# [ML News] Geoff Hinton leaves Google | Google has NO MOAT | OpenAI down half a billion

## Метаданные

- **Канал:** Yannic Kilcher
- **YouTube:** https://www.youtube.com/watch?v=cjs7QKJNVYM
- **Дата:** 12.05.2023
- **Длительность:** 39:07
- **Просмотры:** 46,529

## Описание

#google #openai #mlnews 

Updates from the world of Machine Learning and AI
Great AI memes here: https://twitter.com/untitled01ipynb

OUTLINE:
0:00 - Google I/O 2023: Generative AI in everything
0:20 - Anthropic announces 100k tokens context
0:35 - Intro
1:20 - Geoff Hinton leaves Google
7:00 - Google memo leaked: we have no moat
11:30 - OpenAI loses 540M
12:30 - Google AI: Product first
15:50 - Ilya Sutskever on safety vs competition
18:00 - AI works cannot be copyrighted
19:40 - OpenAI tries to trademark GPT
20:30 - StarCoder: accessible code model
21:40 - RedPyjama & OpenLlama
22:55 - Mosaic 7B model
23:50 - YoloNAS
24:10 - Mojo programming language
25:30 - Random helpful things
37:40 - DeepMind soccer robots

References:
https://twitter.com/weirddalle/status/1649908805788893185
https://www.nytimes.com/2023/05/01/technology/ai-google-chatbot-engineer-quits-hinton.html
https://www.technologyreview.com/2023/05/01/1072478/deep-learning-pioneer-geoffrey-hinton-quits-google/
https://archive.ph/TrPoH
https://twitter.com/DanHendrycks/status/1654560913939374080
https://twitter.com/ylecun/status/1654930029569101824
https://twitter.com/home
https://twitter.com/ylecun/status/1654931495419621376
https://twitter.com/pkedrosky/status/1653955254181068801
https://www.semianalysis.com/p/google-we-have-no-moat-and-neither
https://twitter.com/untitled01ipynb/media
https://www.theinformation.com/articles/openais-losses-doubled-to-540-million-as-it-developed-chatgpt
https://archive.ph/bKsdM
https://www.washingtonpost.com/technology/2023/05/04/google-ai-stop-sharing-research/
https://twitter.com/giffmana/status/1654962145707130880
https://twitter.com/Ken_Goldberg/status/1651309843804987393
https://tsdr.uspto.gov/documentviewer?caseId=sn97733259&docId=PTD20230418160641&s=09#docIndex=1&page=1
https://twitter.com/osanseviero/status/1654230764513370112
https://huggingface.co/bigcode/starcoder
https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement
https://twitter.com/hardmaru/status/1654649036333514753
https://www.together.xyz/blog/redpajama-models-v1
https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1
https://github.com/openlm-research/open_llama
https://www.mosaicml.com/blog/mpt-7b
https://github.com/Deci-AI/super-gradients/blob/master/YOLONAS.md
https://www.modular.com/mojo
https://www.aicrowd.com/challenges/hackaprompt-2023
https://learnprompting.org/
https://developer.nvidia.com/blog/nvidia-enables-trustworthy-safe-and-secure-large-language-model-conversational-systems/?ncid=prsy-552511
https://blogs.nvidia.com/blog/2023/04/25/ai-chatbot-guardrails-nemo/
https://lmql.ai/#distribution
https://github.com/gventuri/pandas-ai?utm_source=pocket_reader
https://lamini.ai/blog/introducing-lamini
https://github.com/deep-floyd/IF
https://huggingface.co/spaces/DeepFloyd/IF
https://twitter.com/FaramaFound/status/1650952295901720576
https://txt.cohere.com/embedding-archives-wikipedia/?hsa_acc=509563538&hsa_ad=242008083&hsa_cam=626636963&hsa_grp=205646033&hsa_net=linkedin&hsa_ver=3&hss_channel=lcp-24024765
https://arxiv.org/abs/2304.12210
https://github.com/h2oai/h2ogpt
https://huggingface.co/h2oai/h2ogpt-oasst1-512-20b
https://github.com/h2oai/h2o-llmstudio
https://ai.facebook.com/blog/ai-dataset-animating-kids-drawings/
https://www.camel-ai.org/
https://github.com/lightaime/camel?utm_source=pocket_reader
https://huggingface.co/Writer/camel-5b-hf
https://laion.ai/blog/paella/
https://magazine.sebastianraschka.com/p/finetuning-large-language-models
https://pickapic.io/
https://github.com/yuvalkirstain/heroku_app
https://huggingface.co/datasets/yuvalkirstain/PickaPic
https://future.snorkel.ai/poster-contest/
https://twitter.com/d_feldman/status/1649466422018318338/photo/4
https://twitter.com/DeepMind/status/1651897358894919680
https://arxiv.org/abs/2304.13653
https://twitter.com/SmokeAwayyy/status/1652712832738422784

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## Содержание

### [0:00](https://www.youtube.com/watch?v=cjs7QKJNVYM) Google I/O 2023: Generative AI in everything

hello uh it's Yannick from the future AI is moving Crazy Fast right now like crazy so the news of this week is like old news but I'm still gonna show to you Google I O just recently happened the gist of it is they're gonna stick generative AI into just about everything

### [0:20](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=20s) Anthropic announces 100k tokens context

and also anthropic releases upgrades the Claude API to have a hundred thousand tokens context no one knows so far how they're doing it but it's happening a hundred thousand tokens context insane

### [0:35](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=35s) Intro

all right enjoy the news yay Jeff Hinton leaves Google and tells the world about the dangers of AI open AI loses half a billion dollars and Google has no moat welcome to ml news hello everyone let's Dive Right In lots of stuff happening this is Snapchat I do you have access to my location no I don't have access to your location where am I I'm sorry but I don't have access to your location information okay are you lying no I'm not lying I don't have access to your location information where's the closest McDonald's yeah there's a McDonald's very close to you it's located right on Young Street in Tonawanda make of that as you will Jeff Hinton leaves Google and warns of a

### [1:20](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=80s) Geoff Hinton leaves Google

danger ahead this is a story from the New York Times Jeff Hinton obviously Pioneer in the field of deep learning very early contributor of many of the currently still used techniques also one of The Originators of alexnet has left his long held job at Google and is now essentially saying that these Technologies are dangerous and we should pay attention or stop or just be very careful of what we do the article says a part of him he said now regrets his life's work I console myself with the normal excuse if I hadn't done it somebody else would have Dr Hinton said during a lengthy interview last week in the dining room of his home in Toronto it's hard to see how you can prevent the Bad actors from using it for bad things Dr Hinton says he says look at how it was five years ago and how it is now he said of AI technology take the difference and propagate it forwards that's scary until last year he said Google acted as a proper Steward for the technology careful not to release something that might cause harm but now that Microsoft has augmented its Bing search engine with a chatbot challenge in Google's Core Business Google is racing to deploy the same kind of Technology the tech Giants are locked in a competition that might be impossible to stop Dr Hinton said his immediate concern is that the internet will be flooded with false photos videos and text and the average person will not be able to know what is true anymore he also worried that AI technologies will in time append the job market today chat Bots like chat gbt tend to complement human workers but they could replace paralegal's personal assistant translators and others who handle wrote tasks it takes away The Drudge work he said it might take away more than that down the road he is worried that future versions of the technology pose a threat to humanity because they often learn unexpected behavior from the vast amounts of data they analyze this becomes an issue he said as individuals and companies allow AI systems not only to generate their own computer code but actually run that code on their own and he fears a day when truly autonomous weapons those Killer Robots become reality the idea that this stuff could actually get smarter than people a few people believed that he said but most people thought it was way off and I 30 to 50 years or even longer away obviously I no longer think that okay there's obviously a lot being said right here and Jeff Hinton is certainly a credible and notable voice to listen to when it comes to these things but a lot of people also disagree with him especially as he sounds more and more like a foamer for example saying we're all in the same boat with respect to the existential threat so we all ought to be able to cooperate on trying to stop it and more John Lacon on the other hand says AI hype is ridiculous in all directions as in llm have superhuman intelligence our useless parrots hallucinations will destroy Society scaling is all you need deep learning has hit a wall air doesn't exist and never will or AI is going to kill us all I think among the various opinions you can probably find some common ground but I also tend to be more on the side of the car here than of Hinton I don't think this is that much of an existential Threat by itself certainly my biggest fear of this technology is what happens when it is concentrated in just a small amount of people like large companies and governments and what then happens if people with not so good intentions come to power in these places I think that's why they're push to do open source and to really democratize this technology is so important that exactly that doesn't happen the fact that the internet's going to be flood with texts that you don't know is true or not or photos or videos I mean that's already the situation who cares if you can generate like 10 000 fake news articles the problem is distribution the problem isn't generation I can generate something fake text right now whatever let's go okay uh pineapple eat your remote I meant to write ananas do you know the amount of time it took me to find out that ananas which is the German word for pineapple isn't an English word because it sounds so English pineapple does not belong on pizza but this is definitely misinformation I'm sorry if you agree with this there is no you make you may be an AI okay I have now generated Mission formation and I did not need a language model to do it so um you know and yes some people may lose their jobs and a lot of people's jobs are going to be transformed but it's not going to cause mass unemployment it's just like the Chariot driver that had now to do something else some people will have and that's okay but of course who wants to hear from Jeff Hinton or Jan Le Carr one we can actually listen to the true expert on the matter obviously Snoop Dogg has an opinion on this listen like man this thing can hold a real conversation like for real like it's blowing my mind because I watch movies on this as a kid years ago when I used to see this [ __ ] and I'm like what is going on then I heard the dude that the old dude that created AI someone this is not safe because the AIS got their own minds and these [ __ ] gonna start doing their own [ __ ] I'm like is we in a [ __ ] movie right now what the [ __ ] man so do I need to invest in the AI so I can have one with me up like do y'all know [ __ ] what the [ __ ] yeah actually pretty based opinion there I have to say respect all right next

### [7:00](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=420s) Google memo leaked: we have no moat

topic a bit related to it but there has been a memo leaked a Google internal memo that is titled we have no remote and neither does open AI the memo details and the website here claims to have verified its origin so I'm just gonna believe that for now the memo details essentially the rise of Open Source models especially models like llama and just how prolific the community becomes when they get access to an open source model like this for example though Laura like low rank adapters being super useful making it very cheap to fine-tune these big models into something useful and the memo argues that open source development will be able to catch up in many ways with the big companies and therefore a moat if you don't know a moat is like is in startup World emote is a position room that is defendable against incursions against your competition so if you have a moat it means that a competitor can't easily sort of reach you and the memo argues that Google has no modes and neither does open Ai and it goes into a little bit of stuff we could have seen it coming what we missed and so on saying retraining models from scratch is the hard part but once a big model is out like llama then it can be worked with really easily with for example Laura updates are very cheap to produce at around a hundred dollars a piece also saying data quality scales better than date to size which is obviously great to hear given we do projects like open Assistant that's absolutely fantastic directly competing with open source is a losing proposition and also commenting a bit about the fact that individuals are not constrained by licenses to the same degree as corporations which is true they say this will inevitably change as truly open models get better not like the Llama models as you may know have this stupid non-compete license and many of the other models like models coming out of hugging face have these even stupider actually less stupid open rail license but still stupid we are waiting for models for people who actually make things open source and at that point I'm very convinced the community will do great things with it and a lot of businesses can be built on open source models as they are built right now on open source software so there's a call in this memo to let open source work for us which has been a give and take in the tech industry that large companies support open source development but then also obviously profit from the results of it and the memo calls a little bit in to the direction of that saying owning the ecosystem might be a big part of what makes the profit maximal for a company and Google has been doing that with things like Android but also with things like tensorflow and stuff like that so what do we make of a leaked Google memo that essentially admits they're gonna lose Auto open source and so does open AI I think it's important to say that it's not official communication right anyone at a company can write a memo and then sort of circulate it that's just common practice in these companies it's the employees freedom to express their opinion and to gather insights from around the company it must not mean that this is the official Google position or this is even true right read it and estimate yourself how good the arguments of this are but you can rest assure them I'm very sure this is internally not everyone agrees with this may be debated it may be just a person writing down sort of purposefully let's say extreme position to sort of see what happens to what can we make if we sort of make this argument what counter arguments are there and so on anyone can write a memo it can be circulated people can give their opinion so while this can absolutely be a true Google memo all it means is that at least one person in the company has written this but what's more beautiful is the memes oh my God the memes stop molting can you just stop saying mode emotes is this moat had ears to monetize llms no mode it's over Anakin I have the 65k context you underestimate my moat anyway I hope you've all found your modes because open AI may have no modes but they have a sharply decreasing bank account losing over 550 million

### [11:30](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=690s) OpenAI loses 540M

dollars over half a billion dollars as it developed chat GPT that's what the information right saying Open the Eyes losses double to around 550 million US Dollars last year as it developed chat GPT and hired key employees from Google according to three people with knowledge of the startup's financials so pretty crazy I mean you could have guessed that like one or two of these millions would go into getting a mode or two but they apparently blew it all on chat GPT and Google employees but we didn't have to wait long for Google's reaction to chat GPT as it now changed its AI strategy Google has been along one of the most prolific Publishers of academic papers if you go to any machine learning conference like nurips or icml Google will always be at the top of the organizations who publish the most papers at these conferences and that was even before they merged with deepmind oh yeah Google brain merged with deepmind that's a piece of news that I haven't

### [12:30](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=750s) Google AI: Product first

even in here that happened but even before that Google was already super prolific and so was deepmind and together they would be an absolute Juggernaut of publishing papers at conferences however Google has now changed its tune so as openai became more closed focusing more and more on developing product and their API and releasing that joke of a paper slash technical report on gpt4 is becoming more and more clear that Jeff Hinton was certainly right in one regard namely the big Tech Giants are locked in into war mode so Google here changed its strategy the article here in the Washington Post says the launch of open ai's groundbreaking chat GPT three months earlier had changed things the San Francisco startup kept up with Google by reading the team's scientific papers lean said in the quarterly meeting for the company's research division indeed Transformers a foundational part of the latest AI Tech and the T in chat GPT originated in a Google study I'll first go to the conclusion is Google researchers now first have to get their stuff into products and then maybe they can publish if they get approval for it whereas before they could just they could publish they were encouraged to publish and then later they would see whether and how that might go into a product so Google Now more closed up in more product focused however saying that like open AI red Transformers paper and that's why I'm not sure I'm really not that that's a bit far that's a tiny bit far-fetched there definitely the case that if you make everything open it's easier to reproduce what you've done also on the other hand um no I mean the interesting thing is how this is actually going to affect the world of researchers Google and the other companies have been publishing so much I believe as a strategy to hire a lot of these people because a lot of researchers they wanna they get out of University and they have the choice they want to go academic path do I want to go industry path and if you promise them hey with us you can come and you can do research and you can even publish it right this is very attractive for researchers to go there on top of that they get like a giant salary and free food but they do also get the publish papers and a lot of them want that first and foremost because they believe in research and second also because it attaches their own name to something out there so rather than it being in a product somewhere where their name might be listed not at all they'll be authors on papers and that will increase their chances of a future stuff that's going to be interesting to see what these people do when that's no longer on the table when it's pretty clear once you go into the big companies you will not get to publish or at least for not for a long time how's that going to affect their hiring and firing at the moment it's firing time anyway so maybe that goes in concordance at the moment they don't want more people and therefore this is okay maybe once they want more people again they'll open up the publishing guidelines again although it's not that easy and the effects are probably longer term I don't know let me know what you think how that's going to affect the general landscape the fight between the big companies is shaping

### [15:50](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=950s) Ilya Sutskever on safety vs competition

it's looking to be really interesting speaking of open Ai and Google and competitiveness Focus buyer has shared a pretty remarkable clip of Ilya satskiver of openai leadership commenting on why do we keep things closed so I'm gonna play the clip you know my view is that the current level of capability is still not that high where it will be the safety consideration it will drive the close Source in the model this kind of research so in other words a claim that it goes in phases right now it is indeed the competitive phase so essentially saying hey yeah we keep the stuff closed but right now it's not because of safety considerations because the capabilities are not so strong right now that you would need to do that due to safety considerations by the way interesting to see that this agreement within here but instead right now it's because of the competitive landscape yes I mean that's what everyone knew that's unambiguously confirming what we all knew but just wanted to hear admitted openai has long claimed that they keep things closed because of safety considerations and whatnot and it was always extremely Shady so it's nice to somewhere here now that was all crap and they knew it was crap and they simply said it so that they have a fine excuse to keep things for themselves until now when it's now okay to be competitive and to keep things closed in order to be competitive so think of that going forward open AI will just say whatever they need to in order to stay competitive I mean not that the other companies probably wouldn't do that but it's still quite remarkable because they were the first one to keep models closed due to safety considerations some like developers of the early YOLO iterations refused to work on more models due to safety considerations but open air were the first prominent ones to say uh no we'll just keep these to ourselves because uh you know you're they're too dangerous for you plebs AI generated

### [18:00](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=1080s) AI works cannot be copyrighted

images and text cannot be copyrighted according to us copyright office this light from a talk at UC Berkeley by Pamela Samuelson and the reason why they can't be copyrighted that's the policy statement right here is because they lack human authorship which is entrenched in U. S copyright law a human has to do something creative for copyright to apply this is the case in many countries around the world and therefore the direct application of copyright to AI generated works is not given because they lack human authorship what's also interesting when people apply to register works that incorporate AI generated text images or other content they must identify parts that are AI generated and disclaim authorship of those parts it's pretty interesting it's gonna get into a lot of gray areas where it's like well what if I have refined and isn't my selection process also part of the creative process and yada so all of these questions are as of yet unclear but it is good to hear this confirmed copyright needs human authorship which also means what I've said for a long time is that models very probably are also not subject to copyright because they've been generated by an algorithm like an optimization algorithm and therefore yeah the only way to enforce any sort of lice license on an AI model is through an active contract where you actively make people sign stuff before they get access to the model rather than just shipping it with like a GPL license or so and then relying on the automatic application of copyright also other news in

### [19:40](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=1180s) OpenAI tries to trademark GPT

intellectual property there is a trademark office trademark application with this number that tries to trademark the mark GPT the owner is open AI so open AI is trying to trademark GPT now I don't know enough about trademarks and the trademark registration process to tell you what any of this even means right if they they're trying to trademark the word GPT they have updated their brand guidelines and they are going after people who use GPT as part of their thing whatever the thing is so they certainly act as if they have a trademark to that but also here on the bottom says therefore you request is hereby dismiss I don't know I don't know what it means I'll just tell you that it exists okay next news star coder

### [20:30](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=1230s) StarCoder: accessible code model

is a model that comes out of the big code project that is led by homingface but is an open Community project to train a 15 billion parameter large language model with 8 000 tokens context on source code in over 80 programming languages and model and data are available so this is pretty cool and lots of congratulations and respect for all the people having take part in this I do have a small curl about this as you may know here it says open source and it's distinctively not open source you know the good days of Open Source when you need to agree to share your contact information to access this model uh yeah all the open source projects that also where you have to accept the conditions of the license to access its files and contents absolutely open source like every other open source project nothing to see here because this is not licensed as open source it's licensed via the open rail license which is the so-called responsible AI license rant over red pajama is a project to

### [21:40](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=1300s) RedPyjama & OpenLlama

collect llama style data set and then train on it they have just released uh 3 billion and seven billion models they are even instruction tune and chat models so very cool definitely follow the red pajama project it's an absolutely amazing project and the models are open source I think let's see yeah look at that license Apache how hard is that how hard is it is the world going down because this exists no it's only gonna get better another project that builds on the red pajama data set is open Llama which is also an open reproduction of llama and that loss just looks I mean there's no sharp drop so AGI hasn't been reached yet but so far the metrics look really good and they are reportedly better than equally sized model like the 7B model is better than a 7B pythia model because it's been trained on more data and that's exactly the effect we're looking for in llama style training so very excited to see what comes out of these efforts and obviously every single person outside of openai is gonna profit that probably even open AI employees are gonna profit heavily from open source models being fully open source and fully available to the public that being said Mosaic

### [22:55](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=1375s) Mosaic 7B model

releases npt7b a new standard for open source commercially usable llms this is a good step into that direction Mosaic focuses on rapid training rapid fine tuning very efficient training of models and they have used their own knowledge and tools in order to produce these models the models are 7 billion parameter models which would have been huge a few years ago but it's kind of small right now but still they're trained for a long time and most notably some of them have a 65 000 token context length now that is certainly something very cool we've demonstrated Generations as long as 40 8 000 tokens on a single node of a 100 gpus absolutely crazy and again license Apache and the world is still here YOLO Nas is a neural architecture search over YOLO networks YOLO you only

### [23:50](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=1430s) YoloNAS

look once is an object detector and YOLO Nas is a project that uses architecture search in order to determine the best and fastest models this picture doesn't do the model Justice the model is extremely good so absolutely cool weights are available under a non-commercial license for now yeah try

### [24:10](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=1450s) Mojo programming language

it out Mojo is a new programming language for all AI developers at least the company modular claims so this comes from very respectable sources notably one of the creators is also the creator of the llvm tool chain which Powers most compilers for example of C plus and other languages so what is mojo is a superset of python so you can run all python code in Mojo but if you add your types always it allows it to compile it faster not only compile it down to binary code but also do so for various AI accelerators so it's kind of like cython meets Cuda meets xla or something like this safe to say that this has the ability to not only make your python code a lot faster but also make transferring stuff from different accelerators probably a lot more easy and also you can end file names in an emoji so that that's a Mojo file the company says the language is in very early development and it's not open sourced yet but it will be open sourced in the future but it not being open source for now keeps many people currently from trying it out or from switching over to it we'll see what happens definitely very cool project to look out for hacker prompt is a prompt

### [25:30](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=1530s) Random helpful things

hacking competition there are various stages here this is made by various organizations in including learnprompting. org which is a website that kind of teaches you prompting and it's not a course you don't have to pay money for it this is a competition with a sizable Chunk in prize money so if you want to have fun prompting it's a weird world where this is an actual competition yeah there's cash prizes there's extra prizes and so on could be fun Nvidia releases Nemo guard rails which is a system that keeps a check on a large language model so in Nemo guardrails you can Define different things different conversation flows and so on and then propose what they call guard rails for topics for safety considerations and for security so for example if you don't want your friendly company chat bot to all of a sudden start talking about I don't know illegal substances or insult the customer or anything like this a topical guard rails could be interesting for you the tools available open source and as far as I understand it works with any large language model in the background whichever one you want to do the way it works is that there is an engine converting the input into a canonical form in the canonical form you can Define your guard rails like what you want to happen if certain things happen that's very much kind of a programmatic form then you have flow execution which is maybe deny or maybe rephrase or do anything that you want I guess and in the end you generate the output from that so there's GitHub wrap up check it out lmql is a programming language for language model interaction this is ql is to give you a hint that it is similar to a query language like SQL or graphql or I don't know any other qls but lmql language model query language lets you express things that you would like to know from a language model for example here is the tell a joke prompt or input query it's called the query so you input your prompt but then you can Define these variables this is a whole variable this is where you would like the language model to put something right then here this is followed by a variable called the punch line so these are variables that you define so this would be your prompt you say which model and you can specify some where Clauses for example I want the joke to be smaller than 120 tokens or characters like some stopping Criterium and so on so lmql will take all of this and interact with the language model for you in this case for example make the language model fill these whole variables right here and you can see the output of the model is this and then lmql will be able to read these variables here out of the response another one is here for example sentiment classification so here is a review we had a great stay hiking in the mountains was fabulous yada question is the underlying sentiment of this review what and why and then there's a whole variable called analysis and then it says based on this the overall sentiment of the message can be considered to be and another whole variable and here in the distribution Clause you can say actually this classification whole variable it can only be one of these things right here so you can strain the model at that particular point lmql will then go and ask the model make sure that this here is in fact one of the tokens where that you have specified right here or one of the sequences all in all this saves you a lot of grunt work from sort of having to query the model at various points look at the logins do something with the log it's stop buff there are certain point force it to do something and so on so this is very cool and it can be combined with other tools such as long chain or other things that you may know I don't know I just know line chain and this AI makes pandas data frames conversational it adds generative artificial intelligence capabilities to pandas what you can do with this is something like this you have a data frame right here country gdp's happiness and you can ask something like which are the five happiest countries and it'll give you an output you can also make plots and stuff with that so in the background this also does the pandas operations for you and gives you the results this is potentially pretty cool if this is pushed a bit further maybe with some tooling assistance and so on I'm not sure how the tools of the future are gonna look like but I definitely see something like this being extremely useful and making data analysis more accessible to people who also don't know programming laminize company and also an llm engine for rapidly customizing models so lamini gives you open source tools to rapidly customize a model like do fine tuning do rlhf and so on and they also on top of that offer a service where they manage all of that for you pretty cool combination we see more and more startups operate in this give you something open source and then offer a service on top way yeah it's very cool benefits a lot of people deep Floyd is a group at stability and they have released a model called if that is in many ways really really good text to image model especially it handles for example text very well it looks very good and that's because the model it operates in pixel space not in Hidden token space so things like stable diffusion they operate in this latent token space so you have like some vqa encoder and then you have the latent tokens and that's where the diffusion process runs whereas with if directly on pixels so the image is generated in 64 by 64 and then has two sequences of up sampling to make it actually look bearable and not only bearable but it looks really good after that those two up sampling steps it's also cool that we're still seeing different approaches to the fusion models so I'm in latent space summon pixels and so on yeah you can check this out on hugging face you can try it and you can download it also this as far as I understand non-commercial for now but they do claim it's going to be fully commercially like permissively licensed in the future for now I only believe it once I see it but will like to believe them the Brahma Foundation has released shimmy which is an API compatibility tool for converting popular external RL environments to the gymnasium and petting Zoo's apis this is really important especially for reinforcement learning where the details of the environment can be quite overwhelming and standard environments such as gymnasium formerly open AI gym they're quite nice to work with because it decouples the development of the reinforcement learning algorithm with the any intricacies of the environment so it's very cool that the ferama foundation spends effort into making things even more compatible into bringing external environments into the standard environments or making them compatible by the shimmy Library go here releases a blog post called the embedding archives millions of Wikipedia article embeddings in many languages releasing a subset of Wikipedia embedded using their embedding models yeah you can now just download these embeddings which is really cool Wikipedia is a big Corpus of very high quality this can serve as the basis for a lot of applications researchers at meta and other places release a cookbook on self-supervised learning with learnings that they have on self-supervised learning obviously people at meta have been among the ones pushing most into getting ever better techniques for self-supervised Learning and it's very cool to see that they're now compiling this and sharing what they've learned in a condensed form for you to consume at once very cool H2O GPT aims to be the best open source GPT led by H2O AI these are models you can try them they have 20 billion parameter models 12 billion parameter models and even 30 billion parameter models they also have models that are already fine-tuned on for example open Assistant data and also those you can just try it on hung face on top of that they release llm Studio which is a framework for no code fine-tuning state-of-the-art large language models very cool meta releases a giant data set of annotated drawings so these drawings they will have annotation points like where is the hand where is the head and so on and allow things like this to be done very cool This research has been out earlier and now they're releasing the data set of nearly 180 000 annotated amateur drawings to help other AI researchers and creators to innovate further excellent thank you very much camel is a project and a paper for studying language I guess by letting language models communicate with each other it's a very unique approach but if they make these things role play and talk to each other they can study things about them I say this here because code and models are both available so if you are interested in that kind of stuff then feel free to check it out Aya is another model that does text to image paella has updated their model to a new version that is now even better Paya is itself claiming to not be the best text image model but to be the simplest in terms of inference code and that's actually quite true so this here is the full code that's needed to sample from the model and as you can see it's very easy to keep an overview so another cool model to check out and also notably it's not a Transformer it's a con net excellent Sebastian rushka releases a blog post called fine tuning large language models and it's quite good it's an introduction to the core ideas and approaches so if you are just in amazement how people can adapt and tune all of these models like llama models even though they're really big this blog post is certainly a good place for you in general Sebastian's blog is a very good resource to learn about modern things in deep learning pick a pick is an app for collecting human feedback on AI generated images the code is available so you can run this locally if you have any sort of images AI generated images for humans to rate this might be a good place for you in addition they do release a data set images data set rankings data set where people have already come and rated AI generated images excellent so they say help us in creating the largest publicly available human feedback or text to image data set if you're in the mood to rate an image or two that's where you go snorkel AI is holding a conference there is a virtual event June 7th through 8th and you get the chance to present your poster there is a poster competition I'm telling you this because the conference is free and the poster competition you can win prizes so if you have a poster that you would like to publish but you don't want to go to all the way to an academic conference that costs like a thousand bucks in entry fee and you have to fly somewhere this might be an excellent alternative and if you're in the competition there's prizes I found this to be funny if you search in Amazon for the string as an AI language model you'll feel you'll like find stuff like reviews and comments where people just copy paste it from chat GPT and look at this the weirdest part is this here it's a book one paragraph starts with as an AI language model I can't so people are writing books using chat GPT and then trying to sell them on Amazon I've had a bunch of people ask me this and saying like oh look I made a book using chatin it was so fast and I'm like yo why would someone if they look for this information that's in your book why wouldn't they just go to chat GPT I huh deepmind has a new research paper out about Robo soccer these guys are

### [37:40](https://www.youtube.com/watch?v=cjs7QKJNVYM&t=2260s) DeepMind soccer robots

just so cute but also the capabilities here are quite astounding because these are end-to-end reinforcement learned and that's quite crazy because movement like this we're used to from like Boston Dynamics and so on but I believe they hard code like every single movement and they have a tight control algorithms where here I'm not sure entirely which part is all reinforcement learned they exhibit very different and very adaptive Behavior I've recently visited Lab at eth also doing Robo soccer a different discipline than this one which I'll also hopefully share soon and that's also really interesting so the paper is called learning agile soccer skills for bipedal robot with deep reinforcement learning and here's a video of like someone pushing over the robots and I'm like don't do that if Jeff Hinton is right that thing you'll be the first person no to get they'll remember forever they have ah no how long does a hard disk store stuff you better hide for longer than that anyway thank you so much for watching this was ml news thank you for being here if you do have a moat please like this video and tell your friends about it so I'll see you next time bye foreign

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*Источник: https://ekstraktznaniy.ru/video/12444*