SPEAKER_00: if you have agi as you said like you can solve the energy problem you can stop once you solve

SPEAKER_01: the energy problem like what i mean you are basically the most valuable company on earth

SPEAKER_02: you know think about that i mean if you can solve economy like if you can solve uh politics basically the structure of governments you know this is the thing that we are hoping to get and there's a race

SPEAKER_05: to getting there do you think everybody gets there at the same time like agi feels no you don't you feel some people will get to agi first first yes yes this week in startups is brought to you by

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SPEAKER_13: get 15 off for your first year all right everybody welcome back to this week in startups we've got a

SPEAKER_15: great guest for you today with a great idea ramin hasani is the ceo and co-founder of liquid ai and we're going to hear all about what liquid ai is doing in a moment but they're kind of headed in a new direction uh trying to make smaller and more efficient language models welcome to the program ramin

SPEAKER_22: thank you for having me uh maybe you know just uh by way of introduction here explain to me what the mission of uh liquid ai is and then let's get into you know sort of language models and you know the

SPEAKER_15: size of models and making them more efficient yeah definitely so i started a company to design

SPEAKER_26: basically like from first principles systems that we can understand from scratch on a completely new

SPEAKER_01: base for artificial intelligence that is rooted in biology and physics so we started looking into brains and see how we can get inspirations from there to design kind of a new math that we can

SPEAKER_26: understand and we can scale basically and that that kind of became kind of a liquid neural network

SPEAKER_24: technology that i invented during my phd program okay liquid neural networks what does it mean

SPEAKER_15: compared to say a traditional ai model large language model so what's the difference what is a liquid neural network let's explain that and is that the term you came up with or is this an industry

SPEAKER_24: yes that's something that i came up with so believe it or not like about seven years ago i started

SPEAKER_01: looking into the brain of a little worm the worm is called c elegans the worm is one of the like in the tree of evolution is one of our fathers okay so it's basically nervous systems and your cellular kind of organization and everything is evolved from this animal this worm has already won um four nobel prizes for us because it shares 75 percent of its genes with humans so and its entire genome is actually

SPEAKER_26: sequenced is one of the only animals on earth we have actually two animals now that its entire nervous system is mapped that means like we know exactly how anatomically like how each part of the nervous system is actually connected to each other right so i thought another nice behavior of this

SPEAKER_02: biological organism is the fact that its nervous system is differentiable what does that mean today's ai systems as as you know them they are basically a set of neurons in a layer wise

SPEAKER_01: architecture next to each other's and they're connected through synapses or weights of the neural network and they become like a giant neural network that can do what chat gbt can do today right we scale

SPEAKER_02: those kind of neural networks into this kind of regime now neural networks the way we train these systems on massive amount of data is with a technology called back propagation okay back propagation of errors the underlying mathematics of the systems is differentiable that means you can propagate errors without interruption inside inside the neural network inside this huge kind of gigantic kind of

SPEAKER_01: a functional form of neural networks okay this property doesn't exist in the human brain in the human brain

SPEAKER_26: neurons spike so you've seen like uh i don't know eeg kind of signals and stuff like you can see that there are

SPEAKER_01: spiking neural networks okay spikes we haven't understood yet like from nervous systems we don't know why spikes work we have no idea we still don't know what's the purpose of the spike i mean some people say they translate an analog to digital kind of conversion to propagate information information much much faster we know a little bit about the learning theory around that like joffrey hinton is actually like he's working on some forward algorithms you know non-back propagation based kind of methods and stuff so there are local kind of learning rules and stuff that that we figured out but there is still so much that we don't know how the brain actually does learning

SPEAKER_02: but when we go back into animals until we arrive at this worm we don't have any nervous system that doesn't spike so that was that that's why i like this worm because you know the nervous system is something that is very similar to the mathematics that we design artificial intelligence with so i started

SPEAKER_24: learning like basically modeling the behavior of cells inside this worm

SPEAKER_02: and then this system became a new type of learning system this learning system is flexible in its behavior meaning that when you train it on data the system still stays adaptable to incoming inputs this is not the case with artificial intelligence systems when you train them they become a kind of a fixed system so when you train the weights of a neural network let's say in the case of let's say gpt4 gpt4 gpt4 has 1.8 trillion parameters each parameter this corresponds to 1.8 trillion weights in the system these weights of the system are already trained and they are fixed now that they are fixed now you can it's now became an intelligent system you can input information in there and then take output information but the system is fixed

SPEAKER_26: liquid neural networks on the other hand they're not fixed they're systems that you can have they can

SPEAKER_02: they can stay adaptable to the input incoming inputs that's the major kind of difference between the two

SPEAKER_15: and that's an advantage because it will make the uh answers more dynamic or more real-time what's the advantage to or more robust

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SPEAKER_51: posting for free on your boy jcal terms and conditions do apply a demonstration would be great

SPEAKER_15: here because this all sounds quite theoretical so maybe we could walk through your product demo or

SPEAKER_58: your powerpoint on how this all works yeah definitely definitely so we are talking about still the science of things like how this became uh liquid ai and this is all based on a worm what worm is it

SPEAKER_26: it's a worm called c elegance it's a two meter meter long worm it's it's a very very tiny worm got it but it's a very popular worm let me show you what would happen when you train a liquid neural network versus a typical neural network okay and for those of you not watching you can

SPEAKER_63: go to this week and startups on youtube and find this episode just look at the recent videos tim

SPEAKER_26: yes what i'm showing is this is basically a dashboard of an autonomous driving system it's a neural network what i'm showing here in the middle you see layers of neural networks that stack

SPEAKER_01: uh to each other and then they receive camera inputs and they make a final decision like they make a driving decision basically

SPEAKER_02: now this system has has been trained on massive amount of kind of uh driving data this is just a lane keeping task because we've done that at mit uh during our research basically so what we what we see here this is this is actually an actual car that is getting uh driven by by this neural network in the camera on the top left what you see is the camera view and uh on the bottom left what you see is an attention map of this neural network that means where does this neural network is paying attention to

SPEAKER_26: where when it is taking driving decisions this neural networks has 500 000 parameters it's a rather small neural network okay now as you see there is also a little bit of noise on top of the image you know

SPEAKER_02: like on the on the camera you see like we put a little bit of noise so that we can disturb and see how robust the decision making of the system is okay and as we see in a typical kind of neural network that you see in the middle you i i put all of these dots that are glowing there are basically single neurons that are getting activated and deactivated basically it is very hard to say what this what what this neural network is doing right because this there's a lot of them and there's a lot of 500 000 parameters how can i actually say what each individual of these systems is doing you know in this task but again if in an abstract way if i bring it back to this image on the bottom left what you see is the attention map the lighter regions are the regions where the network is paying attention to

SPEAKER_76: when it's taking a driving decision god and that would be the road i guess exactly what it has to be

SPEAKER_77: the road yeah it has to be the road but it is basically like outside of the road in this case

SPEAKER_02: as you see the attention is kind of outside and is kind of affected by the noise that we put at the input you know so that's why it is not that much um reliable this is how a typical artificial neural network works now let me change that to a liquid neural network already we switched the parameter heavy part of this neural network we kept the eyes of the network which is this

SPEAKER_38: calm calm kind of layers as you see a convolutional layers basically but we replace basically the

SPEAKER_02: parameter heavy part of the system we then with 19 neurons 19 liquid neurons neurons that are modeled after the worm's brain and then we basically you know like the synapse is also like the connectivity you know it looks like a little bit more scattered it's kind of recurrent kind of connections like you you can see a lot of kind of uh unstructured kind of connections in this system

SPEAKER_72: but this system has 19 neurons and around 1 000 parameters as opposed to the previous system that i showed you that had 500 000 parameters the system is much less yeah it was much smaller and does that make

SPEAKER_15: it more accurate or does it make it faster decisions or both or we just don't know both actually so now

SPEAKER_00: let's look at the bottom left again like the attention map of the system as you see in the attention map

SPEAKER_02: now the focus is on the road and on the sides of the road so the system without any prior it actually

SPEAKER_01: figured out like how how to perform decision making without being like you know like disturbed by anything else now not only this system is very much smaller than a transformer architecture but it's also it can give

SPEAKER_38: you basically much more robust representation very similar to how biological systems perform decision

SPEAKER_83: making so net net a worm is a better driver than a human brain this is what you're is what you're

SPEAKER_45: telling us that seems counterintuitive aren't human brains better than worm brains um and is this because

SPEAKER_15: the silicon that these things are run on and the cameras um aren't able to process fast enough in real time like a human brain so actually a worm brain might be a little bit simpler and easier to run on today's silicon

SPEAKER_02: is that is that what i'm reading into this as uh i mean yeah to some extent but but the the fact that these are just modeled after how nervous systems perform computation in the brain of the worm now we can take those mathematical inspirations and then build machine learning systems that are not just like they're not just mimicking to be a worm or anything they're just like basically the fundamentals of computation in nervous systems now the reason why i told you the worm in the tree of evolution is one of our fathers is the fact that these principles actually scale that means if if nature actually evolved worms into humans

SPEAKER_01: we can take these inspirations from neural computations and even go beyond that you know so there's an opportunity to build ai systems powered by how nature designed nervous systems okay so the worm system

SPEAKER_106: is less robust and narrower than humans but you could scale it up and if a worm had a billion neurons or

SPEAKER_38: a million neurons i don't know how many it has actually the worm has 302 neurons it's a very tiny worm

SPEAKER_22: okay so it's got 302 how many neurons does a human human has hundred billions of neurons got it

SPEAKER_15: okay so there's a big gap between those two but the worms are um simpler and easier to define or

SPEAKER_45: easier to emulate than a human because humans are much more complex with 300 billion yes

SPEAKER_38: yes we can understand this one we can understand the brain of this worm much better than we can

SPEAKER_26: understand the brain of a human being because we still have a lot of questions even we don't understand mice we still don't fully understand monkeys we don't understand a small fruit fly you know

SPEAKER_00: so that's why we need to start from somewhere so i wanted to take a step back and start as a as a

SPEAKER_02: you know computer scientist basically wanted to see like how these kind of systems how how can we look at the origin of these nervous systems and where can we find basically principles that we can at least confirm that exist in biology and then now take the systems and build new type of learning systems okay got

SPEAKER_10: it okay just an inspiration basically i understand yes okay so this is

SPEAKER_106: pretty trippy but i think i'm following so let's get going yes yes so then since then we managed to

SPEAKER_01: drive cars autonomously like with these nervous small nervous systems we showed that you can fly drones

SPEAKER_26: with them okay you can recently united states air force actually showed that you can also fly uh uh full-blown f-16 jets with them this type of worm inspired systems can actually you know do a lot more than just you know like navigating warps but they might not be able to handle the existential crisis or

SPEAKER_15: making a season of the sopranos and something complex like that and creative but it might be able to do something incredibly simple and basic like stay in the middle of this road you know dead center or you know keep this drone in the sky not crashing into something exactly that's what we thought at the

SPEAKER_01: beginning right that this is going to be the property of this learning system but then we started to see

SPEAKER_26: that you can also do much more complex kind of tasks way better than how artificial intelligence systems performing that for example what predictive models for financial markets predictive models for

SPEAKER_01: biological signals let's say like if you want to predict the mortality rate of people in icu based on their biomarkers you know and then if you want to do predictive tasks like that you can see that

SPEAKER_02: these models are really good at doing that in principle we figured out that this type of new type of technology is really good at modeling time series data data it could be video data it could be audio data it could be text it could be user behavior it could be financial time series medical time series

SPEAKER_01: so it is basically a general purpose computer the type of models that we develop these type of models

SPEAKER_26: we've applied them and we checked it the last seven years actually we have seen that these systems are

SPEAKER_01: really good at performing these kind of sequential decision making processes right and that became basically the point where we thought that okay so now it's time to start um maybe making larger and larger systems off of these general purpose computers that we can um you know like we can change the spectrum of how ai is done today because today we are working with a base called transformer architecture right we're building we're basically changing that transformers and gpt's generative pre-trained

SPEAKER_02: transformers into a new foundation which is called liquid foundation models which is called lfms basically

SPEAKER_45: so it's a new thing that is coming basically okay and a time series just so people know when you say

SPEAKER_15: time series it's very simple it's the series of a similar data point but over time so uh perfect would be a stock price over every minute on the stock exchange or as you talked about driving it would be the steering wheels alignment or the speed of the vehicle over every second or millisecond your that's a time series and these are particularly good at studying a time series is what you're saying

SPEAKER_01: yes yes and also these these podcasts you know the audio signal that you're hearing is basically a time

SPEAKER_26: series the video that you're observe you're seeing is also a time series so all video data i mean in some

SPEAKER_02: sense if you think about it that's a time series you know audio it is a time series video is a time series but then language is a little bit different than that language is also a kind of sequential kind of data but it's not the time element is different it's basically just a sequence of words coming after

SPEAKER_58: each other so you could also technically apply liquid neural networks to those kind of problems as well

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SPEAKER_15: startups who are listening well done all complex uh where are you at in terms of this being theory versus execution so we see chat gpt4 we see fsd12 where is your company at in terms of you know commercializing this and did did this all come out of mit i heard you mentioned mit earlier so you went to mit you studied this and uh you know this jet fighter that you know was ai based is that your software or they they also studied this so explain to us where you're at with this company um and maybe some demos of the

SPEAKER_122: product yeah yeah definitely definitely so we started exactly maybe one year ago one year and three days

SPEAKER_26: ago actually the company so the company has four co-founders is uh myself and all mit people so we it's myself is matthias lechner who's another uh cto we have actually invented co-invented the technology uh together and then we have alexander amini another phd student from mit and uh he's graduated now and then the director of computer science and artificial intelligence lab at mit who is daniela rus basically is also a co-founder of our team we started this company on this uh new technology

SPEAKER_01: because we've seen a lot of like you know that our lab at mit was focused on real world applications of ai you know like we really wanted to design ai systems that can go into the real world and solve real world problems you know and that's why like we always had our ai systems deployed in the society like they were always deployed in an environment doing a task you know this would be an an autonomous cart this could be also an you know manipulation of a robotic arm you know this could be any kind

SPEAKER_135: of task a humanoid robot kind of control do they refer to that as c cell at c cell yes at mit the

SPEAKER_137: computer science artificial intelligence laboratory which is known um correct me if i'm wrong for

SPEAKER_15: a lot of robotics that we see in the world absolutely so the rumba and some of those projects came out of that yeah 100 yes yes yes so a lot of the fingerprints on robotics come out of this yes mit's c cell lab and you were part of that now exactly um where are you at in terms of providing this as like a product are you is there an api are people starting to use this are you yeah and how old is the company how much have you raised tell me a little bit about you know now that we got the background on the science behind this and the science is worms we get it yes super interesting let's talk about the application and like making the startup reality because going from theoretical and spinning something out of a university and then making it reality that's a jump

SPEAKER_106: that very few companies are able to make so explain to me where you're at with that big jump

SPEAKER_01: yeah definitely definitely so we started last year 30th of march the company it's very fresh like it has been like 12 months now we raised the substantial amount of kind of seed money i think we we first had a seed round of five million dollars at fifty million dollar valuation and then we actually did like a c2 basically and uh that c2 was also um i think eventually became 37 million dollars and so overall we raised like 42 million dollars in in c in c by at a 300 million dollar valuation the reason for raising the money was basically building the superstar team which is one of the things that we

SPEAKER_02: have like because on if you're building something completely different than 99 of the companies because

SPEAKER_01: every company in the generative ai space and ai space is working on top of a technology called

SPEAKER_26: transformers now we're changing that foundation so you need to have like people like-minded people from all over the world i actually gathered them from mostly from mit and stanford and some of the students of yashio benjo as well so we gathered like this team of people brilliant people they have all

SPEAKER_01: invented new technology for efficient uh alternatives to machine learning systems people that have worked on explainability of ai systems like we have like all sort of kind of capabilities in the team with the purpose of wrapping basically this technology of ours like building on top of the technology core technology which is liquid neural networks for enterprise kind of solutions with a horizontal kind of look to the market so we are basically going after verticals because as i told you it's a general least system i can solve financial problems for banks for large banks i can solve also problems in the space of biotech i can solve problems in the space of autonomy right so it's a horizontal play now as a

SPEAKER_154: startup it's always like the weird way to actually go after all i want to solve all of them but we're

SPEAKER_45: talking about boiling the ocean problem so yeah you're a platform that you're going to provide an

SPEAKER_63: api to people or are you going to go after one of these verticals i guess is the question everybody has

SPEAKER_02: yes yes so we are building an ai infrastructure in which you can train fine tune and play around and use liquid foundation models this product is an enterprise-facing product it comes with a developer package where we actually give it to enterprises enterprises are basically can use this technology and actually enjoy its performance they can see the efficiency of the models mostly you can develop models on the edge we have today language models that run on a raspberry pi raspberry pi just so people

SPEAKER_22: know is the smallest computing unit essentially in the open source hardware community these raspberry

SPEAKER_15: pi's go for 10 bucks 25 bucks it has a certain amount of power to it so you're telling me you're

SPEAKER_106: going to be running this on this neural network on a raspberry pi which is like running it on like a thumb drive basically exactly people can imagine that yeah that's like one of the beauties of the

SPEAKER_26: technology so the technology can be running on a very very tiny they're very energy efficient you

SPEAKER_01: know depending on their they can be small but they can be very powerful now in terms of like how we are going to market and how we are actually commercial on how we are managing to be the ai platform for

SPEAKER_02: all the verticals we have established some contracts across the globe actually with some of the system integrators in the world so in europe we have a contract with cap gemini which is one of the

SPEAKER_01: largest system integrators actually in europe in japan we are working with itochu ctc which is basically the accenture of japan you know in united states we are we are signing up with with ey and conversations with accenture basically so the the target is that system integrators would take the

SPEAKER_26: platform as basically being able to integrate it in the verticals that they are interested in so you

SPEAKER_22: don't have to worry about the commercialization of this you have to provide the people who do

SPEAKER_45: commercialization and license to them so this seems incredibly disruptive um if you are able to do

SPEAKER_15: this for a fraction of the cost um what does this do for uh and the fraction of the hardware if you're successful what does this do to nvidia what does this do to open ai you know they're putting together you know billions of dollars tens of billions of dollars in super computers to train these models you're claiming you're going to be able to do this because it's with the worm brain and it's a much more efficient process with a fraction of the hardware model the hardware footprint so you know head-to-head great question what's going to happen to you know big iron in ai if you're successful

SPEAKER_26: yeah definitely so there are two costs on developing ai systems one cost is like designing the ai systems the other cost is basically usage of ai systems right like you can now now my

SPEAKER_01: ai is inference basically right so now on inference side as i told you we can be between 10 to 1 000

SPEAKER_02: times more efficient than the models that are available today that's basically the the car that basically the energy footprint of the models okay on the training side we can be between 10 to 20 times more efficient than the transformer models that means if i train let's say a 10 billion parameter liquid model it's going to cost me depending on how much information it can process which we call context lengths right depending on the context lengths that they have it can be between 10 to 20

SPEAKER_01: times much more efficient to actually develop this kind of system so that means instead of requiring 10 billions of dollars like 10 billion dollars basically to to develop um gpt4 quality models you

SPEAKER_137: would need a fraction of that basically yeah maybe 500 million or something or 100 million a serious

SPEAKER_22: fraction what does that mean for you know somebody like open ai microsoft some of these cloud computing

SPEAKER_15: platforms that are are they building all this extra hardware and focused on the wrong problem you know that hardware is not the problem it's the architecture and the framework and the paradigm under which

SPEAKER_22: they're building this and they're just building under a much less efficient paradigm is that your claim

SPEAKER_119: here i would say you know the beauty of the transformer architecture and what open ai and everybody else

SPEAKER_02: is after is the fact that these systems are scaled really nicely you can scale them into larger amounts of data and also larger model sizes so what motivates the community on a generative ai is the fact that the larger you make the systems the more powerful they become now if you look at if you look at where we are today with the state of the art we have a claude uh claude opus basically which is the the most powerful model i expect this model to be in the order of like three to five times bigger than gpt4 that means this model is i would say in the range of maybe 10 trillion parameter model

SPEAKER_56: ah now they haven't released claude anthropic hasn't released what that model is but it is

SPEAKER_186: number one on hugging face now with the elo ratings it's even number one 100 it's the number

SPEAKER_26: one kind of performing kind of ai system in the world right now okay like there's now

SPEAKER_02: and tropic is talking about 10xing the size of the models every year that goes forward that means we can expect by the end of next year to have a hundred trillion parameter transformer model the reason why we're why they're doing that is because when the models are actually getting larger they become better and better and maybe maybe we can get into agi and generally kind of ai systems by enlarging kind of the architecture and the focus is just that there are two companies in the world that i think the absolute focus of the companies are building agi is open ai as an antropic right now

SPEAKER_01: so there are kind of gutsy moves like like what we are doing basically we're basically changing the

SPEAKER_02: fundamental architecture we're building new scaling laws basically on top of this thing the scaling laws let's see if we can make liquid neural networks also scale that means if i have one trillion parameter liquid model it might actually be as performant as a 50 or 20 billion parameter 20 trillion parameter transformer model the other way of it is also true if i have a hundred trillion parameter liquid model it might be better than a 20x larger transformer based model so that means these are basically the

SPEAKER_137: kind of moves that we want to make i mean so well if you're successful when will anthropic move over to your platform do you think or are you a competitor to them do you think i think i mean right now like

SPEAKER_26: we are we are going to um another fundraising like series a of liquid and i think after this round we are basically uh getting prepared to actually train very very large models so these models are going to be i mean after the release of those models probably by the end of the year i would say then uh the the community is going to see like that there are alternative kind of models that they can come in and disrupt the way transformers are actually disrupting and they can scale the way transformers

SPEAKER_194: scale basically what hardware are you going to use what platform are you using right now we are using

SPEAKER_26: nvidia gpus as well like it's very similar it's just that the number the amount of gpus that we consume

SPEAKER_58: is about 10 to 20 times less than how got it 5 10 of them what they're using startups and small

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SPEAKER_15: this is the next big shooter drop licensing data balkanization of data hey maybe reddit is available to gemini but not open ai twitter now is you know closing up access or x.com is closed up access for people um and it and the new york times is in a lawsuit with open ai which obviously trained on their data without permission how do you see all of this resolving itself because obviously people are rightfully saying hey i own this data i have the archive of the new york times or i'm disney i own this archive of ip from star wars to marvel where i'm an author and i have these books how do you see

SPEAKER_22: all this shaping up in the in the coming years because is that going to be the limitation that the data you have access to or is it going to be synthetic data rules the day and you're going to be able to just make your own data to train on how do you see all this unfolding yeah definitely i believe like

SPEAKER_190: at the end of the day i think the data providers they should be incentivized to provide their data and they should know they should know that their data is being used basically like you you need to

SPEAKER_24: have a payment scheme basically for people that you're using their data in your in your mind how

SPEAKER_190: would that work do you have any ideas we haven't we haven't gotten there yet like i think i think this

SPEAKER_01: would be like a challenge to to to think about but at the moment what we are trying to do is basically the way everybody does like we are basically purchasing data purchasing data right like you're

SPEAKER_24: basically paying for the data that you use in order to be able to you know like to leave you believe

SPEAKER_15: this is a good idea because it will keep people making data so journalists artists writers thinkers you believe hey this is a a a fair deal here some sort of licensing arrangement where they get paid some reasonable fee to train your models or train claud's models or open ai's models or google's models yeah

SPEAKER_26: 100 the reason being say for example a content creator on on youtube right so if people come and and look at their content basically you know like and they get inspired to build something off of that you see so ai is also like basically doing the same thing right there it's looking at the data

SPEAKER_02: that is basically available and it's getting inspired by that data if it's not directly the copy of that

SPEAKER_26: data right and that that scheme of how we are doing it through like let's say social media kind of channels right it has to happen like very similar ways that we can we can incentivize users of social

SPEAKER_01: medias or users of ai or providers of data for ai systems to also like have this understanding of

SPEAKER_26: this is basically the same thing the same kind of scheme can actually apply here there might be analogies here but again like you really have to be systematic systematically going after this problem which is one of the one of the main main issues like as we're thinking about the scaling our company

SPEAKER_15: it does feel like it's fair if somebody's put a lot of work into it that if an ai was built on top of the new york times corpus that yes they would have permission to do that because it is something that you could partner with the new york times and as opposed to open ai and build this with them and monetize it with them and it's their opportunity to create an ai based on the new york times data not open ais or gemini's everybody should have the ability to opt into these things so i feel like that's a pretty smart approach that you're taking um how long before people will be able to use your platform and swap out gemini or swap out claude or swap out open ai for yours for liquid ai so i mean the first the

SPEAKER_38: first batch of products that are coming is basically already in use with some of the clients is a

SPEAKER_26: developer package as i told you for solving ai problems like this could be let's say like you have a predictive task where you have like video data from surgical kind of processes and the at the output you want to predict basically what phase of surgery we are in for example that's a kind of case cases study where a developer can take our package and then basically use our system in that kind of

SPEAKER_02: real world application to solve that task this is already ready and it's available to some of the enterprises through our system integrator contracts and through directly with some of them we are already

SPEAKER_26: working like in the financial sector in the medical sector in the healthcare and biotech we have been like very active and automotive okay this is already available what's your definition of agi

SPEAKER_137: how do you determine that a system is generally artificially intelligent do you have uh or i mean

SPEAKER_217: you must have heard a million of these different ones that when you're at mit and there's a big

SPEAKER_190: debate around what do you think i think for agi i think that i just want to stick to something that

SPEAKER_26: we can actually still understand and talk about for example a system that is beyond human capable can perform beyond human capabilities given the same resources that means if i'm provided that the same kind of resources is provided to the to the human and to it to to the ai system the ai system is being able to perform that task better or orders of magnitude better than humans got it so given the

SPEAKER_45: same resources we both have access to the internet we both have broadband can i beat this system at chess

SPEAKER_15: no okay but it would be a new game that just came out today could it beat me i guess is the question

SPEAKER_26: exactly yeah and in the age i'm agi can exist in a in a virtual world as well like as you were mentioning these these these are possibilities that are inside a virtual kind of existence it's going to be existing in an internet kind of system but uh in real world you need to have also embodiment so that's why a lot of a lot of work is actually going towards you know like like the humanoid kind of movements of robots like we are building humanoid kind of robots and open ai figure i mean the the new works that are going on like there are so many so many i mean at mit there are many many people working on humanoid kind of research and also like other types of ai systems that you can

SPEAKER_225: integrate in the society you know when you think these yeah so the the point is you know there's

SPEAKER_15: virtual we know that those are creeping up like getting an answer to a legal question or making a marketing plan or writing something you know and obviously chess and verticalized games go it's crushing humans but it's got to be able to translate into the real world and if it's going to be doing picking strawberries we're going to need a robotic arm we're going to need computer vision but all those things seem to be aligning so a robot we had a company called root ai which i think actually had some of its um origins at mit as well with the robotic hands being able to pick strawberries in the real world better than a human faster pick the right ones not crush them put them in a box i think we're kind of there today we're pretty close to it for those kind of applications yeah yeah but

SPEAKER_26: think about think about for application of play uh i want i want to have like a robotic soccer team or a basketball team can we have like those kind of things right that's a level of fine motor skill

SPEAKER_22: probably not yes yeah so when do you think we hit agi in your definition that it's able to beat a human

SPEAKER_190: uh at any task could be basketball could be cooking i think i think then the next two to five years is

SPEAKER_26: going to be very very exciting and i think we are going to see like uh leaps in in performance of these models as the size of the models are growing i would say uh we might actually see uh you know first versions of it like very soon i would say maybe after 100 trillions of parameters this is where in terms of number of capacity in terms of number of parameters we would be equivalent to a human

SPEAKER_100: kind of the amount that is available to what is that two more boosts of 10x so we have like two more boosts of anthropic training there uh cloud cloud four and five probably so yeah somewhere around

SPEAKER_15: cloud five or chat gpt six something in that range of jumps two more jumps which might take another you said two to five years we get some what feels like smarter than any human on the planet i that was mine like smarter than any human on the planet able to be any human on the planet at any test now robotics might be hard because you do have some physical fine motor skills that basketball and soccer seem 100 out there but you know to work in a factory or to cook maybe it does work pretty quickly um yeah how do you think about job destruction societal changes you know this is always something that folks in your career and coming out of mit you know debate late at night when you're having drinks or whatever you're imbibing whatever the vibes are what do you when you're sort of off duty talking with people who are building this stuff what do you how do you think about retiring a whole swath of jobs that are arduous and painful but that also do provide meaning and purpose to some degree or employment generally for humans working in a factory picking strawberries writing marketing copy all

SPEAKER_106: this stuff seems to be at risk so how do you think about job destruction what's the back channel on

SPEAKER_137: this is it coming fast and furious or do you think we're going to be able to manage it as a species

SPEAKER_26: in a society i think we can manage it like any technology that comes in i would say it's going to be disruptive like you can think about like the evolution of technology in all the things that

SPEAKER_01: are in our hands and and it changed the the type of the jobs that you would be actually having

SPEAKER_26: but it's not gonna like replace because right now you can use these systems as an assistant in some sense i think i think that this ai revolution this one in particular is helping us to evolve into a better versions of ourselves like every kind of application that today you see in a generative ai enables is like in the productivity space right so it's increased productivity we can do things faster

SPEAKER_01: we can build things faster because of ai and i feel like this is going to be the trend you know and we're going to frame basically ai systems for basically helping us to become the better versions of ourselves and get things done faster for me the moment that i'm dreaming of happening is the fact

SPEAKER_26: that when ais can actually solve new physics and new mathematics new science right like if ai can can discover you can discover new man i i want i want to give the nai system basically the einstein's equation maxwell's equation and the theory of everything that uh you know cosmologists are working on if i want to give them there and tell the ai system hey continue from here and go figure out what's what's next and that is going to happen now if you solve physics then you can solve the basically the you know the way we built structures like the way we do science if you solve mathematics you can solve the economy of the world you know if you solve uh humanitarian sciences like the conflicts that we would have you know we might actually have ai helping governments basically solve conflicts you know there might be so many use cases of ai enabling like new opportunities for work but this is how i see ai helping us as an assistant as an as an elevator of of the way we live

SPEAKER_106: yeah this is i think the most positive spin on it which is hey yeah you might get rid of some arduous jobs just like we got rid of being a phone operator like people used to have that job people used to work in the mail room i remember when i was starting my career in the 90s working in the mail room or being a bike messenger was like a major career like you there were many jobs that

SPEAKER_15: you could do and you get paid really well bike messengers got paid a sick amount of money in new york to run documents back and forth for law firms from wall street to midtown and they don't exist

SPEAKER_106: anymore for all intents and purposes uh you don't have to run documents because you obviously the fax machine and email change that forever but yeah you're right like you know what if we could actually solve existential problems or you know science problems around clean energy around farming around calories around health you know maybe we just live with massive abundance and i think that's what people have to keep in mind it was like this uh short-term look at it on the john stewart show i don't know if you saw that trending what was your take on the john stewart take that like oh my god we're just doing job destruction here i got a little cameo in there because i was interviewing brian from airbnb and he was talking about like hey we just don't we're not going to need a bunch of customer support people answering repetitive questions which i don't know if that's a great

SPEAKER_63: career or not i don't know if people and there's some people who love being in customers work so like interacting with people but maybe it's not a great job uh long term i think we just get

SPEAKER_213: better choices like as as a species like you you would basically have a choice to to interact with

SPEAKER_26: more with humans right and because let's say for a customer support job right what why a person would be interested in that job i would say the human aspect of it right yeah i like i like to talk to people i like to interact with humans you can do that in the pre in presence of ai just in a different way it might actually be less involved than than how you have to do it or you're forced to basically do we like for for that kind of human attraction i would say ai and and intelligence in general is giving us choice choice is like what is an important kind of element uh of of human civilization as well like the way the way the way we evolved actually became like this kind of uh um uh the the the most powerful species in the in the world is by the fact that we have a lot of choice like choices integrated in our in our site and the ability to have choice i think again as i always say like i'm going back to this of course ai would have like you know like downsides and upsides not all green and everything yeah but i think that the right version of ai is going to be extremely useful what what do i mean by the right version of ai one of the things that is concerning is making this today's ai systems larger and larger as black boxes if you don't understand what you're doing with the system that systems no matter how much control like you're losing control basically you're not you're not going to have like a lot of control in the system the fact that everybody like entropic is actually putting like 20 of their workforce on on on explainability right so explain what this means

SPEAKER_14: for people who don't understand because this is a topic that i think is super important and under

SPEAKER_15: reported on understanding what the machine is doing it's hard for people to believe that people don't

SPEAKER_137: actually understand what the neural networks are doing so take a minute to explain this to folks yeah

SPEAKER_26: definitely so let's let's first define like what do i mean by explanation okay what do i mean but when i say i can explain a system okay i tell you the equation that i think most of your your audience would actually be able to relate to e is equal to mc square that's the ancient's equation right may i

SPEAKER_02: ask you this like do you think this equation is explainable that means what that means like if i have a mass i have an object and i know the mass of this object yep and if we know that if this object is moving with the speed of light yep then then you can compute the energy that it would dissipate at that

SPEAKER_00: kind of fruit yeah you can explain this yes you can explain it's explainable it's it's explained about

SPEAKER_02: across time like it's basically like at any given point in time if i just give you this equation this is called a physic physics equation or physical model okay this is the best type of modeling framework that scientist has ever designed a physical model is a model that is completely 100 explainable and it

SPEAKER_266: explains a kind of reality that you can relate to right on the other side of all in reality that's it

SPEAKER_38: exactly on the other side of the spectrum you have a statistical models i said physical models and

SPEAKER_02: now we have a statistical models statistical models are not 100 explain the behavior of a system but they observe data and from data they infer what is basically the construct of this uh topic that i'm modeling let's say a chat gpt chat gpt is a statistical model okay it's guessing the next word it's

SPEAKER_270: guessing figuring out what the next thing in this thread should be just by observing data right yes

SPEAKER_02: because e is equal to mc squared it doesn't need data anymore it's explainable you just need to plug in

SPEAKER_26: your data and it will always give you like the answer you know but if you were to say the quick brown fox jumped over the lazy dog this is something there's a probabilistic kind of thing you know like you have

SPEAKER_02: to see whether do i this is a statistical model okay chat gpt and systems like that are statistical models now now scale these statistical models into billions of parameters as well this becomes today's ai systems right today's ai systems are black boxes because of the fact that we cannot really understand why if there is an input coming in and an output is getting generated why these output is getting

SPEAKER_190: generated there is no explanation to why this input output no citation to a source exactly what's the

SPEAKER_15: source material explain your work is yes or show your work is what people tend to do in phds right and and in graduate school you have to show your work how did you come to this conclusion you can't just solve the math equation you got to show us how you solved it so we get an idea of that and and in these

SPEAKER_02: neural networks people have not been doing that exactly and now we are basically hopelessly basically trying there is a term called mechanistics in mechanistic interpretability mechanistic interpretability tries to point into a system part of a system a gigantic system and tries to say based on this interaction here i suspect that this this this method is basically doing what you know this part of the system is is um uh you know responsible for biases in my system or something now in the middle of these two spectrums that i plotted for you okay so i told you there's a statistical models and physical models in the middle there is a set of models which we call causal models okay okay causation yeah exactly that means like x implies y and if x x implies y then what you know like basically like more structure into the the way you're designing learning systems what i understood from the liquid neural network kind of thing actually i proved theories around this thing like in my phd thesis is that liquid neural networks i are dynamic causal models there are one step ahead of the statistical models that means you can understand to some extent the behavior of what goes in and comes out and you can explain a little bit about the cause and effect of tasks inside the system not 100 but to a really good extent compared to this statistical because they're simpler they're more basic exactly they are more basic and the math

SPEAKER_26: itself is kind of tractable the mass itself is like something that you can you can um you know you

SPEAKER_01: as as a as an as a uh technical person you you would you would be able to understand the machine

SPEAKER_26: now when i was telling you that we want to design the mission of liquid ai is basically is to design ai

SPEAKER_01: systems that we can understand and efficiently deploy in our society because we understand the math behind our systems it's not like a transformer architecture that i just take it and scale it because of the scales it it it gives your eyes to like very nice kind of capabilities as a black box

SPEAKER_02: but now we are designing systems that are kind of white boxes that at every step of the of of the go

SPEAKER_26: we have a lot more control into how this how how these ai systems are doing decision making

SPEAKER_207: yeah exactly exactly and this is where like i think there are some weird incentives to take the time to

SPEAKER_15: slow down if you're open ai anthropic or gemini you're working on some big project to slow down and say hey we we don't want to make this model bigger until we understand it a little bit better there's a perverse incentives here in capitalism and in this race to see who can get to agi first or who can monetize this first and get their next version out opening ai five six seven you know claude version four five six gemini whatever is there not an incentive to not slow down and not understand it um like why put engineers if you're putting 20 of them why not put zero percent of them on explanations and you know explainability why do explainability when you could just you know put more servers on and get more data and and and beat everybody else that that's the perverse issue here right

SPEAKER_26: the alignment of incentives but i know i know what's their incentive like let's say what's the capital for agi the the the market cap of agi is 600 yeah i mean it would be the market cap of human existence the world that's the world that's the market so that's it so that's where these companies are heading at

SPEAKER_00: you know so the the market like if you have agi as you said like you can solve the energy problem you can solve once you solve the energy problem like what i mean you are basically the most valuable

SPEAKER_01: company on earth you know like think about that i mean if you can solve economy like if you can solve uh politics basically like the structure of governments you know this is the thing that

SPEAKER_04: we are hoping to get and there's a race everybody gets there at the same time like agi feels no you

SPEAKER_05: don't you feel some people will get to agi first first yes yeah yes of course like a lot of people

SPEAKER_26: have i mean of course like i would say open ai and entropic would be the first bets that i would say both of them i don't know which one first but i think they have a head start and they have uh in a lot of kind of information in house to to to get there i don't know about google i don't know where where their where their um priorities are but i think the two companies that are focused on really scaling ai systems into more and more kind of uh powerful uh uh beings i would say at this point

SPEAKER_305: uh i think it's it's going to be um entropic and open air but they both have the they're both taking

SPEAKER_22: the approach that you can just use their system to build whatever you want on top of it so of course if it's open like it's not open but it's available to people to pay for it so then if there was the

SPEAKER_15: ability to i don't know figure out which stock's going to go up you might have a thousand developers realize claude and opening eye are great at this i'm going to make the best trader in the world to

SPEAKER_122: go trade stocks that's true but that's why that's why the release of those kind of huge models is still

SPEAKER_26: it it it by itself is actually like a huge challenge i would say today we haven't seen those kind of systems yet but the systems that are coming in the next two years as i was saying those systems even the release of those systems to public it has to be a rollout it has to be like a trial and error like we really have to see how how what's the reaction we internally like these systems

SPEAKER_01: getting massively tested you know like it's not like they're just they today they get it and then

SPEAKER_26: tomorrow they enable it to to to everybody to get access to right like you need to do a lot of testing of the system to see the capabilities how how they come about do you think that's why there

SPEAKER_207: was that chaos at openai is that maybe they felt like that next version was getting close and that's

SPEAKER_15: why there was sort of chaos because there was that whole sort of speculation like maybe they did

SPEAKER_312: feel like this thing was getting you know agis let's say

SPEAKER_26: i don't know i i don't know i i seriously don't know like because i mean it's uh it's all behind closed doors i i i really just don't know the only thing i would say is like it might look like more of a more of a conflict like just just on mission i would say yeah as as opposed to like how um if the

SPEAKER_122: agi has been achieved or not you know yeah and this is the open source models seem to be doing pretty

SPEAKER_30: strong as well do you think open source wins the day or do you think open source can keep up with

SPEAKER_26: the closed systems or no the the unfortunate answer is no because the closed models are usually like a lot of resources are going a lot of concentrated resources is thrown out uh closed source modes that's like that's just a simple allocation kind of task you know just think about like resource allocation like the massive concentration of resources and in the hand of like open ai and nvidia itself like you know google microsoft like all of these companies right so that alone is also slows down open source open source is going to always play catch up and it's not the gap between the closed source capabilities and open source actually grows as well you know that's also another thing so i don't think the gap is shrinking so unless there's going to be an open source move on

SPEAKER_106: you know like a more facebook you know all the open source credit is moving you know it has moved to

SPEAKER_327: open source models apple's doing open source model so it's going to be really interesting to see if

SPEAKER_58: either of these can catch heat delayed open source think about how llama 2 llama 2 came out delayed open

SPEAKER_26: source is again the same story right the llama 2 came out as a commercial license first right and then they decided to open source it now let's see how llama 3 is getting uh released so it is it is important also like to think about timing on the open source like moves you know it is true that some companies are just putting out like for example mr also played like a amazing role in the open source kind of community right like they they put a model out but then immediately they they put the more powerful models like behind the paywall right so you have to you always have to think about like what what is happening in the game and i would say the unfortunate truth is the fact that closer smalls are really

SPEAKER_307: amazing all right so uh i think you're hiring and things are going pretty well for the firm if

SPEAKER_137: people want to join the firm where can they learn more and uh come join the liquid team

SPEAKER_190: yeah liquid.ai basically like there's a get involved section where you can today we have like around

SPEAKER_26: 25 uh smartest people on earth i would say it's a really crazy concentration of people we have people with uh olympiad medalists in the team like we are people that are solving literally like really complex problems for us we have on the team like inventors of um uh very important ai technologies and uh we have uh good philosophers also in house we have uh joshua bach also like was part of our organization and uh you know like uh it's it's it's always like it's a privilege for myself to work with such an amazing team of talent because this is this has been the power of liquid ai we have been like very good at uh bringing in like a key players into into the space to build like something from scratch a kind of white box kind of intelligence and then hopefully scale it into something that is

SPEAKER_24: meaningful and again we are we are obviously hiring as well and uh we'll continue success with

SPEAKER_15: it and thanks for sharing this crazy vision and uh you know be thoughtful about releasing this stuff let's not end the world let's yes make life awesome for everybody and we'll see you all next time on

SPEAKER_177: this week in startups bye bye great job so much