Open AI Five + Dota 2 Explained
Plus predicate riddles, data scientist skillset Venn diagrams, and art side-hustles
|Apr 30, 2019|
AltDeep is a newsletter focused on microtrend-spotting in data and decision science, machine learning, and AI. It is authored by Robert Osazuwa Ness, a Ph.D. machine learning engineer at an AI startup and adjunct professor at Northeastern University.
Negative Examples: What you don’t need to read and why.
Dose of Philosophy: Or, A Primer in Sounding Not Stupid at Dinner Parties: Goodman’s New Riddle
Ear to the Ground: A new data scientist Venn diagram
Startups You Could Have Started: How to get a Google Cloud data engineering certificate
Data-Sciencing for Fun & Profit: Adding artistic themes to pics and charging for it
The Essential Read: Open AI Five, Dota 2, Explained
r/machinelearning apparently can’t understand why all the libtards care about algorithmic bias
Viral World Economic Forum explainer video on AI hacking minds
Text analysis of pop culture. This time its Avengers Endgame
Dose of Philosophy: Or, A Primer in Sounding Not Stupid at Dinner Parties
Goodman’s New Riddle
The rule of induction says that you should infer that past regularities will continue into the future. Goodman shows that the problem with this rule is that it leads to conflicting conclusions.
Suppose you are a geologist studying gemstones. In the expression “this gemstone is green”, “is green” is what is called a predicate. Suppose this predicate was true for all the gemstones you have ever seen before T = 01-01-2019 0:00. The rule of induction would tell you this predicate will be true going forward — all the gemstones we ever see will be green.
The riddle works by changing “is green” for a new predicate and showing that you get a contradiction. The new predicate is “is grue”, where “grue” means “green and seen before T, or blue and seen after T”.
Now, if all the gemstones we saw before T were green, then the statement “all gemstones are grue” is true. So it seems that all past gemstones were green and all past gemstones were grue, and so the rule of induction tells us that all gemstones will be both green and grue going forward. However, that means that once we pass time T, all gemstones will be both green and blue!
So Goodman shows us that the rule of induction can’t work for all predicates. Goodman concludes that it can only work for what he calls projectable predicates. So what is projectable? Goodman provides a solution, but it feels lacking because it depends on what language we use and have used to describe and predict the behavior of our world.
Bottom line. The riddle and its solution suggest that the continued performance of your classifier depends on the semantics of the problem definition and the labels.
Ear to the ground: A New Data Scientist Venn Diagram
This one would have made negative examples, because it reads a little like something a team leader would write on his blog in a company wiki. But I love data scientist skill-set Venn diagrams. The author also links to some oldies but goodies.
Ironically, in my stats training I was taught never to use a Venn diagram. There is always a better visualization than a Venn diagram.
AI Startups You Could Have Started
Tips on breaking the gravitational pull of big tech. Mini-profiles of AI startups with (shocker) an actual revenue model
Daniel Bourke writes on How I Passed the Google Cloud Professional Data Engineer Certification Exam
Over the past few months, I’ve been taking courses alongside using Google Cloud to prepare for the Professional Data Engineer exam. Then I took it. And I passed. And a few weeks later my hoodie arrived. The certificate came quicker.
This gives a very easy-to-read roundup of Google Cloud-focused data engineering courses.
So what? Most data scientists think about building sophisticated machine learning solutions for a given problem. It is better to grab open source data science tool and solve a problem with minimal tweaking, and then focus on task of deployment in the cloud. Data engineering takes you from solution to product.
Data Science for Fun and Profit
As an example of my point on entrepreneurship and data engineering, it seems rather fashionable these days to build online services that apply artistic themes to images and charging for it. The image was generated by deepart.io.
The Essential Read: Open AI Five, Dota 2, Explained
OpenAI Five is a team of 5 bots that competed against human teams of 5 in combat strategy game Dota 2
It beat the world champions in Dota 2 at an April 13 event called Open AI Finals
What’s a Dota?
Dota 2 is a multiplayer online battle arena game, a style of strategy game where players coordinate to achieve strategic objectives — like destroying or conquering enemy towers, ambushing enemy units, and upgrading and improving their own defenses.
Each team has five players, each player directing their own character with unique abilities.
Dota 2 has a lot going on (in-game economy, all manner of magic spells, etc.) OpenAI Five plays a simplified version of the game. The simplification defines a clear scope for evaluating AI performance against humans.
Traditional games and video games are favored for benchmarking AI because
Games are designed to be challenging but achievable for humans. So they provide a good way of benchmarking AI against human intelligence that non-experts can understand.
The provide a well-defined problem
The rewards (points in the game) are clear, enabling learning based on positive reinforcement
How well did it do?
Open AI Five had good “micro” game, meaning it could adapt quickly to changes in the environment.
It was notable that Open AI Five seemed better than commentators at predicting outcomes. Commentators would say a game was evenly matched, while Open AI five would state that there was a high probability that Open AI Five would win. Or maybe Open AI 5 had just learned to trash talk.
But, but, but…, don’t count out the humans yet
It took humans a day to figure out how to beat the AI. Contrast that with Open AI 5’s 45,000 years of Dota 2 practice.
Humans found Open AI’s confrontational strategy to be narrow-minded.
A team fight is a 5 vs 5 battles which require fast reactions, good positioning, efficient and smart use of various attack abilities and the ability to estimate the power levels of different characters.
This type of multitasking favors bots over humans, especially since bots work with data streaming from an AI while humans look at busy, stylized animations on a screen.
The bots pushed this advantage by adopting a strategy where they forced fights whenever they could. The reinforcement learning likely spent a good deal of time in this strategy space.
Some commenters suggested that this meant less time was spent learning to deal with shifts in macro gameplay. So the humans devised strategies that exploited this “narrow-mindedness” of the bots.