Ep 167: When AI Outsmarts Humans – DeepMind’s Historic Breakthrough

Episode Categories:

Resources

Join the discussion: Ask Jordan questions about Google DeepMind

Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineup

Download the Deepmind paper here. 

Connect with Jordan Wilson: LinkedIn Profile


Embracing the Potential of AI

As the world of artificial intelligence (AI) continues to burgeon, so too does its capability to transcend the confines of mere autocomplete systems. The most recent developments in generative AI and large language models have shown a remarkable capacity to create new discoveries and intelligence that outsmart humans, staking a claim in uncharted territory. We explore how these historic breakthroughs are reshaping the landscape of AI and its potential impact on businesses and decision-makers.

Unlocking Generative AI's Potential

One of the most prominent breakthroughs in the realm of AI was achieved by DeepMind, Google's AI arm. Their recent achievement in unlocking 800 years' worth of knowledge, discovering 2.2 million new crystals, and solving a famous unsolved math problem called cap set using their language model marks a pivotal moment in AI history. This milestone has debunked the misconception that AI is merely an autocomplete tool and has paved the way for the recognition of its potential to create new intelligence and discoveries at an unprecedented pace.

Outperforming Human Capabilities

The capability of large language models and generative AI to reject incorrect answers at a scale 1,000 times that of humans has allowed for new discoveries that were previously unattainable. The ability to explore new frontiers at a rate of 100 years at a time and swiftly create new intelligence that benefits humankind presents a paradigm shift in the capabilities of AI. This level of performance not only outstrips human capabilities but also signifies a profound leap in the potential of AI to lead to accidental discoveries or new intelligence in unexpected areas.

The Dawn of Self-evolving AI Technology

In a groundbreaking demonstration of AI's potential, researchers have showcased how large AI models can create smaller AI models to solve problems without human intervention. This self-evolving AI technology holds promise for various applications, showcasing AI's capability to not only outperform humans but also create its own intelligence. It presents a new frontier where AI has the potential to autonomously evolve and contribute to unprecedented advancements in various domains.

Navigating the Future of AI in Business

As the capabilities of generative AI and large language models continue to unfold, business owners and decision-makers are faced with an evolving landscape. Embracing the potential of AI is no longer a choice but a strategic imperative. The ability of AI to create new intelligence and discoveries underscores the need for businesses to adapt and leverage AI to stay ahead of the curve. The implications of these historic breakthroughs on AI business strategy, the landscape of higher education, and the prospect of AI-enabled innovation in 2024 are topics that demand contemplation and strategic planning.

The AI Frontier Awaits

The revelations from these historic breakthroughs in generative AI and large language models have redefined the boundaries of AI's capabilities. As businesses and decision-makers navigate the future, embracing AI's potential to outperform humans and create new intelligence holds the promise of unlocking unprecedented opportunities. The journey into the AI frontier beckons, and the potential for groundbreaking innovation and transformative impact is within reach.

In conclusion, the age of AI has ushered in a new era where the landscape of generative intelligence is continually expanding. As large language models and generative AI outsmart humans and unveil new discoveries, businesses and decision-makers stand at the cusp of an era ripe with opportunities for leveraging AI's potential. Embracing this potential is not just a choice but an imperative in shaping the future of innovation and advancement across industries.

Topics Covered in This Episode

1. DeepMind's Historic Breakthrough
2. Evolution of AI Capabilities
3. AI's Impact on Various Applications
4. Future Implications of AI


Podcast Transcript

Jordan Wilson [00:00:17]:

People think that AI is just an autocomplete, and that's kind of true, but AI is actually So much more than that, especially when we're talking about generative AI in large language models. So in just one short Year and a half. We've gone from thinking of generative AI and large language models to this ChatGPT craze to now Large language models making new discoveries that have plagued humans for decades. So we're gonna be talking about DeepMind's historic breakthrough And talking about what does it mean now when AI starts to outsmart humans. We're gonna talk about ad That and a lot more today on everyday AI. Thanks for joining us. My name is Jordan Wilson. I am your host.

Daily AI news


Jordan Wilson [00:01:04]:

And if you're new here to everyday AI, welcome. It's a daily livestream podcast and free daily newsletter helping everyday people like you and me not just learn what's going on in the world of generative AI, but how we can all actually Understand it and use it. Right? That's the most important thing is how can we actually use it to grow our companies, to grow our careers. So that's what we're gonna talk about today. But Before we get into this historic breakthrough at DeepMind, let's, as we always do, go. Let's talk about what's going on in the world of AI news. So first, more AI on the campaign trail, and this time, it's coming from Pakistan. So, Artificial intelligence was used to create a virtual reality for Pakistan's former, prime minister, iman Khan or Imran Khan.

Jordan Wilson [00:01:53]:

So why does that matter? Well, Khan is currently in jail and is unable to campaign in person. So this rally was organized by Khan's party, PTI, and it garnered millions of views on social media, leveraging AI technology to replicate Khan's voice and speeches. So this is a new one. Right? We've talked about at least here in the US, with the, the 2024, election cycle ramping up. We've talked about it being used in ads, but here's a new one. Use live in a campaign rally, AI to replicate his voice and, likeness. So interesting. Alright.

Jordan Wilson [00:02:31]:

Number 2, and this is a hot topic for today in AI news, is GPT 4.5 actually out. Well, maybe. Well, let's talk about it. So, we talked about this last week on the Everyday AI Show, and, CEO Sam Altman essentially just denied it on Twitter after, there were rumors and reports and leaked screenshots of, OpenAI's next version of their large language model, GPT 4.5, being a thing, being a reality. Right? So there was a screenshot. People were like, it's not real. The CEO, Sam Altman, said, nope. It's not real, but there's some updates.

Jordan Wilson [00:03:07]:

So over the weekend, a lot of users, including myself, noticed that if you ask, ChatGPT a certain way, what model it is specifically running, it will say gbt 4.5 turbo. However, it's important to note that asking Any large language model, the version of what it's using is not always very accurate at all. But, this is kind of a widespread response that a lot of people are getting. So, you're gonna be hearing a lot of buzz this week as okay. Is are we on, GBT 4 or are we on GBT 4.5? Also, what's important to see is there's been some under the hood updates that no one else has talked about or reported. So I talked about on the Everyday AI Show, this is probably about 2 weeks ago on how the plug ins mode, the GPT 4 version in plug ins mode had actually been rolled back, to January 2022, its knowledge cutoff, which is important because now as of, since, chat g p t has been responding, it's on 4.5, that has been rolled up To April 2023. So the GPT 4 is being used in plug ins mode was, kind of, you know, cut off at the knees for, about a month, and it's been updated again. So Is that a sign that, yes, GPT 4.5 is out? I'm not sure.

Jordan Wilson [00:04:19]:

But, it's it's worth taking a look at, especially After, Google released its Gemini, its new update to Bard, kind of its under the hood, releases with the Gemini Pro now powering Google Bard. All these big companies, they're obviously going back and forth trying to one up each other and trying to make sure that their stocks continue to go up. So keep an eye on that. Alright. Last but not least in the world of AI news, AI can now create more AI with what's being called a self evolving AI. Right? Alright. So researchers have developed a new technology that allows artificial intelligence models to create smaller AI systems without human intervention. Alright? Showcasing the potential for self evolving AI.

Jordan Wilson [00:05:04]:

So this breakthrough, could have applications in improving things like hearing aids, Monitoring pipelines, tracking endangered species. I mean, there's so many different, potential use cases for this. So, in short, larger AI models, as this report shows, can now design smaller, more specific AI applications for everyday use. So this study comes from several companies and universities That include the group AI Zip Inc, as well as researchers from MIT and multiple University of California campuses. So a lot of different researches and private company, being involved in this. So this is something you're gonna be hearing a lot about, especially as we talk about, you know, moving into 2024, what we should be expecting or could be expecting, from generative AI and what's happening in the space or what could happen. Kind of wild. Right? We've we've we've talked about this for For years, right, is this the the first sign of, you know, superintelligence when, AI models can create different versions of themselves? But, it looks like, hey.

Jordan Wilson [00:06:04]:

It's 2023, and we're already there. So before we talk about this historic breakthrough from DeepMind. Let's just go over what we have coming up this week in everyday AI. So, tomorrow It's gonna be a good one. So we're gonna be talking about AI in higher education. If is it broken and how to fix it. So make sure to tune in to that one, tomorrow with Jason Gula, who's the chair of the artificial intelligence. Speaking of colleges in Berkeley, he's the chair of the artificial intelligence council, at Berkeley.

Jordan Wilson [00:06:40]:

Wednesday. If you haven't heard of DID, it is one of the most impressive and unique generative AI tools. So we're talking with Rod Freeman who is the head of content and creative marketing at DID. So that should be exciting. Right? And, hey, where else aside insight. From everyday AI, can you tune in live and talk to the actual people who are shaping generative AI and the tools that we use? So make sure to tune in on Wednesday. On Thursday, we're gonna be talking with Marcus Bernhardt about AI business strategy, and if you're ready for 2024. And then I'm gonna follow that up the day after Friday.

Jordan Wilson [00:07:14]:

Solo show. So many hot takes, you're gonna need oven mitts, alright, to to touch the computer. I'm coming in hot, and and Talking about generative AI in 2024, what's coming, and what it means for you. Alright. So if you haven't already, please make sure to go to your everyday AI.com. Sign up for that free daily newsletter. But let's talk about let's talk about this. Right? Let's talk about the reason, for or the topic for today's show.

Jordan Wilson [00:07:41]:

And thank you to everyone who's joining us, from all over the country and technically all over the world. So Tara's Tara's Tara's joining us from her family road trip. That's great. Thanks for tuning in. Josh, what's going on from, joining us from Dallas, Ellington? Brian, thank you everyone for, for tuning in. Also, Harvey from Texas. We have a lot of people from Texas listening. Alright.

Google DeepMind's new discovery


Jordan Wilson [00:08:03]:

But let's let's talk about this, This new deep mind discovery. Let me first talk about what it is, and then we're going to talk about what it means, and this is pretty historic. Alright? So here's what's going on. So, this just came out late last week. We talked about it on the show and in the newsletter, but I thought this was worth its own kind of deep dive to talk about this. So There's this, I think, very common misconception that all AI is is an autocomplete. Right? So whether we're talking about generative AI, large language models, traditional AI, I think people assume that all AI does is it just it's it's a very advanced autocomplete. It's not creating anything new.

Jordan Wilson [00:08:49]:

It's not creating new intelligence. So, that's wrong because, I mean, as This new breakthrough from Google's, Google's AI arm, if you don't know, is called DeepMind, but they have a language model called FunSearch. And fun searching. The DeepMind team have successfully solved a famous problem in pure mathematics using AI to make valuable and previously unknown discoveries. So here's what happened. They used their, model, fund search, to what has been an unsolved math problem for decades called cap set. Alright. So I'm not a mathematician and maybe someone, tuning in today is and can, enlighten us all.

Jordan Wilson [00:09:33]:

But, from what I can read and what I can understand, cap set is essentially a famous math problem or a math, question, right, that no one has been able to solve for decades. And it's finding the largest set of points in a specific three d space without 3 points forming a straight line. So if you can visualize that, I mean, hopefully, that makes sense. But, essentially, you've had many, many, researchers, mathematicians, and, you know, even artificial intelligence. With the help of artificial intelligence, people have been trying to, solve this or understand, this capsets problem for decades. Right? And DeepMind did it by using, generative AI using their language model fund search. Alright. So Why is this important? Well, it is a new discovery.

Jordan Wilson [00:10:33]:

Right? So this was not Technically, derived from existing data. So, yes, this math problem and and the data that it, technically contains is the parameters. But this new discovery, this is a language model, right, creating a new math like, mathematical discovery. Alright. So, to do this, it combined the large language model with other systems, and how it did is it It used it to reject incorrect answers and generate the correct code. Okay? And this is one of those things that I think People misunderstand or maybe, when you're looking at large language models or generative AI and what it can do for yourself or your company, I think people Sell large language model short, just because of the sheer volume in quantity that a large language model can work at, you know, much faster than any group of humans. So, you know, this this problem, right, this, cap set problem, It obviously has a solution. Right? But like I said, it has perplexed humans for decades, and I think this is one of the first definitive And somewhat easy to understand, instances where a language model has created a new discovery.

First instance of AI outsmarting humans?


Jordan Wilson [00:12:05]:

Ad Right. It has created something that was not there before. This is an autocomplete. Right? So, this this problem, like I said, it has its own Its own rules for solving. It has its own, kind of confinement that the, that the solution must work within. However, Google DeepMind and their fun search model has done this. Right? So let me let me illustrate let me illustrate this. And I'm also wondering from our from our live audience, If you've noticed this or or or what are your thoughts? Right? And, I mean, do you agree? Do you agree? Is this the 1st instance where AI is outsmarting humans? I mean, there's there's arguments that, you know, that has happened before, but This is one of those things where you can look at it and say, this might be one of those kind of like what, Doctor doctor Harvey Castro is is mentioning, here about the AlphaGo, right, the AlphaGo move from, so long ago, Right when you had AI, initially competing in different games, you you know, like AlphaGo, against the best and human players in the world.

Jordan Wilson [00:13:23]:

So this is something, I think, even greater than that. Right? Because in head to head human competitions, Right when AI you know, there's the famous Watson, you know, winning on jeopardy against human contestants. You know? So that was, I think a lot of people's first Interaction with artificial intelligence, seeing it live, you know, on their on their television sets, you know, many years ago. However, That was always AI system versus human. Right? So in theory, You could point at, okay. Well, this is just the AI was just a little better than the human today or, okay. Well, the human today was a little off. Right? A lot of pressure for humans.

Jordan Wilson [00:14:05]:

Right? People people made those, arguments, you know, when these, you know, kind of AI versus human, competitions or, You know, events happened, you you know, decades ago, but this is completely different. Right? This cap set math problem And Google's, Google DeepMind's new discovery is completely different about that because it's it's not pitting, you know, DeepMind against a single human. It's not doing something live. It's not, you know, setting down, you know, Google's, Large language model or their fun search model versus a human. This is against the history of humankind. Right. Countless mathematicians, scientists, researchers have tried to solve this capsat problem for decades to no avail, and Google DeepMind So I kinda I kinda wanna talk, a little bit, just a a little analogy here. Right? And hopefully, This can kind of set our expectation or reset our our mind.

AI creating its own intelligence


Jordan Wilson [00:15:21]:

When we look at generative AI, when we look at large language models, When we look at what they they mean and also what they're capable of, because I think and, you know, more about this Friday, so make sure you tune in to that show. I think that we're gonna see a completely different side of generative AI moving into 2024. I think a lot of companies, You know, enterprise and even tech giants, right, have barely put their toes into, their their collective toes Into artificial intelligence. Right? I think 2024 is gonna be wild, but that's beside the point. So but I wanna illustrate, what I think can hopefully help us better understand or or or get over what I think is a misconception that AI can cannot Create its own intelligence. Right? I mean, one of the news stories today says out you know, says says otherwise. Right? So this, this company, AI Zip Incorporated, and the researchers from MIT and University of California campuses have Show now that large AI models can create smaller AI models to solve problems. I mean, if that doesn't tell you right now, That AI is has the capability to outperform humans, and that AI is more than just an autocomplete, And AI can do a lot more than just work with data that it is given.

Jordan Wilson [00:16:50]:

If that alone doesn't tell you, right, just the fact that researchers are now showing that large language models can create their own AI models. Right? This self self evolving AI system or self solving AI system. If that doesn't show you, let's take a little exploration together. Right? So think of space exploration. Right. So for about oh, yeah. It's been more than 60 years, right, since the fifties, since, humans were physically exploring space. Right.

Jordan Wilson [00:17:25]:

In manned aircrafts, unmanned aircrafts, it's been more than 60 years. So you would think, Right? When there's 1,000,000,000 of dollars of of research going into this and humans have been tackling space space exploration, for decades. Right?


Jordan Wilson [00:18:24]:

That's what I want to set as the baseline for thinking of generative AI, and here's what I mean. I think generative AI and large language models are space explorers, right, in this scenario. Humans have much and I know this sounds weird to even say out loud. Humans are limited Compared to large language models compared to generative AI.

Jordan Wilson [00:19:08]:

Right? You need large teams of people. You need large teams of researchers. You need large teams of funding to explore space. Right? Whereas think of generative AI. Think of large language models. The level at which generative AI can compute is unfathomable To to the human mind. Right? Because there was the the other study that we talked about. This was a couple weeks ago About just the this was also Google and DeepMind.

Jordan Wilson [00:19:46]:

So they said that with, one of their new discoveries that they unlocked 800 years worth of knowledge after discovering 2.2 new 2,200,000 new crystals. Right? So 800 years of knowledge. So that is the difference. Right? And if if we take this analogy of space exploration, Right. What large language models and generative AI can accomplish even just like by using this this same methodology That Google DeepMind used with fun search, right, which was essentially testing all of you you know, creating a system to quickly, At scale, reject incorrect answers. Even just by doing that, by by being able to Reject incorrect answers at 1,000 times the scale of what humans can do. That leads to new discoveries. Right? So in this example that I'm trying to illustrate where a large language model or generative AI is is a a space explorer, We know those stars.

Jordan Wilson [00:20:54]:

Right? We have pictures of the galaxy and from from satellites and NASA and all these things. Right? But Large language models more quickly than humans can explore things at the rate of 100 of years at a time When humans cannot, that's the difference. So large language models in generative AI, by using existing data, Can actually create new intelligence. It can create new discoveries that can benefit humans that Humans may otherwise never be able to come to on their own because it would take too long. Right. That is, I think, one of the most powerful things about large language models, about generative AI that people just look over. People think, oh, you know, large language models, they write you know, they make, my c plus email to my boss into a a minus or a b plus email. Right? They think that's what a large language model is, and, yes, obviously, it can do that.

Future of GenAI


Jordan Wilson [00:21:57]:

But when we talk about all of this new technology that we're gonna be seeing in 2024. It is much more than that. I think we are gonna be talking about on a weekly or maybe daily basis In a year or so, about generative AI, about large language models making new discoveries Nonstop. Right? So, yeah, over the last year, we've heard about a handful of examples. I think we are literally going to be seeing this almost on a daily basis Where large language models generated AI systems are going to be outsmarting humans daily, Tackling societal issues, problems, equations, right, that have plagued humans For decades, I think this is going to become the new reality. Yes, Douglas. It's the, the elephant in the room. Yes.

Jordan Wilson [00:23:00]:

Are we are we moving toward this this terminator type, world. I mean, kind of. Right? Kind of. You know, you even have the the the company, in Asia that is Actually called Skynet and creating this technology. Right? So, yeah, that's that's kind of where we're getting Where, large AIs are creating on their own smaller AIs. When we have artificial intelligence and large language models making new on their own, and then when they're let's let's think about that. What happens when, you know, you combine these these 2, kind of instances, Right? Where, oh, AI is now making discoveries on its own, but then what happens when AI can create, an AI system or an AI model to do something with its this this new intelligence that it discovered. Right? Yeah.

Jordan Wilson [00:23:54]:

It's it's it's hard for me to wrap my brain around. Yes. I talk about AI every single day, but that's hard for me to even fathom. But that is the future of where we're heading, right, when AI is outsmarting humans pretty easily. Right. Cecilia, I I I love this comment here, and thanks for joining. And, hey, if if you listen on the podcast, Check your show notes. You know, we we do this live every day.

Jordan Wilson [00:24:20]:

You can come. You can ask questions when we have guests. But, Cecilia, I I love what Cecilia is saying here. I'm wondering whether sometimes discoveries against the history of humankind is complete history. Is it possible that someone or somebody Who is or are not part of the circle of those trying to solve a problem may have solved it and their interactions on it. Yeah. That's great. Right? Because, yeah, I think you're we're also going to see a lot of accidental discoveries or, new intelligence that is creative, that is created when maybe that was not the outcome.

Jordan Wilson [00:24:56]:

Right? So many of the greatest products that we use, services, were were were not and Created ultimately for what we're using them for. So, Cecilia, absolutely. I think that we are going to see, researchers and generative AI and large language models solving, you know, not just problems that we didn't know existed, but also creating new intelligence in areas where it was not even intended to Create intelligence on. Absolutely. So I hope today's episode is helpful. I'm not gonna continue to talk for 45 minutes, but let's just quickly Let's just quickly recap what we're talking about here with Google DeepMind's historic breakthrough. Alright. So by using their language model fund search, they successfully solved This mathematical equation that has been, eluding humans for decades by solving this math problem cap set and why this is significant.

Jordan Wilson [00:26:06]:

And, you you know, I think this news came out Thursday or Friday, and I haven't seen a lot of people talking about this, which is interesting. But this is significant because This is one of the first times where definitively, we can say that an AI has outsmarted humans and has created a form of new intelligence. Right? And and this and Fun search and and what Google DeepMind is doing is indicative of something so much more than just yes, they crossed off quicker, or or more quickly cross off all of the incorrect answers to this math problem to to more quickly get the correct answer. Yes. That is in theory, how they went about this process, but it signals something so much greater than that. It signals the Enormous potential for large language models and for generative AI moving forward. And I hope that this also opens up your eyes To seeing that large language models are so much more than just writing some content. There's so much more than, you know, creating a marketing plan.

Jordan Wilson [00:27:19]:

Large language models and generative AI as we head into 2024 are creating their own intelligence. I don't know what it means, but we're obviously gonna find out together Because we do this every day. And as more and more of these breakthroughs come, you better believe we're not only gonna be talking about them. We're we're gonna be bringing experts on, for for you all to ask questions and to understand. But I will tell you this. If you have been on the fence, right, About the powers or capabilities of generative AI. If if if you think that generative AI and large language models are nothing more than And autocomplete working off of a fixed dataset. I think you have to open up your your mind to the possibility that Generative AI and large English models can be so much more than that.

Jordan Wilson [00:28:11]:

Right? Yes. In theory, they are trained off of a dataset, but They are creating new discoveries. They are creating new intelligence, And I think that's what's important to know. What's also important is for you to go to your everyday AI .com. Sign up for the free daily newsletter, and like I said, we have some great shows Some great shows coming up the rest of the week that I hope you'll join us. So, as a reminder, tomorrow, We're gonna be talking about AI in higher education. This is gonna be good. I can't wait for this show.

Jordan Wilson [00:28:52]:

Wednesday, we're gonna be talking, with Ron Friedman, the head of content marketing from DID, one of the largest, generative AI tools out there. Bar none. Then we're gonna be talking about AI business strategy in 2024 and my hot takes for 2024 as well. So thank you for joining us. Appreciate you. I hope to see you back tomorrow and every day with more everyday AI. Thanks

Gain Extra Insights With Our Newsletter

Sign up for our newsletter to get more in-depth content on AI