The AI Daily Brief: Artificial Intelligence News and Analysis - AI Wins Not One But Two Different Nobel Prizes

Episode Date: October 11, 2024

This week, Geoffrey Hinton and Dennis Hassabis won Nobel Prizes in Physics and Chemistry for their groundbreaking AI work, sparking conversations about AI’s influence across scientific disciplines. The episode explores Hinton’s AI safety concerns and Hassabis’ work on AlphaFold, transforming protein structure prediction. An unexpected set of honors, these awards spotlight AI’s growing role in advancing research far beyond traditional computer science. Concerned about being spied on? Tired of censored responses? AI Daily Brief listeners receive a 20% discount on Venice Pro. Visit ⁠⁠⁠https://venice.ai/nlw⁠⁠⁠ and enter the discount code NLWDAILYBRIEF. The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614 Subscribe to the newsletter: https://aidailybrief.beehiiv.com/ Join our Discord: https://bit.ly/aibreakdown

Today on the AI Daily Brief, both AI and AI beef hit the Nobel Prizes, and before that in the headlines, the latest AI unicorn. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show notes. Welcome back to the AI Daily Brief Headlines edition, all the daily AI news you need in around five minutes. The theme of this particular episode is all about product updates, funding, and acquisitions. One of the things that you're probably hearing a lot about right now are vertical

applications of AI. This is particularly true in the agent space where companies are taking the highly specific data sets and the highly specific workflows of different industries and building products that are customized for them. Around that theme, even up, a startup building AI for personal injury law firms has hit unicorn status. The company closed their series D fundraising round at a billion dollar valuation, raising $135 million in a deal led by Bain Capital, with participation from funders including Lightspeed, Bessemer, and others. The company says that over 1,000 law firms are currently using their product to help prepare cases and research from large volumes of documents.

Said Rami Kara Bieber, Evenup's co-founder and CEO, many law firms are stretched thin, which is why they need technology to scale their top performers. This is especially critical at personal injury law firms where attorneys often manage over 100 clients a year. Araf Halele, a partner at Bain Capital Ventures, was asked about the question of job loss. He argued, though, that the software is about making lawyers more efficient, not replacing them. Lawyers, he said, can focus on clients, not paperwork. Samir to Lockea, a partner at Bessemer, called the startup the most consequential legal AI company

out there. Next up, Uber is getting in the AI game, getting ready to launch an AI assistant to help drivers transition to electric vehicles. The assistant will be powered by OpenAI's GPT40 model and will be trained to answer questions about EVs. Now, Uber has pledged over $800 million to support drivers' partners. switching to EVs by 2040. And this new assistant is all about answering drivers' questions about this transition.

Basically, it'll be about giving advice on which EVs to purchase and where to find charging stations, although the Uber team says the use cases will be expanded in the future. Drivers will be able to access the assistant from the home menu of the Uber app, and Uber says it will be trained on personal data based on the driver's needs, including information specific to the city they live in, as well as the government incentives that apply to them. Drivers will be able to interact with the assistant using text or voice, Although it's not clear if Uber will support all 40 languages GBT40

4O is capable of communicating in. Speaking of agents, Enterprise Giant SAP is getting in the agent game, promising that its AI co-pilot called Jule will support 80% of its most used business tasks by the end of the year. At the TechEd conference on Tuesday, SAP announced that Jule will include multiple autonomous agents. The agents will be designed to carry out a specific function and able to collaborate to execute more complex tasks.

In addition, Jewel will integrate with Microsoft co-pilot to enable users to do things like check their Outlook calendar from Jewell. The company's head of product engineering said, we were infusing Jewell with multiple autonomous AI agents that will combine their expertise across the business functions to collaboratively accomplish complex workflows. This will free workers to collaborate in areas where human ingenuity is best suited. Walter Sun, the firm's global head of AI, emphasized the idea which is very prominent in the enterprise of human in the loop. Sun said, one analogy we talk about, if you think about these as musicians, each of these expert agents can play an instrument and they're trained to do that. And Jewel, which is a co-pilot, is a

conductor of this orchestra to create music that's beautiful to hear or listen to. And then the humans are the composers. The humans are the one that communicate with Jule and by virtue of that can actually make the request necessary. And Jule, through the help of AI expert agents, will complete the task. Now, even as much energy and momentum as there is around agents, there's also still a lot of skepticism. Scott Bickley, the research practice lead at the Infotech research group, for example, told cio.com, SAPN users are expected to trust that Jule can magically create a series of agents and string together activities in a cogent manner, resulting in comprehensive business workflows. SAP customers must look under the hood here. What data structure is required? What level of complexity

can this engine handle without error? How standardized does your process need to be for this to work? I believe we are fully and aggressively heading into the agent stage of AI, and so these are the types of questions that are going to get surfaced a lot more going forward. Over in the land, a real estate Zillow Group has acquired a virtual staging startup, creatively called virtual staging AI. allows exactly what you would expect. Listing agents will be able to digitally fill photos of an empty house with virtual furniture and decorations.

Zillow plans to integrate the service into their product suite in the near future. Now, this is just the latest in a spree of tech acquisitions for Zillow, who have spent more than a billion dollars on M&A since 2021. Lastly today, some interesting comments from Samsung who have apologized for falling behind in the AI arms race. In a highly unusual public letter, Vice Chairman and head of semiconductors Jun Yong Hewn said, we have caused concerns about our technical competitiveness with some talking about the crisis-facing Samsung. He said that the firm would need to improve its technology and change organizational

structure to catch up. Rather than manufacturing GPUs, Samsung only makes the memory chips that are a component of AI training units. The Wall Street Journal writes, Samsung is trailing its competitors in high band with memory chips, a type integral to AI computing. The article also notes that Samsung is losing to TSM on custom chip making and its consumer electronics division has been conducting layoffs. The rare apologies letter comes in the context of an earnings downgrade. The company was forced to revise guidance, estimating that their third quarter earnings would come in at $6.8 billion. That's roughly four times the figure from the same quarter last year, but just half of their 2021 peak.

Samsung share price is down 24% so far this year, while their closest rivals, TSM and S.K. Heinings are up 70% and 20% respectively. Lots of interesting things going on in the world of AI as always, but that is going to do it for today's headlines. Next up, the main episode. Today's episode is brought to you by Plum. Generative AI promises to supercharge your productivity and give you superpowers, but if you're not an engineer, trying to harness AI can be incredibly frustrating. Hours wasted wrestling with complex tools only to give up when they don't work. We all have tedious tasks we'd love to automate and challenges AI could solve,

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Today's episode is brought to you by Venice. The leading AI company store your entire conversation history and attach it to your identity forever. That's every question you asked, every answer you receive, every image you generate, every thought you share with the machine it's all being spied on. If you trust all the companies, hackers and NSA board members that will ever have access to your AI conversations, then rejoice, for you are well served. For the rest of us, Venice is an alternative. Venice is a powerful AI app for text, image, and code generation that respects you as a sovereign individual and believes privacy and free speech are not only human rights, but necessary for civilizational advancement. Private, permissionless, and uncensored, you can try it for free

without an account. AIA daily brief listeners receive a 20% discount on Venice Pro. Visit venice.com. and enter the discount code, NLW Daily Brief. That's NLW Daily Brief, all one word. Today's episode is brought to you by Super Intelligent. Every single business workflow and function is being remade and reimagined with artificial intelligence. There is a huge challenge, however, of going from the potential of AI to actually capturing that value.

And that gap is what Super Intelligence is dedicated to filling. Superintelligent accelerates AI adoption and engagement to help teams actually use AI to increase productivity and drive business value. An interactive AI use case registry gives your company full visibility into how people are using artificial intelligence right now. Pair that with capabilities building content in the form of tutorials, learning paths, and a use case library, and super intelligent helps people inside your company show how they're getting value out of AI, while providing resources for people to put that inspiration into action. The next three teams that sign up with 100 or more seats are going to get free embedded consulting. That's a

process by which our super intelligent team sits with your organization, figures out the specific use cases that matter most to you, and helps actually ensure support for adoption of those use cases to drive real value. Go to B-Super.a.I to learn more about this AI enablement network, and now back to the show. Welcome back to the AI Daily Brief. Today we are talking about something that took the AI world by kind of a surprise this week, which was not one but two Nobel prizes to AI industry leaders who don't exactly have a direct relationship with the category that they won their prize in. The first of these announcements came out on Tuesday, where Jeffrey Hinton, frequently called a godfather of AI, and at this point best known for his advocacy around AI safety,

was awarded the Nobel Prize for Physics along with John Hopfield. The Nobel Committee said, although computers cannot think, machines can now mimic function such as memory and learning. This year's laureates in physics have helped make this possible. Using fundamental concepts and methods from physics, they have developed technology that use structures and networks to process information. Hinton was the co-author of a paper published back in 1986 that popularized the back propagation algorithm for training multilayered neural networks. He went on to design the foundational image recognition model AlexNet in 2012

with assistance from his then students, Alex Khrushchevsky and Ilya Sutskiver. Ilya, of course, would go on to co-found Open AI and more recently safe superintelligence. Hinton worked on AI at Google from 2013 until last year, when he left in order to be able to, as he put it, freely speak about the risks of AI. Hopfield, meanwhile, wrote one of the seminal papers on neural networks in 1982, and was so foundational to the field that a simple example of a neural network is named after him. The Hopfield network was the first to be able to store and recall memories using its neural structure.

A published scientist since the late 1950s, Hopfield applied his knowledge in biophysics to transfer fundamental principles that could be used to create neural networks. Hinton characterized himself as flabbergasted to receive the prize. In a telephone interview, he said that AI will have a huge influence on our society, adding, it will be comparable with the Industrial Revolution, but instead of exceeding people in physical strength, it's going to exceed people in intellectual ability. We have no experience of what it's like to have things smarter than us.

Hinton said the technology could revolutionize health care or dramatically improved productivity, but warned, we also have to worry about a number of possible bad consequences, particularly the threat of these things getting out of control. And you can definitely feel hinted in these interviews, seeming fairly frustrated at how little people are heating his warnings. He said in one, my guess is in between five and 20 years, there's a probability of half that we'll have to confront the problem of AI trying to take over. He also said in a conversation with the BBC that developments over the last year showed governments were unwilling to reign in military use of AI, while the competition to develop products rapidly meant there was a risk tech companies wouldn't put enough effort into safety. He came off particularly prickly, let's say, in an interview with the New York Times.

Now, part of it may be that he just kept hanging up on them to talk to the BBC, but he also was fundamentally unwilling to even talk or really try to explain what the contributions that he was being recognized for actually meant. Indeed, when Times journalist Cade Metz asked, can you explain in language that the readers of the Times would understand, he referenced the legendary Richard Feynman to basically blow the guy off. Pricklear still was a press conference at the University of Toronto, where he said, I was particularly fortunate to have very many clever students, much cleverer than me,

who actually made things work. They've gone on to do great things. I'm particularly proud of the fact that one of my students fired Sam Altman. Presumably Hinton was talking about Ilius Sitskever. Twitter user Marcus Van de Evry had this assessment about Hinton. Hinton, without a grain of doubt, is an idea that changes the world. But that's where his strength and weakness lies. He is not the leader who turns that idea into a thriving product offering. For that, another type of leader is required, someone that he doesn't resonate with at all. In his eyes, an opportunistic visionary profit-seeking SOB who doesn't give up, people like Jobs, Musk and Altman. Hinton's hunch about leadership types is as bad as his hunch about neural networks was good. So basically,

Marcus is arguing here that Hinton is a priori skeptical of anyone like a Sam Allman. That certainly could be, but it seems to me that the bigger issue might be that in his mind his argument isn't winning. A recent example of this is, of course, California AI legislation SB 1047, which he came out strongly in favor of, but which was ultimately vetoed by Governor Gavin Newsom. As I said at the beginning, though, Hinton wasn't the only AI leader who won a Nobel this week. Google DeepMind CEO Demas Sassabas tweeted, massive congratulations to my good friend and former Google colleague Jeffrey Hinton on winning the Nobel Prize in Physics, incredibly well deserved. Jeff laid the foundations for the deep learning revolution that underpins the modern AI field. Ex-user Bone GBT responded in a few

decades you'll get yours for Alpha Fold. And yet it turns out it wasn't a few decades, but 24 hours. On Wednesday, the Nobel Prize for Chemistry was awarded to a trio of scientists, all of whom advanced the study of protein structure. David Baker of the University of Washington was honored for creating computational tools to design novel proteins for use in medicine and sensors, and Demis Hes Sabaas and John Jumper from Google DeepMind were awarded the prize for their use of AI to predict the structure of proteins. Hider-Linky, chair of the Nobel Committee for chemistry said, one of the discoveries being recognized this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream, predicting protein

structures from their amino acid sequences. Both of these discoveries open up vast possibilities. Hesabas and Jumper were the developers of Alpha Fold, who solved one of the most difficult problems in biochemistry. As the Washington Post put it, unraveling the nuances of how the sequence folded up into lumpy balls or intricate loops was a tough problem. In 1994, scientists began organizing a competition called the critical assessment of protein structure prediction, a kind of Olympics for protein folding, in which scientists would try to predict the structure of proteins whose forms had recently been decoded but not yet publicly released. Progress was slow until 2018 when Hasavas and Jumper began to deploy tools grounded in artificial intelligence to crack the problem.

The second version of their AI tool called AlphaFold 2 could predict protein structure and it turned out just as well as laborious conventional techniques. In a blog post celebrating the prize, Google DeepMind wrote, before AlphaFold predicting the structure of a protein was a complex and time-consuming process. Alpha Fold's predictions have given more than 2 million scientists and researchers from 190 countries a powerful tool for making new discoveries. The AlphaFold 2 paper published in 2021 remains one of the most cited publications of all time. Discussing the award, Hasab said, Receiving the Nobel Prize is the honor of a lifetime. I've dedicated my career to advancing AI because of its unparalleled potential to improve the lives of billions of people. I hope we'll

look back on AlphaFold as the first proof point of AI's incredible potential to accelerate scientific discovery. Jumper added, computational biology has long held tremendous promise for creating practical insights that could be put to use in real-world experiments. Alphafold delivered on this promise. Ahead of us are a universe of new insights and scientific discoveries made possible by the use of AI as a scientific tool. In later comments, Hesabas reflected on how much AI has changed the shape of the scientific world,

saying, Nobel originally set the prizes back 100-plus years ago, so obviously there was no computer science. But I think it's pretty amazing to see the effect of AI on the other sciences and as a tool. I think we'll probably start seeing more of that. Hinton made this point as well. When the New York Times said, is it odd that you've received this award for physics? Hinton said, if there was a Nobel Prize for Computer Science, our work would clearly be more appropriate for that. But there isn't one. The Times responds, that's a great way of putting it, to which Hinton said, it's also a hint. Daniel Lemaire really summed it up when he tweeted, after learning that the 2024-fysics Nobel Prize was given for AI research, we learned that the 2024 Chemistry Nobel Prize was also given for AI research. Computer science is the new master science. So very interesting stuff. Congrats and thanks for your contributions, of course, to all the winners.

That's going to do it for today's AI Daily Brief. Appreciate you listening as always. And until next time, peace.