Artificial Intelligence (AI) is a disruptive and fast-moving field whose developmental trajectory is accelerating rapidly. In fact, the number of publications in this space has been rising dramatically in recent years. Stanford's annual Artificial Intelligence Index Report shows that the number of AI publications has increased from 162,444 in 2010 to 334,497 in 2021 [1]. If you are working in the field of AI, you have probably also noticed the shortening intervals between major industry advances such as OpenAI's DALL·E 2, GPT-3 and ChatGPT, or DeepMind's AlphaFold. Those are just some examples that captured the attention of both the general public and the tech industry as they were extensively reported on and widely circulated on social media.

The viral success of OpenAI's ChatGPT has triggered discussions everywhere these days from the classroom to the boardroom. This amounts to a crucial moment for AI, Alice Xiang, Head of Sony Group's (SONY) AI Ethics Office and AI Lead Research Scientist, told Yahoo Finance Live (video above). "We're really seeing an inflection point with AI ethics, where it's going from being just something that companies are doing on their own… [and now] we're seeing policymakers really dive into this space." Xiang added: "This raises a lot of really interesting questions around how we ensure that we have governance processes in place to make sure AI that's built is compliant with relevant laws." As consumers encounter AI more frequently, and with excitement and fear, regulators are taking notice.

ChatGPT3 became the newest internet sensation last year when it allows users to generate text and answer complex questions in a manner that seems almost human. But, beyond the prowess of ChatGPT3, the underlying impact of the technology -- generative AI -- on business is only just coming into focus. ChatGPT3, together with its image-generating cousin Dall-E, has the potential to revolutionize the way content is created, from blogs to white papers, student essays to business correspondence. It provides access to expert-level syntax and grammar to anyone who uses it. But this also raises some important ethical questions.

OpenAI, the research lab behind the viral ChatGPT chatbot, is in talks to sell existing shares in a tender offer that would value the company at around $29 billion, according to people familiar with the matter, making it one of the most valuable U.S. startups on paper despite generating little revenue. Venture-capital firms Thrive Capital and Founders Fund are in talks to buy shares, the people said. The tender could total at least $300 million in OpenAI share sales, they said. The deal is structured as a tender offer, with the investors buying shares from existing shareholders such as employees, the people said.

Five weeks ago, OpenAI, a San Francisco artificial intelligence lab, released ChatGPT, a chatbot that answers questions in clear, concise prose. The A.I.-powered tool immediately caused a sensation, with more than a million people using it to create everything from poetry to high school term papers to rewrites of Queen songs. Now OpenAI is in the midst of a new gold rush. The lab is in talks to complete a deal that would value it at around $29 billion, more than twice its valuation in 2021, two people with knowledge of the discussions said. The potential deal -- where OpenAI would sell existing company shares in a so-called tender offer -- could total $300 million, depending on how many employees agree to sell their stock, they said.

When Artificial Intelligence (AI) technologies began to make significant advances around two decades ago, societies began to talk more earnestly about both its possibilities and dangers. Now, with the rise of Generative AI (GAI) and tools like ChatGPT and DALL-E 2, AI is starting to enter mainstream industry and society. AI is about to become a disruptive, revolutionary technology. Things are about to get very interesting. Why and what does this mean.

And then there were four. With the Washington Commanders unveiling "Major Tuddy" last week, the commander pig of your dreams, there are now only four teams remaining without mascots in the NFL. The Packers, Chargers, Jets and Giants are the final holdouts from joining the rest of the league in having a fun anthropomorphic figure that not only children can enjoy, but drunk fans can worship like a god as well. At this point there's no reason to hold out, and I felt a need to pitch in and help these teams settle on their new mascots. To do this I used a highly scientific process of getting some keywords courtesy of Google autocomplete, and plugging them into the A.I. art program "DALL E 2" in order to get the perfect mascot designs for these teams.

Remember when predicting protein shapes using AI was the breakthrough of the year? Having solved nearly all protein structures known to biology, AI is now turning to a new challenge: designing proteins from scratch. Far from an academic pursuit, the endeavor is a potential game-changer for drug discovery. Having the ability to draw up protein drugs for any given target inside the body--such as those triggering cancer growth and spread--could launch a new universe of medicines to tackle our worst medical foes. It's no wonder multiple AI powerhouses are answering the challenge.

Ask DALL·E 2, an image generation system created by OpenAI, to paint a picture of "goldfish slurping Coca-Cola on a beach," and it will spit out surreal images of exactly that. The program would have encountered images of beaches, goldfish and Coca-Cola during training, but it's highly unlikely it would have seen one in which all three came together. Yet DALL·E 2 can assemble the concepts into something that might have made Dalí proud. DALL·E 2 is a type of generative model -- a system that attempts to use training data to generate something new that's comparable to the data in terms of quality and variety. This is one of the hardest problems in machine learning, and getting to this point has been a difficult journey.

Microsoft has recently released VALL-E, a new language model for text-to-speech synthesis (TTS) that uses audio codec codes to represent intermediate representations. After being trained on 60,000 hours worth of English speech data, it demonstrated in-context learning abilities in zero-shot situations. VALL-E allows you to create high-quality, personalized speech with just a 3-second recording of an oblique speaker as an acoustic prompt. It allows for prompt-based TTS techniques that are zero-shot and contextual. There is no need to add structural engineering or pre-designed acoustic features.