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.
Created by Denis Dmitriev cofounder of CyberControls.org
NVIDIA CEO and founder Jensen Huang announces the NVIDIA Isaac robot simulator, which integrates physics in super-real time, realistic graphics and AI to speed development and training for the robotics industry.
Artificial intelligence and advanced automation are everywhere including our farm fields and kitchens.