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No-code is code
Today, the release of OpenAI Codex, a new Al system that translates natural language to code, marks the beginning of a shift in how computer software is written.
Over the past few years, there’s been growing talk about “no code” platforms, but this is no new phenomenon. The reality is, ever since the first programmable devices, computer scientists have regularly developed breakthroughs in how we “code” computer software.
The first computers were programmed with switches or punch cards, until the keyboard was invented. Coding became a matter of typing numbers or machine language, until Grace Hopper invented the modern compiler and the COBOL language, ushering in decades of innovation in programming languages and platforms. Languages like Fortran, Pascal, C, Java and Python evolved in a progression, where the newest language (built using an older language) enabled programmers to “code” using increasingly more human language.
Alongside languages, we’ve seen the evolution of “no-code” platforms — including Microsoft Excel, the 1980s granddaddy of no-code — that empower people to program computers in a visual interface, whether in school or in the workplace. Anytime you write a formula in a spreadsheet, or when you drag a block of code on Code.org or Scratch, you’re programming, or “coding,” a computer. “No code” is code. Every decade, a breakthrough innovation makes it easier to write code so that the old way of coding is replaced by the new.
Does this mean coding is dead? No! It doesn’t replace the need for a programmer to understand code. It means coding just got much easier, higher impact and thus more important.
This brings us to today’s announcement. Today, OpenAl announced OpenAI Codex, an entirely new way to “write code” in the natural English language. A computer programmer can now use English to describe what they want their software to do, and OpenAl’s generative Al model will automatically generate the corresponding computer code, in your choice of programming language. This is what we’ve always wanted — for computers to understand what we want them to do, and then do it, without having to go through a complex intermediary like a programming language.
But this is not an end, it is a beginning. With Al-generated code, one can imagine an evolution in every programming tool, in every programming class, and a Cambrian explosion of new software. Does this mean coding is dead? No! It doesn’t replace the need for a programmer to understand code. It means coding just got much easier, higher impact and thus more important, just as when punch cards were replaced by keyboards, or when Grace Hopper invented the compiler.
In fact, the demand for software today is greater than ever and will only continue to grow. As this technology evolves, Al will play a greater role in generating code, which will multiply the productivity and impact of computer scientists, and will make this field accessible to more and more computer programmers.
There are already tools that let you program using only drag-and-drop, or to write code using your voice. Improvements in these technologies and new tools, like OpenAI Codex, will increasingly democratize the ability to create software. As a result, the amount of code — and the number of coders — in the world will increase.
This also means that learning how to program — in a new way — is more important than ever. Learning to code can unlock doors to opportunity and also help solve global problems. As it becomes easier and more accessible to create software, we should give every student in every school the fundamental knowledge to not only be a user of technology but also a creator.
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GitHub previews new AI tool that makes coding suggestions
GitHub has unveiled a new product that leverages artificial intelligence to help you write code more efficiently. Named GitHub Copilot, today’s new product can suggest lines of code and even sometimes entire functions.
GitHub has partnered with OpenAI to develop this tool. It doesn’t replace developers, it’s just a tool that should improve productivity and make it easier to learn how to code. GitHub frames this new tool as an AI pair programmer.
The model behind GitHub Copilot has been trained on billions of lines of code — many of them are hosted and available publicly on GitHub itself. When you’re writing code, GitHub Copilot suggests code as you type. You can cycle through suggestions, accept or reject them.
In order to figure out what you’re currently coding, GitHub Copilot tries to parse the meaning of a comment, the name of the function you are writing or the past couple of lines. The company shows a few demos on its website.
Image Credits: GitHub
In particular, you can describe a function in plain English in a comment and then convert it to actual code. If you’re getting started with a new language or you’ve been using no-code or low-code tools in the past, that feature could be useful.
If you’re writing code every day, GitHub Copilot can be used to work with a new framework or library. You don’t have to read the documentation from start to finish as GitHub Copilot already knows the specific functions and features of the framework you’re working with. It could also replace many Stack Overflow queries.
GitHub Copilot integrates directly with Visual Studio Code. You can install it as an extension or use it in the cloud with GitHub Codespaces. Over time, the service should improve based on how you interact with GitHub Copilot. As you accept and reject suggestions, those suggestions should get better.
Currently available as a technical preview, GitHub plans to launch a commercial product based on GitHub Copilot. It currently works best with Python, JavaScript, TypeScript, Ruby and Go.
Image Credits: GitHub
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Anthropic is the new AI research outfit from OpenAI’s Dario Amodei, and it has $124M to burn
As AI has grown from a menagerie of research projects to include a handful of titanic, industry-powering models like GPT-3, there is a need for the sector to evolve — or so thinks Dario Amodei, former VP of research at OpenAI, who struck out on his own to create a new company a few months ago. Anthropic, as it’s called, was founded with his sister Daniela and its goal is to create “large-scale AI systems that are steerable, interpretable, and robust.”
The challenge the siblings Amodei are tackling is simply that these AI models, while incredibly powerful, are not well understood. GPT-3, which they worked on, is an astonishingly versatile language system that can produce extremely convincing text in practically any style, and on any topic.
But say you had it generate rhyming couplets with Shakespeare and Pope as examples. How does it do it? What is it “thinking”? Which knob would you tweak, which dial would you turn, to make it more melancholy, less romantic, or limit its diction and lexicon in specific ways? Certainly there are parameters to change here and there, but really no one knows exactly how this extremely convincing language sausage is being made.
It’s one thing to not know when an AI model is generating poetry, quite another when the model is watching a department store for suspicious behavior, or fetching legal precedents for a judge about to pass down a sentence. Today the general rule is: the more powerful the system, the harder it is to explain its actions. That’s not exactly a good trend.
“Large, general systems of today can have significant benefits, but can also be unpredictable, unreliable, and opaque: our goal is to make progress on these issues,” reads the company’s self-description. “For now, we’re primarily focused on research towards these goals; down the road, we foresee many opportunities for our work to create value commercially and for public benefit.”
The goal seems to be to integrate safety principles into the existing priority system of AI development that generally favors efficiency and power. Like any other industry, it’s easier and more effective to incorporate something from the beginning than to bolt it on at the end. Attempting to make some of the biggest models out there able to be picked apart and understood may be more work than building them in the first place. Anthropic seems to be starting fresh.
“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,” said Dario Amodei, CEO of the new venture, in a short post announcing the company and its $124 million in funding.
That funding, by the way, is as star-studded as you might expect. It was led by Skype co-founder Jaan Tallinn, and included James McClave, Dustin Moskovitz, Eric Schmidt and the Center for Emerging Risk Research, among others.
The company is a public benefit corporation, and the plan for now, as the limited information on the site suggests, is to remain heads-down on researching these fundamental questions of how to make large models more tractable and interpretable. We can expect more information later this year, perhaps, as the mission and team coalesces and initial results pan out.
The name, incidentally, is adjacent to anthropocentric, and concerns relevancy to human experience or existence. Perhaps it derives from the “Anthropic principle,” the notion that intelligent life is possible in the universe because… well, we’re here. If intelligence is inevitable under the right conditions, the company just has to create those conditions.
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As concerns rise over forest carbon offsets, Pachama’s verified offset marketplace gets $15 million
Restoring and preserving the world’s forests has long been considered one of the easiest, lowest-cost and simplest ways to reduce the amount of greenhouse gases in the atmosphere.
It’s by far the most popular method for corporations looking to take an easy first step on the long road to decarbonizing or offsetting their industrial operations. But in recent months the efficacy, validity and reliability of a number of forest offsets have been called into question thanks to some blockbuster reporting from Bloomberg.
It’s against this uncertain backdrop that investors are coming in to shore up financing for Pachama, a company building a marketplace for forest carbon credits that it says is more transparent and verifiable thanks to its use of satellite imagery and machine learning technologies.
That pitch has brought in $15 million in new financing for the company, which co-founder and chief executive Diego Saez Gil said would be used for product development and the continued expansion of the company’s marketplace.
Launched only one year ago, Pachama has managed to land some impressive customers and backers. No less an authority on things environmental than Jeff Bezos (given how much of a negative impact Amazon operations have on the planet), gave the company a shoutout in his last letter to shareholders as Amazon’s outgoing chief executive. And the largest e-commerce company in Latin America, Mercado Libre, tapped the company to manage an $8 million offset project that’s part of a broader commitment to sustainability by the retailing giant.
Amazon’s Climate Pledge Fund is an investor in the latest round, which was led by Bill Gates’ investment firm Breakthrough Energy Ventures. Other investors included Lowercarbon Capital (the climate-focused fund from über-successful angel investor, Chris Sacca), former Uber executive Ryan Graves’ Saltwater, the MCJ Collective, and new backers like Tim O’Reilly’s OATV, Ram Fhiram, Joe Gebbia, Marcos Galperin, NBA All-star Manu Ginobili, James Beshara, Fabrice Grinda, Sahil Lavignia and Tomi Pierucci.
That’s not even the full list of the company’s backers. What’s made Pachama so successful, and given the company the ability to attract top talent from companies like Google, Facebook, SpaceX, Tesla, OpenAI, Microsoft, Impossible Foods and Orbital Insights, is the combination of its climate mission applied to the well-understood forest offset market, said Saez Gil.
“Restoring nature is one of the most important solutions to climate change. Forests, oceans and other ecosystems not only sequester enormous amounts of CO2 from the atmosphere, but they also provide critical habitat for biodiversity and are sources of livelihood for communities worldwide. We are building the technology stack required to be able to drive funding to the restoration and conservation of these ecosystems with integrity, transparency and efficiency” said Saez Gil. “We feel honored and excited to have the support of such an incredible group of investors who believe in our mission and are demonstrating their willingness to support our growth for the long term.”
Customers outside of Latin America are also clamoring for access to Pachama’s offset marketplace. Microsoft, Shopify and SoftBank are also among the company’s paying buyers.
It’s another reason that investors like Y Combinator, Social Capital, Tobi Lutke, Serena Williams, Aglaé Ventures (LVMH’s tech investment arm), Paul Graham, AirAngels, Global Founders, ThirdKind Ventures, Sweet Capital, Xplorer Capital, Scott Belsky, Tim Schumacher, Gustaf Alstromer, Facundo Garreton and Terrence Rohan were able to commit to backing the company’s nearly $24 million haul since its 2020 launch.
“Pachama is working on unlocking the full potential of nature to remove CO2 from the atmosphere,” said Carmichael Roberts from BEV, in a statement. “Their technology-based approach will have an enormous multiplier effect by using machine learning models for forest analysis to validate, monitor and measure impactful carbon neutrality initiatives. We are impressed by the progress that the team has made in a short period of time and look forward to working with them to scale their unique solution globally.”
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OthersideAI raises $2.6M to let GPT-3 write your emails for you
When I send an email, it’s special. A crafted, beautiful thing that — who am I kidding, it’s mostly automatic. So why not automate it? OthersideAI is taking this idea (with a $2.6 million seed round) beyond the auto-responders and smart replies, using OpenAI’s GPT-3 language generation engine to turn bullet points into full, personalized messages.
GPT-3, or Generative Pre-trained Transformer 3, is of course the latest version of the AI model that writes such convincing copy that everyone under the sun has let it write their column about it, and then attempted to surprise readers by revealing the fact at the end. (There are usually a few tells, though.)
Access is carefully limited, though, and the team at OthersideAI has a cozy but uncharacterized relationship with OpenAI . It began when the team was working on their previous project, and found they had more emails than they could handle. At the time, GPT-3’s predecessor GPT-2 was in vogue.
“We built a cold email thing with it, but then we thought — that might be the business we should be pursuing,” said CEO Matt Shumer. “So we decided to go all in.”
He and his colleagues Jason Kuperberg and Miles Feldstein built a demo that got a bit of attention when they posted it to Twitter, and soon obtained access to the new version of the GPT engine.
OpenAI arguably already did the hard part by building this astonishing language engine, but it’s not as simple as letting it run wild in someone’s inbox. Unrestrained, GPT-3 will chase its own tail down a rabbit hole, producing truly strange stuff, as any player of AI Dungeon can attest.
“GPT-3 makes an amazing demo, but putting it in a product is another story,” said Shumer. “Our job is in a sense to tame its creativity.”
The resulting product turns a summary or bullet points into a complete email, and looks like this in action:
Image Credits: OthersideAI
If you don’t like the result, or there’s an error, or you just like torturing AIs, you can hit the button and it’ll generate it again, differently. Tweak it a bit first and the system will understand that in the future you’d prefer the new way.
The GPT systems are trained on millions of words and phrases, and then generate text inspired by that corpus after being given an input to work from. In this case the system takes as input not just your bullet points, but other information from the email chain and the user’s past preferences.
That way it picks up not just context: it may say “It was great to sit down for coffee with you” if coffee is referenced even if you only wrote “good to meet” in the bullet. And it also learns your style, preferring certain words or phrases or learning that you like to sign off a certain way.
It can make good guesses at technical and financial details, such as in making a job offer:
Of course, for something so important, you may wonder: why bother letting an AI do it at all?
It’s sort of like how a car can go 120 MPH, but you never drive it faster than 80 (okay… 90). You want to know the thing isn’t going to fall apart as soon as it leaves its most obvious use case. For OthersideAI’s model, this means being robust enough to handle “serious” emails even if it’s most likely to spend its time replacing rote messages.
Kuperberg said the company, which has almost 10,000 people waiting to get into its test version, has seen interest from engineers and developers as well as sales and support people. One instantly sees the application in a support or sales scenario where a handful of scripted questions or replies can be re-generated to be different every time, or slightly adjusted for the person or situation. That avoids the feeling of receiving a “form email” even though it amounts to the same thing.
I mentioned the possibility of helping people who have trouble typing — someone who must write emails letter by letter using gaze detection might find this extremely compelling. Shumer said this hadn’t been on their radars to begin with but that in the last few weeks they’ve seen interest from this direction.
Shumer was careful to assure that security comes first and this isn’t a data-sucking operation — obviously no one would want to use a tool that reads your email and uses that info for nefarious purposes, with the notable exception of Gmail.
They feel secure in their approach, noting that Google seems more interested in selecting the right reply for the context, and text generation tools aren’t robust enough to handle the inputs OthersideAI’s GPT-3-based system handles with ease.
“If you want to make an email in the tone of the user, it can’t guess about the details. It needs a human. This isn’t a generated response, it’s taking direction,” Shumer said.
The $2.6 million seed round was led by Madrona Venture Group, with Active Capital, Hustle Fund, Chapter One and more participating. It’s all going toward building the team so the company can build a full-scale product.
Ultimately, they envision this as a small-scale test for a larger system of interlocking AIs that can safely and securely connect with one another, answering questions and providing information in a human-like way but with only the minimum human involvement. Obviously that’s somewhat of a long-term goal, but given all the talk for a decade or so about replacing email has come to nothing, perhaps it’s time to embrace it but let someone (or something) else take on a bit of the load.
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This 16-game arcade for AIs tests their playing prowess
Figuring out just what an AI is good at is one of the hardest thing about understanding them. To help determine this, OpenAI has designed a set of games that can help researchers tell whether their machine learning agent is actually learning basic skills or, what is equally likely, has figured out how to rig the system in its favor.
It’s one of those aspects of AI research that never fails to delight: the ways an agent will bend or break the rules in its endeavors to appear good at whatever the researchers are asking it to do. Cheating may be thinking outside the box, but it isn’t always welcome, and one way to check is to change the rules a bit and see if the system breaks down.
What the agent actually learned can be determined by seeing if those “skills” can be applied when it’s put into new circumstances where only some of its knowledge is relevant.
For instance, say you want to learn if an AI has learned to play a Mario-like game where it travels right and jumps over obstacles. You could switch things around so it has to walk left; you could change the order of the obstacles; or you could change the game entirely and have monsters appear that the AI has to shoot while it travels right instead.
If the agent has really learned something about playing a game like this, it should be able to pick up the modified versions of the game much quicker than something entirely new. This is called “generalizing” — applying existing knowledge to a new set of circumstances — and humans do it constantly.
OpenAI researchers have encountered this many times in their research, and in order to test generalizable AI knowledge at a basic level, they’ve designed a sort of AI arcade where an agent has to prove its mettle in a variety of games with varying overlap of gameplay concepts.
The 16 game environments they designed are similar to games we know and love, like Pac-Man, Super Mario Bros., Asteroids, and so on. The difference is the environments have been build from the ground up towards AI play, with simplified controls, rewards, and graphics.
Each taxes an AI’s abilities in a different way. For instance in one game there may be no penalty for sitting still and observing the game environment for a few seconds, while in others it may place the agent in danger. In some the AI must explore the environment, in others it may be focused on a single big boss spaceship. But they’re all made to be unmistakably different games, not unlike (though obviously a bit different from) what you might find available for an Atari or NES console.
Here’s the full list, as seen in the gif below from top to bottom, left to right:
- Ninja: Climb a tower while avoiding bombs or destroying them with throwing stars.
- Coinrun: Get the coin at the right side of the level while avoiding traps and monsters.
- Plunder: Fire cannonballs from the bottom of the screen to hit enemy ships and avoid friendlies.
- Caveflyer: Navigate caves using Asteroids-style controls, shooting enemies and avoiding obstacles.
- Jumper: Open-world platformer with a double-jumping rabbit and compass pointing towards the goal.
- Miner: Dig through dirt to get diamonds and boulders that obey Atari-era gravity rules.
- Maze: Navigate randomly generated mazes of various sizes.
- Bigfish: Eat smaller fish than you to become the bigger fish, while avoiding a similar fate.
- Chaser: Like Pac-Man, eat the dots and use power pellets strategically to eat enemies.
- Starpilot: Gradius-like shmup focused on dodging and quick elimination of enemy ships.
- Bossfight: 1 on 1 battle with a boss ship with randomly selected attacks and replenishing shields.
- Heist: Navigate a maze with colored locks and corresponding keys.
- Fruitbot: Ascend through levels while collecting fruit and avoiding non-fruit.
- Dodgeball: Move around a room without touching walls, hitting others with balls and avoiding getting hit.
- Climber: Climb a series of platforms collecting stars along the way and avoiding monsters.
- Leaper: Frogger-type lane-crossing game with cars, logs, etc.
You can imagine that an AI might be created that excels at the grid-based ones like Heist, Maze, and Chaser, but loses the track in Jumper, Coinrun, and Bossfight. Just like a human — because there are different skills involved in each. But there are shared ones as well: understanding that the player character and moving objects may have consequences, or that certain areas of the play area are inaccessible. An AI that can generalize and adapt quickly will learn to dominate all these games in a shorter time than one that doesn’t generalize well.
The set of games and methods for observing and rating agent performance in them is called the ProcGen benchmark, since the environments and enemy placements in the games are procedurally generated. You can read more about them, or learn to build your own little AI arcade, at the project’s GitHub page.
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Slack investor Index Ventures backs Slack competitor Quill
Slack created a new solution for workplace communication, one copied by many, even Microsoft. But the product, which is meant to help individuals and businesses collaborate, has been critiqued for sending too many notifications, with some claiming it’s sabotaged workplace productivity.
Quill, a startup led by Ludwig Pettersson, Stripe’s former creative director and design aficionado, claims to offer “meaningful conversations, without disturbing your team.” The company has raised a $2 million seed round led by Sam Altman with participation from General Catalyst, followed by a $12.5 million Series A at a $62.5 million valuation led by Index Ventures partner and former Slack board observer Sarah Cannon, TechCrunch has learned.
Quill and Cannon declined to comment.
The company, based in San Francisco, has created a no-frills messaging product. Still in beta, Quill plans to encourage fewer, more focused conversations with a heavy emphasis on threads, sources tell TechCrunch . The product is less of a firehose than Slack, says former Y Combinator president Altman, where one can get stuck for extended periods of time filtering through direct messages, threads and channels.
“It’s relentlessly focused on increasing the bandwidth and efficiency of communication,” Altman tells TechCrunch. “The product technically works super well–it surfaces the right information in the feed and it’s pretty intelligent about how it brings the right people into conversations.”
Pettersson previously worked with Altman at his current venture, OpenAI, a research-driven business focused on development that steers artificial intelligence in a “friendlier” direction. Pettersson was a member of the company’s technical staff in 2016 and 2017, creating OpenAI’s initial design.
Index Ventures, for its part, appears to be doubling down on the growing workplace communications software category. The firm first invested in Slack, which completed its highly-anticipated direct listing earlier this year, in 2015. Slack went on to raise hundred millions more, reaching a valuation of over $7 billion in 2018.
Since going public, Slack has struggled to find its footing on the public markets, in large part due to the growing threat of Microsoft Teams, the software giant’s Slack-like product that debuted in 2016. Quickly, Microsoft has gobbled up market share, offering convenient product packages including beloved tools used by most businesses. As of July, Teams had 13 million daily active users and the title of Microsoft’s fastest-growing application in its history. Slack reported 12 million daily active users earlier this month.
Startups like Quill pose a threat to Slack, too. It created the playbook for workplace chat software and proved the massive appetite for such tools; companies are bound to iterate on the model for years to come.
Quill is also backed by OpenAI’s chairman and chief technology officer Greg Brockman and Elad Gil, a former Twitter executive and co-founder of Color Genomics.
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OpenAI Five crushes Dota2 world champs, and soon you can lose to it too
Dota2 is one of the most popular, and complex, online games in the world, but an AI has once again shown itself to supersede human skill. In matches over the weekend, OpenAI’s “Five” system defeated two pro teams soundly, and soon you’ll be able to test your own mettle against — or alongside — the ruthless agent.
In a blog post, OpenAI detailed how its game-playing agent has progressed from its younger self — it seems wrong to say previous version, since it really is the same extensive neural network as many months ago, but with much more training.
The version that played at Dota2’s premiere tournament, The International, gets schooled by the new version 99 percent of the time. And it’s all down to more practice:
In total, the current version of OpenAI Five has consumed 800 petaflop/s-days and experienced about 45,000 years of Dota self-play over 10 realtime months (up from about 10,000 years over 1.5 realtime months as of The International), for an average of 250 years of simulated experience per day.
To the best of our knowledge, this is the first time an RL [reinforcement learning] agent has been trained using such a long-lived training run.
One is tempted to cry foul at a data center-spanning intelligence being allowed to train for 600 human lifespans. But really it’s more of a compliment to human cognition that we can accomplish the same thing with a handful of months or years, while still finding time to eat, sleep, socialize (well, some of us) and so on.
Dota2 is an intense and complex game with some rigid rules but a huge amount of fluidity, and representing it in a way that makes sense to a computer isn’t easy (which likely accounts partly for the volume of training required). Controlling five “heroes” at once on a large map with so much going on at any given time is enough to tax a team of five human brains. But teams work best when they’re acting as a single unit, which is more or less what Five was doing from the start. Rather than five heroes, it was more like five fingers of a hand to the AI.
Interestingly, OpenAI also discovered lately that Five is capable of playing cooperatively with humans as well as in competition. This was far from a sure thing — the whole system might have frozen up or misbehaved if it had a person in there gumming up the gears. But in fact it works pretty well.
You can watch the replays or get the pro commentary on the games if you want to hear exactly how the AI won (I’ve played but I’m far from good. I’m not even bad yet). I understand they had some interesting buy-back tactics and were very aggressive. Or, if you’re feeling masochistic, you can take on the AI yourself in a limited-time event later this week.
We’re launching OpenAI Five Arena, a public experiment where we’ll let anyone play OpenAI Five in both competitive and cooperative modes. We’d known that our 1v1 bot would be exploitable through clever strategies; we don’t know to what extent the same is true of OpenAI Five, but we’re excited to invite the community to help us find out!
Although a match against pros would mean all-out war using traditional tactics, low-stakes matches against curious players might reveal interesting patterns or exploits that the AI’s creators aren’t aware of. Results will be posted publicly, so be ready for that.
You’ll need to sign up ahead of time, though: The system will only be available to play from Thursday night at 6 PM to the very end of Sunday, Pacific time. They need to reserve the requisite amount of computing resources to run the thing, so sign up now if you want to be sure to get a spot.
OpenAI’s team writes that this is the last we’ll hear of this particular iteration of the system; it’s done competing (at least in tournaments) and will be described more thoroughly in a paper soon. They’ll continue to work in the Dota2 environment because it’s interesting, but what exactly the goals, means or limitations will be are yet to be announced.
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Y Combinator president Sam Altman is stepping down amid a series of changes at the accelerator
Sam Altman, the well-known president of the prolific Silicon Valley accelerator Y Combinator, is stepping down, the firm shared in a blog post on Friday.
Altman is transitioning into a chairman role with other YC partners stepping up to take on his day-to-day responsibilities, as first reported by Axios. Sources tell TechCrunch YC has no succession plans. YC’s core program is currently led by chief executive officer Michael Seibel, who joined the firm as a part-time partner in 2013 and assumed the top role in 2016.
The news comes amid a series of shake-ups at the accelerator, which is expected to demo its latest batch of 200-plus companies in San Francisco March 18 and 19. In Friday’s blog post, YC expands on some of those changes, including the firm’s decision to move it’s HQ to San Francisco, which TechCrunch reported earlier this week.
“We are considering moving YC to the city and are currently looking for space,” YC writes. “The center of gravity for new startups has clearly shifted over the past five years, and although we love our space in Mountain View, we are rethinking whether the logistical tradeoff is worth it, especially given how difficult the commute has become. We also want to be closer to our Bay Area alumni, who disproportionately live and work in San Francisco.”
In addition to moving it’s HQ up north, YC has greatly expanded the size of its cohorts — so much so that it’s next demo day will have two stages — and it’s writing larger checks to portfolio companies.
Altman, who joined YC as a partner in 2011 and was named president in 2014, will focus on other efforts, including OpenAI, a research organization in which he co-chairs. Altman was the second-ever YC president, succeeding YC co-founder Paul Graham in 2014. Graham is currently an advisor to YC.
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OpenAI’s ‘Dota 2’ neural nets are defeating human opponents
Artificially intelligent systems taking on human competitors is a grand tradition of computer science; thankfully, we’re still in the cute stages that don’t feel quite like War Games yet. For its part, OpenAI has been trying its hand at Dota 2 competitive play, and its bots are starting to win against some skilled opponents under certain conditions.
The Elon Musk co-founded venture is aiming to raise awareness for where AI technologies are now and how the tech industry can promote safe advances that benefit everyone in the future. While playing an unabashedly nerdy video game better than human opponents may seem to be a weird place to expend extensive resources, it’s all the continuation of where AlphaGo and Deep Blue have taken us before: building machines that can beat humans in seemingly simple games.
Unlike decidedly more turn-based games like chess or Go, Dota 2 is a title that requires plenty of real-time decision-making. While Google’s AlphaGo sometimes took minutes to decide how to respond to a particularly well-crafted move, OpenAI Five, as it’s called, does not have that luxury, as its opponent would be making moves in the meantime. These games are operating at 30 frames per second for an average of 45 minutes, OpenAI says, resulting in about 80,000 frames, of which the system analyzes one-quarter.
This blog post has plenty of the nitty-gritty technical details if you’re interested.
This is plenty resource-intensive — OpenAI Five is running on 124,000 cores on Google Cloud — and while this isn’t OpenAI’s first public experimentation playing Dota 2, what makes this interesting is that, compared to its previous efforts in 1v1 matches, this is a team of five distinct neural nets working together to best human opponents.
For its part, OpenAI gave some interesting data points about OpenAI Five, particularly how it learns by playing 180 years’ worth of Dota 2 games against itself every day.
OpenAI is understandably still tackling the parameters of the full game and is struggling in some aspects; as a result, there are certain rules by which both OpenAI and its human opponents must operate during matches, including not using certain characters, items and strategies. Even with these current restrictions, which the group fully outlines on the blog post, the team is aiming to compete at a Dota 2 esports world championship in August, where it will be fully tested.
OpenAI will be hosting a Twitch-streamed Dota 2 tourney next month to showcase what it has built as it competes against top players.
At the end of the day, a lot of this “Human versus AI” excitement is a bit over-exalted; these are games being approached by insanely powerful computer programs that can do one thing and only one thing. A lot of the media narrative around how artificial intelligence is already beating human experts is valid in a certain light, but kind of undermines the complex work being done by the people building these programs. This all probably plays into OpenAI’s interests, however, which seem to be focused quite a bit on driving home how quickly we’re progressing toward artificial general intelligence.
It’s going to probably be a bit before an AI-controlled system starts trouncing opponents in Fortnite, but for a fixed-perspective strategy game like Dota 2, there is room for boundary-pushing hyper-focused AI programs to bulk up on gameplay knowledge and perhaps deliver some wins.
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