AI voice cloning technology refers to the use of advanced machine learning algorithms—particularly deep learning—to replicate an individual's voice with high realism.

These models analyze audio data to capture unique voice attributes such as timbre, rhythm, intonation, and emotional cues, enabling the generation of synthetic speech that closely mimics a target speaker.

Modern voice cloning can produce convincing results with minimal original voice input, sometimes requiring as little as three seconds of recorded speech.

Core technologies include deep neural networks and neural vocoders, which significantly improve naturalness and expressiveness of the synthesized voice output .

The development of voice cloning systems begins with the collection of original voice recordings from the speaker.

These recordings undergo preprocessing to extract relevant speech features and enhance temporal sequences.

Then, deep learning models are trained to reproduce these features, including lip movement synchrony, emotional expression, and nuanced voice dynamics.

For example, companies like Deepdub leverage neural networks to clone actors’ voices for film and game dubbing, replicating emotions such as crying or shouting—even when these are absent in the initial samples—thus preserving performance authenticity across languages .

Leading platforms providing voice cloning capabilities range from commercial services to open-source frameworks.

Deepdub focuses on content localization through high-fidelity voice replication, allowing seamless dubbing of videos while maintaining the actor's distinct voice characteristics.

NeuralSpace’s VoiceAI

platform excels in realistic voice synthesis with support for local dialects and offers convenient integration and transcription features suitable for diverse applications.

Additionally, open-source projects like OpenAI’s Whisper contribute automatic speech recognition and translation functionality that facilitates the training and refinement of voice models .

The applications of AI voice cloning span multiple domains.

In entertainment, it revolutionizes dubbing in film and video games by preserving original voice performances across languages.

It also supports media restoration via re-voicing archival audio and enables immersive gaming experiences with dynamic voice content.

Accessibility benefits include generating personalized voice assistants for people with speech disabilities and creating tailored listening comprehension materials in language learning contexts.

Such materials can address the scarcity of authentic yet accessible audio at low proficiency levels by synthesizing voices with controlled accent, pacing, and intonation.

Conversely, the technology facilitates misuse; notable among these abuses are fraud schemes where criminals clone victims’ voices from brief audio snippets to perform social engineering scams or misinformation campaigns, causing increasing concern among security experts .

The legal landscape surrounding voice cloning is evolving but remains fragmented globally.

Current concerns center on copyright, personality rights, and explicit consent for using someone’s voice digitally.

Jurisdictions such as the US, the European Union, and countries in Asia have begun drafting or proposing legislation targeting voice cloning misuse, yet comprehensive international regulation is still lacking.

Past legal cases have highlighted disputes over voice rights, emphasizing the necessity of balancing technological advancement and personal privacy.

The ease of access to sophisticated voice cloning tools by non-experts exacerbates challenges for effective legal enforcement, and ongoing debates call for updated intellectual property frameworks and stringent consent mechanisms to protect individuals from unauthorized voice replication .

Real-world examples illustrate both the promise and pitfalls of voice cloning.

On the positive side, Deepdub’s AI-driven dubbing has facilitated international film releases, enabling actors’ voices to be preserved intimately in multiple languages, thus enhancing viewer experience.

NeuralSpace’s culturally nuanced synthetic voices have bolstered customer service applications, improving accessibility for speakers of lesser-known dialects and minority languages.

Conversely, voice cloning technology has enabled a marked increase in online voice scams, where criminals impersonate relatives or corporate executives to extort money from victims, demonstrating serious risks associated with unchecked proliferation of this technology .

Ethically, voice cloning raises crucial issues.

The primary concern is consent: cloning a voice without permission infringes on personal identity and autonomy.

There are psychological harms when listeners cannot distinguish between genuine and synthetic speech, potentially eroding trust in communications and media.

Ethical responsibility extends to contexts such as education, where deploying synthetic voices without transparency might mislead learners or damage credibility.

Instructors using AI voice cloning are advised to maintain clear disclosure, ensure materials are factually sound, and respect learners’ right to genuine interaction.

Safeguards are needed to prevent deceptive applications and to foster ethical best practices within academic settings .

In education, particularly foreign language instruction at low proficiency levels (e.g., ILR 1 - 1+), AI voice cloning offers valuable potential.

Generating bespoke listening comprehension resources with synthetic voices allows instructors to tailor accent clarity, speaking speed, and intonation to student needs, overcoming scarcity of authentic input materials that suit beginners.

This capability can enhance learner engagement and provide consistent, repeatable exposure to language input.

However, educators must balance innovation with ethical use, ensuring that synthesized voices support rather than replace genuine communicative experiences, and that learners are informed of the nature of AI-generated content .

In summary, AI voice cloning is a rapidly advancing technology powered by deep learning that enables realistic voice synthesis with minimal data.

It has diverse applications across entertainment, accessibility, and education but also poses legal and ethical challenges, particularly regarding consent, misuse, and societal trust.

Emerging regulatory frameworks aim to address these concerns, but the technology’s accessibility necessitates ongoing vigilance.

In educational contexts, voice cloning presents promising tools for creating tailored listening comprehension materials for low proficiency language learners, provided it is applied transparently and responsibly.

AI voice cloning technology refers to the use of advanced machine learning algorithms—particularly deep learning—to replicate an individual's voice with high realism.

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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These models analyze audio data to capture unique voice attributes such as timbre, rhythm, intonation, and emotional cues, enabling the generation of synthetic speech that closely mimics a target speaker.

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

important vocal nuances
intonation
timbre

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

Difficult to distinguish from human voice

Modern voice cloning can produce convincing results with minimal original voice input, sometimes requiring as little as three seconds of recorded speech.

Title:

Content: Artificial intelligence developed by the Israelis is able to duplicate films with the voices of the original
Israeli startup Deepdub has developed a service for translating video content into new languages using machine learning algorithms.
The company uses neural networks to generate the speech of original actors from movies, TV shows, games and advertising.
AI processes voices and then translates them into a new language, preserving the timbre, rhythm, intonation and other important voice nuances in the work of a professional artist.
Deepdub promises to solve the main problems of industries related to voiceover and duplication.
According to Aberdeen, an average of five minutes of storytelling takes an hour to work in a recording studio.
And if the dubbing actor wants to match the original track as much as possible, the time spent on dubbing can increase by another two times.
At the same time, the cost of such services is a significant part of the budget for filmmakers and video game developers - a minute of duplication of one person per language on average costs about $75 for a simple video.
The startup aims to create a one-stop solution for content makers who want to go international or reduce their own costs for translators.
Using synthetic voices based on the original voice patterns of the actors, the startup duplicates movies, games and advertising in the post-production phase.
According to the developers, in addition to reducing costs, dubbing with the help of neural networks gives another advantage - AI reproduces the voice of the original, so the viewer will always watch the film in the form in which it was conceived by the director and performed by the actor. "Our artificial intelligence models allow you to reproduce the characteristics of the voice, using no more than three minutes of original voice data.
The AI-generated voice in the target language can be reproduced at any level of expressiveness, including crying, screaming, talking with food in the mouth, and more — even if the original voice data did not contain this information, ” said Deepdub co-founder Nir Krakowski.
Additionally, Deepdub ’ s algorithms track facial expressions and lip movements of actors.
Thus, the output of the audio track of the startup fully corresponds to how the voice actors work.
The current version of the platform supports six languages, and the startup plans to add six more before the end of next year.
In the long term, the company also intends to take its algorithms to the next level – to teach AI to duplicate voices in real time.
Among the famous works of Deepdub Krasowski mentioned the English-language thriller “Every time I die ” 2019.
The startup dubbed the film into Spanish and Portuguese.
According to the developers, this is one of the first cases when a full-fledged film with synthesized voices was released in the world.
Recently, Deepdub raised $20 million in the series A round. The company will direct the funds received to improve its technologies.

What is necessary for voice cloning to work?

Core technologies include deep neural networks and neural vocoders, which significantly improve naturalness and expressiveness of the synthesized voice output .

Title:

Content: There were 107 press releases posted in the last 24 hours and 446,378 in the last 365 days.
VoiceAI, technology for local dialects, transforming global customer service experiences
NeuralSpace launches AI voices in local languages
NeuralSpace logo
NeuralSpace's VoiceAI platform
LONDON, UNITED KINGDOM, December 10, 2023 / EINPresswire.com / --
NeuralSpace, a leader in linguistic artificial intelligence, has launched a transformative voice technology feature within its VoiceAI platform, offering realistic artificial sounds in Arabic, Hindi and English.
The innovation, unveiled on December 11, marks a major development in sound technology, offering speech formulation with unparalleled precision, especially in local dialects such as Saudi Arabia.
Three main advantages characterize NeuralSpace ’ s offerings:
Cultural Interaction: Artificial sounds capture the essence of local dialects, fostering deep communication between users and technology in regions such as Saudi Arabia, India and the English-speaking world.
Instantaneous Interaction: VoiceAI technology provides instantaneous, natural-sounding voice feedback, which is essential for dynamic speech generation in virtual assistants and voice response systems.
Ease of integration: The technology is sophisticated but easy to use, allowing developers to effortlessly optimize their applications.
VoiceAI for NeuralSpace meets the growing demand for accurate transcription, speech analytics and authentic synthetic sounds.
These AI sounds are finely tuned to real-world interactions, providing engaging and realistic user experiences.
Felix Le Mans, CEO and co-founder of NeuralSpace, said:
“NeuralSpace was founded with a vision to make the technology globally available in any language.
Today, with the release of our natural AI sounds, we are one step closer to turning this dream into reality.
Our human-quality Saudi, Indian and English voices in AI are not just revolutionizing technological innovation; they are gateways to make dialectical interactions with technology a tangible experience for everyone. ”
About VoiceAI: This platform and application programming interface (API) allow companies to copy, translate and analyze voice data with exceptional accuracy, especially in Arabic and Hindi.
Key features include up to 91% copy accuracy for these languages, surpassing major tech brands.
The platform also ensures high-level data security through in-house deployment, NeuralSpace ’ s ISO 27001 certification and compliance with the General Data Protection Law.
NeuralSpace invites you to integrate VoiceAI into your systems, and promises to change customer experiences.
The platform can be accessed via a code-free web platform, REST APIs, and Python SDK, ensuring easy integration.
About NeuralSpace: At the forefront of linguistic artificial intelligence, NeuralSpace is committed to technologies that enhance human-machine communication.
VoiceAI embodies this vision, reflecting the diversity and richness of human languages.
For more information, demos or enquiries, visit www.neuralspace.ai or contact sales @ neuralspace.ai.
NeuralSpace ’ s latest innovation is more than just a technological advance; it ’ s a step towards a future where you not only understand technology but also speak with a voice that resonates with the cultural richness of human languages.
Melanie Swamy-Brown
NeuralSpace
melanie @ neuralspace.ai
Visit us on social media:
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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?

The development of voice cloning systems begins with the collection of original voice recordings from the speaker.

What is necessary for voice cloning to work?

These recordings undergo preprocessing to extract relevant speech features and enhance temporal sequences.

Title:

Content: City News
14 / 02 / 2023 14: 31
[AI fever] Baidu new patent authorized, or combined with Wenxin to promote the real virtual image
Beijing Baidu Network Technology Co., Ltd.
Patent granted for “Driving Methods, Training Methods and Devices for Deep Learning Networks ”.
It is said that the patent can make the virtual human voice
Lip muscle movement is more natural and close to real people.
With the help of this technology and Baidu class ChatGPT project Wen Xin
(ERNIE BOT), Baidu is expected to launch a more natural dialogue with users of high fidelity virtual image.
The patent abstract shows that the present disclosure provides a virtual object-driven method, a deep learning network training method and device, and a device.
Preparation, media and products in the field of artificial intelligence, in particular deep learning, computer vision, virtual / augmented reality and images
Processing technology field, can be applied to virtual digital people, metaverse and other scenes.
The specific implementation scheme of the patent includes: in response to the acquired voice data, determining the initial speech characteristics based on the voice data;
First speech features are processed by time sequence enhancement to obtain target speech features; based on target speech features and target virtual object reference plane
to generate a lip image sequence for the target virtual object; and to perform a lip image sequence for the target virtual object according to the lip image sequence
Driver to enable the target virtual object to perform lip actions that match the voice data. (ct)
【 2023 Year of the Rabbit 】 Happy Chinese New Year, Happy Chinese New Year, Happy Chinese New Year, Happy Chinese New Year!
Let ’ s look at the forecast for 2023, and the year of the rabbit is in full swing!
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[Subscription Reward] $50 Supermarket Gift Voucher and 2023 Annual Calendar Card
The interest rate hike cycle is not urgent, the most important thing is to "fix"! etnet major banks fixed deposit rate competition!
[2023 Year of the Rabbit] Use the Nine House Flying Star to promote the Lunar New Year! Fortune, Peach Blossom Fortune, and Health Fortune Enhance! (Text Version)

Then, deep learning models are trained to reproduce these features, including lip movement synchrony, emotional expression, and nuanced voice dynamics.

Title:

Content: The exciting new AI transforming search — and maybe everything — explained

Generative AI is here. Let’s hope we’re ready.

The world’s first generative AI-powered search engine is here, and it’s in love with you. Or it thinks you’re kind of like Hitler. Or it’s gaslighting you into thinking it’s still 2022, a more innocent time when generative AI seemed more like a cool party trick than a powerful technology about to be unleashed on a world that might not be ready for it.

If you feel like you’ve been hearing a lot about generative AI, you’re not wrong. After a generative AI tool called ChatGPT went viral a few months ago, it seems everyone in Silicon Valley is trying to find a use for this new technology. Generative AI is essentially a more advanced and useful version of the conventional artificial intelligence that already helps power everything from autocomplete to Siri. The big difference is that generative AI can create new content, such as images, text, audio, video, and even code — usually from a prompt or command. It can write news articles, movie scripts, and poetry. It can make images out of some really specific parameters. And if you listen to some experts and developers, generative AI will eventually be able to make almost anything, including entire apps, from scratch. For now, the killer app for generative AI appears to be search.

One of the first major generative AI products for the consumer market is Microsoft’s new AI-infused Bing, which debuted in January to great fanfare. The new Bing uses generative AI in its web search function to return results that appear as longer, written answers culled from various internet sources instead of a list of links to relevant websites. There’s also a new accompanying chat feature that lets users have human-seeming conversations with an AI chatbot. Google, the undisputed king of search for decades now, is planning to release its own version of AI-powered search as well as a chatbot called Bard in the coming weeks, the company said just days after Microsoft announced the new Bing.

In other words, the AI wars have begun. And the battles may not just be over search engines. Generative AI is already starting to find its way into mainstream applications for everything from food shopping to social media.

Microsoft and Google are the biggest companies with public-facing generative AI products, but they aren’t the only ones working on it. Apple, Meta, and Amazon have their own AI initiatives, and there are plenty of startups and smaller companies developing generative AI or working it into their existing products. TikTok has a generative AI text-to-image system. Design platform Canva has one, too. An app called Lensa creates stylized selfies and portraits (sometimes with ample bosoms). And the open-source model Stable Diffusion can generate detailed and specific images in all kinds of styles from text prompts.

There’s a good chance we’re about to see a lot more generative AI showing up in a lot more applications, too. OpenAI, the AI developer that built the ChatGPT language model, recently announced the release of APIs, or application programming interfaces, for its ChatGPT and Whisper, a speech recognition model. Companies like Instacart and Shopify are already implementing this tech into their products, using generative AI to write shopping lists and offer recommendations. There’s no telling how many more apps might come up with novel ways to take advantage of what generative AI can do.

Generative AI has the potential to be a revolutionary technology, and it’s certainly being hyped as such. Venture capitalists, who are always looking for the next big tech thing, believe that generative AI can replace or automate a lot of creative processes, freeing up humans to do more complex tasks and making people more productive overall. But it’s not just creative work that generative AI can produce. It can help developers make software. It could improve education. It may be able to discover new drugs or become your therapist. It just might make our lives easier and better.

Or it could make things a lot worse. There are reasons to be concerned about the damage generative AI can do if it’s released to a society that isn’t ready for it — or if we ask the AI program to do something it isn’t ready for. How ethical or responsible generative AI technologies are is largely in the hands of the companies developing them, as there are few if any regulations or laws in place governing AI. This powerful technology could put millions of people out of work if it’s able to automate entire industries. It could spawn a destructive new era of misinformation. There are also concerns of bias due to a lack of diversity in the material and data that generative AI is trained on, or the people who are overseeing that training.

Nevertheless, powerful generative AI tools are making their way to the masses. If 2022 was the “year of generative AI,” 2023 may be the year that generative AI is actually put to use, ready or not.

The slow, then sudden, rise of generative AI

Conventional artificial intelligence is already integrated into a ton of products we use all the time, like autocomplete, voice assistants like Amazon’s Alexa, and even the recommendations for music or movies we might enjoy on streaming services. But generative AI is more sophisticated. It uses deep learning, or algorithms that create artificial neural networks that are meant to mimic how human brains process information and learn. And then those models are fed enormous amounts of data to train on. For example, large language models power things like ChatGPT, which train on text collected from around the internet until they learn to generate and mimic those kinds of texts and conversations upon request. Image models have been fed tons of images and captions that describe them in order to learn how to create new content based on prompts.

After years of development, most of it outside of public view, generative AI hit the mainstream in 2022 with the widespread releases of art and text models. Models like Stable Diffusion and DALL-E, which was released by OpenAI, were first to go viral, and they let anyone create new images from text prompts. Then came OpenAI’s ChatGPT (GPT stands for “generative pre-trained transformer”) which got everyone’s attention. This tool could create large, entirely new chunks of text from simple prompts. For the most part, ChatGPT worked really well, too — better than anything the world had seen before.

Though it’s one of many AI startups out there, OpenAI seems to have the most advanced or powerful products right now. Or at least, it’s the startup that has given the general public access to its services, thereby providing the most evidence of its progress in the generative AI field. This is a demonstration of its abilities as well as a source of even more data for OpenAI’s models to learn from.

OpenAI is also backed by some of the biggest names in Silicon Valley. It was founded in 2015 as a nonprofit research lab with $1 billion in support from the likes of Elon Musk, Reid Hoffman, Peter Thiel, Amazon, and former Y Combinator president Sam Altman, who is now the company’s CEO. OpenAI has since changed its structure to become a for-profit company but has yet to make a profit or even much by way of revenue. That’s not a problem yet, as OpenAI has gotten a considerable amount of funding from Microsoft, which began investing in OpenAI in 2019. And OpenAI is seizing on the wave of excitement for ChatGPT to promote its API services, which are not free. Neither is the company’s upcoming ChatGPT Plus service.

Other big tech companies have for years been working on their own generative AI initiatives. There’s Apple’s Gaudi, Meta’s LLaMA and Make-a-Scene, Amazon’s collaboration with Hugging Face, and Google’s LaMDA (which is good enough that one Google engineer thought it was sentient). But thanks to its early investment in OpenAI, Microsoft had access to the AI project everyone knew about and was trying out.

In January 2023, Microsoft announced it was giving $10 billion to OpenAI, bringing its total investment in the company to $13 billion. From that partnership, Microsoft has gotten what it hopes will be a real challenge to Google’s longtime dominance in web search: a new Bing powered by generative AI.

AI search will give us the first glimpse of how generative AI can be used in our everyday lives ... if it works

Tech companies and investors are willing to pour resources into generative AI because they hope that, eventually, it will be able to create or generate just about any kind of content humans ask for. Some of those aspirations may be a long way from becoming reality, but right now, it’s possible that generative AI will power the next evolution of the humble internet search.

After months of rumors that both Microsoft and Google were working on generative AI versions of their web search engines, Microsoft debuted its AI-integrated Bing in January in a splashy media event that showed off all the cool things it could do, thanks to OpenAI’s custom-built technology that powered it. Instead of entering a prompt for Bing to look up and return a list of relevant links, you could ask Bing a question and get a “complete answer” composed by Bing’s generative AI and culled from various sources on the web that you didn’t have to take the time to visit yourself. You could also use Bing’s chatbot to ask follow-up questions to better refine your search results.

Microsoft wants you to think the possibilities of these new tools are just about endless. And notably, Bing AI appeared to be ready for the general public when the company announced it last month. It’s now being rolled out to people on an ever-growing wait list and incorporated into other Microsoft products, like its Windows 11 operating system and Skype.

This poses a major threat to Google, which has had the search market sewn up for decades and makes most of its revenue from the ads placed alongside its search results. The new Bing could chip away at Google’s search dominance and its main moneymaker. And while Google has been working on its own generative AI models for years, its AI-powered search engine and corresponding chatbot, which it calls Bard, appear to be months away from debut. All of this suggests that, so far, Microsoft is winning the AI-powered search engine battle.

Or is it?

Once the new Bing made it to the masses, it quickly became apparent that the technology might not be ready for primetime after all. Right out of the gate, Bing made basic factual errors or made up stuff entirely, also known as “hallucinating.” What was perhaps more problematic, however, was that its chatbot was also saying some disturbing and weird things. One person asked Bing for movie showtimes, only to be told the movie hadn’t come out yet (it had) because the date was February 2022 (it wasn’t). The user insisted that it was, at that time, February 2023. Bing AI responded by telling the user they were being rude, had “bad intentions,” and had lost Bing’s “trust and respect.” A New York Times reporter pronounced Bing “not ready for human contact” after its chatbot — with a considerable amount of prodding from the reporter — began expressing its “desires,” one of which was the reporter himself. Bing also told an AP reporter that he was acting like Hitler.

In response to the bad press, Microsoft has tried to put some limits and guardrails on Bing, like limiting the number of interactions one person can have with its chatbot. But the question remains: How thoroughly could Microsoft have tested Bing’s chatbot before releasing it if it took only a matter of days for users to get it to give such wild responses?

Google, on the other hand, may have been watching this all unfold with a certain sense of glee. Its limited Bard rollout hasn’t exactly gone perfectly, but Bard hasn’t compared any of its users to one of the most reviled people in human history, either. At least, not that we know of. Not yet.

Again, Microsoft and Google aren’t the only companies working on generative AI, but their public releases have put more pressure on others to roll out their offerings as soon as possible, too. ChatGPT’s release and OpenAI’s partnership with Microsoft likely accelerated Google’s plans. Meanwhile, Meta is working to get its generative AI into as many of its own products as possible and just released a large language model of its own, called Large Language Model Meta AI, or LLaMA.

With the rollout of APIs that help developers add ChatGPT and Whisper to their applications, OpenAI seems eager to expand quickly. Some of these integrations seem pretty useful, too. Snapchat now has a chatbot called “My AI” for its paid subscribers, with plans to offer it to everyone soon. Initial reports say it’s just ChatGPT in Snapchat, but with even more restrictions about what it will talk about (no swearing, sex, or violence). Instacart will use ChatGPT in a feature called “Ask Instacart” that can answer customers’ questions about food. And Shopify’s Shop app has a ChatGPT-powered assistant to make personalized recommendations from the brands and stores that use the platform.

Generative AI is here to stay, but we don’t yet know if that’s for the best

Bing AI’s problems were just a glimpse of how generative AI can go wrong and have potentially disastrous consequences. That’s why pretty much every company that’s in the field of AI goes out of its way to reassure the public that it’s being very responsible with its products and taking great care before unleashing them on the world. Yet for all of their stated commitment to “building AI systems and products that are trustworthy and safe,” Microsoft and OpenAI either didn’t or couldn’t ensure a Bing chatbot could live up to those principles, but they released it anyway. Google and Meta, by contrast, were very conservative about releasing their products — until Microsoft and OpenAI gave them a push.

Error-prone generative AI is being put out there by many other companies that have promised to be careful. Some text-to-image models are infamous for producing images with missing or extra limbs. There are chatbots that confidently declare the winner of a Super Bowl that has yet to be played. These mistakes are funny as isolated incidents, but we’ve already seen one publication rely on generative AI to write authoritative articles with significant factual errors.

These screw-ups have been happening for years. Microsoft had one high-profile AI chatbot flop with its 2016 release of Tay, which Twitter users almost immediately trained to say some really offensive things. Microsoft quickly took it offline. Meta’s Blenderbot is based on a large language model and was released in August 2022. It didn’t go well. The bot seemed to hate Facebook, got racist and antisemitic, and wasn’t very accurate. It’s still available to try out, but after seeing what ChatGPT can do, it feels like a clunky, slow, and weird step backward.

There are even more serious concerns. Generative AI threatens to put a lot of people out of work if it’s good enough to replace them. It could have a profound impact on education. There are also questions of legalities over the material AI developers are using to train their models, which is typically scraped from millions of sources that the developers don’t have the rights to. And there are questions of bias both in the material that AI models are training on and the people who are training them.

On the other side, some conservative bomb-throwers have accused generative AI developers of moderating their platforms’ outputs too much and making them “woke” and biased against the right wing. To that end, Musk, the self-proclaimed free-speech absolutist and OpenAI critic as well as an early investor, is reportedly considering developing a ChatGPT rival that won’t have content restrictions or be trained on supposedly “woke” material.

And then there’s the fear not of generative AI but of the technology it could lead to: artificial general intelligence. AGI can learn and think and solve problems like a human, if not better. This has given rise to science fiction-based fears that AGI will lead to an army of super-robots that quickly realize they have no need for humans and either turn us into slaves or wipe us out entirely.

There are plenty of reasons to be optimistic about generative AI’s future, too. It’s a powerful technology with a ton of potential, and we’ve still seen relatively little of what it can do and who it can help. Silicon Valley clearly sees this potential, and venture capitalists like Andreessen Horowitz and Sequoia seem to be all-in. OpenAI is valued at nearly $30 billion, despite not having yet proved itself as a revenue generator.

Generative AI has the power to upend a lot of things, but that doesn’t necessarily mean it’ll make them worse. Its ability to automate tasks may give humans more time to focus on the stuff that can’t be done by increasingly sophisticated machines, as has been true for technological advances before it. And in the near future — once the bugs are worked out — it could make searching the web better. In the years and decades to come, it might even make everything else better, too.

Oh, and in case you were wondering: No, generative AI did not write this explainer.

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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?

For example, companies like Deepdub leverage neural networks to clone actors’ voices for film and game dubbing, replicating emotions such as crying or shouting—even when these are absent in the initial samples—thus preserving performance authenticity across languages .

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

What company has developed a service to translate original video content into multiple languages using machine learning algorithms?

What is necessary for voice cloning to work?

Leading platforms providing voice cloning capabilities range from commercial services to open-source frameworks.

Deepdub focuses on content localization through high-fidelity voice replication, allowing seamless dubbing of videos while maintaining the actor's distinct voice characteristics.

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

What company has developed a service to translate original video content into multiple languages using machine learning algorithms?

NeuralSpace’s VoiceAI

platform excels in realistic voice synthesis with support for local dialects and offers convenient integration and transcription features suitable for diverse applications.

Title:

Content: OpenAI, a non-profit artificial intelligence developer, claims that its speech recognition software is highly efficient.
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The non-profit organization OpenAI, whose goal is to develop and implement artificial intelligence (AI) for the benefit of all mankind, released the Whisper system in open access (release).
Whisper is an automatic speech recognition technology that, according to OpenAI, allows for “reliable ” transcription in multiple languages.
In addition, Whisper automatically translates speech from different languages into English.
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Speech transcription occurs automatically.
Voice Record
Automatic speech recognition (ASR) has always challenged artificial intelligence and machine learning.
Thanks to the Whisper program, OpenAI has taken another successful step in the development of this direction.
A lot of different options
There are a huge number of different speech recognition systems that are the basis of the software and services of such technology giants as Google, Meta and Amazon.
A distinctive feature of Whisper is that this platform has been trained for 680,000 hours based on data collected from the Internet in multilingual and multitasking mode.
This approach has improved the recognition of unique accents, background noise variations, as well as technical terminology and jargon.
"The main intended users of Whisper models are AI specialists who study the reliability, potential, errors, generalization and limitations of the current model.
However, Whisper can also be potentially useful as a solution for automatic speech recognition among developers, especially for English speech recognition. "said OpenAI representatives in the GitHub repository.
Anyone can download Whisper from GitHub; it ’ s completely free.
Models have demonstrated their capabilities
In a press release, OpenAI said: "The models performed well in ASR in about 10 languages.
Tailored to perform certain tasks, such as detecting voice activity, dictaphone recording, and classifying speakers, they can demonstrate a host of useful benefits.
However, they have not yet been thoroughly tested in these areas. ”
Limitations of technology
In certain areas, such as text forecasting, Whisper has certain disadvantages.
Since the system was trained on a large number of "noisy" data sets, the company warns that Whisper may include in its transcriptions words that were not actually spoken.
This may be due to the need to predict the next word and at the same time try to decipher the audio stream.
In addition, Whisper does not work equally well in different languages.
When working with native speakers of languages that are underrepresented in training data or models, the system allows a higher percentage of errors.
The company says: “Although Whisper models cannot be used for real-time transcription out of the box, their speed and versatility allow specialists to develop products based on them that can recognize and translate speech almost instantly.
The true value of applications built on Whisper models suggests that the disparate performance of these systems can have real economic benefits. "
OpenAI is not limited to Whisper technology.
Currently, the company is also engaged in the creation of commercial projects such as DALL-E 2 and GPT-3.
In addition, OpenAI constantly conducts purely theoretical developments, one of which is artificial intelligence systems, which are trained by studying recorded video materials.
Source: GitHub Repository
1. (https: / / github.com / openai / Video-Pre-Training)
2. (https: / / github.com / openai / whisper / blob / main / model-card.md)
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Additionally, open-source projects like OpenAI’s Whisper contribute automatic speech recognition and translation functionality that facilitates the training and refinement of voice models .

How will AI Voice help educators with building courses?

The applications of AI voice cloning span multiple domains.

In entertainment, it revolutionizes dubbing in film and video games by preserving original voice performances across languages.

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

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

It also supports media restoration via re-voicing archival audio and enables immersive gaming experiences with dynamic voice content.

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

Accessibility benefits include generating personalized voice assistants for people with speech disabilities and creating tailored listening comprehension materials in language learning contexts.

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 address the scarcity of authentic yet accessible audio at low proficiency levels by synthesizing voices with controlled accent, pacing, and intonation.

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?

Conversely, the technology facilitates misuse; notable among these abuses are fraud schemes where criminals clone victims’ voices from brief audio snippets to perform social engineering scams or misinformation campaigns, causing increasing concern among security experts .

What are some risks regarding AI voice clning?

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

could become a popular method of attacking banks and financial services

What is necessary for voice cloning to work?

The legal landscape surrounding voice cloning is evolving but remains fragmented globally.

Current concerns center on copyright, personality rights, and explicit consent for using someone’s voice digitally.

Jurisdictions such as the US, the European Union, and countries in Asia have begun drafting or proposing legislation targeting voice cloning misuse, yet comprehensive international regulation is still lacking.

Past legal cases have highlighted disputes over voice rights, emphasizing the necessity of balancing technological advancement and personal privacy.

The ease of access to sophisticated voice cloning tools by non-experts exacerbates challenges for effective legal enforcement, and ongoing debates call for updated intellectual property frameworks and stringent consent mechanisms to protect individuals from unauthorized voice replication .

Real-world examples illustrate both the promise and pitfalls of voice cloning.

On the positive side, Deepdub’s AI-driven dubbing has facilitated international film releases, enabling actors’ voices to be preserved intimately in multiple languages, thus enhancing viewer experience.

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

What company has developed a service to translate original video content into multiple languages using machine learning algorithms?

NeuralSpace’s culturally nuanced synthetic voices have bolstered customer service applications, improving accessibility for speakers of lesser-known dialects and minority languages.

Conversely, voice cloning technology has enabled a marked increase in online voice scams, where criminals impersonate relatives or corporate executives to extort money from victims, demonstrating serious risks associated with unchecked proliferation of this technology .

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

could become a popular method of attacking banks and financial services

Ethically, voice cloning raises crucial issues.

The primary concern is consent: cloning a voice without permission infringes on personal identity and autonomy.

There are psychological harms when listeners cannot distinguish between genuine and synthetic speech, potentially eroding trust in communications and media.

What are some risks regarding AI voice clning?

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

Difficult to distinguish from human voice

Ethical responsibility extends to contexts such as education, where deploying synthetic voices without transparency might mislead learners or damage credibility.

Title:

Content: 7 Machine Learning Algorithms to Know

Written by Coursera • Updated on

Machine learning algorithms power many services in the world today. Here are seven to know as you look to start your career.

Machine learning (ml) can do everything from analyzing x-rays to predicting stock market prices to recommending binge-worthy television shows. With such a wide range of applications, it’s little surprise that the global machine learning market is projected to grow from $21.7 billion in 2022 to $209.91 billion by 2029, according to Fortune Business Insights [1].

At the core of machine learning are algorithms, which are trained to become the machine learning models used to power some of the most impactful innovations in the world today. In this article, you will learn about the different learning styles used to turn ml algorithms into ml models followed by seven of the most important algorithms to know as you begin your own machine learning journey.

Top machine learning algorithms to know

Machine learning algorithms are the fundamental building blocks for machine learning models. From classification to regression, here are seven algorithms to know:

1. Linear regression

Linear regression is a supervised learning algorithm used to predict and forecast values within a continuous range, such as sales numbers or prices.

Originating from statistics, linear regression performs a regression task, which maps a constant slope using an input value (X) with a variable output (Y) to predict a numeric value or quantity. Linear regression uses labeled data to make predictions by establishing a line of best fit, or “regression line”, that is approximated from a scatter plot of data points. As a result, linear regression is used for predictive modeling rather than categorization.

2. Logistic regression

Logistic regression, or “logit regression,” is a supervised learning algorithm used for binary classification, such as deciding whether an image fits into one class or another.

Originating from statistics, logistic regression technically predicts the probability that an input can be categorized into a single primary class. In practice, however, this can be used to group outputs into one of two categories (“the primary class” or “not the primary class”). This is achieved by creating a range for binary classification, such as any output between 0-.49 is put in one group and any between .50 - 1.00 is put in another.

As a result, logistic regression in machine learning is typically used for binary categorization rather than predictive modeling.

3. Naive Bayes

Naive Bayes is a set of supervised learning algorithms used to create predictive models for either binary or multi-classification. Based on Bayes’ theorem, Naive Bayes operates on conditional probabilities, which are independent of one another but indicate the likelihood of a classification based on their combined factors.

For example, a program created to identify plants might use a naive Bayes algorithm to categorize images based on particular factors, such as perceived size, color, and shape. While each of these factors is independent of one another, the algorithm would note the likelihood of an object being a particular plant using the combined factors.

4. Decision tree

A decision tree is a supervised learning algorithm used for classification and predictive modeling.

Resembling a graphic flowchart, a decision tree begins with a root node, which asks a specific question of data and then sends it down a branch depending on the answer. These branches each lead to an internal node, which in turn asks yet another question of the data before directing it toward another branch depending on the answer. This continues until the data reaches an end node, also called a leaf node, that doesn’t branch any further.

Decision trees are common in machine learning because they can handle complex data sets with relative simplicity.

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Explanation and Examples of Decision Trees

5. Random forest algorithm

A random forest algorithm uses an ensemble of decision trees for classification and predictive modeling.

In a random forest, many decision trees (sometimes hundreds or even thousands) are each trained using a random sample of the training set (a method known as “bagging”). Afterward, researchers put the same data into each decision tree in the random forest and tally their end results. The most common result is then selected as the most likely outcome for the data set.

Although they can become complex and require significant time, random forests correct the common problem of “overfitting” that can occur with decision trees. Overfitting is when an algorithm coheres too closely to its training data set, which can negatively impact its accuracy when introduced to new data later.

6. K-nearest neighbor (KNN) algorithm

A K-nearest neighbor is a supervised learning algorithm used for classification and predictive modeling.

True to its name, KNN algorithms classify an output by its proximity to other outputs on a graph. For example, if an output is closest to a cluster of blue points on a graph rather than a cluster of red points, then it would be classified as a member of the blue group. This approach means that KNN algorithms can be used to either classify known outcomes or predict the value of unknown ones.

7.  K means algorithm

K means is an unsupervised algorithm used for classification and predictive modeling.

Much like KNN, K means uses the proximity of an output to a cluster of data points to identify it. Each of the clusters is defined by a centroid, a real or imaginary center point for the cluster. K means is useful on large data sets, especially for clustering, though it can falter when handling outliers.

Training machine learning algorithms: four methods

Everyone learns differently – including machines. In this section, you will learn about four different learning styles used to train machine learning algorithms: supervised learning, unsupervised learning, reinforcement learning, and semi-supervised learning.

Supervised learning

A supervised learning algorithm uses a labeled data set to train an algorithm, effectively guaranteeing that it has an answer key available to cross-reference predictions and refine its system. As a result, supervised learning is best suited to algorithms faced with a specific outcome in mind, such as classifying images.

For example, an algorithm meant to identify different plant types might be trained using images that are already labeled with their names (e.g., “rose,” “pumpkin,” or “aloe vera”). Through supervised learning, the algorithm would be able to identify the differentiating features for each plant classification effectively and eventually do the same with an unlabeled data set.

Much as a teacher supervises their students in a classroom, the labeled data likewise supervises the algorithm’s solutions and directs them toward the right answer.

Unsupervised learning

An unsupervised learning algorithm uses an unlabeled data set to train an algorithm, which must analyze the data to identify distinctive features, structures, and anomalies. Unlike supervised learning, researchers use unsupervised learning when they don’t have a specific outcome in mind, instead, they use the algorithm to cluster data and identify patterns,   associations, or anomalies.

For example, a business might feed an unsupervised learning algorithm unlabeled customer data to segment their target market. Once they have established a clear customer segmentation, the business could then use this data to direct their future marketing efforts, like social media marketing

Unsupervised learning is akin to a learner working out a solution themselves without the supervision of a teacher.

Reinforcement learning

In reinforcement learning a machine or AI agent attempts to accomplish a task, receives feedback as it does so, and then iterates a new approach until it has devised the optimal solution. As a result, reinforcement learning is akin to the way that a child learns to maneuver a new environment: first, they explore, then interact with it, and over time learn how to seamlessly maneuver the space.

Due to the feedback loops required to develop better and better strategies, reinforcement learning is often used in video game environments where conditions can be controlled and feedback reliably given. Over time, the machine or AI learns through the accumulation of feedback until it achieves the optimal path to its goal.

Semi-supervised learning

Semi-supervised learning (SSL) trains algorithms using a small amount of labeled data alongside a larger amount of unlabeled data. Semi-supervised learning is often used to categorize large amounts of unlabeled data because it might be unfeasible or too difficult to label all data itself.

Typically, a researcher using SSL would first train an algorithm with a small amount of labeled data before training it with a large amount of unlabeled data. For example, an SSL algorithm analyzing speech might first be trained on labeled soundbites before being trained on unlabeled sounds, which are likely to vary in pitch and style from the labeled data.

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Learn more about machine learning

A career in machine learning begins with learning all you can about it. Even the best machine learning models need some training first, after all.

To start your own training, you might consider taking the University of Washington’s Machine Learning Specialization, which introduces course takers to the fundamentals of prediction, classification, clustering, and information retrieval. DeepLearning.AI’s Deep Learning Specialization, meanwhile, introduces course takers on how to build and train deep neural networks.

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Article sources

1. Fortune Business Insights. “The global machine learning (ML) market is expected to grow from $21.17 billion in 2022 to $209.91 billion by 2029,  https://www.fortunebusinessinsights.com/machine-learning-market-102226.” Accessed April 27, 2022.

Written by Coursera • Updated on

This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

Learn without limits

What are some risks regarding AI voice clning?

Instructors using AI voice cloning are advised to maintain clear disclosure, ensure materials are factually sound, and respect learners’ right to genuine interaction.

What are some risks regarding AI voice clning?

Safeguards are needed to prevent deceptive applications and to foster ethical best practices within academic settings .

In education, particularly foreign language instruction at low proficiency levels (e.g., ILR 1 - 1+), AI voice cloning offers valuable potential.

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

Generating bespoke listening comprehension resources with synthetic voices allows instructors to tailor accent clarity, speaking speed, and intonation to student needs, overcoming scarcity of authentic input materials that suit beginners.

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?

Enhance pronunciation abilities
Respond to different learning demands
Enhance language comprehension

This capability can enhance learner engagement and provide consistent, repeatable exposure to language input.

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

However, educators must balance innovation with ethical use, ensuring that synthesized voices support rather than replace genuine communicative experiences, and that learners are informed of the nature of AI-generated content .

What are some risks regarding AI voice clning?

In summary, AI voice cloning is a rapidly advancing technology powered by deep learning that enables realistic voice synthesis with minimal data.

It has diverse applications across entertainment, accessibility, and education but also poses legal and ethical challenges, particularly regarding consent, misuse, and societal trust.

Emerging regulatory frameworks aim to address these concerns, but the technology’s accessibility necessitates ongoing vigilance.

In educational contexts, voice cloning presents promising tools for creating tailored listening comprehension materials for low proficiency language learners, provided it is applied transparently and responsibly.

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

Respond to different learning demands
Enhance language comprehension