AI Wins Not One But Two Different Nobel Prizes

0:00Today 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.

0:33This 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.

1:10Said Rami Karabieber, 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 Halayli, 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 Dalokhia, a partner at Bessemer, called the startup the most consequential legal AI company out there.

1:39Next 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 GPT-40 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.

2:21Drivers 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 is capable of communicating in. Speaking of agents, enterprise giant SAP is getting in the agent game, promising that its AI co-pilot called Juul will support 80 % of its most-used business tasks by the end of the year. At the TechEd conference on Tuesday, SAP announced that Juul 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, Juul will integrate with Microsoft Co-Pilot to enable users to do things like check their Outlook calendar from Juul.

2:55The company's head of product engineering said, We are infusing Juul 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,

3:36Now, 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, SAP end-users are expected to trust that Juul 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.

4:16Over in the land of real estate, Zillow Group has acquired a virtual staging startup, creatively called Virtual Staging AI. The tech 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've 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.

4:46In a highly unusual public letter, Vice Chairman and Head of Semiconductors Joon Yong-hyun 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-bandwidth memory chips, a type integral to AI computing. The article also notes that Samsung is losing to TSMC on custom chip making, and its consumer electronics division has been conducting layoffs.

5:20The rare apology 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's share price is down 24 % so far this year, while their closest rivals TSMC and SK Hyninks 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.

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7:36There is a huge challenge, however, of going from the potential of AI to actually capturing that value. And that gap is what Superintelligent 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 Superintelligent 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.

8:13The 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 Superintelligent 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 bsuper.ai 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.

8:56The 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 functions 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.

9:32He went on to design the foundational image recognition model AlexNet in 2012 with assistance from his then-students, Alex Krzyzewski and Ilya Sutskiver. Ilya, of course, would go on to co-found OpenAI, 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.

10:05A 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 healthcare or dramatically improve 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.

10:42And you can definitely feel Hinton in these interviews seeming fairly frustrated at how little people are heeding 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, the developments over the last year showed governments were unwilling to rein 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.

11:13Now, 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. Prickler 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.

11:44They'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 Ilya Sutskever. Twitter user Marcus Vandeevery had this assessment about Hinton. Hinton, without a grain of doubt, is an ideal leader who launches 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.

12:14people 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 Altman. 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, though, Hinton wasn't the only AI leader who won a Nobel this week.

12:47Google DeepMind CEO Demas Hasabas 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 BoneGBT responded, in a few decades, you'll get yours for AlphaFold. 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.

13:21And Demis Hassabis and John Jumper from Google DeepMind were awarded the prize for their use of AI to predict the structure of proteins. Heiner Linke, 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. Hassabis and Jumper were the developers of AlphaFold, 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.

13:55In 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 Hassabis and Jumper began to deploy tools grounded in artificial intelligence to crack the problem. The second version of their AI tool called AlphaFold2 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.

14:28AlphaFold's predictions have given more than 2 million scientists and researchers from 190 countries a powerful tool for making new discoveries. The AlphaFold2 paper, published in 2021, remains one of the most cited publications of all time. Discussing the award, Hassabis said,

14:54Jumper added,

15:09In later comments, Hassabis 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.

15:36The Times responds, that's a great way of putting it, to which Hinton said, it's also a hint. Daniel LaMere really summed it up when he tweeted, after learning that the 2024 Physics 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.