[ML News] AI-generated patent approved | Germany gets an analog to OpenAI | ML cheats video games
deep learning machine learning arxiv explained neural networks ai artificial intelligence paper deep learning tutorial what is deep learning introduction to deep learning ai inventor dabus thaler steve thaler stephen thaler ai patent creativity machine aleph alpha openai german openai aleph alpha openai german aleph alpha machine learning game cheat ai cheat video games machine learning video games deepmind wordcraft neural flame
#mlnews #dabus #alephalpha

OUTLINE:
0:00 - Intro
0:20 - Sponsor: Weights & Biases
3:45 - AI legally recognized as patent inventor
8:35 - Alpeh Alpha raises USD 27Mio to build European OpenAI
10:20 - AMP advances AI aided recycling
11:20 - DeepMind builds XLand RL environment
13:15 - Cognitive Behavioral Therapy as an app
16:15 - Wordcraft interactive AI text editor
17:05 - ML used to cheat in console games
18:10 - Google's OpenBuildings Dataset
20:00 - Most ML COVID tools are flawed
21:10 - DALL-E mini released
21:55 - Helpful Libraries
25:20 - FSF funds papers discussing CoPilot


SPONSOR: Weights & Biases
https://wandb.ai

References:
AI legally recognized as patent inventor
https://www.globallegalpost.com/news/south-africa-issues-worlds-first-patent-listing-ai-as-inventor-161068982
https://www.abc.net.au/news/2021-08-01/historic-decision-allows-ai-to-be-recognised-as-an-inventor/100339264
https://artificialinventor.com/frequently-asked-questions/
https://artificialinventor.com/dabus/
https://www.worldscientific.com/doi/abs/10.1142/S2705078521500053
https://www.worldscientific.com/doi/epdf/10.1142/S2705078521500053
https://imagination-engines.com/dabus.html
https://imagination-engines.com/about.html
https://www.nextbigfuture.com/2016/03/sander-olson-interviewed-dr-stephen.html
https://www.actiac.org/system/files/Dawn19%20-%20Dr.%20Thaler.pdf

Alpeh Alpha raises USD 27Mio to build European OpenAI
https://techcrunch.com/2021/07/27/german-startup-aleph-alpha-raises-27m-series-a-round-to-build-europes-openai/

AMP advances AI aided recycling
https://www.robotics247.com/article/amp_robotics_marks_data_pick_rate_milestones_automated_recycling

DeepMind builds XLand RL environment
https://deepmind.com/blog/article/generally-capable-agents-emerge-from-open-ended-play
https://deepmind.com/research/publications/open-ended-learning-leads-to-generally-capable-agents

Cognitive Behavioral Therapy as an app
https://www.nytimes.com/2021/06/01/health/artificial-intelligence-therapy-woebot.html

Wordcraft interactive AI text editor
https://syncedreview.com/2021/07/21/deepmind-podracer-tpu-based-rl-frameworks-deliver-exceptional-performance-at-low-cost-66/
https://arxiv.org/abs/2107.07430
https://www.youtube.com/watch?v=9p4mfA0Fyd8

ML used to cheat in console games
https://au.pcmag.com/games/88121/machine-learning-is-now-being-used-to-cheat-in-multiplayer-games

Google's OpenBuildings Dataset
https://ai.googleblog.com/2021/07/mapping-africas-buildings-with.html
https://sites.research.google/open-buildings/

Most ML COVID tools are flawed
https://www.technologyreview.com/2021/07/30/1030329/machine-learning-ai-failed-covid-hospital-diagnosis-pandemic/

DALL-E mini released
https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA
https://huggingface.co/spaces/flax-community/dalle-mini

Helpful Libraries
https://www.openai.com/blog/triton/
https://github.com/openai/triton
https://github.com/microsoft/FLAML
https://github.com/clip-italian/clip-italian
https://deepmind.com/research/open-source/melting-pot
https://github.com/deepmind/meltingpot
https://www.roboti.us/license.html
https://github.com/openai/gym/issues/2259
https://github.com/jkterry1

FSF funds papers discussing CoPilot
https://www.fsf.org/blogs/licensing/fsf-funded-call-for-white-papers-on-philosophical-and-legal-questions-around-copilot
https://www.gnu.org/philosophy/who-does-that-server-really-serve.en.html

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 An AI is now officially listed as the inventor in a patent. Aleph Alpha raises $27 million to build Europe's open AI and an open source replication of Dalí is released. Welcome to ML News. All right, before we get into all the stuff, this video is sponsored by weight and biases. weights and biases is a one stop shop for machine learning researchers to track their experiments, save their models, recreate their old experiments, share work with others and generally analyze their results. weights and biases allows you with one single line of code to track your experiments, which means that weights and biases will track the execution run of your experiment, it will track the results, it will track saved models and checkpoints, upload it all to a convenient central place in your profile. And that allows you to analyze visualize all of your experiments and data. Think of it like effortless tensor board in the cloud. weights and biases has integrations across all of the deep learning frameworks, PyTorch, TensorFlow, hugging face, you name it, they probably have an integration available. Today, I want to tell you about a new feature that they have, which is called tables. Now the name is deceptively simple. Table is simply a grid of stuff. But in weights and biases, tables allow you to view things like data sets, but also outputs of your runs, any kind of artifact you have, you can analyze in tables, tables allow you to sort group filter and do anything with the data you're looking at. And you can take advantage of all the visualization capabilities that you're used to from weights and biases dashboards. For example, here, we automatically visualize the results of pixel level annotations. I mean, look at that left hand side, that model sucks. Look at the bottom, why is the sky labeled as trees, clearly you have to do something here. So as you can see, you can analyze the output of your runs, you can see where the model still makes mistakes by filtering for the samples that are classified incorrectly, if for some reason, weights and biases doesn't have a visualization for your type of data, which is unlikely, if they don't have it, they allow you to actually integrate with their framework in order to produce one, the capabilities here are really endless. Here you can see we visualize anything from sound files to training plots to spectrograms, whatever you can think of. So as a special bonus, viewers of this channel only get 80% off today off the basic plan, which you don't need actually, because it's free. Yes, it's completely free. There's really nothing stopping you from going there and making an account personal accounts, free unlimited experiments. If you're a bit more involved, if you want a team, and if that team is large and does a lot of tracking, you'll have to give them some money, but their main income comes from big enterprises that want to use this internally. If you are such a big enterprise, don't hesitate to give them a call and give them a lot of money. In that way, you'll be supporting all the free accounts for all us plebs, there are special options for academic research teams, which do get free team accounts. And you can also self host if you need to be compliant with some sort of regulations. So again, go over to weights and biases and check it out. There's a lot of features that I haven't even talked about yet, such as hyper parameter optimization that's done automatically, check it out. And now let's get into the news. I'm back. Yay. What did I miss? What has been going on? How do I do? How do I do news? I forgot. All right. The global legal post right South Africa issues world's first patent listing AI as inventor. So this person right here is Professor Ryan Abbott, he and his legal team have been fighting around the world applying for patents that list the AI named Davos as the inventor of two particular inventions. So now they finally succeeded in South Africa. And also as ABC news writes, an Australian court has equally ruled that AI can be listed as an inventor on a patent application. Now the situation is a little bit complex, and I'm not a lawyer, so don't take my word for it. But the ownership of the patent rests with the creator of Davos of the AI, while Davos is listed as the inventor. So here's one of the things that Davos apparently invented, it's kind of a fractal thing. So they're saying this is kind of a food container or something. And the fractality somehow makes it good. And you can connect containers together. But there's also this light emitting thing that has kind of a fractal ish pulse or something that makes it really noticeable. And this here is Stephen taller, who is the inventor of Davos and therefore the owner of the patent. Now I was immensely interested into this. And I have spent way too much time researching this here is kind of a few takeaways. First, I thought this is a PR stunt, come on, you know, why can't you just list yourself as an inventor, because ultimately AI is like a tool, right? And how does an AI even come up with new ideas? Like what counts as new ideas? And like, how does an AI come up with this? Or this? Like, what was the part that the AI did? What was the starting point? What was it do? Like, I'm so confused. Okay. So this is the website of the team of the legal professionals that got the patents through to through the courts. And they answer some of these questions. And their claim here is that in the various legal systems, the granting of a patent requires the inventor to perform like the invention step, like there's a specific step in the conception of an idea that is like the innovative step. And it is actually criminal offense to list the wrong individual as an inventor. So the inventor does the creative step. And you have to list that person as the inventor. Otherwise, it's criminal offense. Now, the question is, if legally the AI did that inventive step, whatever that means, technically, you should list the AI there because you can't list any of your employees, you can't list yourself because you've only controlled and built the AI, but the AI did the actual step that the law requires to be listed under the inventor. And apparently, they claim at places patent applications have been rejected because of this. So from this perspective, it kind of makes sense that you should be able to list the AI as the inventor. Now counter to that, some legal systems also reject this notion, saying only a natural person can be an inventor. And therefore, on some of these inventions, simply no patent can be granted, which would be discouraging from researching stuff. Remember, AI is used to make inventions in such field as drug discovery, where the AI simply comes up with new compounds, and then you test them. So in a way, the inventive step is performed by the AI, if you could not apply for a patent in that, that would discourage research in these directions. Alright, so this seemed to me like to be a reasonable explanation, but that's only the surface right here. I was much more interested in the question of how, how does this system that I have never heard of come up with new invention. And here on this hideous website of this legal team, this question appears to be answered and cut. So this has gotten so long through the edits that it just completely blows the format of ML news. So what we're going to do is we're going to cut the rest of this into its own video, because this is really weird. This DABA system is weird. This whole case is weird. The too long didn't read is there might be a valid legal reason why AI needs to be listed as an inventor on a patent. Also, at the same time, this is probably a giant PR stunt. And the inventions themselves are they're nothing. So, you know, look forward to the next video, make up your own mind. Let's go on with the news. Alright, German startup Aleph Alpha raises 27 million US dollar series a round to build Europe's open AI from tech crunch. This is Jonas Andrulles, the founder of Aleph Alpha with headquarters in Heidelberg in Germany, which is not too far from here. And the goal is to build the equivalent of open AI, but in a European fashion. So it says the German AI startup of office now raised 23 million euro, which is 27 million in real money in a series a founding co led by early bird VC, Lake Star and UBC partners, the team says it will have a strong commitment to open source communities such as a Luther AI academic partnerships and will be pushing European values and ethical standards, it says supporting fairer access to modern AI research aimed at counteracting the ongoing D democratization, monopolization and loss of control or transparency. So while these are laudable goals, and I really hope they achieve and stick to these goals, remember that open AI has said the same at the beginning and now open AI is mostly interested in closing down access to their stuff and charging for it. But luckily, venture capitalists, which are the main founders of this venture right here are not known to ever wanting their money back or anything like this. So this should just be a breeze for Aleph Alpha. So I wish Jonas and co founder Samuel and anyone part of Aleph Alpha all the best and big success in their endeavors. It's going to be fun having sort of a counterforce to the US here in Europe. Robotics 24 seven says a and pay robotics marks milestone in data pick rates for automated recycling. So speaking of companies and raising money, this company is now raising series B for about 55 million US dollars. And they're in the space of garbage sorting and disposal and recycling. So they've developed these analysis and gripper technologies. And this is incredibly cool to watch. I mean, we're always talking about AI taking away our jobs. I don't think people will be too sad that AI is going to take away their jobs in this particular field. So here the AI automatically analyzes the streams of garbage and sorts them by the materials in them. And these blocks of cans just look really cool. Also, there is such a thing as waste expo didn't know excellent must be a blast. Next news DeepMind releases a paper called open ended learning leads to generally capable agents. So what they do is they build an environment called x land. This is kind of a 3d environment and the agents in here you can see on the top left and top right, this is what they see apparently, and they have to fulfill various goals in these environments, you can build any kind of environment you want in x land, then you can tell the agents to achieve that apparently the paper is about when you instruct the agents to learn multiple goals, many goals at the same time, or after one another, they become generally capable as opposed to just having a single objective and then ending up with a very narrow skilled agent. Now x land can be used to not only have many different environment spatially, but also have many different tasks or games in this environment. So they've captured the flag king of the hill, and so on. In the paper, they actually detail how they use population based methods in order to train these agents, how good they are at zero shop learning and so on. And this is all pretty cool. However, these things and results aren't that new, we already knew that population based training is probably good if you want to achieve some generally skilled agents, we already knew that multi objective or objective conditioned learning is probably a good thing. Ultimately, the agents here are simply an observation encoder into an LSTM. And then they take in the goal conditioning. And then it's a standard actor critic reinforcement learning. I guess what I want to say is that the research isn't necessarily super new or exciting, but you can get a lot, lot, lot of publicity if you build something that's 3d and looks really cool. So if you want, you can build your own stuff in x land if you work at DeepMind, because I don't think it's open source. So ha ha. The New York Times writes something bothering you tell it to woe bot, and it is about the system that delivers cognitive behavioral therapy through an app. So cognitive behavioral therapy is one of the more successful approaches to treat things like depression or anxieties, it is rather formulaic, as this article describes. And therefore, it lends itself at least a little bit to be incorporated into some kind of algorithm. So the article is a discussion of is this good? Is this bad? The pros are that usually a human therapist is very expensive, and there aren't enough of them, especially in times of a global health crisis. On the other hand, critics argue that these algorithms aren't yet good enough to replace a human because they cannot intrinsically understand the things that the humans say. And you get the idea. The New York Times accompanies this person right here, Eli, who has tried out the app for a given period of time, Eli details how the app sometimes fails. Responding to my boss doesn't appreciate the work I do, and I can't seem to get her approval. The bot answers with that sounds difficult. Does this happen more in the morning or at night? It is a little bit of an improvement, I guess over something like Eliza. However, it still seems to be a rather formulaic. So my own personal opinion is this, if I have some problems, there are books that I can read self help books that guide me through the process of somehow solving my own problems. These books are necessarily impersonal, they are written by a person, but they're not personalized to me in any way. It's the same text for every single person that buys the book. So if a book like this can help me, then certainly a little bit of an algorithmized version of a book like this might help me too. You know, there are ways to make it worse, but I don't think much. So if you think that there are good books that have helped you in the past to overcome personal issues or problems or any kind of improvement, then it's entirely possible that an app like this does the same thing. I don't think we have to necessarily seek to replace therapists, but there are a lot of people who cannot afford therapists or don't have one close by. And in this case, such an app can probably help. Now, of course, it's also easy to see that people will feel as though that actually replaces a competent therapist and not seek the attention of an actual therapist when it's needed. So at the end, Eli breaks up with woe bot saying he was unimpressed by the bots advice for beating back loneliness and despair, but he is not entirely sorry that he tried it out. The mere act of typing out his problems was helpful. And through the process, he pinpointed what he actually needed to feel better. Yes. So it worked. Now Eli is seeing a human therapist in Philadelphia for $110 a session. Next news synced writes Google's wordcraft text editor advances human AI collaborative story writing. So the text editor isn't out yet just a paper and a demo video where a human writes something and then clicks on a button and then the machine sort of continues the story. This seems to be sort of a GPT three ish thing with an interface that just helps you select from different continuations and does the prompt engineering in a smart way for you, you can even customize the prompt, you can ask them on to elaborate on particular parts of the story, and then choose from various continuation. I think that's pretty cool if it ever will appear online, which I'm not sure, given that it's Google. But if it ever will appear, something like this might lead humans to just come up with new ideas through this thing. So pretty cool. Next news, PC mag writes machine learning is now being used to cheat in multiplayer games. So there's apparently this video here that demonstrates that a bot is used for cheating in games. Now aim bots have been a thing for a while. But apparently this thing works in a little bit of a different way. And it also works on consoles, which for now has been a kind of a difficult thing for aim bots. So what you do is you hook up your console to a video capture card feed that into your PC and the PC would actually send commands to your controller. So you'd hold the controller, but your controls would sort of be overwritten at times by the input of the cheat engine. And that makes detecting these cheats rather hard to use. Now it just says that machine learning is used in order to control this right here. You could also imagine this being just kind of a classic aim bot that just recognizes some pixels and then shoots at it. But apparently it's machine learning based. So you know, it's an ML news. Thanks. Next news, Google releases the open buildings data set, which is a data set that across satellite images of Africa has annotations of over 516 million buildings. This goes along with a paper where they detailed the challenges that they had to overcome to do this. So you can devise various failure modes right here. So all of these pictures, for examples are not buildings, the top left are water pools, top right are rocks. Then here there are some buildings, but the thing in the red square is not a building is just a bunch of walls, the left are containers. This is very difficult. Google has annotated over I think a million images 1.75 million images or sorry, Google has annotated 1.75 million buildings in 100,000 images by hand and then trained a system on it. The paper details how difficult that was how much you have to use augmentation and regularization in order to do that. But in the end, they've come up with this giant data set that you can now use, you can actually explore the data set in this interactive explorer right here. So you can switch between this view, which is I'm not sure how helpful that is, or this view I have discovered. So if you zoom in right here, I have discovered however, that sometimes I feel at least like this piece here, is this an actual building, it says it's a very high confidence building, I'm not sure, honestly, also this thing here, this might be one, but it seems like it works pretty well. Just overall, the challenges are also recognizing buildings in both rural areas, where they kind of blend into the environment and recognizing buildings in commercial or dense populated areas where you mainly have to separate buildings from each other. So pretty cool, give the open buildings data set a try if you're interested. Next MIT technology review writes hundreds of AI tools have been built to catch COVID, none of them helped yet another article about the shortcomings of machine learning research. And the take of this article is somehow you know, more effort is needed and criticizing ML research. In the meantime, I have a bit of a more cynical approach right here. Like we've known long enough about the publication pressure in ML research. And to use a buzzword topic like COVID in order to get a paper published by simply applying whatever your thing is in research, whatever your topic is, and using it on some kind of COVID data set in order to get a publication out of it, because people think like, oh, this is, you know, relevant, we need to publish fast. Now, I don't think the main motivation of 99% of this research was actually to develop something that actually works. Old methods are slapped onto new topics in order to get publications. And we will continue to see that in the future as well. Don't expect any of these things to work in the first place. Next news, Dali mini is an open source replication effort of open AI's Dali. So these people have built a version of Dali that is much smaller, but has first signs of actually working. Remember, Dali goes from text to images, and you can actually try it out yourself on an online interactive demo on hogging face. Here's my query for a creepy clown and the model does not disappoint. It seems like there's still a gap, probably a gap in size model size and data set size, until this project reaches the level of Dali if ever but still it's pretty cool. And I love the avocado chair just as much as the Dali one. Okay, we come to the helpful library section of ML news helpful libraries. First helpful library is kind of big news. Open AI releases Triton, which is a language that allows you to build custom CUDA kernels. And these CUDA kernels are super duper duper fast. And you don't have to know low level C++ CUDA in order to produce them. So there's a blog post and code to go along with it, detailing in very detail what's now possible with Triton. And apparently, open AI has made this in such a way that people who have no previous experience with CUDA programming are able to produce kernels that are as fast or faster than the kernels that were previously programmed by experienced CUDA programmers. So if you have something that doesn't have a efficient CUDA kernel yet, maybe give Triton a try. Next helpful library flammel fast and lightweight auto ML is a library for cost effective hyper parameter optimization. So apparently, you enter your problem to optimize and your cost and the library will optimize your hyper parameter towards your cost taking into account how much each hyper parameter setting costs to explore. So for example, if you have something like model size as a hyper parameter, it will preferably try the smaller sizes first because they cost less and you can search more before it then scales up that hyper parameter. Pretty cool. Give it a try. Next helpful library Italian clip. Remember clip scores images and text together and Italian clip is now available particularly can classify such things as a and oh, I'm kidding. It's a it's a cool project. Check it out if you are Italian speaking or building Italian speaking products. Next helpful library deep mind releases melting pot and evaluation suite for multi agent reinforcement learning. Now other than excellent this one is actually open. It's an environment in deep mind 2d lab and has various scenarios for multi agent reinforcement learning. And this actually looks like you can do some research with it and multi agent reinforcement learning especially something like cooperative multi agent reinforcement learning is one of these areas that is still largely unexplored and we don't have super good algorithms for it yet. So if you're looking for some research to do this might be a cool topic. There's an old helpful library with some news mojo co the 3d simulator that has been used for a long time for doing things like continuous reinforcement learning control problems and so on is now free the product requires a license but they do give out a free license to anyone at least until the 31st of October 2021. So if the availability of the license has blocked you so far, give it a try now. Also in RL news open AI gym has a new maintainer that is going to address the poll requests that are there project has been kind of dead for a long time and the new maintainer makes it clear that there aren't going to be new environments, major breaking changes, environment wrappers, anything like this, I think they simply want to make the gym usable and up to date as it is pretty cool. If you're a gym user, this should give you some stability and compatibility with current libraries. The new maintainer is JK Terry. Thanks for your work. So in last news for today, the free software foundation calls for white papers on the philosophical and legal questions around copilot. Apparently they're contacted understandably a lot with regards to copilot and the kind of legal ramifications of copyright and patents in what copilot does. If you don't know what copilot is, watch ml news from a while ago. In essence, they give you 500 bucks if you publish a paper through them that somehow elaborates on parts of these topics. So areas of interest are is copilot training on public repositories infringing copyright? Is it fair use? How likely is the output of copilot generate actionable claims of violations on GPL licensed works and so on. So there are some submission guidelines and I wonder if there's a way I can submit my ml news segment to this. Where's my 500 bucks, Richard? Come on. So the criticism of the free software foundation is that copilot is what they call service as a software substitute, which is a term they came up with to replace as software as a service to make it more clear. Of course, Richard Stallman here writes, the basic point is you can have control over a program someone else wrote if it's free, but you can never have control over service someone else runs. So never use a service where in principle running a program would do never. Richard says never. Okay, new.org. Let's look at that a certificate. What kind of certificate is there? Details. It's by let's encrypt. G is let's encrypt the program or a service. I wonder what's up, Richard, you're perfectly capable of generating SSL certificates using open SSL, a free program that you can run yet you elect to use a service like let's encrypt. Well, isn't that a jolly? All right, this was already way too long. This was it for this week's ml news. Please check out weights and biases. They're a great system. And I'll see you next time. Bye bye.
 An AI is now officially listed as the inventor in a patent. Aleph Alpha raises $27 million to
 build Europe's open AI and an open source replication of Dalí is released. Welcome to ML News.
 All right, before we get into all the stuff, this video is sponsored by
 weight and biases. weights and biases is a one stop shop for machine learning researchers to track
 their experiments, save their models, recreate their old experiments, share work with others
 and generally analyze their results. weights and biases allows you with one single line of code
 to track your experiments, which means that weights and biases will track the execution run
 of your experiment, it will track the results, it will track saved models and checkpoints,
 upload it all to a convenient central place in your profile. And that allows you to analyze
 visualize all of your experiments and data. Think of it like effortless tensor board in the cloud.
 weights and biases has integrations across all of the deep learning frameworks, PyTorch,
 TensorFlow, hugging face, you name it, they probably have an integration available. Today,
 I want to tell you about a new feature that they have, which is called tables. Now the name is
 deceptively simple. Table is simply a grid of stuff. But in weights and biases, tables allow you to
 view things like data sets, but also outputs of your runs, any kind of artifact you have,
 you can analyze in tables, tables allow you to sort group filter and do anything with the data
 you're looking at. And you can take advantage of all the visualization capabilities that you're
 used to from weights and biases dashboards. For example, here, we automatically visualize the
 results of pixel level annotations. I mean, look at that left hand side, that model sucks. Look at
 the bottom, why is the sky labeled as trees, clearly you have to do something here. So as
 you can see, you can analyze the output of your runs, you can see where the model still makes
 mistakes by filtering for the samples that are classified incorrectly, if for some reason,
 weights and biases doesn't have a visualization for your type of data, which is unlikely,
 if they don't have it, they allow you to actually integrate with their framework in order to produce
 one, the capabilities here are really endless. Here you can see we visualize anything from sound
 files to training plots to spectrograms, whatever you can think of. So as a special bonus, viewers
 of this channel only get 80% off today off the basic plan, which you don't need actually,
 because it's free. Yes, it's completely free. There's really nothing stopping you from going
 there and making an account personal accounts, free unlimited experiments. If you're a bit more
 involved, if you want a team, and if that team is large and does a lot of tracking, you'll have to
 give them some money, but their main income comes from big enterprises that want to use this
 internally. If you are such a big enterprise, don't hesitate to give them a call and give them a lot
 of money. In that way, you'll be supporting all the free accounts for all us plebs, there are
 special options for academic research teams, which do get free team accounts. And you can also self
 host if you need to be compliant with some sort of regulations. So again, go over to weights and
 biases and check it out. There's a lot of features that I haven't even talked about yet, such as
 hyper parameter optimization that's done automatically, check it out. And now let's get into the news.
 I'm back. Yay. What did I miss? What has been going on? How do I do? How do I do news? I forgot.
 All right. The global legal post right South Africa issues world's first patent listing AI as
 inventor. So this person right here is Professor Ryan Abbott, he and his legal team have been
 fighting around the world applying for patents that list the AI named Davos as the inventor of
 two particular inventions. So now they finally succeeded in South Africa. And also as ABC news
 writes, an Australian court has equally ruled that AI can be listed as an inventor on a patent
 application. Now the situation is a little bit complex, and I'm not a lawyer, so don't take my
 word for it. But the ownership of the patent rests with the creator of Davos of the AI, while Davos
 is listed as the inventor. So here's one of the things that Davos apparently invented, it's kind
 of a fractal thing. So they're saying this is kind of a food container or something. And the
 fractality somehow makes it good. And you can connect containers together. But there's also this
 light emitting thing that has kind of a fractal ish pulse or something that makes it really
 noticeable. And this here is Stephen taller, who is the inventor of Davos and therefore the owner
 of the patent. Now I was immensely interested into this. And I have spent way too much time
 researching this here is kind of a few takeaways. First, I thought this is a PR stunt, come on,
 you know, why can't you just list yourself as an inventor, because ultimately AI is like a tool,
 right? And how does an AI even come up with new ideas? Like what counts as new ideas? And like,
 how does an AI come up with this? Or this? Like, what was the part that the AI did? What was the
 starting point? What was it do? Like, I'm so confused. Okay. So this is the website of the
 team of the legal professionals that got the patents through to through the courts. And they
 answer some of these questions. And their claim here is that in the various legal systems, the
 granting of a patent requires the inventor to perform like the invention step, like there's
 a specific step in the conception of an idea that is like the innovative step. And it is actually
 criminal offense to list the wrong individual as an inventor. So the inventor does the creative
 step. And you have to list that person as the inventor. Otherwise, it's criminal offense.
 Now, the question is, if legally the AI did that inventive step, whatever that means,
 technically, you should list the AI there because you can't list any of your employees,
 you can't list yourself because you've only controlled and built the AI, but the AI did the
 actual step that the law requires to be listed under the inventor. And apparently, they claim
 at places patent applications have been rejected because of this. So from this perspective, it kind
 of makes sense that you should be able to list the AI as the inventor. Now counter to that,
 some legal systems also reject this notion, saying only a natural person can be an inventor. And
 therefore, on some of these inventions, simply no patent can be granted, which would be discouraging
 from researching stuff. Remember, AI is used to make inventions in such field as drug discovery,
 where the AI simply comes up with new compounds, and then you test them. So in a way, the inventive
 step is performed by the AI, if you could not apply for a patent in that, that would discourage
 research in these directions. Alright, so this seemed to me like to be a reasonable explanation,
 but that's only the surface right here. I was much more interested in the question of how,
 how does this system that I have never heard of come up with new invention. And here on this
 hideous website of this legal team, this question appears to be answered and cut. So this has gotten
 so long through the edits that it just completely blows the format of ML news. So what we're going
 to do is we're going to cut the rest of this into its own video, because this is really weird. This
 DABA system is weird. This whole case is weird. The too long didn't read is there might be a valid
 legal reason why AI needs to be listed as an inventor on a patent. Also, at the same time,
 this is probably a giant PR stunt. And the inventions themselves are they're nothing.
 So, you know, look forward to the next video, make up your own mind. Let's go on with the news.
 Alright, German startup Aleph Alpha raises 27 million US dollar series a round to build
 Europe's open AI from tech crunch. This is Jonas Andrulles, the founder of Aleph Alpha with
 headquarters in Heidelberg in Germany, which is not too far from here. And the goal is to build
 the equivalent of open AI, but in a European fashion. So it says the German AI startup of
 office now raised 23 million euro, which is 27 million in real money in a series a founding co
 led by early bird VC, Lake Star and UBC partners, the team says it will have a strong commitment to
 open source communities such as a Luther AI academic partnerships and will be pushing
 European values and ethical standards, it says supporting fairer access to modern AI research
 aimed at counteracting the ongoing D democratization, monopolization and loss of control or transparency.
 So while these are laudable goals, and I really hope they achieve and stick to these goals,
 remember that open AI has said the same at the beginning and now open AI is mostly interested
 in closing down access to their stuff and charging for it. But luckily, venture capitalists, which
 are the main founders of this venture right here are not known to ever wanting their money back or
 anything like this. So this should just be a breeze for Aleph Alpha. So I wish Jonas and co
 founder Samuel and anyone part of Aleph Alpha all the best and big success in their endeavors.
 It's going to be fun having sort of a counterforce to the US here in Europe.
 Robotics 24 seven says a and pay robotics marks milestone in data pick rates for automated
 recycling. So speaking of companies and raising money, this company is now raising series B for
 about 55 million US dollars. And they're in the space of garbage sorting and disposal and recycling.
 So they've developed these analysis and gripper technologies. And this is incredibly cool to watch.
 I mean, we're always talking about AI taking away our jobs. I don't think people will be too sad
 that AI is going to take away their jobs in this particular field. So here the AI automatically
 analyzes the streams of garbage and sorts them by the materials in them. And these blocks of cans
 just look really cool. Also, there is such a thing as waste expo didn't know excellent must be a blast.
 Next news DeepMind releases a paper called open ended learning leads to generally capable agents.
 So what they do is they build an environment called x land. This is kind of a 3d environment
 and the agents in here you can see on the top left and top right, this is what they see apparently,
 and they have to fulfill various goals in these environments, you can build any kind of environment
 you want in x land, then you can tell the agents to achieve that apparently the paper is about when
 you instruct the agents to learn multiple goals, many goals at the same time, or after one another,
 they become generally capable as opposed to just having a single objective and then ending up with
 a very narrow skilled agent. Now x land can be used to not only have many different environment
 spatially, but also have many different tasks or games in this environment. So they've captured
 the flag king of the hill, and so on. In the paper, they actually detail how they use population based
 methods in order to train these agents, how good they are at zero shop learning and so on. And this
 is all pretty cool. However, these things and results aren't that new, we already knew that
 population based training is probably good if you want to achieve some generally skilled agents,
 we already knew that multi objective or objective conditioned learning is probably a good thing.
 Ultimately, the agents here are simply an observation encoder into an LSTM. And then
 they take in the goal conditioning. And then it's a standard actor critic reinforcement learning.
 I guess what I want to say is that the research isn't necessarily super new or exciting, but you
 can get a lot, lot, lot of publicity if you build something that's 3d and looks really cool. So if
 you want, you can build your own stuff in x land if you work at DeepMind, because I don't think
 it's open source. So ha ha. The New York Times writes something bothering you tell it to woe bot,
 and it is about the system that delivers cognitive behavioral therapy through an app. So cognitive
 behavioral therapy is one of the more successful approaches to treat things like depression or
 anxieties, it is rather formulaic, as this article describes. And therefore, it lends itself at least
 a little bit to be incorporated into some kind of algorithm. So the article is a discussion of is
 this good? Is this bad? The pros are that usually a human therapist is very expensive, and there
 aren't enough of them, especially in times of a global health crisis. On the other hand,
 critics argue that these algorithms aren't yet good enough to replace a human because they cannot
 intrinsically understand the things that the humans say. And you get the idea. The New York
 Times accompanies this person right here, Eli, who has tried out the app for a given period of time,
 Eli details how the app sometimes fails. Responding to my boss doesn't appreciate the work I do,
 and I can't seem to get her approval. The bot answers with that sounds difficult. Does this
 happen more in the morning or at night? It is a little bit of an improvement, I guess over something
 like Eliza. However, it still seems to be a rather formulaic. So my own personal opinion is this,
 if I have some problems, there are books that I can read self help books that guide me through
 the process of somehow solving my own problems. These books are necessarily impersonal, they are
 written by a person, but they're not personalized to me in any way. It's the same text for every
 single person that buys the book. So if a book like this can help me, then certainly a little bit of
 an algorithmized version of a book like this might help me too. You know, there are ways to make it
 worse, but I don't think much. So if you think that there are good books that have helped you
 in the past to overcome personal issues or problems or any kind of improvement, then it's
 entirely possible that an app like this does the same thing. I don't think we have to necessarily
 seek to replace therapists, but there are a lot of people who cannot afford therapists or don't have
 one close by. And in this case, such an app can probably help. Now, of course, it's also easy to
 see that people will feel as though that actually replaces a competent therapist and not seek the
 attention of an actual therapist when it's needed. So at the end, Eli breaks up with woe bot saying
 he was unimpressed by the bots advice for beating back loneliness and despair, but he is not entirely
 sorry that he tried it out. The mere act of typing out his problems was helpful. And through the
 process, he pinpointed what he actually needed to feel better. Yes. So it worked. Now Eli is seeing
 a human therapist in Philadelphia for $110 a session. Next news synced writes Google's
 wordcraft text editor advances human AI collaborative story writing. So the text editor
 isn't out yet just a paper and a demo video where a human writes something and then clicks on a
 button and then the machine sort of continues the story. This seems to be sort of a GPT three ish
 thing with an interface that just helps you select from different continuations and does the prompt
 engineering in a smart way for you, you can even customize the prompt, you can ask them on to
 elaborate on particular parts of the story, and then choose from various continuation. I think
 that's pretty cool if it ever will appear online, which I'm not sure, given that it's Google. But
 if it ever will appear, something like this might lead humans to just come up with new ideas through
 this thing. So pretty cool. Next news, PC mag writes machine learning is now being used to cheat
 in multiplayer games. So there's apparently this video here that demonstrates that a bot is used
 for cheating in games. Now aim bots have been a thing for a while. But apparently this thing
 works in a little bit of a different way. And it also works on consoles, which for now has been a
 kind of a difficult thing for aim bots. So what you do is you hook up your console to a video
 capture card feed that into your PC and the PC would actually send commands to your controller.
 So you'd hold the controller, but your controls would sort of be overwritten at times by the
 input of the cheat engine. And that makes detecting these cheats rather hard to use. Now it just says
 that machine learning is used in order to control this right here. You could also imagine this being
 just kind of a classic aim bot that just recognizes some pixels and then shoots at it. But apparently
 it's machine learning based. So you know, it's an ML news. Thanks. Next news, Google releases the
 open buildings data set, which is a data set that across satellite images of Africa has annotations
 of over 516 million buildings. This goes along with a paper where they detailed the challenges
 that they had to overcome to do this. So you can devise various failure modes right here. So all
 of these pictures, for examples are not buildings, the top left are water pools, top right are rocks.
 Then here there are some buildings, but the thing in the red square is not a building is just a bunch
 of walls, the left are containers. This is very difficult. Google has annotated over I think a
 million images 1.75 million images or sorry, Google has annotated 1.75 million buildings in 100,000
 images by hand and then trained a system on it. The paper details how difficult that was how much
 you have to use augmentation and regularization in order to do that. But in the end, they've come up
 with this giant data set that you can now use, you can actually explore the data set in this
 interactive explorer right here. So you can switch between this view, which is I'm not sure how
 helpful that is, or this view I have discovered. So if you zoom in right here, I have discovered
 however, that sometimes I feel at least like this piece here, is this an actual building, it says
 it's a very high confidence building, I'm not sure, honestly, also this thing here, this might be one,
 but it seems like it works pretty well. Just overall, the challenges are also recognizing
 buildings in both rural areas, where they kind of blend into the environment and recognizing
 buildings in commercial or dense populated areas where you mainly have to separate buildings from
 each other. So pretty cool, give the open buildings data set a try if you're interested.
 Next MIT technology review writes hundreds of AI tools have been built to catch COVID,
 none of them helped yet another article about the shortcomings of machine learning research.
 And the take of this article is somehow you know, more effort is needed and criticizing ML research.
 In the meantime, I have a bit of a more cynical approach right here. Like we've known long enough
 about the publication pressure in ML research. And to use a buzzword topic like COVID in order
 to get a paper published by simply applying whatever your thing is in research, whatever your topic is,
 and using it on some kind of COVID data set in order to get a publication out of it, because
 people think like, oh, this is, you know, relevant, we need to publish fast. Now, I don't think the
 main motivation of 99% of this research was actually to develop something that actually works.
 Old methods are slapped onto new topics in order to get publications. And we will continue to see
 that in the future as well. Don't expect any of these things to work in the first place.
 Next news, Dali mini is an open source replication effort of open AI's Dali. So these people have
 built a version of Dali that is much smaller, but has first signs of actually working. Remember,
 Dali goes from text to images, and you can actually try it out yourself on an online
 interactive demo on hogging face. Here's my query for a creepy clown and the model does not disappoint.
 It seems like there's still a gap, probably a gap in size model size and data set size,
 until this project reaches the level of Dali if ever but still it's pretty cool. And I love
 the avocado chair just as much as the Dali one. Okay, we come to the helpful library section of
 ML news helpful libraries. First helpful library is kind of big news. Open AI releases Triton,
 which is a language that allows you to build custom CUDA kernels. And these CUDA kernels are
 super duper duper fast. And you don't have to know low level C++ CUDA in order to produce them. So
 there's a blog post and code to go along with it, detailing in very detail what's now possible with
 Triton. And apparently, open AI has made this in such a way that people who have no previous
 experience with CUDA programming are able to produce kernels that are as fast or faster
 than the kernels that were previously programmed by experienced CUDA programmers. So if you have
 something that doesn't have a efficient CUDA kernel yet, maybe give Triton a try. Next helpful
 library flammel fast and lightweight auto ML is a library for cost effective hyper parameter
 optimization. So apparently, you enter your problem to optimize and your cost and the library will
 optimize your hyper parameter towards your cost taking into account how much each hyper parameter
 setting costs to explore. So for example, if you have something like model size as a hyper parameter,
 it will preferably try the smaller sizes first because they cost less and you can search more
 before it then scales up that hyper parameter. Pretty cool. Give it a try. Next helpful library
 Italian clip. Remember clip scores images and text together and Italian clip is now available
 particularly can classify such things as a and oh, I'm kidding. It's a it's a cool project. Check
 it out if you are Italian speaking or building Italian speaking products. Next helpful library
 deep mind releases melting pot and evaluation suite for multi agent reinforcement learning.
 Now other than excellent this one is actually open. It's an environment in deep mind 2d lab
 and has various scenarios for multi agent reinforcement learning. And this actually looks
 like you can do some research with it and multi agent reinforcement learning especially something
 like cooperative multi agent reinforcement learning is one of these areas that is still
 largely unexplored and we don't have super good algorithms for it yet. So if you're looking for
 some research to do this might be a cool topic. There's an old helpful library with some news
 mojo co the 3d simulator that has been used for a long time for doing things like continuous
 reinforcement learning control problems and so on is now free the product requires a license but they
 do give out a free license to anyone at least until the 31st of October 2021. So if the
 availability of the license has blocked you so far, give it a try now. Also in RL news open AI gym has
 a new maintainer that is going to address the poll requests that are there project has been kind of
 dead for a long time and the new maintainer makes it clear that there aren't going to be new
 environments, major breaking changes, environment wrappers, anything like this, I think they simply
 want to make the gym usable and up to date as it is pretty cool. If you're a gym user, this should
 give you some stability and compatibility with current libraries. The new maintainer is JK Terry.
 Thanks for your work. So in last news for today, the free software foundation calls for white papers
 on the philosophical and legal questions around copilot. Apparently they're contacted understandably
 a lot with regards to copilot and the kind of legal ramifications of copyright and patents
 in what copilot does. If you don't know what copilot is, watch ml news from a while ago.
 In essence, they give you 500 bucks if you publish a paper through them that somehow elaborates on
 parts of these topics. So areas of interest are is copilot training on public repositories infringing
 copyright? Is it fair use? How likely is the output of copilot generate actionable claims
 of violations on GPL licensed works and so on. So there are some submission guidelines and I wonder
 if there's a way I can submit my ml news segment to this. Where's my 500 bucks, Richard? Come on.
 So the criticism of the free software foundation is that copilot is what they call service as a
 software substitute, which is a term they came up with to replace as software as a service to make
 it more clear. Of course, Richard Stallman here writes, the basic point is you can have control
 over a program someone else wrote if it's free, but you can never have control over service someone
 else runs. So never use a service where in principle running a program would do never.
 Richard says never. Okay, new.org. Let's look at that a certificate. What kind of certificate is
 there? Details. It's by let's encrypt. G is let's encrypt the program or a service. I wonder what's
 up, Richard, you're perfectly capable of generating SSL certificates using open SSL, a free program
 that you can run yet you elect to use a service like let's encrypt. Well, isn't that a jolly?
 All right, this was already way too long. This was it for this week's ml news. Please check out
 weights and biases. They're a great system. And I'll see you next time. Bye bye.