Cloud Hosting

Description

In a recent address at the CogX event in London, Lila Ibrahim, the Chief Operating Officer (COO) of Google DeepMind, emphasized the imperative for international cooperation in the field of artificial intelligence (AI). She called for global AI regulation to manage risks effectively while harnessing the technologys vast potential. Ibrahims statements come in the wake Read more

In a recent address at the CogX event in London, Lila Ibrahim, the Chief Operating Officer (COO) of Google DeepMind, emphasized the imperative for international cooperation in the field of artificial intelligence (AI). She called for global AI regulation to manage risks effectively while harnessing the technologys vast potential. Ibrahims statements come in the wake of the UK governments push to position the country as a leader in AI safety and innovation. In contrast to this national focus, Ibrahim underscored that AIs impact and challenges transcend national boundaries, requiring a collaborative, worldwide approach.

The United Kingdom has been making strides in positioning itself as a hub for AI safety. Chancellor Rishi Sunak announced in June a vision to make the UK the global center for AI safety. This aspiration aligns with the UK governments broader goal of becoming a true science and technology superpower by 2030, with a significant emphasis on safety and innovation.

Secretary of State for Universities, Michelle Donelan, echoed this vision during her address at the tech-focused CogX event. She asserted that safety would be the UKs unique selling point in the AI arms race. Donelan contended that safety considerations would be the determining factor in the global competition to lead in AI innovation.

Both Lila Ibrahim and Michelle Donelan concurred that the responsibility for ensuring AI safety rests with a collaborative effort involving organizations and governments. They stressed the importance of cooperation and coordination on a global scale to address the challenges posed by AI.

The UK governments AI Safety Summit, scheduled for November 1-2 at Bletchley Park, is a pivotal event in this endeavor. Donelan outlined the summits objectives, which include identifying and agreeing upon AI risks, fostering collaborative research, and establishing regulatory measures to ensure AI serves as a force for good.

One of the key concepts introduced by Secretary Donelan is responsible capability scaling. This approach encourages AI developers to be proactive in monitoring and managing risks associated with their AI systems. Developers are expected to outline how they plan to control risks and take necessary actions, which may include slowing down or pausing AI projects until improved safety mechanisms are in place.

Donelan emphasized the importance of making responsible capability scaling a standard practice in the AI industry. She likened it to having a smoke alarm in ones kitchen, suggesting that it should become an integral part of AI development to ensure the safety of AI technologies.

Lila Ibrahims call for international cooperation in regulating AI underscores the global nature of AIs impact and potential risks. While individual countries can make significant strides in AI development and safety, the interconnectedness of the digital world demands a collaborative approach.

The rapid advancement of AI capabilities further amplifies the need for swift and effective international regulation. As AI technologies continue to evolve and proliferate, the risks associated with them also become more complex and widespread. International coordination can facilitate the sharing of knowledge, best practices, and regulatory frameworks, ensuring that AI benefits humanity while minimizing potential harm.

The United Kingdoms commitment to becoming a leader in AI safety and innovation is evident through its policies and initiatives. Chancellor Rishi Sunaks vision of making the UK a global AI safety hub aligns with the governments broader ambition to excel in science and technology. By prioritizing safety, the UK seeks to differentiate itself in the global competition for AI leadership.

The call for international cooperation on AI regulation, as advocated by Google DeepMinds COO Lila Ibrahim, resonates with the urgency of addressing the challenges posed by artificial intelligence on a global scale. While the UK governments focus on AI safety is commendable, both Ibrahim and Secretary Michelle Donelan emphasize that the solutions to AIs complex issues require collaborative efforts beyond national borders. The upcoming AI Safety Summit in the UK serves as a crucial platform for fostering international cooperation, sharing expertise, and advancing responsible AI development practices. As AI continues to reshape industries and societies worldwide, the imperative for collective action in ensuring its safe and beneficial deployment becomes increasingly evident.

Link:
Google DeepMind COO Urges Immediate Global Collaboration on ... - Cryptopolitan

Only a handful of European cities host a sizable population of highly-coveted artificial intelligence (AI) engineers, leaving most of the continent scrambling to catch up.

London, the birthplace of Alan Turing, who is considered one of the founding figures of modern computer science and AI, is home to about 24,600 AI engineers, according to figures from venture capital firm Sequoia Capital. The citys AI cluster features major employers in the AI space of the likes of Google DeepMind and a flourishing community of AI start-ups that can find in the city both the capital and the talent they need to scale-up.

There are a number of great universities and apprenticeships here in the UK that allow us to bring some of the best talent into the company and into our partner ecosystem, Vishal Marria, the companys CEO of Quantexa, a UK decision intelligence firm,told fDi after the unveiling a $105m hub for research and development in AI solutions in the UK capital in July.

Beyond London, Paris has the second largest population of AI engineers with 7624, followed by Zurich with 5800.

But the European city with the highest concentration of AI engineers relative to the overall population of tech engineers is Dublin, where almost two in 10 (17%) software engineers have a specialisation in AI.

Similarly to London, one factor driving the outlier concentration in Dublin is that the city has proved a friendly base for tech giants, argues the Sequoia report, which was published in June. Meta, Google and Microsoft among the top five companies hiring AI talent globally have built a considerable presence here, taking advantage of Irelands attractive tax regime for research and development.

If both the EU and other major European powerhouses like the UK have big AI ambitions, the overall level of AI talent available is still relatively low. Across the whole of Europe, only 1.4% of the population of software engineers has a specialisation in AI, with that percentage growing to 7% for engineers with some AI experience, according to Sequoia figures. These figures are even lower in the US and China, where only 1.1% and 0.5%, respectively, of all software engineers have an AI specialisation, Sequoia figures show.

With its wealth of talent, Europe is positioning itself as a leader in the accelerating world of AI, reads the report. While talent is amassing at the tech giants, these talent pools become aircraft carriers as entrepreneurial employees inevitably depart to start their own companies, generating yet more demand for AI skills. With assertive policy incentives in the pipeline, anyone with a stake in AI is keeping their eyes on the region.

More:
Mind the tech gap: the AI divide in Europe - fDi Intelligence

An age-old adage, often introduced to us during our formative years, is designed to nudge us beyond our self-centered, nascent minds: "Two heads are better than one." This proverb encourages collaborative thinking and highlights the potency of shared intellect.

Fast forward to 2023, and we find that this wisdom holds true even in the realm of artificial intelligence: Multiple language models, working in harmony, are better than one.

Recently, a team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) embodied this ancient wisdom within the frontier of modern technology. They introduced a strategy that leverages multiple AI systems to discuss and argue with each other to converge on a best-possible answer to a given question. This method empowers these expansive language models to heighten their adherence to factual data and refine their decision-making.

The crux of the problem with large language models (LLMs) lies in the inconsistency of their generated responses, leading to potential inaccuracies and flawed reasoning. This new approach lets each agent actively assess every other agents responses, and uses this collective feedback to refine its own answer. In technical terms, the process consists of multiple rounds of response generation and critique. Each language model generates an answer to the given question, and then incorporates the feedback from all other agents to update its own response. This iterative cycle culminates in a final output from a majority vote across the models' solutions. It somewhat mirrors the dynamics of a group discussion where individuals contribute to reach a unified and well-reasoned conclusion.

One real strength of the approach lies in its seamless application to existing black-box models. As the methodology revolves around generating text, it can also be implemented across various LLMs without needing access to their internal workings. This simplicity, the team says, could help researchers and developers use the tool to improve the consistency and factual accuracy of language model outputs across the board.

Employing a novel approach, we dont simply rely on a single AI model for answers. Instead, our process enlists a multitude of AI models, each bringing unique insights to tackle a question. Although their initial responses may seem truncated or may contain errors, these models can sharpen and improve their own answers by scrutinizing the responses offered by their counterparts," says Yilun Du, an MIT PhD student in electrical engineering and computer science, affiliate of MIT CSAIL, and lead author on a new paper about the work. "As these AI models engage in discourse and deliberation, they're better equipped to recognize and rectify issues, enhance their problem-solving abilities, and better verify the precision of their responses. Essentially, we're cultivating an environment that compels them to delve deeper into the crux of a problem. This stands in contrast to a single, solitary AI model, which often parrots content found on the internet. Our method, however, actively stimulates the AI models to craft more accurate and comprehensive solutions."

The research looked at mathematical problem-solving, including grade school and middle/high school math problems, and saw a significant boost in performance through the multi-agent debate process. Additionally, the language models showed off enhanced abilities to generate accurate arithmetic evaluations, illustrating potential across different domains.

The method can also help address the issue of "hallucinations" that often plague language models. By designing an environment where agents critique each other's responses, they were more incentivized to avoid spitting out random information and prioritize factual accuracy.

Beyond its application to language models, the approach could also be used for integrating diverse models with specialized capabilities. By establishing a decentralized system where multiple agents interact and debate, they could potentially use these comprehensive and efficient problem-solving abilities across various modalities like speech, video, or text.

While the methodology yielded encouraging results, the researchers say that existing language models may face challenges with processing very long contexts, and the critique abilities may not be as refined as desired. Furthermore,the multi-agent debate format, inspired by human group interaction, has yet to incorporate the more complex forms of discussion that contribute to intelligent collective decision-making a crucial area for future exploration, the team says. Advancing the technique could involve a deeper understanding of the computational foundations behind human debates and discussions, and using those models to enhance or complement existing LLMs.

"Not only does this approach offer a pathway to elevate the performance of existing language models, but it also presents an automatic means of self-improvement. By utilizing the debate process as supervised data, language models can enhance their factuality and reasoning autonomously, reducing reliance on human feedback and offering a scalable approach to self-improvement," says Du. "As researchers continue to refine and explore this approach, we can get closer to a future where language models not only mimic human-like language but also exhibit more systematic and reliable thinking, forging a new era of language understanding and application."

"It makes so much sense to use a deliberative process to improve the model's overall output, and it's a big step forward from chain-of-thought prompting," says Anca Dragan, associate professor at the University of California at Berkeleys Department of Electrical Engineering and Computer Sciences, who was not involved in the work. "I'm excited about where this can go next. Can people better judge the answers coming out of LLMs when they see the deliberation, whether or not it converges? Can people arrive at better answers by themselves deliberating with an LLM? Can a similar idea be used to help a user probe a LLM's answer in order to arrive at a better one?"

Du wrote the paper with three CSAIL affiliates: Shuang Li SM '20, PhD '23; MIT professor of electrical engineering and computer science Antonio Torralba; and MIT professor of computational cognitive science and Center for Brains, Minds, and Machines member Joshua Tenenbaum. Google DeepMind researcher Igor Mordatch was also a co-author.

The rest is here:
Multi-AI collaboration helps reasoning and factual accuracy in large ... - MIT News

A New York Times article this morning, titled How to Tell if Your AI Is Conscious, says that in a new report, scientists offer a list of measurable qualities based on a brand-new science of consciousness.

The article immediately jumped out at me, as it was published just a few days after I had a long chat with Thomas Krendl Gilbert, a machine ethicist who, among other things, has long studied the intersection of science and politics. Gilbert recently launched a new podcast, called The Retort, along with Hugging Face researcher Nathan Lambert, with an inaugural episode that pushes back on the idea of todays AI as a truly scientific endeavor.

Gilbert maintains that much of todays AI research cannot reasonably be called science at all. Instead, it can be viewed as a new form of alchemy that is, the medieval forerunner of chemistry, that can also be defined as a seemingly magical process of transformation.

Many critics of deep learning and of large language models, including those who built them, sometimes refer to AI as a form of alchemy, Gilbert told me on a video call. What they mean by that, he explained, is that its not scientific, in the sense that its not rigorous or experimental. But he added that he actually means something more literal when he says that AI is alchemy.

The people building it actually think that what theyre doing is magical, he said. And thats rooted in a lot of metaphors, ideas that have now filtered into public discourse over the past several months, like AGI and super intelligence. The prevailing idea, he explained, is that intelligence itself is scalar depending only on the amount of data thrown at a model and the computational limits of the model itself.

But, he emphasized, like alchemy, much of todays AI research is not necessarily trying to bewhat we know as science, either. The practice of alchemy historically had no peer review or public sharing of results, for example. Much of todays closed AI research does not, either.

It was very secretive, and frankly, thats how AI works right now, he said. Its largely a matter of assuming magical properties about the amount of intelligence that is implicit in the structure of the internet and then building computation and structuring it such that you can distill that web of knowledge that weve all been building for decades now, and then seeing what comes out.

I was particularly interested in Gilberts thoughts on alchemy given the current AI discourse, which seems to me to include some doozies of cognitive dissonance: There was the Senates closed-door AI Insight Forum, where Elon Musk called for AI regulators to serve as a referee to keep AI safe, while actively working on using AI to put microchips in human brains and making humans a multiplanetary species. There was the EU parliament saying that AI extinction risk should be a global priority, while at the same time, OpenAI CEO Sam Altman said hallucinations can be seen as positive part of the magic of generative AI and that superintelligence is simply an engineering problem.

And there was DeepMind co-founder Mustafa Suleyman, who would not explain to MIT Technology Review how his company Inflections Pi manages to refrain from toxic output Im not going to go into too many details because its sensitive, he said while calling on governments to regulate AI and appoint cabinet-level tech ministers.

Its enough to make my head spin but Gilberts take on AI as alchemy put these seemingly opposing ideas into perspective.

Gilbert clarified that he isnt saying that the notion of AI as alchemy is wrong but that its lack of scientific rigor needs to be called what it really is.

Theyre building systems that are arbitrarily intelligent, not intelligent in the way that humans are whatever that means but just arbitrarily intelligent, he explained. Thats not a well-framed problem, because its assuming something about intelligence that we have very little or no evidence of, that is an inherently mystical or supernatural claim.

AI builders, he continued, dont need to know what the mechanisms are that make the technology work, but they are interested enough and motivated enough and frankly, also have the resources enough to just play with it.

The magic of generative AI, he added, doesnt come from the model. The magic comes from the way the model is matched to the interface. The magic people like so much is that I feel like Im talking to a machine when I play with ChatGPT. Thats not a property of the model, thats a property of ChatGPT of the interface.

In support of this idea, researchers at Alphabets AI division DeepMind recently published work showing that AI can optimize its own prompts and performs better when prompted to take a deep breath and work on this problem step-by-step, though the researchers are unclear exactly why this incantation works as well as it does (especially given the fact that an AI model does not actually breathe at all.)

One of the major consequences of the alchemy of AI is when it intersects with politics as it is now with discussions around AI regulation in the US and the EU, said Gilbert.

In politics, what were trying to do is articulate a notion of what is good to do, to establish the grounds for consensus that is fundamentally whats at stake in the hearings right now, he said. We have a very rarefied world of AI builders and engineers, who are engaged in the stance of articulating what theyre doing and why it matters to the people that we have elected to represent our political interests.

The problem is that we can only guess at the work of Big Tech AI builders, he said. Were living in a weird moment, he explained, where the metaphors that compare AI to human intelligence are still being used, but the mechanisms are not remotely well understood.

In AI, we dont really know what the mechanisms are for these models, but we still talk about them like theyre intelligent. We still talk about them liketheres some kind of anthropological ground that is being uncovered and theres truly no basis for that.

But while there is no rigorous scientific evidence backing for many of the claims to existential risk from AI, that doesnt mean they arent worthy of investigation, he cautioned. In fact, I would argue that theyre highly worthy of investigation scientifically [but] when those things start to be framed as a political project or a political priority, thats a different realm of significance.

Meanwhile, the open source generative AI movement led by the likes of Meta Platforms with its Llama models, along other smaller startups such as Anyscale and Deci is offering researchers, technologists, policymakers and prospective customers a clearer window onto the inner workings of the technology. But translating the research into non-technical terminology that laypeople including lawmakers can understand, remains a significant challenge.

That is the key problem with the fact that AI, as alchemy and not science, has become a political project, Gilbert explained.

Its a laxity of public rigor, combined with a certain kind of willingness to keep your cards close to your chest, but then say whatever you want about your cards in public with no robust interface for interrelating the two, he said.

Ultimately, he said, the current alchemy of AI can be seen as tragic.

There is a kind of brilliance in the prognostication, but its not clearly matched to a regime of accountability, he said. And without accountability, you get neither good politics nor good science.

VentureBeat's mission is to be a digital town square for technical decision-makers to gain knowledge about transformative enterprise technology and transact. Discover our Briefings.

Read more here:
Why generative AI is 'alchemy,' not science - VentureBeat