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In December 2023, Google announced the launch of its new large language model (LLM) named Gemini. Gemini now provides the artificial intelligence (AI) foundations of Google products; it is also a direct rival to OpenAIs GPT-4.

But why is Google considering Gemini as such an important milestone, and what does this mean for users of Googles services? And generally speaking, what does it mean in the context of the current hyperfast-paced developments of AI?

Read more: Google's Gemini: is the new AI model really better than ChatGPT?

Google is betting on Gemini to transform most of its products by enhancing current functionalities and creating new ones for services such as search, Gmail, YouTube and its office productivity suite. This would also allow improvements to their online advertising business their main source of revenue as well as for Android phone software, with trimmed versions of Gemini running on limited capacity hardware.

For users, Gemini means new features and improved capacities that would make Google services harder to shun, strengthening an already dominant position in areas such as search engines. The potential and opportunities for Google are considerable, given the bulk of their software is easily upgradable cloud services.

But the huge and unexpected success of ChatGPT attracted a lot of attention and enhanced the credibility of OpenAI. Gemini will allow Google to reinstate itself as a major player in AI in the public view. Google is a powerhouse in AI, with large and strong research teams at the origin of many major advances of the last decade.

There is public discussion about these new technologies, both on the benefits they provide and the disruption they create in fields such as education, design and health care.

At its core, Gemini relies on transformer networks. Originally devised by a research team at Google, the same technology is used to power other LLMs such as GPT-4.

A distinctive element of Gemini is its capacity to deal with different data modalities: text, audio, image and video. This provides the AI model with the capacity to execute tasks over several modalities, like answering questions regarding the content of an image or conducting a keyword search on specific types of content discussed in podcasts.

But more importantly, that the models can handle distinct modalities enables the training of globally superior AI models, compared to distinct models trained independently for each modality. Indeed, such multimodal models are deemed to be stronger since they are exposed to different perspectives of the same concepts.

For example, the concept of birds may be better understood through learning from a mix of birds textual descriptions, vocalizations, images and videos. This idea of multimodal transformer models has been explored in previous research at Google, Gemini being the first full-fledged commercial implementation of the approach.

Such a model is seen as a step in the direction of stronger generalist AI models, also known as artificial general intelligence (AGI).

Given the rate at which AI is advancing, the expectations that AGI with superhuman capabilities will be designed in the near future generates discussions in the research community and more broadly in the society.

On one hand, some anticipate the risk of catastrophic events if a powerful AGI falls into the hands of ill-intentioned groups, and request that developments be slowed down.

Others claim that we are still very far from such actionable AGI, that the current approaches allow for a shallow modelling of intelligence, mimicking the data on which they are trained, and lack an effective world model a detailed understanding of actual reality required to achieve human-level intelligence.

On the other hand, one could argue that focusing the conversation on existential risk is distracting attention from more immediate impacts brought on by recent advances of AI, including perpetuating biases, producing incorrect and misleading content prompting Google to pause its Gemini image generator, increasing environmental impacts and enforcing the dominance of Big Tech.

The line to follow lies somewhere in between all of these considerations. We are still far from the advent of actionable AGI additional breakthroughs are required, including introducing stronger capacities for symbolic modelling and reasoning.

In the meantime, we should not be distracted from the important ethical and societal impacts of modern AI. These considerations are important and should be addressed by people with diverse expertise, spanning technological and social science backgrounds.

Nevertheless, although this is not a short-term threat, achieving AI with superhuman capacity is a matter of concern. It is important that we, collectively, become ready to responsibly manage the emergence of AGI when this significant milestone is reached.

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Google's Gemini showcases more powerful technology, but we're still not close to superhuman AI - The Conversation

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In the new lawsuit filed by Elon Musk last week against OpenAI, its CEO Sam Altman, and its president Greg Brockman, the word nonprofit appears 17 times. Board comes up a whopping 62 times. AGI? 66 times.

The lawsuits claims, which include breach of contract, breach of fiduciary duty, and unfair competition, all circle around the idea that OpenAI put profits and commercial interests indeveloping artificial general intelligence(AGI) ahead of the duty of its nonprofit arm (under the leadership of its nonprofit board) to protect the public good.

This is an issue, of course, that exploded after OpenAIs board suddenly fired Sam Altman on November 17, 2023 followed by massive blowback from investors including Microsoft and hundreds of OpenAI employees posting heart emojis indicating they were on Altmans side. Altman was quickly reinstated, while several OpenAI board members got the boot.

Plenty of people have pointed out that Musk, as an OpenAI co-founder who is now competing with the company with his own startup X.ai, is hardly an objective party. But Im far more interested in one important question: How did nerdy nonprofit governance issues tied to the rise of artificial general intelligence spark a legal firestorm?

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Well, it all winds back to the beginning of OpenAI, which Musks lawsuit lays out in more detail than we have previously seen: In 2015, Musk, Altman and Brockman joined forces to form a nonprofit AI lab that would try to catch up to Google in the race for AGI developing it for the benefit of humanity, not for a for-profit company seeking to maximize shareholder profits.

But in 2023, the lawsuit claims, Altman, Brockman and OpenAI set the Founding Agreement aflamewith flagrant breaches such as breaching the nonprofit boards fiduciary duty and breach of contract, including what transpired during the days after Altman was fired by the nonprofit board on November 17, 2023, and subsequently reinstated.

Much of the controversy winds back to the fact that Open AI isnt just any old nonprofit. In fact, I reported on OpenAIs unusual and complex nonprofit/capped profit structure just a few days before Altmans firing.

In that piece, I pointed to the Our structure page on OpenAIs website that says OpenAIs for-profit subsidiary is fully controlled by the OpenAI nonprofit. While the for-profit subsidiary is permitted to make and distribute profit, it is subject to the nonprofits mission.

Elon Musks lawsuit, however, shed even more light on the confusing alphabet soup of companies that are parties in the case. While OpenAI, Inc. is the nonprofit, OpenAI, LP; OpenAI LLC; OpenAI GP, LLC; OpenAI Opco, LLC; OpenAI Global, LLC; OAI Corporation, LLC and OpenAI Holdings, LLC, all appear to be for-profit subsidiaries.

As I wrote in November, according to OpenAI, the members of its nonprofit board of directors will determine when the company has attained AGI which it defines as a highly autonomous system that outperforms humans at most economically valuable work. Thanks to the for-profit arm that is legally bound to pursue the Nonprofits mission, once the board decides AGI, or artificial general intelligence, has been reached, such a system will be excluded from IP licenses and other commercial terms with Microsoft, which only apply to pre-AGI technology.

But as the very definition of AGI is far from agreed-upon, what does it mean to have a half-dozen people deciding on whether or not AGI has been reached? what does the timing and context of that possible future decision mean for its biggest investor, Microsoft that is now a non-voting member of the nonprofit board?Isnt that a massive conflict of interest?

Musk certainly seems to think so. The lawsuit says: Mr. Altman and Mr. Brockman, in concert with Microsoft, exploited Microsofts significant leverage over OpenAI, Inc. and forced the resignation of a majority of OpenAI, Inc.s Board members, including Chief Scientist Ilya Sutskever. Mr. Altman was reinstated as CEO of OpenAI, Inc. on November 21. On information and belief, the new Board members were hand-picked by Mr. Altman and blessed by Microsoft. The new Board members lack substantial AI expertise and, on information and belief, are ill equipped by design to make an independent determination of whether and when OpenAI has attained AGIand hence when it has developed an algorithm that is outside the scope of Microsofts license.

Musk is not the first to push back on OpenAIs nonprofit status. I think the story that Musk tells in his complaint validates and deepens the case were making in California, said Robert Weissman, president of Public Citizen, a nonprofit consumer advocacy organization which wrote a letter on January 9 requesting that the California Attorney General investigate OpenAIs nonprofit status. The letter raised concerns that OpenAI may have failed to carry out its non-profit purposes and is instead acting under the effective control of its for-profit subsidiary affiliate.

And legal experts I spoke to say that Musk has a strong point in this regard: James Denaro, attorney and chief technologist at the Washington DC-based CipherLaw, told me that Musk does make a strong policy argument that if a company can launch as a non-profit working for the public benefit, collect pre-tax donations, and then transfer the IP into a for-profit venture, this would be a highly problematic paradigm shift for technology companies.

Musks lawsuit is not surprising because of the nonprofit vs. profit structural issues that have plagued OpenAI, added Anat Alon-Beck, associate professor at Case Western University School of Law, who focuses on corporate law and governance and recently wrote a paper about shadow governance by observing board members at tech companies.

According to the paper, It was not until November 2023 that mainstream media started paying more attention to the concept of board observers, after OpenAI, the corporate entity that brought the world ChatGPT, gave Microsoft a board observer seat following the drama in OpenAIs boardroom. But what the mainstream media did not explore in its coverage of the board observer concept was its seemingly less interesting nature as a non-voting board membership, which was an important element in the complex relationship between OpenAI and Microsoft. This signaled deepening ties between the two companies that also eventually got the attention of the DOJ and FTC, as well as the influential role of CVC [corporate venture capital] in funding and governing the research and development of OpenAI.

This lawsuit was due because of OpenAIs structure, she said, adding that OpenAI should be worried.

You should always be worried because when you pick such a weird structure like OpenAI did, theres uncertainty, she said. In law, when were representing large companies, we want to have efficiency, low transaction costs and predictability. We dont know how courts gonna look at fiduciary duties. We dont know because of court hasnt decided on that. Im sorry, but its a bad structure. They could have accomplished [what they wanted] using a different type of structure.

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Why OpenAI's nonprofit mission to build AGI is under fire again | The AI Beat - VentureBeat

In the dynamic field of artificial intelligence, the quest for Artificial General Intelligence (AGI) represents a pinnacle of innovation, promising to redefine the interplay between technology and human intellect. Jensen Huang, CEO of NVIDIA, a trailblazer in AI technology, recently brought this topic to the forefront of technological discourse. During a forum at Stanford University, Huang posited that AGI might be realized within the next five years, a projection that hinges critically on the definition of AGI itself.

According to Huang, if AGI is characterized by its ability to successfully pass a diverse range of human tests, then this milestone in AI development is not merely aspirational but could be nearing actualization. This statement from a leading figure in the AI industry not only sparks interest but also prompts a reassessment of our current understanding of artificial intelligence and its potential trajectory in the near future.

The landscape of artificial intelligence today is a testament to remarkable achievements and yet, simultaneously, a reminder of the challenges that remain. A notable milestone in AI's current capabilities is its success in passing legal bar exams, a feat that underscores its proficiency in processing and applying extensive legal knowledge. This accomplishment not only demonstrates AI's advanced analytical skills but also its potential to revolutionize sectors reliant on data interpretation and legal expertise.

However, the prowess of AI is not without its limitations. In more specialized fields, such as gastroenterology, AI continues to grapple with complexities. These fields require not only a deep understanding of intricate subject matter but also the ability to navigate nuances and subtleties that are often second nature to human experts. The contrast between AI's success in legal examinations and its struggles in specialized medical tests highlights the current disparity in AI's ability to mimic human expertise across diverse domains.

Jensen Huang, in his forecast, envisions a rapidly evolving AI landscape. Within the next five years, he anticipates AI to make significant strides in conquering a broader range of complex tasks, extending beyond its current scope. Huang's projection suggests a future where AI could adeptly handle challenges in specialized fields, matching, or even surpassing, human expertise in areas where it currently falters. This expectation is not merely a prediction of incremental improvement but a forecast of transformative advancement, signaling a shift towards a more versatile and capable AI. The realization of these goals would mark a substantial leap forward in AI technology, potentially reshaping numerous industries and impacting the way we approach problem-solving and innovation.

Venturing into the realm of AGI involves delving deep into the complexities of human thought processes, a venture that remains one of the most challenging aspects of AI development. Human cognition is a rich tapestry of logical reasoning, emotional intelligence, creativity, and contextual understanding elements that are inherently difficult to quantify and replicate in machines. This challenge forms the crux of the AGI puzzle.

Huang, reflecting on this challenge, emphasized that engineering AGI is an intricate task, primarily due to the elusive nature of human cognition. It's not just about programming an AI to perform tasks; it's about imbuing it with an understanding of the world that mirrors the human mind's flexibility and depth. This task, as Huang suggested, is not just a technological hurdle but also a philosophical and scientific one, requiring insights from various disciplines to fully grasp the essence of human thought.

The expansion of AI, especially towards AGI, necessitates a robust infrastructure, particularly in semiconductor technology. Fabrication plants, or fabs, are critical in this respect, serving as the backbone for producing advanced AI chips. However, Huang offers a nuanced view of this requirement. He acknowledges the growing need for fabs to sustain AI's growth but also draws attention to the ongoing improvements in chip efficiency and AI algorithms.

This perspective suggests a strategic approach to AI development: a balance between increasing physical production capacities and enhancing the technological prowess of each component. It's not just about quantity; it's about quality and efficiency. This approach aims to maximize the potential of each chip, reducing the need for mass production and focusing on smarter, more efficient designs. Huang's insight reflects NVIDIA's commitment to not only expanding AI's physical infrastructure but also pushing the boundaries of what each element within that infrastructure can achieve.

As we stand at the threshold of potentially achieving AGI, the implications for society and various industries are profound. AGI promises to revolutionize fields like healthcare, finance, education, and transportation, offering solutions that are currently beyond our grasp. This transformative potential extends to everyday life, reshaping how we interact with technology and each other.

NVIDIA, at the helm of this AI revolution, faces both challenges and opportunities in its pursuit of AGI. The company's role in driving AI advancements is undeniable, but the journey towards AGI is laden with complex ethical, technical, and philosophical questions. As NVIDIA continues to push the boundaries of AI, its strategies, innovations, and foresight will be pivotal in navigating the uncharted waters of AGI. The path forward is an exciting one, filled with possibilities that could redefine our world. In this race towards AGI, NVIDIA stands not just as a participant but as a key architect of the future.

Link:

Could We Achieve AGI Within 5 Years? NVIDIA's CEO Jensen Huang Believes It's Possible - Unite.AI

OpenAI fired back at Elon Musks lawsuit against the company by releasing screenshots of emails from Musk during his time at OpenAI that show he supported making it a for-profit company and said a merger with Tesla was the only way to compete with Google.

What inspired Elon Musk's design for Tesla's Cybertruck?

Elon left OpenAI, saying there needed to be a relevant competitor to Google/DeepMind and that he was going to do it himself, OpenAI wrote in a blog post published late Tuesday. He said hed be supportive of us finding our own path.

Musk, an OpenAI co-founder, sued the company and CEO Sam Altman last week, alleging that its multi-billion-dollar partnership with Microsoft betrays its founding commitment to benefiting humanity over making profit. The lawsuit has a sparked debate among Musks fellow Silicon Valley billionaires about the future of AI.

OpenAI, Inc. has been transformed into a closed-source de facto subsidiary of the largest technology company in the world: Microsoft, Musks lawsuit says. Under its new board, it is not just developing but is actually refining an [artificial general intelligence] to maximize profits for Microsoft, rather than for the benefit of humanity.

OpenAI said in its response Tuesday that Musk wanted to start with a $1 billion funding commitment to avoid sounding hopeless, after Altman and their other co-founder Greg Brockman initially planned to raise $100 million.

By 2017, OpenAI said, the company realized building AGI, or artificial general intelligence, would require billions of dollars per year far more than the company and Musk thought it would be able to raise as a nonprofit.

OpenAI said Musk wanted majority equity, board control, and the CEO position after the team discussed creating a for-profit entity that year and withheld his funding during those discussions. But OpenAI said it couldnt agree to Musks terms because it would have been against the mission for one person to have absolute control over the company.

In 2018, OpenAI said, Musk suggested merging the company with Tesla, which Musk said in one of the emailscould serve as its cash cow to compete with Google.

Musk decided to leave OpenAI that year, the company said, adding that he had warned that our probability of success was 0. Musk planned to build his own AGI competitor within Tesla, OpenAI said.

When he left in late February 2018, he told our team he was supportive of us finding our own path to raising billions of dollars, OpenAI said. The company included a screenshot of an email from December 2018, in which Musk wrote: Even raising several hundred million wont be enough. This needs billions per year immediately or forget it.

Musks lawsuit also seeks an order for OpenAI to open its research and technology to the public. OpenAI said Tuesday that it provides broad access to its AI models, including free versions, and that Musk understood the mission did not imply open-sourcing AGI.

In one email, OpenAI co-founder Ilya Sutskever wrote to Musk, saying it will make sense to start being less open as the company continued developing its AI, and that Open in openAI means that everyone should benefit from the fruits of AI after its built, but its totally OK to not share the science.

Musk responded to that, Yup.

Were sad that its come to this with someone whom weve deeply admired someone who inspired us to aim higher, then told us we would fail, started a competitor, and then sued us when we started making meaningful progress towards OpenAIs mission without him, OpenAI said.

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OpenAI fired back at Elon Musk's lawsuit by releasing his emails - Quartz