The Artificial Intelligent startup that Elon Musk help found OpenAI, created an AI bot which beats the world's top professionals at 1v1 matches of Dota 2. Watch us play on main stage during The International.
The AI bot learned the game from scratch by itself through playing the game. This is a step towards building AI systems which accomplish well-defined goals in complex, complicated situations involving real humans.
AI4ALL is a national nonprofit working to increase diversity and inclusion in artificial intelligence. We support educational programs around the country that give underrepresented high school students early exposure to humanistic AI. Our vision is a world where AI is created by people who represent the diverse general population.
AI4ALL is a group of mission-driven academics, technologists, scientists, and business leaders based in San Francisco, CA. Join us and help support a diverse new generation of AI creators.
Choose a project to pursue: anything from self-driving cars to identifying cancer genes.
Attend intensive sessions with AI leaders in the classroom and on field trips.
Work with peers with different viewpoints and experiences to explore the capabilities of AI.
Stay connected with the AI4ALL alumni community to keep learning, meet mentors, and teach others.
Find out more at www.AI-4-All.org
As automation technologies such as machine learning and robotics play an increasingly great role in everyday life, their potential effect on the workplace has, unsurprisingly, become a major focus of research and public concern. The discussion tends toward a Manichean guessing game: which jobs will or won’t be replaced by machines?
In fact, as our research has begun to show, the story is more nuanced. While automation will eliminate very few occupations entirely in the next decade, it will affect portions of almost all jobs to a greater or lesser degree, depending on the type of work they entail. Automation, now going beyond routine manufacturing activities, has the potential, as least with regard to its technical feasibility, to transform sectors such as healthcare and finance, which involve a substantial share of knowledge work.
Read the full article on McKinsey.com: Where machines could replace humans—and where they can’t (yet)
We are convinced that deep learning will be a transformative technology that will dramatically improve medicine, education, agriculture, transport and many other fields, with the greatest impact in the developing world. But for this to happen, the technology needs to be much easier to use, more reliable, and more intuitive than it is today. We are working on hybrid “man and machine” solutions that harness the strengths of both humans and computers; building a library of ready-to-use applications and models; developing a complete educational framework; and writing fast and easy to use software for both developers and end users.
Practical Deep Learning For Coders
This is a very different kind of course, taught in a very different way. We have spent as much time studying the research into effective education techniques as we have studying the research into deep learning—one of the biggest differences that you'll see as a result is that we teach "top down" rather than "bottom up". For instance, you'll learn how to use deep learning to solve your problems in week 1, but will only start to learn why it works in week 2! And you'll spend a lot more time learning how to write effective code and use effective processes than you will on learning mathematical formalisms. For full details on the teaching approach, please see our article A unique path to deep learning expertise. And for more information about some of the great education researchers that have inspired and taught us, read our article Providing a Good Education in Deep Learning.
While developing negotiating chatbot agents, Facebook researchers found that the bots spontaneously developed their own non-human language as they improved their techniques, highlighting how little we still know about how artificial intelligences learn.
At one point, the researchers write, they had to tweak one of their models because otherwise the bot-to-bot conversation “led to divergence from human language as the agents developed their own language for negotiating.”
In other words, the model that allowed two bots to have a conversation—and use machine learning to constantly iterate strategies for that conversation along the way—led to those bots communicating in their own non-human language.
The larger point of the report is that bots can be pretty decent negotiators—they even use strategies like feigning interest in something valueless, so that it can later appear to “compromise” by conceding it.
Already, there’s a good deal of guesswork involved in machine learning research, which often involves feeding a neural net a huge pile of data then examining the output to try to understand how the machine thinks. But the fact that machines will make up their own non-human ways of conversing is an astonishing reminder of just how little we know, even when people are the ones designing these systems.