AI voice cloning technology uses advanced machine learning techniques to create synthetic voices that closely imitate human speech patterns and vocal characteristics.

At its core, the technology relies on neural networks, particularly Generative Adversarial Networks (GANs) and transformer-based architectures, which learn from short audio samples—sometimes as brief as three to fifteen seconds—to replicate the unique qualities of an individual's voice.

These models analyze nuances such as pitch, tone, pace, and emotional inflections, enabling the production of speech that sounds highly realistic and natural.

Over recent years, breakthroughs in AI research and computational power have significantly enhanced the quality and accessibility of voice cloning.

Companies like ElevenLabs and OpenAI have developed systems capable of generating multilingual voice outputs that retain a speaker’s emotional tone and personality, making the synthetic voices applicable in diverse linguistic and cultural contexts .

The applications of AI voice cloning are varied and expanding across multiple sectors.

In entertainment, voice cloning is employed to recreate actors' voices for films and audiobooks, enabling posthumous performances or modifications without needing the original artist.

Accessibility stands out as a significant area of impact, with personalized synthetic voices enhancing communication options for individuals with speech impairments or degenerative conditions, thereby improving independence and quality of life.

In language education—an area directly relevant to foreign language instructors—voice cloning offers the potential to generate authentic, contextually appropriate listening comprehension materials tailored to low proficiency learners.

Such materials can include diverse accents, emotional tones, and multilingual content, enriching the learning experience while addressing the scarcity of suitable audio resources at lower proficiency levels.

However, alongside these positive uses, there are malicious applications; AI voice cloning facilitates sophisticated scams and fraud schemes where impostors mimic trusted voices to deceive victims, extort money, or bypass security systems.

Recent real-world incidents reveal scammers exploiting AI-cloned voices for financial extortion, underscoring the risks involved.

Meanwhile, content creators experiment with AI-synthesized voices in multiple languages for storytelling or localization, highlighting evolving creative possibilities .

Legally, the regulation of voice cloning technology remains in a nascent and fragmented state worldwide.

Copyright law traditionally mandates human authorship for protection, leaving AI-generated voices without clear ownership or copyright status under current frameworks, particularly in the United States.

This gap complicates questions concerning the rights to use, reproduce, or commercially exploit cloned voices.

The right of publicity, which protects individuals against unauthorized commercial use of their name or likeness—including voice—varies widely by jurisdiction and often depends on state-level legislation.

In China, courts have taken a firmer stance, ruling that AI voice cloning without consent constitutes infringement, resulting in damages awarded to voice owners.

Regulatory efforts are advancing; the European Union is implementing an AI Act starting in 2026 aimed at addressing risks associated with AI-generated content, including voice fakes.

Japan is advocating for voluntary measures among AI operators instead of immediate legislative changes, focusing on intellectual property protection and ethical standards.

Despite these efforts, there is no comprehensive international legal framework yet to uniformly govern AI voice cloning, and important questions remain about consent, data privacy, compensation, and enforcement across jurisdictions .

Ethical issues surrounding AI voice cloning revolve chiefly around privacy violations, consent, misinformation, and potential psychological and social harm.

Recording and synthesizing a person’s voice without consent raises serious privacy concerns and risks unauthorized exploitation, particularly for vulnerable individuals who may be more easily manipulated.

The proliferation of synthetic voices contributes to the growing problem of deepfakes—fabricated media that can be used to spread misinformation, undermine trust, or commit fraud—intensifying societal harms such as erosion of confidence in audio evidence and public discourse.

Ethicists and policymakers stress the imperative for transparency, accountability, and the incorporation of robust detection tools able to identify synthetic voices to curb misuse.

In educational contexts, ethical questions include maintaining authenticity and respect for original speakers when generating synthetic listening materials, as well as considering students’ psychological responses to AI-generated voices.

Industry leaders and researchers advocate embedding ethical AI principles, including fairness, inclusivity, user control, and respect for consent, into voice cloning development.

Global collaborations, such as international AI safety summits and corporate ethical guidelines (e.g., Adobe’s ethical AI principles), illustrate attempts to balance innovative potential with protecting individual rights and fostering social trust .

In sum, AI voice cloning presents transformative possibilities for education, entertainment, and accessibility, with particular promise for generating realistic listening comprehension materials across languages and proficiency levels.

However, the rapid advancement and democratization of this technology raise critical challenges that foreign language educators and other users must consider.

Ethical deployment requires obtaining proper consent, ensuring transparency about synthetic content, and remaining vigilant about potential misuse.

Legal frameworks are evolving but remain insufficiently mature to address all the nuanced issues surrounding voice cloning.

Awareness of these factors will enable educators to harness AI voice cloning responsibly and effectively as a valuable tool in curriculum design and lesson preparation, while navigating the complex technical, ethical, and legal landscape that accompanies its application.

AI voice cloning technology uses advanced machine learning techniques to create synthetic voices that closely imitate human speech patterns and vocal characteristics.

Title:

Content: Language No Barrier: New AI tool can clone voice and make it say anything in multiple languages

Imagine former US President Donald Trump speaking in flawless Hindi. A new AI voice cloning tool by ElevenLabs can clone voices and make them say anything in a bunch of different languages. All it needs is a three-second sample clip and a bit of text as the prompt.

Mehul Reuben Das May 03, 2023 13:42:20 IST

Imagine former US President Donald Trump speaking in flawless Hindi. A new AI voice cloning tool by ElevenLabs can clone voices and make them say anything in a bunch of different languages. All it needs is a three-second sample clip and a bit of text as the prompt.

As advanced as AI-based voice cloning has been, there were a few limitations to it. For example, voice generators were mainly bound by the language of the source they were trained in or rather the language of the prompt that was given to them. Now, ElevenLabs, an American AI software developing company has come up with a tool that does away with that limitation.

ElevenLabs’ new AI tool can clone the voice of a person and make it say anything in a bunch of different languages. All it needs is a clear voice sample of about 3 seconds, although it also suggests that the longer the sample, the better will be the results. The new tool even manages to maintain the speaker’s original tone of voice.

Limitless possibilities
The creators expect that this would ‘extend the possibilities’ in a variety of industries such as publishing, gaming creation, and media. “Introducing Eleven Multilingual v1: Our New Speech Synthesis Model! We’re thrilled to launch our new model supporting seven new languages: French, German, Hindi, Italian, Polish, Portuguese, and Spanish.”

“This advancement expands the horizons for creators, game developers, and publishers, and it paves the way for using generative media to create more localized, accessible, and imaginative content,” the company said.

Following the announcement, Twitter was flooded with comments, with users testing the software on various famous faces.

To demonstrate just how capable the new voice cloning AI tool is, Eluna AI, a platform for AI-content creators experimented with a sample clip of former US President Donald Trump.

In their demo, Eluna AI showcased a small clip of Donald Trump, speaking in his native tongue, English, followed by French, Spanish, German, Hindi and Italian, almost perfectly. The generated samples even gets the accent right, almost if he was a native speaker of the new target language.

Another user changed David Attenborough’s voice to German while the BBC broadcaster discussed the prospects of AI. “With this tool, anyone can do it,” stated the Twitter user. “It’s also worth noting that fraudsters will undoubtedly exploit this one.”

ElevenLabs says that its model was designed to understand the emotion and reasoning underlying words in order to produce natural-sounding and persuasive audio. ElevenLabs has said that some numerals and symbols are currently spoken wrongly, but that it is striving to rectify this.

Who is this new tool meant to help?
The US-based company believes its technology will be valuable in a variety of areas, including education and journalism.

Game developers and publishers can build engaging, localised experiences for worldwide audiences, breaking through language barriers and connecting with players and listeners to maximise engagement and efficiency while maintaining quality and accuracy.

“Educational institutions now have the ability to develop audio content for varied users in their target languages, enhancing language comprehension and even pronunciation abilities, as well as responding to different teaching methods and learning demands,” says the report.

Because of the model’s capacity to produce unique character-like voices, audiobook and content developers may also find it beneficial.

The conundrum of AI voice genertors
Despite its benefits, ElevenLabs’ launch comes amid widespread concern about the wave of new voice cloning technology. An unidentified TikTok user went popular last month after sharing an AI-generated Drake song starring The Weeknd.

It sparked concerns about the grey area of AI-created music and whether it constitutes a copyright violation. Nonetheless, ElevenLabs claims to be ‘fully committed’ to respecting creators’ rights in the midst of technological advancements.

“At Eleven, we think that we should seek to make the best use of emerging technology, but not at any expense,” it stated.

“As we create them, we make every effort to put suitable controls in place that reduce the possibility of detrimental abuse. With this in mind, we are entirely committed to both protecting intellectual property rights and taking action against infringement.”

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World

Music labels are worried because of AI, thanks to a surprising new song ft. Drake X The Weeknd

Someone used AI to create a whole new song featuring Drake and The Weeknd. But because the singers never sang the song, artists and music labels are worried, how AI voice modulation and AI-generated songs can disrupt the industry.

World

Artificial Imposter: 47% of Indian phone users have experienced AI voice scams, highest in the world

About half of Indian adults, or 47%, have experienced AI voice scams, nearly double the global average of 25%. Furthermore, nearly 69% of adults say they can't differentiate between a real and an AI-generated voice.

Deepdub, a film dubbing company, uses an AI to process voice and translate into a different language and promises to preserve what?

At its core, the technology relies on neural networks, particularly Generative Adversarial Networks (GANs) and transformer-based architectures, which learn from short audio samples—sometimes as brief as three to fifteen seconds—to replicate the unique qualities of an individual's voice.

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Speech cloning raises concerns, OpenAI again explains its text-to-speech tool
2024 / 6 / 10 9: 57: 21 Source: IT House Author: Yuanyang Editor: Yuanyang
OpenAI explained its text-to-speech tool for the second time in a few months, and again stressed that the tool is not currently widely available and may not be available in the future.
“Whether or not we end up deploying this technology at scale, it ’ s important for people around the world to understand where it ’ s going, ” OpenAI said in a statement on its website Friday.“ That ’ s why we want to explain how the model works, how we use it for research and education, and how we implement security measures around the technology. ”
At the end of last year, OpenAI shared its “voice engine ” with a small number of external users.
The engine uses text input and a 15-second clip of human voices to “generate a natural sounding human voice that is very similar to the original speaker. ”
The tool could create fake, multilingual human voices, and the company said at the time that it had chosen to preview the technology rather than launch it at scale to “enhance social resilience ” to the threat posed by“ increasingly realistic artificial intelligence-generated models. ”
As part of these efforts, OpenAI says it is actively phasing out ways to use voice recognition for bank account verification, exploring policies to protect individual voices for use in AI, educating the public about the risks of AI, and accelerating the development of technologies to track audiovisual content so users can identify whether they are interacting with real people or synthetic content.
However, despite these efforts, concerns about the technology remain.
Bruce Reed, the head of artificial intelligence for US President Joe Biden, has said that voice cloning is one of the reasons he can't sleep at night.
The Federal Trade Commission said in March that scammers were using AI technology to increase the credibility of their fraud, using voice cloning tools that make it harder for people to distinguish between AI-generated voices and human voices.
“We continue to engage with U.S. and international partners from government, media, entertainment, education, civil society, and more to ensure we incorporate their feedback in the build process, ” the company also noted, once the“ Voice Engine ” is equipped with its latest model, GPT-4o, it will also bring new threats.
The company says it is actively “conducting ‘ red team testing ’ on GPT-4o to identify and address known and unknown risks from areas such as social psychology, bias and fairness, and misinformation. ”
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Deepdub, a film dubbing company, uses an AI to process voice and translate into a different language and promises to preserve what?

What is necessary for voice cloning to work?

These models analyze nuances such as pitch, tone, pace, and emotional inflections, enabling the production of speech that sounds highly realistic and natural.

Deepdub, a film dubbing company, uses an AI to process voice and translate into a different language and promises to preserve what?

What has the US Federal Trade Commission warned about the use of voice cloning tools?

Difficult to distinguish from human voice

Over recent years, breakthroughs in AI research and computational power have significantly enhanced the quality and accessibility of voice cloning.

Title:

Content: Generative AI: What Does the Future Look Like?by@nisharathod
829 reads
829 reads

Generative AI: What Does the Future Look Like?

by Nisha Nemasing RathodOctober 13th, 2023

Too Long; Didn't Read

Explore the transformative potential of Generative AI, its applications, and ethical considerations in shaping the future.

People Mentioned

Generative AI stands at the forefront of technology, embodying the pinnacle of machine creativity and innovation. This facet of artificial intelligence goes beyond mere data analysis, stepping into a realm where machines can generate new content that's comparable to, or in some cases, indistinguishable from that created by humans. The spectrum of its potential is vast and encompasses various industries, including art, music, healthcare, and even finance. It is the magic wand that can potentially turn abstract ideas into palpable visuals, create music, or even generate realistic human faces.

The allure of Generative AI has spurred a significant surge in interest and investment in AI technologies. As we inch closer to a future where AI might become as ubiquitous as the internet, the importance of a solid educational foundation in this domain can't be overstated. For those keen on delving into the intricacies of Generative AI, embarking on an educational journey through reputable sources is imperative. Courses like the Artificial Intelligence Course provide a comprehensive overview of AI principles and applications, while the MIT AI Course dives deep into the advanced algorithms and techniques that drive generative models. These courses not only equip individuals with the knowledge to comprehend the mechanisms of Generative AI but also furnish a platform to contribute to a future where the interaction between machines and imagination will be commonplace. Through a blend of curiosity, education, and innovation, the horizon of what can be achieved with Generative AI is bound to expand, heralding a new era of technological advancement.

Historical Background of Generative AI

The trajectory of Generative AI has been fascinating, tracing back to the early inklings of machine learning. Here’s a concise chronicle of its evolution encapsulated in a table:

Year

Milestone

2006

Introduction of Restricted Boltzmann Machines (RBMs)

2014

Inception of Generative Adversarial Networks (GANs) by Ian Goodfellow

2016

Emergence of Variational Autoencoders (VAEs)

2018

Advancements in GANs leading to the creation of realistic human faces

The journey commenced with the introduction of Restricted Boltzmann Machines (RBMs) in 2006, which laid the groundwork for more sophisticated generative models. The real breakthrough came in 2014 with Ian Goodfellow's invention of Generative Adversarial Networks (GANs), a novel framework that pitted two neural networks against each other to generate new, synthetic instances of data. The narrative of generative models was further enriched in 2016 with the emergence of Variational Autoencoders (VAEs), which brought about a more stable and controlled way of generating data. The timeline witnessed a significant milestone in 2018 when advancements in GANs led to the generation of realistic human faces, emblematic of the immense potential of Generative AI. Through these evolutionary strides, Generative AI has burgeoned from foundational models to a powerhouse of synthetic data generation, continually pushing the boundaries of what machines can create.

Core Technologies and Algorithms

At the heart of Generative AI lie robust algorithms and technologies that fuel the generation of new, unprecedented data. Among these, Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are the linchpins.

Generative Adversarial Networks (GANs) operate on a dualistic framework where two neural networks, a generator and a discriminator, contest with each other. The generator creates new data instances while the discriminator evaluates them against real data. This adversarial process iteratively refines the generator's output, enabling the creation of highly realistic synthetic data. For instance, GANs are employed in creating realistic imagery, as seen in the generation of artificial faces or artwork.

On the other hand, Variational Autoencoders (VAEs) are probabilistic models that generate new data by learning and approximating the data distribution of the input. They are vital in scenarios where controlled generation is imperative, such as in drug discovery, where creating molecular structures requires a defined set of parameters.

Other pivotal algorithms include Transformer models like GPT-3, which have shown prowess in text generation, creating coherent and contextually relevant text based on given prompts.

Delving deep into these core technologies requires a solid educational grounding. Pursuing an Artificial Intelligence Course can provide the requisite knowledge and hands-on experience. These courses offer a deep dive into the underlying principles, algorithms, and applications of Generative AI, furnishing a profound understanding that is essential for anyone aspiring to explore or work in this exhilarating field of artificial intelligence. Through rigorous coursework and practical applications, individuals can grasp the mechanics of these algorithms and their real-world implications, preparing them for a future where they can contribute to the burgeoning landscape of Generative AI.

Current Applications of Generative AI

Generative AI has carved out a significant niche across a myriad of fields, proving to be a catalyst for innovation and enhancement.

Generative AI plays a critical role in data augmentation in technical domains, which is pivotal in training robust machine learning models. For instance, in medical imaging, Generative AI helps augment datasets by generating synthetic medical images, aiding in better disease diagnosis.

The realm of art has witnessed a renaissance with the advent of Generative AI. Algorithms like GANs have been used to create new artworks, exemplified by the project "Next Rembrandt," which generated a new painting in the style of the legendary artist Rembrandt by analyzing his existing body of work.

The music industry, too, has been touched by the wand of Generative AI. OpenAI's MuseNet is a testament to this, as it can generate musical compositions in various styles, spanning multiple instruments. Its ability to create harmonies that resonate with human emotions showcases the prowess of Generative AI in music creation.

Generative AI's foray into the gaming industry has also been noteworthy. It aids in creating realistic textures, environments, and characters, enhancing the gaming experience manifold. Games like 'Minecraft' have utilized GANs to generate realistic texture packs, illustrating the potential of Generative AI in creating immersive gaming environments.

Furthermore, Generative AI has made strides in voice synthesis and natural language processing. Tools like Descript's Overdub and OpenAI’s GPT-3 have revolutionized content creation and text generation, making the processes more efficient and creative.

These applications underline the versatility and potential of Generative AI in not only augmenting existing fields but also in paving the way for new avenues of exploration and creativity. Through a blend of technology and imagination, Generative AI continues to redefine the boundaries of what's possible across a wide spectrum of disciplines.

Future Prospects of Generative AI

The horizon of Generative AI is continuously expanding, promising a future replete with groundbreaking advancements across various domains.

In the sphere of drug discovery, Generative AI is poised to play a pivotal role. By generating molecular structures that could potentially be new drug candidates, it accelerates the traditionally long and arduous process of drug development. For instance, generative models can help identify novel compounds with desired properties, significantly reducing the time and resources required to bring new treatments to market.

Climate modeling is another area where Generative AI can have a profound impact. By generating realistic simulations of climate scenarios, it aids researchers in understanding and predicting climate change dynamics. This, in turn, can guide policy-making and planning, contributing to a more sustainable future.

Moreover, Generative AI holds promise in automating and enhancing design processes in fields like architecture and engineering. By generating design proposals based on specified criteria it can foster creativity and efficiency, facilitating the realization of innovative and optimized designs.

In the realm of education, Generative AI could customize learning materials to cater to individual needs and preferences, thus personalizing education. It could generate practice problems, essays, or interactive lessons, enhancing the learning experience.

Furthermore, Generative AI can contribute to enhancing cybersecurity by generating scenarios to test and bolster security systems against a myriad of threats. By simulating cyber-attacks, it helps in identifying vulnerabilities and fortifying security infrastructures.

The burgeoning capabilities of Generative AI herald a future where its applications could be transformative. From healthcare to environmental sustainability and from design innovation to personalized education, Generative AI is set to be a cornerstone of technological advancements, propelling society into an era of unprecedented innovation and problem-solving.

Ethical Implications

The ascent of Generative AI is not without ethical quandaries, with deepfakes being a glaring exemplar. Deepfakes, fabricated videos generated using GANs, can create realistic-looking footage of real individuals saying or doing things they never did. Another notable example of deepfake technology being misused is the case of a deepfake video that went viral in 2020 featuring the Belgian Prime Minister Sophie Wilmès. In the fabricated video, the Prime Minister appeared to state that the country had decided to combat the climate crisis by committing to a bold ecological agenda, including the decision to collaborate with the fictional 'BLUR' initiative. The video was actually created by the Extinction Rebellion Belgium group to raise awareness about climate change and to prompt action from the government. This scenario underscored the potential of deepfakes to spread misinformation and potentially influence public opinion or governmental actions, albeit with the intention of promoting a social cause in this particular instance.

Aside from deepfakes, Generative AI can perpetuate biases present in the training data, leading to discriminatory or prejudiced outcomes. Moreover, the potential misuse in generating synthetic data could infringe on privacy rights and intellectual property.

Mitigating these challenges necessitates a multi-faceted approach. Technological solutions like better detection algorithms can help identify deepfakes and other synthetic media. Legal frameworks need to evolve to address the new kinds of misuse that Generative AI enables, ensuring accountability and protection against malicious use. Moreover, promoting transparency and ethical practices in AI development and deployment is imperative.

Education plays a crucial role in ethical AI usage. Artificial Intelligence Course delves into the ethical dimensions of AI, equipping individuals with the knowledge to navigate the moral landscape of Generative AI. Through a combination of technological innovation, legal reform, and ethical education, the adversities posed by Generative AI can be significantly mitigated, steering its development toward a more responsible and beneficial trajectory.

Wrapping Up

Generative AI encapsulates a realm of endless possibilities, poised to significantly alter the landscape of numerous industries. From revamping creative domains like art and music and augmenting technical fields through data generation to potentially accelerating life-saving drug discoveries, its potential is both profound and far-reaching. However, as we tread further into the expanses of Generative AI, the ethical implications demand meticulous attention and scrutiny. The advent of deepfakes and the potential perpetuation of biases underscore the necessity for responsible innovation and robust legal frameworks.

Education emerges as a cornerstone in navigating the complexities and harnessing the potential of Generative AI responsibly. Through comprehensive courses, individuals can attain a deep understanding of both the technological and ethical facets of Generative AI. Armed with knowledge, aspiring innovators can contribute to a future where Generative AI serves as a catalyst for positive transformation while mitigating risks and ethical quandaries.

As we stand on the cusp of a future intertwined with Generative AI, fostering a culture of ethical awareness, continuous learning, and responsible innovation is imperative. It is through such a balanced approach that the promise of Generative AI can be fully realized, ushering in a new epoch of technological advancement and societal betterment.

Companies like ElevenLabs and OpenAI have developed systems capable of generating multilingual voice outputs that retain a speaker’s emotional tone and personality, making the synthetic voices applicable in diverse linguistic and cultural contexts .

Deepdub, a film dubbing company, uses an AI to process voice and translate into a different language and promises to preserve what?

The applications of AI voice cloning are varied and expanding across multiple sectors.

Title:

Content: The scary side of artificial intelligence

The pros of AI outweigh the Cons or threats in our day-to-day lives, and there’s no magic pill to protect us from the scary threats of AI.

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  • Updated On Oct 3, 2023 at 03:02 PM IST

Artificial intelligence (AI), as we all know, is the simulation of human intelligence leveraging computer systems and machines. AI programs can continuously learn and evolve with experience. In technical terms, AI systems render the output by churning and analyzing the data for correlations and patterns and help make future predictions using these patterns.

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Today AI is being used extensively in all facets of our lives starting from lifestyle, e-commerce, home appliances, automobiles, navigation, transport, healthcare, agriculture, education, financial planning, chatbots, marketing, cyber security, virtual assistant, industrial automation and many more.

Clearly, we have and are leveraging the strengths of AI to the fullest to automate the routine tasks and make it auto-learn over a period. There’s no doubt that AI has revolutionized the lives of all – organizations, consumers etc. But as they say, “Everything comes at a price”. This analogy is quite true for AI as well, there have been a lot of threats that have emerged using AI and are being utilized by the Cyber criminals to dupe the ignorant users, cause mental stress and extort money.

continued below

Executive Workshop

I’m not going to delve into all of threats but would like to elucidate some of the real ones that have come to the surface recently:

Voice cloning or mimicry

As the name suggests, it uses machine learning algorithms to simulate anyone’s voice. It requires certain voice data to analyze and learn the specific patterns of any individual. There are certain advantages of cloning a voice that can be used in voice assistants, chatbots, navigation systems and other areas. Voice cloning has existed for a while but was restricted to experts who possess the technical knowledge to be able to use the same. But now AI Voice cloning tools are available on the open Internet that anyone can leverage with literally no technical knowledge or cost to clone anyone’s voice. All you need to do is just Google and it will pop up and get a bunch of both free and paid AI Voice cloning tools.

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In fact, recently one of the global leaders in online protection published a report stating how AI is fueling a rise in online voice scams, with just three seconds of audio required to clone a person’s voice. This is extremely creepy. Just imagine, if a Cyber-criminal clones your voice and tricks your near ones by making them believe that they are talking to you, and request for money due to any distress reasons. In fact, these are not assumptions but real-life incidents that are happening across the world every minute.

Deepfakes AI

Deepfakes AI are being used to impersonate an individual by manipulating videos, images, audios, and other content available on the social media platforms using deep learning techniques. It mainly transforms existing content from one person to another. This is another area that has been exploited by the Cyber criminals in the recent past, and there are a few deepfake cases that have made waves on the Internet. One of the most famous ones being Deepfake created by a VFX artist of one of Hollywood’s most famous male actors. This piece was so well made by rendering thousands of pictures using artificial versions of the actor. There are similar Deepfakes incidents involving other global celebrities and leaders. The damage and misuse of the deepfakes videos could be extremely dangerous for the human society and can be used by terrorists to incite riots and create nuisance.

Creating virtual people

This is another area where AI is being used to create people that do not exist. There are a lot of websites that allow you to generate a random image by just specifying the age group, gender & ethnicity, and you can continue to refresh these images at the click of a button. Be mindful of the fact that these images pertain to people that do not exist in this world. On the similar lines, there’s an emerging trend of AI news anchors that possess a human-like appearance and are fed data that they can read using text-to-speech technology. Recently, there's been a surge of AI-Driven robotic new anchors in multiple language channels. While the main objective of this initiative is to provide royalty free or cost-effective human images for branding and other purposes. However, the overuse of these technologies causes Fear of missing out (FOMO), Feeling of inadequacy, dissatisfaction, and isolation may potentially lead to psychological and emotional vulnerabilities.

One of the Hollywood movies, ‘HER’ portrayed an introverted and lonely writer (Joaquin Phoenix) who purchases an AI virtual assistant personified through a female voice to assist him in his writing job. However, when he finds out about its ability to learn and adapt, he falls in love with it. The film delves into emotional vulnerability and the impact of AI on human relationships.

Needless to say, that the pros of AI outweigh the Cons or threats in our day-to-day lives, and there’s no magic pill to protect us from the scary threats of AI. However, there are certain preventive tips that one can follow to mitigate any such risks. Do spread awareness with your family and friends:

  • Set some code words with family, and friends to verify the identity during emergency circumstances
  • Optimize social media presence by limiting your connections with only known contacts and avoid connecting with any unknown ones
  • Avoid saying ‘Yes’ as a greeting while receiving unknown calls as it can be used to authorize credit card charges or other purposes
  • Continuously update privacy settings on social media to allow viewing by friends and family only
  • Avoid clicking on any suspicious links or calls received without proper due diligence
  • Do not share your passwords and other sensitive information with anyone
  • Verify the credentials of any online information before circulating it through various mediums
  • Avoid public Wi-Fi connections offered at coffee shops or airports, which requires to enter password
  • Closely monitor the time spent by younger generation on social media and online gaming
  • Finally, in case of any suspicion, double check and report it to the host site or concerned authorities
The author is the CIO of MOVIN

Disclaimer: The views expressed are solely of the author and ETCIO does not necessarily subscribe to it. ETCIO shall not be responsible for any damage caused to any person/organization directly or indirectly.

  • Published On Oct 3, 2023 at 03:02 PM IST

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In entertainment, voice cloning is employed to recreate actors' voices for films and audiobooks, enabling posthumous performances or modifications without needing the original artist.

Title:

Content: AI clones child’s voice in fake kidnapping scam

‘I never doubted for one second it was her,’ mother says

Anthony Cuthbertson
Thursday 13 April 2023 12:34
Comments

A mother has said she was “100 per cent” convinced by an AI voice clone of her daughter that scammers used in a faked kidnapping attempt.

Jennifer DeStefano from Arizona picked up a call from an unknown number and heard what she believed to be her 15-year-old daughter Brie “sobbing”.

The voice on the other end of the line said, “mom, I messed up”, before a male voice took over and made threatening demands.

“This man gets on the phone, and he’s like, ‘listen here, I’ve got your daughter’,” Ms DeStafano told local news show WKYT.

The apparent kidnapper told the mother: “You call the police, you call anybody, I’m going to pop her so full of drugs, I’m going to have my way with her, and I’m going to drop her off in Mexico.”

In the background, Ms DeStefano said she could hear her daughter saying “help me, mom, please help me” and crying.

“It was 100 per cent her voice,” Ms DeStefano said.

“It was never a question of who is this? It was completely her voice, it was her inflection, it was the way she would have cried – I never doubted for one second it was her. That was the freaky part that really got me to my core.”

The apparent kidnapper demanded $1 million for the daughter to be released, before lowering the figure to $50,000. Ms DeStefano only realised that her daughter was safe after a friend called her husband and confirmed that she was safe.

The scammer, who police are still investigating, appears to have used artificial intelligence voice clone technology, which has become increasingly competent in recent years at mimicking people’s voices.

It is also relatively easy to access and use, with AI tools freely available on the internet.

AI-generated voices have already been used in films to replicate actors like James Earl Jones who voiced the original Darth Vader character in the Star Wars franchise.

It has also been touted as a way for authors to produce audiobooks without spending hours in a studio reading their work.

The technology has also raised fears that it could be misused by scammers or people attempting to create deepfake videos, with one voice clone startup warning that it could be “harmful in the wrong hands”.

In a press release last year, the firm Respeecher said: “The widespread attention and adoption of deepfake technologies will accelerate the need to regulate the use of the technology.”

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Eleven Labs is extending its voice cloning services for which industries?

Accessibility stands out as a significant area of impact, with personalized synthetic voices enhancing communication options for individuals with speech impairments or degenerative conditions, thereby improving independence and quality of life.

Title:

Content: In recent years, the rapid development of artificial intelligence has become the focus of innovation and subversion in various industries.
One of the most groundbreaking AI developments is the emergence of generative models that have the potential to revolutionize the way we live, work and communicate.
This article delves into the fascinating journey of generative AI, tracing it from the critically acclaimed ChatGPT to the state-of-the-art Midjourney mode.
As we explore the incredible capabilities and wide-ranging implications of these AI systems, we will also discuss the challenges and ethical considerations of this powerful technology.
Join us as we explore the evolving landscape of generative AI and reveal its transformative potential for our future.
The dawn of generative artificial intelligence: the emergence of ChatGPT
ChatGPT, a language model developed by OpenAI, marks a pivotal moment in the world of generative artificial intelligence.
Built on the GPT-3 architecture, ChatGPT demonstrates a major leap forward in natural language understanding and generation, becoming a prime example of the capabilities of large-scale convertor models.
With 175 billion parameters, GPT-3 is already a powerful AI model, but ChatGPT, by fine-tuning the model specifically for conversational applications, pushes the boundaries further.
As a result, ChatGPT has shown unprecedented performance in generating human-like text, excelling at tasks such as translating, summarizing, and answering questions.
One of the most notable milestones is the release of the ChatGPT API in 2021, which provides developers with access to the model, leading to the creation of many AI-driven applications. ChatGPT ’ s success not only marks the arrival of a new era of generative AI, but also sets the stage for subsequent breakthroughs, such as the Midjourney model, which continues to push the limits of AI capabilities.
Setting the stage: The impact of ChatGPT on communication
The advent of ChatGPT has had a profound impact on the way we communicate, both professionally and personally. ChatGPT revolutionized a variety of industries and applications, from customer service to content creation, by being adept at understanding and generating human-like text.
For example, companies have adopted chatbots powered by ChatGPT to enhance the customer support experience, providing faster, more accurate, context-relevant 24-hour responses.
This has led to increased customer satisfaction and reduced workload for human support staff.
In addition, with the advent of ChatGPT, the creative world has also undergone major changes.
Content creators, journalists, and marketers can now use AI-powered writing assistants to help them generate ideas, write compelling content, and even improve their writing by offering suggestions and corrections.
This leads to increased efficiency and productivity while maintaining a high level of quality.
In personal communications, ChatGPT has enabled the development of AI-driven language learning tools, translation services, and even mental health support platforms to help remove language barriers and provide empathy support to those in need.
As ChatGPT continues to shape communication across domains, it lays the groundwork for more advanced generative AI models, such as Midjourney, that promise to further refine and expand the possibilities for human-AI collaboration.
Disrupting the industry: AI-driven content creation
The introduction of generative AI models such as ChatGPT has led to a paradigm shift in the content creation industry, ushering in a new era of AI-driven content generation.
These models demonstrate exceptional ability to understand context, style, and tone, making them valuable tools for a variety of content-related applications.
Marketing and advertising professionals are among the first to benefit from AI-driven content creation, using generative models to generate attractive copywriting, social media content, and even long blog posts.
This simplifies the workflow and allows marketers to craft tailored messages to target specific audiences more effectively.
As a result, businesses reported improved conversion rates, increased brand awareness, and increased customer engagement.
In the news and media industry, AI-driven content generation has been used to create automated news summaries, draft incident reports, and generate data-driven articles at an unprecedented rate.
This enables news organizations to provide timely and accurate information to their readers, while journalists can focus on more complex tasks such as investigative reporting and in-depth analysis.
The education sector is also witnessing a shift in AI-driven content creation as these models facilitate personalized learning experiences by generating customized learning materials, quizzes, and exercises tailored to individual learning styles and progress.
Moreover, AI-driven content creation has found its place in the entertainment industry, with generative models being used to draft scripts, create video game narratives, and even write poetry.
As generative AI continues to evolve, we can expect further disruption across industries, unlocking new creative possibilities and redefining the way we approach content creation.
The Evolution of Artificial Intelligence: From ChatGPT to Midjourney
ChatGPTMidjourney, The evolution of artificial intelligence shows the significant advances and ever-expanding potential of artificial intelligence.
With ChatGPT, natural language processing and understanding has revolutionized, paving the way for new AI models to explore different areas. Midjourney and Minecraft focus on generating images from text prompts, using this progress to transform the world of digital art and advertising.
The development of these two groundbreaking AI technologies highlights the versatility and capabilities of AI, and as progress continues, we can look forward to a more dynamic and integrated future where natural language processing and images converge to create innovative, powerful solutions in all areas.
Advances in technology: GPT-4 and beyond
As AI research evolves at an incredible pace, the development of generative models such as GPT-4 is expected to lead to more breakthroughs.
These next-generation models may leverage new technologies and architectures to address existing limitations and expand the scope of AI applications.
One of the main areas of concern for future models is to improve the efficiency and scalability of the training process, which will help reduce the computational resources and energy consumption required to develop large-scale AI systems.
Researchers are exploring techniques such as sparse attention, model compression, and distillation to create more compact and efficient models without compromising their performance.
Another key aspect of AI evolution is addressing issues related to fairness, accountability, and transparency.
Future models, such as GPT-4 and beyond, are expected to integrate advanced mechanisms to minimize bias, ensure ethical AI deployment, and provide more explainable insights into the decision-making process.
In addition, researchers are working to improve the multi-modal capabilities of AI models, enabling seamless integration and understanding of various data types such as text, images, audio, and video.
This will help develop more complex applications, from artificially-generated art and music to intelligent virtual assistants capable of understanding and responding to complex multi-modal inputs.
Finally, the future of generative AI will likely involve exploring human-AI collaborative systems, where AI models serve as valuable tools to augment human intelligence rather than replace it.
By developing AI systems that complement human skills and expertise, we can unlock the full potential of generative AI, drive innovation in countless areas and shape the future of technology.
Expand Capabilities: New Artificial Intelligence Applications
As generative AI models like GPT-4 continue to evolve, we can anticipate a range of new applications that push the boundaries of current AI capabilities.
These advances will open up new possibilities and promote innovation across industries.
Personalized medicine: AI-driven diagnostic and treatment recommendations will become increasingly precise, enabling personalized medicine that takes into account individual genetic, lifestyle, and environmental factors.
This will lead to more effective treatment plans and better patient outcomes.
• Smart cities: AI applications will play an important role in urban planning and management, optimizing energy consumption, traffic flow and waste management, and ultimately creating a more sustainable and efficient urban environment.
Scientific discovery: AI-driven large-scale data set analysis will accelerate discoveries in areas such as genomics, drug discovery, and materials science.
AI models will be able to identify patterns and generate hypotheses, facilitating a new era of data-driven scientific breakthroughs.
Creative Collaboration: Generative AI will be a valuable tool for artists, writers and musicians, enabling them to create novel and complex works by exploring and combining different ideas and styles, ultimately expanding the boundaries of human creativity.
• Emotional intelligence: Future AI models will better understand and respond to human emotions, paving the way for more compassionate and supportive virtual assistants, mental health support platforms, and personalized learning experiences.
Enhanced cybersecurity: AI-driven threat detection and response systems will become more sophisticated, enabling businesses and individuals to better protect their digital assets and maintain privacy in an increasingly interconnected world.
• Environmental protection: AI will play a key role in monitoring and protecting ecosystems, analyzing satellite imagery and sensor data to track deforestation, biodiversity loss, and climate change impacts, ultimately informing more effective conservation strategies.
These emerging AI applications will, among other things, reshape industries, improve the quality of our lives, and drive technological advances that will redefine the possibilities of the AI era.
Global Transformation: The Role of Artificial Intelligence in Society
As generative AI models such as GPT-4 continue to advance, their impact on society is becoming more profound, driving a global transformation that permeates all aspects of our lives.
This powerful technology has the potential to redefine the way we work, communicate, learn and interact with the world around us.
Evolution of the workforce: AI-driven automation will reshape the job market, streamline repetitive work and increase productivity across industries.
As a result, the workforce will shift to more creative, analytical and interpersonal roles that require constant learning and adaptation to remain competitive.
• Inclusive communication: Advanced AI-driven translation services will break down language barriers, foster cross-cultural collaboration, and foster a more connected global community.
This will open up new opportunities for international cooperation and exchange of ideas.
• Education revolution: AI-driven personalized learning experiences will democratize education, enabling learners across the globe to access high-quality resources and tailored instruction, regardless of socioeconomic status or location.
This will help close the education gap and promote lifelong learning.
Advances in healthcare: The role of AI in healthcare will continue to grow, from early diagnosis and precision medicine to virtual health assistants and telemedicine, improving access to care and improving overall health for millions of people around the world.
• Social impact: AI can be harnessed to address pressing global challenges such as climate change, poverty and inequality.
By harnessing AI-driven insights and predictive models, policymakers can make more informed decisions, develop more effective interventions, and ultimately create a more sustainable and equitable world.
Ethics and governance: As AI is increasingly integrated into society, it is critical to establish ethical guidelines, legal frameworks, and international standards to ensure responsible and transparent AI deployment, prevent unintended consequences, and promote equitable access to AI-driven benefits.
The ongoing global transformation, driven by AI technologies, presents both enormous opportunities and challenges.
As we navigate this uncharted territory, collaboration must be fostered between researchers, policymakers, industry leaders and the public to harness the potential of AI while minimizing its risks and ultimately shaping a future that benefits all of humanity.
The intersection of artificial intelligence and ethics
As AI technologies evolve and permeate every aspect of our lives, the intersection of AI and ethics becomes increasingly critical to ensure responsible and fair AI deployment.
This involves addressing several key ethical issues that arise in the development, implementation, and governance of AI systems.
• Bias and fairness: AI models may inadvertently propagate and amplify biases present in training data, leading to unfair outcomes and perpetuating existing inequalities.
Ensuring fairness in AI systems involves rigorous testing, strategies to reduce bias, and the use of diverse and representative datasets in model development.
Transparency and interpretability: Many AI systems, especially deep learning models, are often seen as “black boxes ” due to their complex internal workings.
Enhancing the transparency and interpretability of AI models is critical to building trust, promoting accountability, and making users aware of the decision-making process behind AI-generated outputs.
• Privacy and data security: AI systems often rely on large amounts of data, which raises concerns about privacy, data ownership, and security.
Building strong data protection measures, anonymization technologies, and an AI approach to protecting privacy are critical to protecting user privacy and maintaining public trust.
• Autonomy and human institutions: The growing reliance on AI-driven decision-making raises concerns about the possible erosion of human autonomy and agency.
It is necessary to strike the right balance between AI assistance and human control to ensure that AI systems augment human capabilities, not replace or destroy them.
Accountability and legal frameworks: Identifying responsibilities and obligations in situations where AI systems cause harm or make wrong decisions is a complex challenge.
Developing appropriate legal frameworks and governance structures is critical to addressing these issues and ensuring accountability in AI-driven outcomes.
• AI for Good: A fundamental ethical consideration is to ensure that AI technologies are developed and deployed for the benefit of all, rather than exacerbating existing inequalities or concentrating power in the hands of a few.
This includes promoting equitable access to AI-driven benefits and promoting AI-driven solutions to address pressing global challenges.
Addressing these ethical issues requires a multidisciplinary approach that includes collaboration between AI researchers, ethicists, policymakers, industry leaders, and the wider public.
By prioritizing ethical issues in the development and deployment of AI, we can harness the potential of this transformative technology while minimizing its risks and ultimately shaping a future that benefits all of humanity.
The Contribution of Artificial Intelligence to Sustainable Development
AI has the potential to contribute significantly to sustainable development by providing innovative solutions to complex global challenges.
By aligning AI advances with the United Nations Sustainable Development Goals (SDGs), we can use this transformative technology to create a more equitable and sustainable future for all.
No Poverty (SDG 1): AI can be used to identify areas of extreme poverty, optimize resource allocation, and improve the effectiveness of targeted interventions.
Machine learning models can also predict socioeconomic trends, enabling policymakers to develop smarter poverty reduction strategies.
Zero Hunger (SDG 2): AI-powered precision agriculture technologies can help optimize crop yields, reduce resource waste, and minimize the environmental impact of farming practices.
In addition, AI can be used to monitor and predict global food supply chains, ensuring more efficient distribution and reducing food waste.
Good Health and Well-Being (SDG 3): AI could revolutionize healthcare by enabling early diagnosis, personalized medicine, and more effective treatment plans.
In addition, AI-powered telemedicine and virtual health assistants can improve access to quality healthcare in remote or underserved areas.
Quality Education (SDG 4): AI-driven personalized learning platforms can democratize access to education, tailor teaching to individual learning styles and needs, and help close education gaps around the world.
• Gender Equality (SDG 5): AI can help identify and reduce biases in a variety of areas, including hiring, lending, and healthcare, ensuring more equitable outcomes for all genders.
In addition, AI-driven tools can be used to analyze and monitor gender disparities to inform policies aimed at promoting gender equality.
• Clean Water and Sanitation (SDG 6): AI can optimize water management and infrastructure planning to help ensure access to clean water and sanitation for all.
Machine learning models can also be used to monitor and predict water quality for timely interventions to prevent pollution.
• Affordable Clean Energy (SDG 7): AI can optimize energy consumption in buildings, transportation and industry, drive energy efficiency and reduce greenhouse gas emissions.
In addition, AI can support the integration of renewable energy into the grid and facilitate the transition to a more sustainable energy system.
• Climate Action (SDG 13): Artificial intelligence can be used to monitor and predict the impacts of climate change, make climate models more accurate, and inform mitigation and adaptation strategies.
In addition, AI-driven tools can help optimize carbon sequestration and emissions reduction initiatives.
These examples illustrate only a small part of the potential contribution of AI to sustainable development.
By prioritizing the development and deployment of AI solutions in line with the Sustainable Development Goals, we can unleash the transformative power of AI to address pressing global challenges and create a more sustainable and equitable future.
Midjourney: The Next Generation of Artificial Intelligence
Midjourney, an innovative artificial intelligence software developed by Midjourney Inc., an independent research lab based in San Francisco, is taking the AI world by storm.
This groundbreaking program is capable of generating images from natural language descriptions or “cues, ” similar to other AI tools such as OpenAI ’ s DALL-E and Stable Diffusion.
While Midjourney ’ s exact technical basis remains a topic of speculation, some experts believe it may be based on steady diffusion.
After entering the open beta phase on July 12, 2022, Midjourney, has proven itself to be a profitable business.
Led by David Holz, co-founder of Leap Motion, the Midjourney team offers its users a unique experience by leveraging Discord bot commands to create digital artwork.
Currently, the project can only be accessed through the official Discord server, which can send messages directly to the bot or invite it to a third-party server.
The user simply enters a prompt using the / imagine command, after which the robot generates a set of four images.
Users can then choose which images they want to zoom in on.
The company is also working on developing a web interface to improve accessibility.
Midjourney has three subscription levels that offer users a free trial to generate images.
Once users have completed about 25 tasks, they must subscribe to continue using the service.
David Holtz envisions artists as collaborators rather than competitors, and he says that many artists use Midjourney to quickly prototype art concepts for clients before starting their own work.
However, not everyone is excited about Midjourney ’ s impact on the art world.
Some artists argue that the platform degrades the value of original work by including it in training sets.
To address these issues, Midjourney ’ s terms of service include a DMCA removal policy that allows artists to request removal of their work from the training set if they believe it infringes their copyright.
The advertising industry has rapidly adopted AI tools such as Midjourney, DALL-E, and Stable Diffusion.
These platforms enable advertisers to effectively produce original content and brainstorm, creating countless new possibilities.
Industry experts, such as Advertising Age, predict that these AI tools could lead to personalized ads, innovative special effects, and increased efficiency in e-commerce advertising.
As Midjourney continues to make waves in AI, it ’ s clear that this next generation of AI tools has the potential to revolutionize digital art and advertising.
By providing a unique and effective way to generate images from natural language cues, Midjourney enables artists and advertisers alike to push the boundaries of creativity.
As AI technology continues to advance, it will be fascinating to see how Midjourney and similar platforms evolve to meet the changing needs of their users.
Looking forward to the future of human and artificial intelligence cooperation
As we look to the future of human-AI collaboration, we can foresee a world in which AI becomes an integral part of our daily lives, empowering humans, and transforming the way we work, learn, and communicate with each other.
This vision of the future depends on a harmonious partnership between humans and AI, in which each brings its unique strengths and expertise.
AI will increasingly automate routine and repetitive tasks in the workplace, enabling humans to focus on more creative, strategic, and complex problem-solving activities.
This shift will redefine job roles and lead to the emergence of new career paths that harness human ingenuity and compassion.
Collaboration between AI systems and human experts will drive innovation, increase productivity, and facilitate smarter decisions across industries.
There will also be a major shift in education, with personalized learning platforms driven by AI democratizing access to quality education and tailoring teaching to individual learning styles and needs.
Human teachers will continue to play a key role in guiding students, facilitating discussion, and fostering social and emotional development.
Synergies between AI and human educators will help bridge the education gap around the world and promote lifelong learning.
In healthcare, AI will assist healthcare professionals in diagnosing diseases, designing personalized treatment plans, and monitoring patient progress.
Collaboration between AI-driven tools and human expertise will improve the accuracy and efficiency of healthcare services, lead to better patient health outcomes, and reduce the burden on healthcare professionals.
The future of human-AI collaboration will also need to address ethical considerations and ensure responsible AI deployment.
A strong partnership between humans and AI systems will require AI technologies that are transparent, unbiased and protect privacy, respect human autonomy and promote trust.
Policymakers, researchers, and industry leaders will need to work together to establish ethical guidelines, legal frameworks, and international standards to navigate the challenges posed by the widespread integration of AI into society.
Ultimately, the future of human-AI collaboration envisions a world where AI enhances human capabilities, empowers individuals, and drives sustainable development.
By promoting a harmonious partnership between humans and AI, we can unlock the full potential of this transformative technology to shape a brighter future for all.
Preparing for a Generative World Driven by Artificial Intelligence
As generative AI models continue to advance and shape our world, individuals, organizations, and governments must adapt and be prepared to deal with the far-reaching impact of these technologies.
Here are some key steps to help navigate a generative AI-driven world:
Education and skills development: Continuous learning and improving skills are essential for individuals to remain relevant in the workforce.
Emphasis on digital knowledge, programming, data analysis, and other AI-related skills will be critical, along with honing creativity, critical thinking, and emotional intelligence, all of which are unique to humans and complement AI capabilities.
Industry Transformation: Businesses and organizations must keep up with AI developments and explore how generative AI models can be used to optimize their operations, drive innovation, and enhance decision-making.
This could involve investing in AI research and development, integrating AI-driven tools into existing workflows, and redefining work roles to support human-AI collaboration.
Regulatory framework and ethics: Governments and policymakers should strive to establish a comprehensive regulatory framework and ethical guidelines for the development and deployment of AI.
These guidelines should address issues such as bias, transparency, privacy and accountability to ensure responsible and fair AI integration into society.
• AI infrastructure and investments: Ensuring access to a robust AI infrastructure, including high-speed internet, computing resources, and advanced algorithms, is critical to the development and widespread adoption of generative AI models.
Governments and private sector entities should work together to invest in AI research, infrastructure and start-ups to promote a thriving AI ecosystem.
• Workforce Transition Support: As AI technologies lead to shifts in the job market, governments and organizations must support workforce transitions, including retraining programs, career counseling, and social safety nets to help individuals adapt to new job roles and opportunities in an AI-driven world.
• Public awareness and engagement: Raising public awareness of the potential benefits and risks of generative AI is essential to ensure informed decision-making and promote healthy public discussion about the role of AI in society.
Encouraging collaboration among AI researchers, ethicists, policymakers, and the wider public will promote a more inclusive and diverse view of the impact and potential applications of AI.
By taking these positive steps, individuals, organizations, and governments can effectively prepare for a world powered by AI, maximizing the benefits of this transformative technology while minimizing risks and ensuring a fairer and sustainable future for all.
The importance of AI literacy
In today ’ s rapidly evolving digital landscape, where artificial intelligence is increasingly present and influential in all aspects of our lives, AI knowledge has become increasingly important.
AI literacy refers to an understanding of AI concepts, principles, and applications, as well as the ability to engage critically and responsibly in AI technologies.
Developing AI literacy is critical for the following reasons:
• Informed decision making: AI knowledge enables individuals to make informed decisions when interacting with AI-driven tools and services.
A basic understanding of AI principles helps people assess the potential benefits, risks, and limitations of AI technologies, enabling them to make more informed choices in their personal and professional lives.
• Ethical considerations: AI knowledge promotes awareness of ethical issues surrounding AI, such as bias, privacy, transparency, and accountability.
By understanding these issues, individuals can engage in meaningful conversations about responsible AI development and deployment, helping to shape ethical guidelines and regulatory frameworks for managing AI technologies.
• Workforce readiness: As AI continues to transform industries and job roles, AI knowledge equips individuals with the necessary skills and knowledge to adapt to an AI-driven workforce.
Individuals can improve their employability and thrive in an increasingly automated job market by developing AI-related skills such as programming, data analytics, and machine learning.
• AI adoption and trust: AI knowledge contributes to the successful adoption of AI technologies across all sectors.
By understanding the capabilities and limitations of AI, individuals can better understand how AI complements and enhances human capabilities, promotes trust and drives the integration of AI solutions across different applications.
Empowerment and inclusion: AI knowledge helps ensure that more people have access to the benefits of AI.
By promoting AI education and awareness, we can engage individuals from diverse backgrounds in the development, deployment, and governance of AI technologies to promote a more inclusive and equitable AI-driven world.
Critical Thinking and Creativity: Developing AI knowledge encourages critical thinking and creativity as individuals learn to navigate the complex interactions between humans and AI systems.
This promotes a deeper understanding of how AI can be used to solve problems, drive innovation, and improve human capabilities.
In summary, AI knowledge is critical in today ’ s AI-driven world as it empowers individuals to make informed decisions, address ethical issues, adapt to a changing workforce, and contribute to the responsible development and deployment of AI technologies.
By investing in AI education and promoting broad AI knowledge, we can ensure a more inclusive, equitable and sustainable future driven by AI.
Strategies for realizing the full potential of artificial intelligence
Harnessing the full potential of AI requires individuals, organizations, and governments to take a proactive, collaborative approach in order to effectively maximize its positive impact.
To do this, it is critical to promote AI education and literacy among people of different ages and professional backgrounds.
This will enable informed decision-making, critical thinking and responsible participation in AI technologies.
Emphasizing the importance of cooperation between humans and AI systems will help maximize the benefits for both parties.
By combining the unique strengths of humans and AI, productivity, innovation and problem-solving can be significantly improved.
Establishing a comprehensive ethical framework and guidelines for the development and deployment of AI is critical to addressing issues such as bias, transparency, privacy, and accountability.
This will ensure responsible and fair AI integration into society.
Public-private sector partnerships could be promoted by promoting cooperation between Governments, private sector entities, academic institutions and non-governmental organizations.
This partnership will support AI research, development and adoption, drive innovation, create jobs, and foster a thriving AI ecosystem.
Investments in AI infrastructure, research, and start-ups are critical to providing robust AI resources such as high-speed internet, computing resources, and advanced algorithms.
This will support the development and widespread adoption of cutting-edge AI technologies.
Inclusion and diversity in AI research, development and governance should be promoted.
Encouraging the participation of individuals with diverse backgrounds, skills, and perspectives will ensure a more equitable distribution of AI-driven benefits and a better understanding of potential risks and challenges.
Focusing on sustainable development and harnessing the potential of AI to solve global challenges is key.
In areas such as healthcare, education, energy, agriculture and environmental management, AI-driven solutions can contribute to social, economic and environmental well-being.
Adjusting the legal and regulatory framework to take into account the unique challenges and opportunities presented by AI technologies is necessary.
In an AI-driven environment, it is critical to implement policies that protect individual rights, ensure safety and security, and promote fair competition.
Finally, international cooperation in AI research, development and policy development should be encouraged.
Sharing knowledge, resources and best practices across borders will help address global challenges and harness the collective potential of AI technologies.
By adopting these strategies, stakeholders can effectively harness the full potential of AI, drive innovation, improve human capabilities, and promote sustainable development.
Through a proactive and collaborative approach, we can shape a future in which AI technologies are responsibly developed and deployed for the benefit of all.

In language education—an area directly relevant to foreign language instructors—voice cloning offers the potential to generate authentic, contextually appropriate listening comprehension materials tailored to low proficiency learners.

How will educational institutions benefit from developing AI voice audio content for languages learners?

Respond to different learning demands
Enhance language comprehension

Such materials can include diverse accents, emotional tones, and multilingual content, enriching the learning experience while addressing the scarcity of suitable audio resources at lower proficiency levels.

Deepdub, a film dubbing company, uses an AI to process voice and translate into a different language and promises to preserve what?

How will educational institutions benefit from developing AI voice audio content for languages learners?

Respond to different learning demands
Enhance language comprehension

However, alongside these positive uses, there are malicious applications; AI voice cloning facilitates sophisticated scams and fraud schemes where impostors mimic trusted voices to deceive victims, extort money, or bypass security systems.

What do cybersecurity experts believe about the use of synthesized voice?

could become a popular method of attacking banks and financial services

Recent real-world incidents reveal scammers exploiting AI-cloned voices for financial extortion, underscoring the risks involved.

What do cybersecurity experts believe about the use of synthesized voice?

could become a popular method of attacking banks and financial services

Meanwhile, content creators experiment with AI-synthesized voices in multiple languages for storytelling or localization, highlighting evolving creative possibilities .

Title:

Content: ChatGPT ’ s new feature makes it possible to synthesize realistic vocals in just 15 seconds OpenAI: not available yet
Author: Au Jingluo
Published: Updated:
OpenAI has decided to postpone the release of its new software, Voice Engine, which can copy and synthesize realistic human voices in just 15 seconds of recording. OpenAI says it is too risky to release the tool now.
Voice Engine was developed in 2022 and the original version was used for the Text-to-Speech feature of ChatGPT.
However, OpenAI wants to expand it into a tool for simulating speech, copying the sounds of real people.
“We want to start a conversation about the responsible application of synthetic sound and how society responds to these new capabilities, ” OpenAI said in a blog post.“ Based on the results of small-scale testing, we will be more informed about whether and how to deploy this technology at scale. ”
A giant AI logo at the Artificial Intelligence Conference in Shanghai, China, on July 6, 2023. (Reuters)
In the article, OpenAI shares practical examples of partners trying this technology.
For example, the edtech company Age of Learning uses it to generate script voiceovers, while the AI voice company HeyGen uses user recordings to translate directly, such as using audio samples from French users to simulate users reading English.
It doesn ’ t take much variety to make a realistic simulation, and one of the demonstrations uses only 15 seconds of recording to successfully replicate.
OpenAI says it won ’ t release the technology publicly at this time because it ’ s too risky.
The company called on the community to discuss “policies to protect the use of individual voices in AI ” and“ educate the public about the capabilities and limitations of AI technology, including the possibility of fraudulent content. ”

Legally, the regulation of voice cloning technology remains in a nascent and fragmented state worldwide.

Title:

Content: Generative AI and US copyright law are on a collision course

The way in which US copyright law interacts with the burgeoning field of generative AI remains nebulous, and could cause problems for generative AI training that will come to a head in court cases.

US copyright law has yet to catch up to the numerous challenges posed by the wildfire adoption of generative AI, and the first real movement on major legal questions is likely to come from upcoming trials.

One of those key questions is the copyright status of works generated by artificial intelligence — for example, whether a Midjourney image that the system generated in response to a user prompt is protected by copyright, and, if so, who owns that copyright.

No one owns copyrights on AI-generated work

The answer, for the moment, is that nobody owns the copyright on AI-generated works, because nobody can. The US Copyright Office has taken the position that human authorship is required for copyright to exist on a work, and that no such authorship exists in the case of AI-created writing or images — neither the creator of the AI nor the provider of the prompt used to generate a particular work “owns” that output.

That’s unlikely to change due to legislation or administrative action in the near future, according to Ron Lazebnik, clinical associate professor at Fordham University School of Law. Litigation, however, could challenge that standard, he noted.

“Either someone tries to register something with the copyright office, who says no, and then they sue challenging that denial, or alternatively, someone who may have used an AI but never let the copyright office know that, they become the plaintiff,” Lazebnik said. “Beyond that, it’s not clear on how we would be able to get a court to say whether or not an AI generated work is attributed to the person who prompted the AI.”

It's within the Copyright Office’s purview to change that rule, according to Philippa Loengard, executive director of the Kernochan Center for Law, Media and the Arts at Columbia Law School — but she warned that such action remains unlikely.

“My guess is that AI regulation won’t start with copyright,” she said. “I think, and I could be wrong, that there are so many issues being discussed in the realm of AI that I’m not sure that changing the requirements of human authorship is going to be of paramount importance.”

The larger issue for AI in terms of copyright law is likely to be the concept of fair use, particularly as it applies to the training data used to create the large language models (LLMs) underpinning generative AI.

Fair use, in brief, is a defense to copyright claims written into federal law. The four factors that courts have to consider when deciding whether a particular use of copyright material without permission is “fair use,” are the character and purpose of the use (educational or other not-for-profit use is much more likely to be deemed fair than commercial use), the nature of the original work, the amount of the original work used, and the market effect on the original work.

Copyright a stumbling block for AI model training

Given those factors, it’s perhaps unsurprising that the lawsuits against companies like OpenAI have already begun. Most notably, a group of authors that includes comedian and writer Sarah Silverman sued OpenAI and Meta in July over the company’s use of their books to train ChatGPT.

The core issue in that lawsuit is the use of a data set called “BookCorpus,” which, the plaintiffs say, contained their copyright material. OpenAI and Meta are likely to argue that the market effect on Silverman’s and others works is negligible, and that the “character and purpose” of the use is different than that which prompted the writing of the books in the first place, while the plaintiffs are likely to highlight the for-profit nature of Meta and OpenAI’s use, as well as the use of entire works in training data.

Precedent, however, may be on the AI companies’ side — the Google Books case, which was a fair use action brought by the Author’s Guild of America against Google’s mass book digitization project in 2005. The case’s history is complicated, including appeals went on for a decade, and was ultimately settled in Google’s favor.

Whether that’s likely to be predictive, however, is debatable, according to Loengard, and much could depend on a judge’s willingness to challenge a large, profitable industry.

“By the time it ended, Google Books had become a tool of many researchers,” she said. “So there’s this idea that the cat’s out of the bag — and of course, the court wouldn’t say this out loud, and I’m not saying this is what they did, but they could look at it and say that once something has entered mainstream commerce that it’s harder to reel it back in and regulate it.”

Obviously derivative works could be another copyright battleground for the AI industry, given that the technology has already been used to produce convincing imitations of popular singers and songwriters.  The right of publicity — a different legal concept covering the rights to a person’s name, image and likeness, could become a cause of action for the performance itself — i.e., the sound of Taylor Swift’s voice. But copyright could still become an issue if the underlying song is sufficiently similar to one written by Swift.

“If it’s a song close to what those artists might be expecting to produce, [copyright law could be implicated], in theory, depending on how close AI comes to writing a song that resembles one that that artist could write,” said Lazebnik.

The unsettled state of play around AI and copyright law is not simply a US phenomenon, although most countries have yet to pass detailed legislation around it. The EU’s AI Act, as well as frameworks for more general AI regulation passed by the US and China, do not change the confused state of play around copyright issues. One country that has done so is Japan, which clarified in June that the use of copyright works for AI training is permitted, even for commercial purposes.

But active regulation of these issues may still be far off in the US, according to the experts.

“The legislature hasn’t created a new carve-out in copyright in quite some time,” said Lazebnik. “If they think fair use isn’t sufficient and that it’s something they want to encourage, they might amend our laws, but at the moment, they would have to see an appellate court saying ‘no, this isn’t fair use.’”

“[Congress] has no pending legislation at all,” noted Loengard. “Everyone is studying this, nobody has come to any conclusions about it.”

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Copyright law traditionally mandates human authorship for protection, leaving AI-generated voices without clear ownership or copyright status under current frameworks, particularly in the United States.

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Content: The Future Is Here
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AI This Week: Can AI's Environmental Impact Be Justified?

A new report shows the massive energy needs of AI systems. Plus: a look at how the music industry is fighting AI voice clones.

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Headlines This Week

  • A new report from Politico suggests a “billionaire-backed network” of Silicon Valley-linked AI “advisors” are working to control the regulatory agenda in Washington D.C. Boy, I sure wish stuff like that were illegal instead of just business-as-usual.
  • Despite apparently having lobbying money aplenty, it looks like the AI industry is still struggling to monetize some of its platforms. The Wall Street Journal reports that some major AI platforms, like Microsoft’s Github Copilot, have been hemorrhaging money.
  • Last but not least: A newly introduced draft bill would institute protections for musical artists against AI. We spoke with a music industry executive about what the policy could mean for his business.

The Top Story: AI’s Growing Environmental Impact

One of the ongoing problems connected to the AI industry is just how much power it takes to run its systems. The pursuit of enough juice to run the high-octane algorithms behind apps like Bard, Bing, and ChatGPT is causing concern even among AI’s biggest proponents. Now, a new study shows the electrical resources needed for the growing AI industry could make its environmental impact even worse than previously thought.

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The study, authored by Alex de Vries, who is a PhD candidate at the VU Amsterdam School of Business and Economics, claims that the AI industry’s energy needs may soon match those of a small country. “Given the expected production in the coming few years, by 2027 newly manufactured AI devices will be responsible for as much electricity consumption as my home country, the Netherlands,” de Vries told Insider this week. “This is also in the same range as the electricity consumption of countries like Sweden or Argentina.”

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While the rate of electricity consumption in the tech industry has remained relatively steady for years, de Vries says that the advent of the AI chatbot wars between tech giants like Microsoft, Google, and OpenAI, may have spawned a new era. De Vries’ study looks specifically at the electricity consumption of the AI sector as it applies to something called the “inference phase” of AI production. While most environmental impact studies have so far focused on the amount of energy it takes to train large language models like GPT-4, less attention has been paid to “inferencing,” which is the process by which language models produce new information as the result of prompts. This phase of energy consumption can be massive and, sometimes, accounts for a majority of energy expended during the AI life cycle, de Vries writes. Due to the potential ballooning nature of AI energy needs over the next several years, the scholar argues that developers “focus [not only] on optimizing AI, but also to critically consider the necessity of using AI in the first place.”

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De Vries’ study, like a number of other prominent environmental impact studies that have been published recently, begs the question: in an age of climate change and accelerating environmental distress, can generative AI really be justified? I mean, do we really need ChatGPT, automated email-writing, and AI stickers? Or are we killing the environment needlessly for too little in trade off?

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At best, these platforms offer increased convenience for consumers and some cost savings for corporations, but—for now—that’s about it. Much of the mystification around AI has helped hide the fact that generative AI technologies aren’t—in many cases—particularly revolutionary and, in some cases, aren’t even new. Sure, there are some novel scientific applications of AI that, once refined, could have a major impact. But it would be hard to argue that those applications are the ones getting the lion’s share of attention or resources. Mostly, it’s stuff like ChatGPT that hogs the spotlight (and venture capital).

When viewed through the lens of the technology’s massive environmental impact, it seems hard to justify what is little more than an over-hyped form of content automation. Better deepfakes aren’t worth killing the planet over.

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The Interview: Mitch Glazier on the Music Industry’s Fraught Relationship to AI

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This week we spoke with Mitch Glazier, the Chairman and CEO of the Recording Industry Association of America, which lobbies on behalf of the music industry. RIAA is supporting a newly introduced piece of legislation that seeks to institute legal protections against the use of AI to replicate the visual or audio likenesses of artists. In recent months, there’s been  an explosion of AI “deepfake” content that blatantly rips off well-known musicians and celebrities. The  Nurture Originals, Foster Art, and Keep Entertainment Safe (NO FAKES) Act, which is currently a discussion draft bill, would enshrine a number of legal protections for artists who don’t want their visual or audio likenesses used in unauthorized deepfakes. This interview has been edited for brevity and clarity.

What role did RIAA have in connection to this bill and what do you hope this bill achieves?

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We were one of several groups that were asked to give input so that they could develop the bill. SAG-AFTRA was also heavily involved. The idea was to create federal legislation because all of the protections for name, image, likeness and voice are in the states right now—and they’re all different.

What is it about AI that makes it a threat to your industry (the music business)? What’s the problem with this technology?

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The problem really isn’t the technology. AI can be licensed for a variety of great purposes and uses. The problem is when AI is made based on a particular artist’s voice or image without consent, credit, and compensation. Basically, what the bill says is, ‘The technology’s great and if an artist wants to license the use of their essence—their voice, their likeness—that’s they’re choice. But what you can’t do is clone their voice without their permission.’ It’s just a basic right that exists for all other types of property—and that should exist here. It should be up to the artist to decide whether or not they want to allow you to use their image or their voice.

So it seems like this legislation actually opens the door for more AI music but the idea is that, if a particular artist is involved in an AI or a deepfake production, they would have to get credit and, presumably, get paid, right?

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Well, the bill says “authorized,” so the artist would have to authorize it. If they want to negotiate compensation they can. Some may not want to. But the key is that “authorization” part. The author has to say, “Yes, you can use my voice, my image.” If they don’t say yes, you can’t do it.

What are your hopes for the passage of the bill? It seems like there are some pretty prominent Senators supporting it.

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We’re very optimistic. This issue goes beyond the music and entertainment industry and it goes beyond the issue of art. It relates to every individual’s right to control their own essence, for lack of a better term. We’ve been encouraged by great bipartisan support right off the bat. To get two Democrats and two Republicans—including the chairman of the Intellectual Property Subcommittee (Amy Klobuchar D-Minnesota)—to put out this discussion draft and to do it in a way in which they’re inviting people to respond and come into the conversation, is a great sign.

Catch up on all of Gizmodo’s AI news here, or see all the latest news here. For daily updates, subscribe to the free Gizmodo newsletter.

This gap complicates questions concerning the rights to use, reproduce, or commercially exploit cloned voices.

Title:

Content: OpenAI's Johansson gaffe pushes voice cloning into spotlight

Law enforcement warned that AI clones could be used to extort money from loved ones over the phone

By AFP

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Scarlett Johansson voiced an AI character in the film "Her". — Reuters file

Published: Mon 27 May 2024, 7:51 PM

Last updated: Mon 27 May 2024, 7:52 PM

OpenAI was forced to apologise to actor Scarlett Johansson last week for using her voice -- or something very similar -- on its latest chatbot, throwing the spotlight on to voice-cloning tech.

Although OpenAI denied the voice they used was Johansson's, their case was not helped by CEO Sam Altman flagging the new model with a one-word message on social media -- "Her".

Johansson voiced an AI character in the film "Her", which Altman has previously said is his favourite film about the technology.

Right from the start, AI voice cloning has proved problematic.

Last year, British firm Elevenlabs went viral for all the wrong reasons when it released its voice-cloning software.

Internet pranksters immediately began pushing out deepfaked celebrities -- Harry Potter star Emma Watson was shown reading Hitler's Mein Kampf.

Law enforcement warned that AI clones could be used to extort money from loved ones over the phone.

The technology has developed rapidly in the past year becoming far more realistic and nuanced.

Danish entrepreneur Victor Riparbelli, CEO of British AI firm Synthesia, told AFP it was largely down to a program called Tortoise that was released two years ago.

The program's developers threw thousands of hours of voice data into their model in an unstructured way and discovered it not only learnt what to say but how to say it.

"That was a pretty big paradigm shift," Riparbelli said on the sidelines of last week's VivaTech conference in Paris.

Tortoise was an open source program and Elevenlabs was the first to go to market using it.

OpenAI uses similar systems though they do not release any details.

Much of the controversy around voice cloning has focused on concerns over people misusing the software.

But the claim against OpenAI is unusual because it is the company itself accused of playing fast and loose.

"It was very unfortunate that OpenAI did that -- really not very good," Katya Laine, CEO of TALKR.ai, told AFP at VivaTech.

"If they actually cloned her voice without her knowing then I think that's very very bad," said Riparbelli.

The two entrepreneurs are among hundreds harnessing AI voice programs for uses that they argue will make companies more efficient.

Laine's firm provides virtual voice assistants -- essentially AI customer service agents.

She said her firm's system could now resolve 25 to 30 percent of calls without any human involvement.

Synthesia specialises in video avatars, which Riparbelli said allowed any office worker to turn text or slides into a video performed by a realistic AI.

Both Riparbelli and Laine allow their clients to use their own avatars, off-the-shelf products or those supplied by the likes of OpenAI and Elevenlabs.

Riparbelli said Synthesia used actors whose likenesses and voices were licensed for two years with an option to renew after the initial period.

The problems arise if actors' voices are used without their consent.

The fiasco overshadowed a developer conference in Paris last week when OpenAI was showing off a suite of new tools.

In front of a big screen in an auditorium, Romain Huet, OpenAI's Head of Developer Experience, breezily chatted into his phone.

Seconds later, his short voice sample had been processed and could be heard commentating over a generated video -- in five languages.

The demonstration showed how quickly the field is moving, but the headlines had already been written.

The Washington Post asked in a newsletter "How dumb is OpenAI?", other commentators were suggesting wunderkind Altman was nothing more than a huckster.

Nonetheless, Riparbelli was open to OpenAI's argument that they had used another actor who just sounded liked Johansson.

"If it's not her but someone who sounds a lot like her... where do you draw that line," Riparbelli asked.

"If they're not allowed to use someone who sounds a lot like her, then it sets a very odd precedent."

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The right of publicity, which protects individuals against unauthorized commercial use of their name or likeness—including voice—varies widely by jurisdiction and often depends on state-level legislation.

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How AI will reshape the music industry, according to 5 entertainment lawyers

This story is available exclusively to Insider subscribers. Become an Insider and start reading now.

  • AI-generated music is set to create a slew of new legal challenges for artists and rights holders.
  • Copyright and right of publicity laws are top of mind for entertainment attorneys as AI songs surge.
  • Here are four key takeaways from a recent panel on AI music hosted by the law firm Manatt.

AI-generated tracks simulating artists like Drake and The Weeknd have spread across social media in recent weeks, forcing record labels, publishers, and streaming platforms to reckon with its potential business impact on musicians.

Some artists like Grimes are embracing the technology by setting up AI content-creation tools in an attempt to profit from the use of their voices. Others, such as Ice Cube, are rejecting the technology outright.

Regardless of artists' views, adoption of the technology is on the rise. Entertainment lawyers are planning strategies for how they can protect their clients, including using existing tech designed to detect and enforce copyright protections under the Digital Millennium Copyright Act, or DMCA, and invoking right of publicity laws.

"Music is unfortunately a canary in a coal mine sometimes with new technology," said Jordan Bromley, entertainment group leader at Manatt, Phelps, & Phillips, LLP, speaking on a May 9 panel hosted by the firm.

Manatt's panel included Bromley; his colleagues Nathaniel Bach and Sarah Moses; Mitch Glazier, chairman and CEO of the Recording Industry Association of America; and Jeffrey Bennett, the general counsel for the Hollywood actors and music artists union SAG-AFTRA.

Here are four main takeaways on the legal and business challenges of AI in music called out by the panelists:

1. Understanding AI training models will be important for enforcing copyright law

Technology exists today to identify copyright violations in songs after they are released.

The Verge reported that Universal Music Group used DMCA detection tools to submit a takedown request for an AI-generated track imitating Drake and The Weeknd that included a traceable tag from the producer Metro Boomin, for example.

But the precise manner that AI-generated music, trained on a human artist's body of work, could violate copyright laws is still being defined.

"We don't always have information about how a particular AI song is created and how the software works," Manatt's Bach said. "Figuring out where the rubber meets the road there in terms of the internal working processes, what was done by the AI, what was done with the human, and for what purpose, is going to be really central."

The copyright rules surrounding AI training models are currently being litigated in other parts of the art world. The company Getty Images and several visual artists have separately sued AI image generators like Stable Diffusion, Midjourney, and Stability AI around alleged use of their works in training programs.

During Manatt's panel, RIAA's Mitch Glazier raised the idea that an AI model trained on existing songs should legally be defined as a mechanical reproduction, which would require a license.

2. Right of publicity may play an important role in AI legal cases

Right of publicity laws, which protect an individual's right to control the commercial use of their personal characteristics, could be a useful tool for lawyers looking to protect artists from AI-generated imitations.

If a song that includes an AI-generated voice that mimics a well-known artist is making money, it may violate right of publicity laws in certain states.

"You have to look at what is the intent and commercial benefit to the person who is putting it out, and then what is the commercial consequence to the market itself and to the artist on the other works that they have out in the marketplace," RIAA's Glazier said.

Because right of publicity laws are written at the state level, they can be more time consuming to enforce and aren't a perfect solution, the attorneys said.

"It's sort of a patchwork of state laws," SAG-AFTRA's Bennett said. "It's time to take it out and see what we can do with it."

3. Streaming platforms like Spotify and YouTube will play a big role in AI's future

The sheer volume of AI songs spit out onto streaming platforms could create detection challenges for rights holders. Music platforms like Spotify and YouTube could set limits on how much AI music spreads, the attorneys said.

"Even if we get the tools, we need to think about enforcement very carefully, because this is a whole new game of whack-a-mole," Manatt's Bromley said.

Separately, the business incentives between the platforms and rights holders may not be aligned.

Some streaming apps may push to add AI-generated tracks to their libraries as a way to cut costs by reducing the royalty payments due to human artists, record labels, and publishers.

"If you're a [digital streaming platform] and you want to add your bottom line, you create AI technology that can make functional music and you throw all that on your playlists and you shift 10% of total content costs to your bottom line," Bromley said. "If a company wants to work with the music business and be on the right side of history, they won't do that."

Record labels are already looking for ways to regulate how streaming platforms work with AI tools. Universal Music Group sent letters in March to Spotify and Apple Music requesting that they block artificial-intelligence companies from using their catalogs to train AI models, Billboard reported.

4. AI isn't inherently bad, and could spark new creative work in music

While much of the conversation in Manatt's panel centered on the risks that AI presents to the music industry, there were a few notes of optimism about how the technology could benefit musicians.

"The other side of this coin is this is a great creative tool if embraced properly and used appropriately and responsibly," Bromley said.

AI-generated works that share earnings with an artist, such as Grimes' Elf.tech tool, could end up creating a new revenue line for performers. Universal Music Group announced on Tuesday a partnership with AI-music startup Endel to help its artists and labels create "soundscapes for daily activities like sleep, relaxation, and focus by harnessing the power of AI."

"If you look at history, those who have decided to become partners and work with the music community have thrived with the music community," RIAA's Glazier said. "Those who have decided that they're going to use legal loopholes and try to pay less, or pay nothing, you don't really hear about them much today."

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In China, courts have taken a firmer stance, ruling that AI voice cloning without consent constitutes infringement, resulting in damages awarded to voice owners.

Title:

Content: The first trial of the "AI voice infringement case" sentenced the voice actor to be paid 250,000 yuan
Author: Chen Jinan
Published: Updated:
Beijing Internet Court on Tuesday (23) on the country's first "AI voice infringement case" issued a judgment, found that as a voice actor, the plaintiff's voice rights and interests in the case involving AI voice, the defendant's use of the plaintiff's voice, the development of AI text-to-speech products without legal authorization, constitutes infringement, written apology, and compensation for the plaintiff's losses of 250,000 yuan.
The court heard that the voice actor Yin had accidentally discovered that his voice had been AI and sold in an APP called "Magic Voice Workshop" in the name of "Magic Xiaoxuan". Yin believed that the behavior violated his voice rights.
The defendant denied the infringement. Among them, Beijing Intelligent Technology Co., Ltd. believed that the sound products in its APP had a legitimate source, from a software company of the defendant. The software company said that the sound used by the software company was from the defendant AV Culture Media Co., Ltd. Right.
The court held that sound as a personality, with personal characteristics, any natural person's voice should be protected by law, the authorization of the sound does not mean the authorization of the sound, without permission, unauthorized use, permission of others to use the sound in the sound, constitutes infringement, and so made the above judgment.

Regulatory efforts are advancing; the European Union is implementing an AI Act starting in 2026 aimed at addressing risks associated with AI-generated content, including voice fakes.

Title:

Content: Legal Safeguards for Intellectual Property Weak on AI; Japanese Govt Panel Notes Issues, Offers Little Remedy

The draft outline of a government panel’s interim report has acknowledged that Japan’s existing laws are limited in their ability to prevent infringements of intellectual property rights by artificial intelligence technologies. It calls on AI operators to voluntarily make efforts to protect those rights.

The outline approved Thursday by the panel tasked with discussing the protection of intellectual property rights in the era of AI also underlined the importance of providing some compensation to creators and other rights holders, but did not go as far as to call for new legislation governing AI.

Chief Cabinet Secretary Yoshimasa Hayashi touched on the issue of protecting intellectual property rights during his press conference the same day. “Additional efforts must be promoted through an appropriate combination of laws, technologies and contracts at each stage,” Hayashi said.

When instructed to imitate the painting style of a given artist, generative AI can easily produce a picture that looks like an artwork the artist might have created. Similarly, this technology can easily generate a voice by learning the voices of real-life singers and voice actors.

Creators around the world have become increasingly concerned that their rights could be infringed by AI, which can very easily learn artworks and mimic skills that human creators used their ability and effort to develop.

The draft outline explicitly states that there are instances in which current laws cannot respond to such situations. Abilities nurtured during the creation of artworks, and literary or artistic “styles” that do not lend themselves to precise definitions, fall outside what is protected under the Copyright Law, and the outline explains that the possibility of things such as the “voice” of a voice actor being protected as a form of publicity rights by the law “cannot be said to be high.”

In addition, based on moves to introduce AI regulations in Europe and the United States, the outline states that the “formation of international rules and international standardization might be necessary.”

The European Union has passed AI legislation that includes penalties for violations of AI development and operation rules, and this will start being applied in 2026. By contrast, momentum for crafting a similar legal framework in Japan has not been growing.

A Cabinet Office official said the draft outline was not intended to be a channel “for offering an opinion on creating something new, but rather something arranged based on current laws.”

Uniform measures ‘difficult’

The draft outline also suggested that creators could be protected by way of having “profits returned” in the event that their work is used for learning by AI.

Individual creators and AI operators are not on an equal footing when it comes to bargaining power. Accordingly, the outline called on these operators to establish opportunities to properly adjust and reconcile usage conditions and other details with the creators.

The Copyright Law allows the training of AI models with copyrighted material without permission from copyright holders. However, the outline said that even in these cases it would be “possible” to reach an agreement with copyright holders on providing compensation to them, and that “it could be expected” that AI operators would offer such compensation.

However, the draft outline also stated that establishing “uniform, systematic measures” would be difficult. As things stand, it will be up to operators to take it upon themselves to make efforts on this issue.

What are some risks regarding AI voice clning?

Japan is advocating for voluntary measures among AI operators instead of immediate legislative changes, focusing on intellectual property protection and ethical standards.

Despite these efforts, there is no comprehensive international legal framework yet to uniformly govern AI voice cloning, and important questions remain about consent, data privacy, compensation, and enforcement across jurisdictions .

Ethical issues surrounding AI voice cloning revolve chiefly around privacy violations, consent, misinformation, and potential psychological and social harm.

What are some risks regarding AI voice clning?

Recording and synthesizing a person’s voice without consent raises serious privacy concerns and risks unauthorized exploitation, particularly for vulnerable individuals who may be more easily manipulated.

The proliferation of synthetic voices contributes to the growing problem of deepfakes—fabricated media that can be used to spread misinformation, undermine trust, or commit fraud—intensifying societal harms such as erosion of confidence in audio evidence and public discourse.

Title:

Content: Promoting responsible AI: Balancing innovation and regulation

As AI technology advances, it is essential to remain mindful of familiar and emerging risks. Education is critical to fostering responsible AI innovation, as understanding the technology and its limitations raises standards and benefits everyone.

In this Help Net Security interview, Nadir Izrael, co-founder & CTO of Armis, discusses the global efforts and variations in promoting responsible AI, as well as the necessary measures to ensure responsible AI innovation in the United States.

What are your initial impressions of the Biden-Harris Administration’s efforts to advance responsible AI? Are they on the right track in managing the risks associated with AI?

The effort to address the issue of responsible AI is a proactive step in the right direction. However, whether they are on the right track in managing the risks associated with AI is still uncertain and will depend on various factors, including finding the right balance between innovation and regulation.

In a free market, there may not be sufficient incentives to prioritize responsible AI research and development. So, it’s commendable that the administration is taking initiative in this regard. It is crucial for the initiatives to strike a balance that allows for innovation and financial reward. Without such flexibility, AI developers may seek ways to bypass the initiatives, potentially undermining their intended purpose.

While the blueprint put forward is a great start, it is merely that, a start. The AI Bill of Rights is laying the foundation for our journey towards a responsible framework regarding the right policy and regulations in this space.

The Administration seeks public input through a Request for Information (RFI) on critical AI issues. What significance does public input hold in shaping the government’s strategy to manage AI risks and harness opportunities? How might this engagement with the general public impact policy decisions?

The administration’s request for public input demonstrates an inclusive approach that recognizes the importance of diverse perspectives. Seeking public input is a vital component as it allows the government to gather insights from a wide range of stakeholders, including industry, academia, civil society, and the general public. This input is instrumental in identifying unique opportunities and risks associated with AI, shaping policies, and establishing regulations that align with the values and concerns of the public.

Engaging with the general public also promotes awareness and understanding of AI-related issues while fostering trust between the government and the public. By involving the public in the development of AI policies and regulations, the government can promote responsible deployment of AI that serves the best interests of society.

The US Department of Education’s report on AI in education highlights both the opportunities and risks associated with AI in teaching and learning. Could you elaborate on some of the risks mentioned in the report, particularly algorithmic bias? How can trust, safety and appropriate guardrails be ensured in implementing AI in educational settings?

There are a variety of risks with AI that we should address. Some of those include:

1. Ethical concerns: AI systems are often marketed to consumers as objective tools. They are, however, tools designed by humans with their own subjective experiences and biases that are introduced into the technologies. These ethical challenges must be identified and introduced to teachers and students.

2. Algorithmic bias and discrimination: AI systems can perpetuate and amplify existing biases and discrimination. First, an AI system might be trained on biased data. Second, the AI system might learn to discriminate based on features that are correlated with protected characteristics, such as race or gender, even if those features are not explicitly categorized in the training data. This can lead to unfair treatment of certain groups of people, including students.

3. Cybersecurity risks: The use of AI in education can increase cybersecurity risks, particularly if sensitive student data is collected and stored in a vulnerable manner. As AI learns from the data that it has been fed, it may also inadvertently share confidential information that it has collected with other users.

4. Privacy concerns: The use of AI in education can raise privacy concerns, particularly if sensitive student data is collected and used without proper consent or protection.

5. Inaccuracy: AI systems are only as accurate as the data they are trained on, and errors in the data can lead to inaccurate results and decisions. Known as “hallucinations,” AI can produce inaccurate results that are not backed by data or evidence.

6. Overgeneralization: AI systems can overgeneralize from limited data, again leading to inaccurate or unfair decisions.

From a cybersecurity perspective, we must address privacy and security concerns. Bad actors are successfully using confidentiality attacks to draw out sensitive information from AI systems. Without proper security measures, institutions and individuals are at risk. To protect students, for example, institutions may put in place policies curbing the use of AI tools in specific instances or provide educational content cautioning them against sharing confidential information with AI platforms.

Algorithmic biases, inaccuracies, overgeneralizations represent intrinsic limitations of the technology since the models are a reflection of the data they are trained on. Even if care is taken to ensure input data is fact-checked and accurate, hallucinations may still occur. Therefore, a human element is still important in the use of AI. Fact checks and discerning eyes can help weed out inaccuracies. Councils guided by community-oriented ethical guidelines can help reduce biases.

Regarding national security, what concerns about AI systems need to be addressed? Could you provide some examples of cyber threats to AI systems?

From a geopolitical perspective, we cannot ignore the adversarial nature of the AI innovation race. AI-powered cyberwarfare is an incredibly impactful, cost-effective tool for adversaries to disrupt world order. AI can be weaponized against both networks and people and used to gain competitive advantage.

For example, criminal groups could use AI-powered hacking tools to disrupt critical infrastructure. In light of recent warnings that Chinese state-sponsored hackers had compromised industries including transportation and maritime, this concern is paramount.

Additionally, the proliferation of the use of deepfakes, voice and image creation and manipulation is a particularly concerning threat on the rise. These convincing manipulations could be used to extract sensitive national security information.

Policy makers are aware of these risks and are working to foster a competitively robust environment that will allow US tech companies to innovate, while protecting national security interests and the individual rights of our citizens.

How do the actions taken by the Biden-Harris Administration compare to global efforts in promoting responsible AI? Are there any notable differences or areas where the US could learn from other countries’ approaches?

The UK, EU and Canada have released ethical guidelines encouraging responsible AI development in their countries.

The US is joining the club with the Biden administration’s latest announcement. Though still in the early stages, the administration is putting serious consideration into it by relying on experts’ insights and requesting public input.

What additional steps or measures are necessary to ensure responsible AI innovation in the United States? Are there any areas that still need to be addressed or potential risks that require further attention?

Designing and developing the right model calls for an increased emphasis on public-private partnerships between the public and private sectors. We are off to a good start but the legacy collaborative models of the past won’t suffice. We know other countries are making rapid advances in AI research, so we need to move at a speed and velocity never before seen in addressing this topic and creating a productive and protective environment.

AI technology is very much a revolution in progress. On top of the familiar risks, we should be mindful and continuously evaluate new risks that the technology may present. A key component to ensure responsible AI innovation is education. When people understand what is behind the technology they consume and its current limitations, the standard rises, and we all benefit.

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What are some risks regarding AI voice clning?

Ethicists and policymakers stress the imperative for transparency, accountability, and the incorporation of robust detection tools able to identify synthetic voices to curb misuse.

What are some risks regarding AI voice clning?

In educational contexts, ethical questions include maintaining authenticity and respect for original speakers when generating synthetic listening materials, as well as considering students’ psychological responses to AI-generated voices.

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Content: Monday, 30 October 2023 01: 26 GMT
The ethical and legal features of manipulating music sounds using artificial intelligence
(MENAFN- Al-Bayan)
Today, we live in the era of digital technology, where modern technologies are playing an increasingly important role in the music industry, and one of the technologies, which have significantly affected this industry are artificial intelligence techniques, which enable producers and musicians to modify the sounds of songs in advanced ways, yet these technologies raise multiple issues in terms of legal and ethical responsibility.
The problem of modifying song sounds using AI software is legally complex.
This adjustment can include changing sounds, removing errors, adjusting times, and more.
If these technologies are used without permission from artists or song rights holders, it can be a violation of intellectual property rights.
Often these works require licenses and legal agreements with artists, and rights holders.
Legally, the assessment of liability depends on several factors.
First of all, it is necessary to verify the existence of specific licenses and agreements, which allow for the modification of votes.
If there is a written agreement that allows for this type of amendment, the legal liability is greatly reduced, however, if AI technologies are used to modify votes without explicit permission or authorization, this can lead to infringement of intellectual property rights, exposing the responsible party to legal accountability.
Some countries have adopted strict legislation to protect the rights of artists and owners of intellectual property rights.
For example in the United States such cases are considered under copyright and trademark laws, and sometimes penalties for intellectual property rights violations can be harsh, including significant financial compensation.
On the other hand, there are ethical aspects related to the proper use of AI techniques in modifying the sounds of songs, and artists and producers should consider the extent to which these modifications affect the quality of songs and their resonance to the audience, and the increasing manipulation of sounds may lead to loss of originality, and human communication in music.
In conclusion there must be a balance between the use of technology and legal and ethical responsibility in the music industry.
Producers and artists should abide by applicable laws and regulations, and tread carefully with AI technologies, to ensure that intellectual property rights are respected and the quality and authenticity of music is preserved.
In the end, the parties concerned must tread cautiously and transparently on this issue.
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Industry leaders and researchers advocate embedding ethical AI principles, including fairness, inclusivity, user control, and respect for consent, into voice cloning development.

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Content: In recent years, the field of artificial intelligence (AI) has made significant strides, particularly in the form of generative AI. These generative AI tools refer to algorithms and models that can generate novel content, such as text, images, and music, that mimic human creativity. While this technology has brought about exciting possibilities and potential applications, it has also sparked concerns and fears about its implications. So, should we fear generative AI?

We have to start by examining both the potential benefits and risks associated with generative AI. On the one hand, generative AI has the potential to revolutionize industries and enhance human creativity while also benefiting scientific research and innovation. It can assist artists, designers, and musicians by delivering new ideas and inspiration and generating unique art, music, and visuals. By augmenting human creativity, generative AI pushes the boundaries of artistic expression. Moreover, researchers can also use generative AI to explore complex datasets, generate hypotheses, and make predictions, accelerating drug discovery, and facilitating the design of novel materials in fields like materials science.

Generative AI also presents significant potential benefits in the realm of cybersecurity. With the ever-evolving landscape of cyber threats, traditional security measures often struggle to keep up. Generative AI can theoretically enhance defense mechanisms by creating realistic synthetic data that resemble legitimate network traffic, helping to identify and detect anomalies and potential intrusions. This technology can also simulate sophisticated attack scenarios, which let security pros test and fortify their systems against emerging threats. Similarly, generative AI can potentially assist in developing robust authentication systems, creating biometric data that’s difficult to replicate, and bolster security in areas such as facial recognition and fingerprint identification.

However, alongside these promising applications, there are legitimate concerns surrounding this technology. One of which surrounds its misuse. Attackers can use generative AI algorithms to create deepfakes, which are highly-realistic videos or images that manipulate or falsify information. This poses an existential threat to the integrity of information and bad actors can exploit them for malicious purposes, such as spreading disinformation, blackmail, or political manipulation. As generative AI progresses, it becomes increasingly challenging to distinguish between what’s real and what’s generated, potentially eroding trust in media and exacerbating the spread of propaganda.

Generative AI likewise raises ethical concerns because of its reliance on biased datasets, leading to the production of content that perpetuates societal biases and reinforces inequalities. Furthermore, there are broader societal and economic worries related to job displacement. As generative AI automates tasks previously performed by humans, we now have legitimate fears of job losses in various industries. While the technology may create new jobs in the long run, we can expect a challenging transition period for those directly impacted. Proactive measures are necessary to address these challenges and ensure equitable distribution of the benefits brought by generative AI.

To mitigate the risks and maximize the benefits of generative AI, organizations and stakeholders can implement the following measures. We need to promote increased awareness and education about generative AI among the public, policymakers, and stakeholders. This will foster informed discussions and decisions about the responsible development and deployment of generative AI systems. It’s important to establish clear guidelines and regulations to address issues such as deepfakes, data biases, and privacy concerns.

Stakeholders should prioritize transparency and accountability by making algorithms and models explainable, letting users understand content generation and address biases. We also have to hold developers accountable for misuse. The industry has to incorporate ethical considerations into the design and development process, curating diverse training datasets, and aligning the generated content with ethical standards. Fairness, inclusivity, and societal well-being should guide the development process, and collaborative efforts between researchers, ethicists, and domain experts can establish responsible guidelines and best practices for generative AI.

Interdisciplinary research and collaboration are vital to addressing some of the challenges. Collaboration between experts in AI, psychology, sociology, law, and ethics can offer a comprehensive understanding of the societal impacts and implications of this technology. This interdisciplinary approach can help identify potential risks, devise mitigation strategies, and ensure that generative AI aligns with human values and aspirations. Similarly, ongoing monitoring, evaluation, and regulation are necessary to keep pace with the advancements. As the technology evolves, it’s essential we adapt and update regulations to address emerging risks and concerns.

Generative AI holds immense potential to enhance human creativity, drive innovation, and transform various industries. However, we have to approach this technology with caution and address the associated risks and concerns. Generative AI has the potential for misuse, the perpetuation of biases, and socioeconomic impacts. By implementing the measures I just outlined, we can harness the benefits of generative AI while mitigating its fears and potential harms. With responsible development and deployment, generative AI can contribute to a more creative, innovative, and inclusive future.

Guy Albertini, associate vice president and CISO, Rutgers University

What are some risks regarding AI voice clning?

Global collaborations, such as international AI safety summits and corporate ethical guidelines (e.g., Adobe’s ethical AI principles), illustrate attempts to balance innovative potential with protecting individual rights and fostering social trust .

In sum, AI voice cloning presents transformative possibilities for education, entertainment, and accessibility, with particular promise for generating realistic listening comprehension materials across languages and proficiency levels.

How will AI Voice help educators with building courses?

How will educational institutions benefit from developing AI voice audio content for languages learners?

Respond to different learning demands
Enhance language comprehension

However, the rapid advancement and democratization of this technology raise critical challenges that foreign language educators and other users must consider.

Ethical deployment requires obtaining proper consent, ensuring transparency about synthetic content, and remaining vigilant about potential misuse.

Legal frameworks are evolving but remain insufficiently mature to address all the nuanced issues surrounding voice cloning.

Awareness of these factors will enable educators to harness AI voice cloning responsibly and effectively as a valuable tool in curriculum design and lesson preparation, while navigating the complex technical, ethical, and legal landscape that accompanies its application.

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Could AI be the next college teaching assistant? Some Colorado professors believe so

It’s a calm afternoon in the computer lab-style classroom at the University of Northern Colorado (UNC). Junior Vivian Johnson is busy typing ideas into her computer. Today’s assignment is all about adapting beloved children’s stories using Chat GPT. Johnson asked the AI software to write “Cat In The Hat” in a science fiction style.

“It switched it to, like, the hats gave them magic powers and then strange things started happening in the town,” she said. “And they found out it was because of this mad scientist that was trying to get powers from the plants and animals.”

But the exercise is not just about writing silly stories. The driving question is, can AI be used as a tool – just like essays and class projects – to teach children’s literature?

The elementary education students continue to refine the stories by adding more vocabulary words, or making the stories readable at a first-grade level. It’s fun and educational.

For example, student Holly Hyman worked with her group partners to ask Chat GPT to rewrite their story with more of a focus on their main character’s family history.

“Despite the love and warmth of her adoptive family, Lily often found herself wondering about her past. Who were her parents? Why did they leave her behind?” she read from the program. “Hey, there you go, that’s some character development!”

Johnson said she already knew what Chat GPT was, but she never viewed AI as a teaching tool. Now, it’s a whole new discovery. She hopes to use the technology to create content for second language learners in her future classroom.

“I love this class, it’s just so interesting,” Johnson said. “The buzzword ‘AI’ is really scary to some people, and I'll admit that I was even kind of that way. But I've noticed a lot more on the benefits of AI…I feel like we get a lot of good strategies out of this.”

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Artificial Intelligence has been used to edit photos, enhance home appliances and more. But now it’s being widely introduced in the classroom. This story activity is one of many class activities designed to show these future teachers what innovative things AI can do.

“They can have their students…analyze those stories,” said Matthew Farber, professor of a UNC class called "Integration of Technology into Content and Pedagogy." “Then they get to work closer to the evaluation of narrative and literary devices through using AI.”

As AI seeps into the classroom, it hasn’t come without critique. Universities are enacting policies on using the technology to write papers. Some teachers ban it altogether. Even on the student side, more than half of 1,000 surveyed college students believe using AI is cheating.

But Farber believes it won’t replace teachers or their work. He compared AI to computers and the initial fear they would become teaching machines. He referenced how Seymour Papert, the creator of Lego, helped change the perspective on them.

“Papert said, ‘It's a children's machine, children learn by teaching the computer,’” Farber recalled. “That is the approach that I'm taking with AI, that AI is not teaching students so much as we'll learn by teaching AI and working with AI.”

Farber also emphasized that many kids have already interacted with AI outside of the classroom, and not just in the form of Chat GPT.

“Anytime they play an app or a video game that has a second player, whether you're playing chess online, or you're playing Minecraft, all of the other non-playable characters are AI,” he said. “So how do we harness that to be a power for good?”

He believes children should not be passive consumers of technology, but rather should learn how to reflect on it and see it as a tool.

“The big takeaway is, how do we play with this tool in a thoughtful way, in an ethical way, that will then help us understand our humanity better?” he said. “It’s really important to embrace it.”

‘A profound benefit, if we get it right’

AI is still a new concept for teaching. Last year’s Educator Confidence Report found around 75% of educators wanted to increase their use of it. But only 20% felt confident enough to do so.

Alex Kotran is the co-founder of the AI Education Project (aiEDU). It helps equip teachers with AI lessons and knowledge to prepare students for a world where AI is everywhere. He started the project in part because his mother, who’s a teacher in Akron, Ohio, asked him to present an “AI 101” class to her students.

“It was more surprising, like surely (she must) have a teacher that talks about this, I mean, how does she talk to students about the future of work?” he said. “How is it possible that school students who are most impacted by this, like they're not part of these conversations? It just doesn't make sense.”

He looked around for curricula in schools, but could only find lessons in computer science classes and summer camps. He wants to close that gap in education and bring it out of STEM. He believes every graduating student should be learning about AI to be successful in the job market.

"If you're a truck driver, if you're an accountant, if you're an investment banker—like, every vocation is going to be impacted in some way. Some of them are going to be significantly impacted,” he said.

aiEDU has connected with the Colorado Education Initiative to form a steering committee that will look into best practices for AI by testing and scaling approaches.

“There's only a few states, Colorado being an example, that have done consistent convening… (and) have a sustained conversation about AI in education,” he said. “Superintendents and school board associations are seeing this and they're realizing, ‘Okay, this is actually something that our school leaders really care about, and they want to see action.’”

Data from an international educational journal shows the use of AI in the classroom can actually increase student engagement and learning outcomes. But Kotran thinks some AI tools are not completely accurate yet and might be used as educational crutches to metacognitive skill development.

“It'll definitely be a net good,” he said. “But the question is, is it going to be so good?”

Testing its limitations

Kotran is not the only skeptic. University of Colorado Boulder student Ashley Stafford pulls up a picture of a needle-felt cat on their computer during their "Generative AI" class. Today, the class is focused on accessibility. The assignment is to find any photo on the internet, describe it in great detail to a partner and draw it.

”The head has two big eyes that are brown with huge irises,” Stafford said to their group partner, Chi Chi Kari. “The creature is lying on its side slash stomach in a relaxed position.”

Professor Larissa Schwartz is simulating a real-life technology called Be My Eyes, which allows blind or low-sighted people to call volunteers via video chat to describe what is in front of them. Since launching, the program has recently added an AI feature which will describe the contents of a photo in place of a human.

But when they put the original photo into the AI software CoPilot, things go awry.

“I'm like, ‘Describe the image’ and it's like, ‘It's something of a delightful fantasy world. Its simplicity and unique design,’” Stafford recalls. “I'm like, ‘Dude, just tell me what it looks like,’ you know? It’s not my friend and I want it to stop acting like it is.”

Other students found CoPilot could not identify key celebrities in the photos or distinguish various Asian symbols.

Student Jackson Greer, who studies creative technology and design, said it’s hard to argue the technology isn’t helpful to some degree – it’s either trusting the computer or getting no information at all. But he said AI needs to be specific and built for its purpose to be effective.

"If it's just describing how whimsical and magical the cheese in the fridge is, like, I don't care (if I was) a blind person, I want to know where the cheese is,” he said.

Another common exercise Schwartz will do is have her class blindfold themselves and try to upload a picture to CoPilot using VoiceOver technology. As students pressed the tab button furiously to find the right setting, they got frustrated as the computer whizzed through the commands verbally. Some even found the program would give complicated commands, like clicking “Command + Shift + Option Up Arrow.”

“You couldn't go back,” Stafford said about the activity. “So I'm tabbing through it, and it says the option I need and I accidentally clicked past it. So I had to like go all the way back around, which was very annoying…I kept peeking under the blindfold.”

But the class isn’t all doom and gloom. Throughout the course, Professor Larissa Schwartz touches on how AI is used in industry, education and accessibility. She’s shown how blind athletes rely on AI to guide them on trails, or how designers use it for creative inspiration.

“I think if you can collaborate with your ideas, and then moving that into these different applications, you're only going to come up with a better solution,” Schwartz said.

Some students said they wouldn’t know about the breadth and depth of AI if it were not for this class.

“I know myself and some other people were coming in expecting a more technical aspect of like, here's how to program AI in Python,” Stafford said. “But it's been really interesting to see the more human applications of it.”

But Schwartz knows that all of these technologies are not going to be reliable. She said caution and consideration when using AI is key, especially for those with a disability.

“Anyone working within technology, whether you're developing or you're designing, you need to have an understanding of every single type of person that is using technology, not just people that can have all five senses,” she said.

Regardless, AI is not going away. Global data company Statista predicts more than 240 million people in the U.S. will be using AI by the end of the decade. That includes educators and students.

I'm the General Assignment Reporter and Back-Up Host for KUNC, here to keep you up-to-date on news in Northern Colorado — whether I'm out in the field or sitting in the host chair. From city climate policies, to businesses closing, to the creativity of Indigenous people, I'll research what is happening in your backyard and share those stories with you as you go about your day.
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