[ML News] Anthropic raises $124M, ML execs clueless, collusion rings, ELIZA source discovered & more
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#mlnews #anthropic #eliza

Anthropic raises $124M for steerable AI, peer review is threatened by collusion rings, and the original ELIZA source code was discovered.

OUTLINE:
0:00 - Intro
0:40 - Anthropic raises $124M
3:25 - 65% of execs can't explain AI predictions
4:25 - DeepMind releases AndroidEnv
6:10 - Collusion rings in ML Conferences
7:30 - ELIZA's original source code discovered
10:45 - OpenAI raises $100M fund
11:25 - Outro

References:
https://techcrunch.com/2021/05/28/anthropic-is-the-new-ai-research-outfit-from-openais-dario-amodei-and-it-has-124m-to-burn/
https://www.anthropic.com/news/announcement
https://www.anthropic.com/
https://openai.com/blog/introducing-openai/
https://deepmind.com/research/publications/androidenv
https://cacm.acm.org/magazines/2021/6/252840-collusion-rings-threaten-the-integrity-of-computer-science-research/fulltext#FNA
https://venturebeat.com/2021/05/25/65-of-execs-cant-explain-how-their-ai-models-make-decisions-survey-finds/
https://techcrunch.com/2021/05/26/openais-100m-startup-fund-will-make-big-early-bets-with-microsoft-as-partner/
https://sites.google.com/view/elizagen-org/the-original-eliza
http://psych.fullerton.edu/mbirnbaum/psych101/eliza.htm
https://en.wikipedia.org/wiki/Carl_Rogers
https://openai.com/fund/

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 Anthropic raises 124 million for steerable AI, peer review is threatened by collusion rings, and the original Eliza source code was discovered. This and much more in ML News. Hello and welcome to ML News, your absolutely irregular update of what happens in the ML world. I thought I'd try something new and if you like this format, let me know. If you don't like this format, let me know even more, please. So we're going to go over a bunch of stories of what happened in the last week or so in the ML world. And the first story here is that Anthropic tech crunch writes, the new AI research company by Dario Amodei of OpenAI and his sister Daniela Amodei is a new startup that focuses by their own website on reliable, interpretable, and steerable AI systems. They have raised $124 million in a series A round led by Jan Tallin, the co founder of Skype and other people such as Eric Schmidt and Dustin Moskovitz. Their press release says Anthropic's goal is to make the fundamental research advances that will let us build more capable, general and reliable AI systems, then deploy these systems in a way that benefits people. And the research principles center around AI as a systematic science, safety and scaling and developing tools and measurements to measure our advance towards general or capable AI that benefits everyone. If you think that sounds a little bit like OpenAI sounded at the beginning, you're very correct. If you go back to the very first blog post of OpenAI introducing OpenAI, it sounds a lot similar saying that AI should be as broadly and evenly distributed as possible in the spirit of liberty, and so on. Now other than OpenAI, Anthropic, by the way, it's not Anthropic AI, as I understand, it's just Anthropic. Anthropic is not a non profit. And I'm pretty sure the investors do expect a return on their money, even though the company focuses on research initially. So while it sounds very much like OpenAI, I would expect that Anthropic does shoot towards some profitable venture in the future. So maybe at least when they say it should benefit everyone, we might expect that if they ever release an API, at least that will be open to anyone. Yeah, remember those times where the repositories of OpenAI said the checkpoint is available at this link? I guess we're going to see what happens. I'm mainly excited about another group of capable people coming together and doing something different. They have a lot of careers open. And if you see yourself in any of these roles, don't hesitate to apply, I guess. Though I don't want to rag too much on OpenAI, their track record and their projects is pretty impressive. And a lot of what they've done has contributed to the greater AI world in a very, very beneficial way. I'm still happy that OpenAI exists rather than it didn't. So good job, everyone. Next news 65% of execs can't explain how their AI models make decisions survey finds. Venturebeat writes that a new survey from FICO and Corinium, they surveyed 100 C level analytic and data executives to understand how organizations are developing AI. And apparently 65% of them can't explain how AI model decisions or predictions are made, which of course is used by people to bring the warning bells and say, well, we don't understand AI. But remember, these are C level executives, they don't even understand how an Excel spreadsheets makes its decisions and they don't need to. So make of this as you will, if you want to go and read the whole study survey and the report, I'll link it in the description. It's pretty interesting, honestly. And obviously, it is important that we do understand why AI makes the decisions it does. Next news, DeepMind releases Android Env, the Android learning environment. This is pretty cool, it builds on top of the Android emulator, and it gives unified descriptions of the interface and tasks so that you can do reinforcement learning on Android apps. So there's many possibilities here, you can do multitask learning because you use different apps, you can do perception because you need to actually see the screen, there's a lot of opportunity to hard code things, not to hard code things to learn gestures. And potentially you can interact with any app that runs on Android. So this is pretty cool. And it is a cool bridge in between the real toy environments that we have until now, to something like robotics in the real world where you need lots of time, and you can't just reset all the time. And an Android operating system is actually something that people interact with every day. So they do provide this on GitHub, and they do provide a bunch of example tasks such that you see how you can build your own. If you're interested in reinforcement learning and the bridge to the real world and maybe robotics, I think this would be a good start. It's cool to see something from DeepMind again, that is rather open source, the apps that are already there come in a variety from maps to the browser to little games. And apparently even the Battle of polytopia is integrated as a wait a minute. Oh, come on. Well, at least the rest is open source. There is a technical report if you're interested, go read it, check out the GitHub repo. Now that our mood is so great, collusion rings threaten the integrity of computer science research warns Michael L. Littman in an article at the communications of the ACM. A collusion ring is essentially a bunch of people that secretly work together, bid on each other's papers, and then write positive reviews about these papers in the conference review process. They also lobby other reviewers and area chairs in order to accept these papers. So the colluders give each other positive reviews with the hope that their papers get accepted without being of proper quality. Apparently the author of this article is aware that this is happening at one of the large machine learning conferences, though they do not give the name of the conference or of the colluders. The article is mainly to raise awareness about the existence of the problem. And I'm sure if they're aware of something, this is not the only collusion ring. In fact, I am aware of a lot of shady practices in the reviewing system. I know, shocking discovery. If you couple the anonymity of peer review with the super intense pressure of getting published, you'll get shady behavior. Beats me how this happens. And our last story, Joseph Weizenbaum's original source code for the Eliza program was discovered Eliza, of course, the program we all love sparking humanity's interest in AI, and then absolutely failing to live up to that standard. So Jeff Schrager writes here that the original source code was discovered in the archives of MIT. Now if you expected a GitHub repo, I'm sorry to disappoint you this is a scan of a personal folder where the source code is pasted. It is implemented in a language called math slip. And its most successful application is the so called doctor script that implements a Rogerian therapist. Based on the conversational principles of Carl Rogers, Rogerian conversation essentially means that you restate the opinions of your conversational partner until your conversational partner agrees that you have properly understood them. This can be used in a therapeutic context in order to reflect people's opinions back upon them and elaborate more. So there are many online implementations of something like Eliza that you can play around with. So this one, for example, if I type in I'm sad, it asks me, did you come to me because you are sad? Yes, that's why I came here. What is it that you really want to know? I'd like to know why banana tastes sour after drinking tea? Why do you ask? As you can see, this is a sort of a regex type script. What it does is it looks at what you're saying, and then it sort of replaces this into some pre canned responses. And then it has some other modes, like if you say I'd like to know it responds with why do you ask if you say no, it asks why are you negative and so on. So it's sort of a pattern matching algorithm. And people were really excited about this at the beginning. But then of course, the brittleness of the system comes to bear really quickly, because all it can do is sort of reflect back onto you what you've already said. Now don't get me wrong, Carl Rogers was not advocating for an approach like this. This is simply a part of the approach. Rogers was actually a quite competent person. And I think his approaches are used successfully all over the world until today. So in the source code, you're going to see the reg exes or patterns that Eliza uses, you're going to see the substitutions and what it responds to, followed by the actual implementation of the program itself. So if you want to dive into something other than pytorch and TensorFlow, knock yourselves out. And it's Yannick from the future, I almost forgot, OpenAI is opening a $100 million fund to help AI companies have a profound positive impact. They want to spread it very thick. So they only want to invest in a small number of early stage startups in the field, where artificial intelligence can have a transformative effect like healthcare, climate change and education, though the application form is just open. So you can apply if you want some piece of that $100 million. Go for it. Yay. Okay, that was it for this week's ML news. Maybe there's going to be one next week. Who knows? There's no schedule here. Tell me if you like this and tell me what you think about the individual things. Go raise yourself 124 million for your own AI company. I'll see you next time.
 Anthropic raises 124 million for steerable AI, peer review is threatened by collusion rings,
 and the original Eliza source code was discovered. This and much more in ML News.
 Hello and welcome to ML News, your absolutely irregular update of what happens in the ML world.
 I thought I'd try something new and if you like this format, let me know. If you don't like this
 format, let me know even more, please. So we're going to go over a bunch of stories of what
 happened in the last week or so in the ML world. And the first story here is that Anthropic tech
 crunch writes, the new AI research company by Dario Amodei of OpenAI and his sister
 Daniela Amodei is a new startup that focuses by their own website on reliable, interpretable,
 and steerable AI systems. They have raised $124 million in a series A round led by Jan Tallin,
 the co founder of Skype and other people such as Eric Schmidt and Dustin Moskovitz. Their press
 release says Anthropic's goal is to make the fundamental research advances that will let
 us build more capable, general and reliable AI systems, then deploy these systems in a way that
 benefits people. And the research principles center around AI as a systematic science,
 safety and scaling and developing tools and measurements to measure our advance towards
 general or capable AI that benefits everyone. If you think that sounds a little bit like OpenAI
 sounded at the beginning, you're very correct. If you go back to the very first blog post of OpenAI
 introducing OpenAI, it sounds a lot similar saying that AI should be as broadly and evenly
 distributed as possible in the spirit of liberty, and so on. Now other than OpenAI, Anthropic,
 by the way, it's not Anthropic AI, as I understand, it's just Anthropic. Anthropic is not a non profit.
 And I'm pretty sure the investors do expect a return on their money, even though the company
 focuses on research initially. So while it sounds very much like OpenAI, I would expect that
 Anthropic does shoot towards some profitable venture in the future. So maybe at least when
 they say it should benefit everyone, we might expect that if they ever release an API, at least
 that will be open to anyone. Yeah, remember those times where the repositories of OpenAI said the
 checkpoint is available at this link? I guess we're going to see what happens. I'm mainly excited
 about another group of capable people coming together and doing something different. They
 have a lot of careers open. And if you see yourself in any of these roles, don't hesitate to apply,
 I guess. Though I don't want to rag too much on OpenAI, their track record and their projects
 is pretty impressive. And a lot of what they've done has contributed to the greater AI world
 in a very, very beneficial way. I'm still happy that OpenAI exists rather than it didn't. So
 good job, everyone. Next news 65% of execs can't explain how their AI models make decisions
 survey finds. Venturebeat writes that a new survey from FICO and Corinium, they surveyed 100 C level
 analytic and data executives to understand how organizations are developing AI. And apparently
 65% of them can't explain how AI model decisions or predictions are made, which of course is used
 by people to bring the warning bells and say, well, we don't understand AI. But remember,
 these are C level executives, they don't even understand how an Excel spreadsheets makes its
 decisions and they don't need to. So make of this as you will, if you want to go and read the whole
 study survey and the report, I'll link it in the description. It's pretty interesting, honestly.
 And obviously, it is important that we do understand why AI makes the decisions it does.
 Next news, DeepMind releases Android Env, the Android learning environment. This is pretty cool,
 it builds on top of the Android emulator, and it gives unified descriptions of the interface
 and tasks so that you can do reinforcement learning on Android apps. So there's many
 possibilities here, you can do multitask learning because you use different apps, you can do
 perception because you need to actually see the screen, there's a lot of opportunity to
 hard code things, not to hard code things to learn gestures. And potentially you can interact with any
 app that runs on Android. So this is pretty cool. And it is a cool bridge in between the real toy
 environments that we have until now, to something like robotics in the real world where you need
 lots of time, and you can't just reset all the time. And an Android operating system is actually
 something that people interact with every day. So they do provide this on GitHub, and they do
 provide a bunch of example tasks such that you see how you can build your own. If you're interested
 in reinforcement learning and the bridge to the real world and maybe robotics, I think this would
 be a good start. It's cool to see something from DeepMind again, that is rather open source,
 the apps that are already there come in a variety from maps to the browser to little games. And
 apparently even the Battle of polytopia is integrated as a wait a minute. Oh, come on.
 Well, at least the rest is open source. There is a technical report if you're interested,
 go read it, check out the GitHub repo. Now that our mood is so great, collusion rings
 threaten the integrity of computer science research warns Michael L. Littman in an article
 at the communications of the ACM. A collusion ring is essentially a bunch of people that secretly
 work together, bid on each other's papers, and then write positive reviews about these papers
 in the conference review process. They also lobby other reviewers and area chairs in order to accept
 these papers. So the colluders give each other positive reviews with the hope that their papers
 get accepted without being of proper quality. Apparently the author of this article is aware
 that this is happening at one of the large machine learning conferences, though they do not give the
 name of the conference or of the colluders. The article is mainly to raise awareness about the
 existence of the problem. And I'm sure if they're aware of something, this is not the only collusion
 ring. In fact, I am aware of a lot of shady practices in the reviewing system. I know,
 shocking discovery. If you couple the anonymity of peer review with the super intense pressure
 of getting published, you'll get shady behavior. Beats me how this happens. And our last story,
 Joseph Weizenbaum's original source code for the Eliza program was discovered Eliza, of course,
 the program we all love sparking humanity's interest in AI, and then absolutely failing
 to live up to that standard. So Jeff Schrager writes here that the original source code was
 discovered in the archives of MIT. Now if you expected a GitHub repo, I'm sorry to disappoint
 you this is a scan of a personal folder where the source code is pasted. It is implemented
 in a language called math slip. And its most successful application is the so called doctor
 script that implements a Rogerian therapist. Based on the conversational principles of Carl
 Rogers, Rogerian conversation essentially means that you restate the opinions of your conversational
 partner until your conversational partner agrees that you have properly understood them. This can
 be used in a therapeutic context in order to reflect people's opinions back upon them and
 elaborate more. So there are many online implementations of something like Eliza that
 you can play around with. So this one, for example, if I type in I'm sad, it asks me,
 did you come to me because you are sad? Yes, that's why I came here.
 What is it that you really want to know? I'd like to know why
 banana tastes sour after drinking tea? Why do you ask? As you can see, this is a sort of a regex
 type script. What it does is it looks at what you're saying, and then it sort of replaces this into
 some pre canned responses. And then it has some other modes, like if you say I'd like to know it
 responds with why do you ask if you say no, it asks why are you negative and so on. So it's sort
 of a pattern matching algorithm. And people were really excited about this at the beginning. But
 then of course, the brittleness of the system comes to bear really quickly, because all it can do is
 sort of reflect back onto you what you've already said. Now don't get me wrong, Carl Rogers was not
 advocating for an approach like this. This is simply a part of the approach. Rogers was actually
 a quite competent person. And I think his approaches are used successfully all over the world until
 today. So in the source code, you're going to see the reg exes or patterns that Eliza uses, you're
 going to see the substitutions and what it responds to, followed by the actual implementation of the
 program itself. So if you want to dive into something other than pytorch and TensorFlow,
 knock yourselves out. And it's Yannick from the future, I almost forgot, OpenAI is opening a
 $100 million fund to help AI companies have a profound positive impact. They want to spread it
 very thick. So they only want to invest in a small number of early stage startups in the field,
 where artificial intelligence can have a transformative effect like healthcare, climate
 change and education, though the application form is just open. So you can apply if you want some
 piece of that $100 million. Go for it. Yay. Okay, that was it for this week's ML news. Maybe there's
 going to be one next week. Who knows? There's no schedule here. Tell me if you like this and tell
 me what you think about the individual things. Go raise yourself 124 million for your own AI company.
 I'll see you next time.