Social Media Sidebar


Please sign up, comment on articles and bring your friends!

Current poll

PlanetTech is asking:

What do you think about our new web site?

Love it, indeed
Really good solution
Same as old one
The old one was better
This is a new option

Quote of the day

Just because something doesn’t do what you planned it to do doesn’t mean it’s useless.


Thomas Edison

Machines Teaching Each Other Could Be the Biggest Exponential Trend in AI

Posted in Software on 22nd Jan, 2018 11:47 PM by Alex Muller

During an October 2015 press conference announcing the autopilot feature of the Tesla Model S, which allowed the car to drive semi-autonomously, Elon Musk said each driver would become an “expert trainer” for every Model S. Each car could improve its own autonomous features by learning.


More significantly, when one Tesla learned from its own driver—that knowledge could then be shared with every other Tesla vehicle.

As Fred Lambert with Electrik reported shortly after, Model S owners noticed how quickly the car’s driverless features were improving. In one example, Teslas were taking incorrect early exits along highways, forcing their owners to manually steer the car along the correct route. After just a few weeks, owners noted the cars were no longer taking premature exits.
“I find it remarkable that it is improving this rapidly,” said one Tesla owner.
Intelligent systems, like those powered by the latest round of machine learning software, aren’t just getting smarter: they’re getting smarter faster. Understanding the rate at which these systems develop can be a particularly challenging part of navigating technological change.
Ray Kurzweil has written extensively on the gaps in human understanding between what he calls the “intuitive linear” view of technological change and the “exponential” rate of change now taking place. Almost two decades after writing the influential essay on what he calls “The Law of Accelerating Returns”—a theory of evolutionary change concerned with the speed at which systems improve over time—connected devices are now sharing knowledge between themselves, escalating the speed at which they improve.
[Learn more about thinking exponentially and the Law of Accelerating Returns.]
“I think that this is perhaps the biggest exponential trend in AI,” said Hod Lipson, professor of mechanical engineering and data science at Columbia University, in a recent interview.
“All of the exponential technology trends have different ‘exponents,’” Lipson added. “But this one is potentially the biggest.”
According to Lipson, what we might call “machine teaching”—when devices communicate gained knowledge to one another—is a radical step up in the speed at which these systems improve.
“Sometimes it is cooperative, for example when one machine learns from another like a hive mind. But sometimes it is adversarial, like in an arms race between two systems playing chess against each other,” he said.
Lipson believes this way of developing AI is a big deal, in part, because it can bypass the need for training data.
“Data is the fuel of machine learning, but even for machines, some data is hard to get—it may be risky, slow, rare, or expensive. In those cases, machines can share experiences or create synthetic experiences for each other to augment or replace data. It turns out that this is not a minor effect, it actually is self-amplifying, and therefore exponential.”
Lipson sees the recent breakthrough from Google’s DeepMind, a project called AlphaGo Zero, as a stunning example of an AI learning without training data. Many are familiar with AlphaGo, the machine learning AI which became the world’s best Go a player after studying a massive training data-set comprised of millions of human Go moves. AlphaGo Zero, however, was able to beat even that Go-playing AI, simply by learning the rules of the game and playing by itself—no training data necessary. Then, just to show off, it beat the world’s best chess playing software after starting from scratch and training for only eight hours.
Now imagine thousands or more AlphaGo Zeroes instantaneously sharing their gained knowledge.
This isn’t just games though. Already, we’re seeing how it will have a major impact on the speed at which businesses can improve the performance of their devices.
One example is GE’s new industrial digital twin technology—a software simulation of a machine that models what is happening with the equipment. Think of it as a machine with its own self-image—which it can also share with technicians.
A steam turbine with a digital twin, for instance, can measure steam temperatures, rotor speeds, cold starts, and other data to predict breakdowns and warn technicians to prevent expensive repairs. The digital twins make these predictions by studying their own performance, but they also rely on models every other steam turbine has developed.
As machines begin to learn from their environments in new and powerful ways, their development is accelerated by communicating what they learn with each other. The collective intelligence of every GE turbine, spread across the planet, can accelerate each individual machine’s predictive ability. Where it may take one driverless car significant time to learn to navigate a particular city—one hundred driverless cars navigating that same city together, all sharing what they learn—can improve their algorithms in far less time.
As other AI-powered devices begin to leverage this shared knowledge transfer, we could see an even faster pace of development. So if you think things are developing quickly today, remember we’re only just getting started.

Tags: AIsoftwaredeep learningartificial intelligence

Read original article » Back to category



Recent headlines

  • Posted in Science on 2019-01-05 00:26:50
    China Lands Chang'e 4 on the Far Side of the more
    Posted in Science on 2018-12-31 00:24:45
    Support for Human Gene Editing to Fix Diseases in more
    Posted in Science on 2018-12-31 00:15:06
    Will Mimicking The Nervous System Advance more
    Posted in Science on 2018-12-26 15:04:48
    NASA Gives Us Sugar in Space to Confirm Building more
    Posted in Business on 2018-12-21 22:48:10
    US Air Force Funds SpaceX Starlink For $28.7 more
Posted in Business on 2013-10-10 01:33
China is working towards a manned lunar mission in more
Posted in Business on 2013-10-20 07:17
Spacex says China is their main competitor for more
Posted in Software on 2013-10-20 06:43
Pirate Bay Browser Clocks 1,000,000 more
Posted in Medicine on 2013-10-10 02:10
Google reportedly investing hundreds of millions into more
Posted in Medicine on 2013-10-14 03:13
Endothelial Cells Can Repair and Regenerate Organs, more
Posted in Science on 01.01.2010
Spacex says China is their main competitor for more
Posted in Science on 01.01.2010
Staring at Your Phone Could Be Making You Short more
Posted in Science on 01.01.2010
Oculus Rift virtual reality headset coming to mobile, more
Posted in Science on 01.01.2010
China is working towards a manned lunar mission in more
Posted in Science on 01.01.2010
Delivering drugs via nanoparticles to target more

Recent Blog Posts

  • Posted by AlexMuller
    Even light drinking increases risk of more
    Posted by AlexMuller
    Researchers have discovered how to slow more
    Posted by AlexMuller
    Human retinas grown in a dish explain how color vision more
    Posted by AlexMuller
    The smartphone app that can tell you’re depressed before more
    Posted by AlexMuller
    Neural networks don’t understand what optical illusions more

Login to your Account

Login to your PlanetTech Account here

Remember me

Create a New Account

You just need username and password

The following errors occured:
Verify password:
Remember me