Tag Archives: Machine learning

Updates from GE

I Machine, You Human: How AI Is Helping GE Build A Powerhouse Of Knowledge
By Tomas Kellner – Every fall, GE Global Research holds a scientific gathering called the Whitney Symposium highlighting the latest scientific trends. Last year the two-day event explored industrial applications of artificial intelligence. We sat down with Mark Grabb and Achalesh Pandey, two GE scientists looking for ways to apply AI to jet engines, medical scanners and other machines.

“We are starting to see significant performance increases from the combination of deep learning and reinforcement learning, where you have a human in the loop correcting the system,” Grabb said. “Once you build a smooth user experience and get the system going, people don’t even know they are correcting the AI along the way.”

At GE, we are writing software like Predix, which is the cloud-based operating system for machines that allows us to connect them to the Industrial Internet. But we also have a tremendous number of domain experts. There’s a lot of physics and domain knowledge that’s required to build good analytics and machine learning models. We have actually built AI systems that help data scientists more quickly and more effectively capture the domain knowledge across all the people inside GE building these models. So AI comes in even in the developing of analytics. more> https://goo.gl/OMZ9TS

The body is the missing link for truly intelligent machines

BOOK REVIEW

Basin and Range, Author: John McPhee.
Descartes’ Error, Author: Antonio Damasio.

By Ben Medlock – Things took a wrong turn at the beginning of modern AI, back in the 1950s. Computer scientists decided to try to imitate conscious reasoning by building logical systems based on symbols. The method involves associating real-world entities with digital codes to create virtual models of the environment, which could then be projected back onto the world itself.

In later decades, as computing power grew, researchers switched to using statistics to extract patterns from massive quantities of data. These methods are often referred to as ‘machine learning’. Rather than trying to encode high-level knowledge and logical reasoning, machine learning employs a bottom-up approach in which algorithms discern relationships by repeating tasks, such as classifying the visual objects in images or transcribing recorded speech into text.

But algorithms are a long way from being able to think like us. The biggest distinction lies in our evolved biology, and how that biology processes information. Humans are made up of trillions of eukaryotic cells, which first appeared in the fossil record around 2.5 billion years ago. A human cell is a remarkable piece of networked machinery that has about the same number of components as a modern jumbo jet – all of which arose out of a longstanding, embedded encounter with the natural world.

We only have the world as it is revealed to us, which is rooted in our evolved, embodied needs as an organism. Nature ‘has built the apparatus of rationality not just on top of the apparatus of biological regulation, but also from it and with it’,

In other words, we think with our whole body, not just with the brain. more> https://goo.gl/oBgkRF

The end of code —

By Edward C. Monaghan – Over the past several years, the biggest tech companies in Silicon Valley have aggressively pursued an approach to computing called machine learning.

In traditional programming, an engineer writes explicit, step-by-step instructions for the computer to follow. With machine learning, programmers don’t encode computers with instructions. They train them.

If you want to teach a neural network to recognize a cat, for instance, you don’t tell it to look for whiskers, ears, fur, and eyes. You simply show it thousands and thousands of photos of cats, and eventually it works things out.

But here’s the thing: With machine learning, the engineer never knows precisely how the computer accomplishes its tasks. The neural network’s operations are largely opaque and inscrutable. It is, in other words, a black box.

The implications of an unparsable machine language aren’t just philosophical. A world run by neurally networked deep-learning machines requires a different workforce.

Analysts have already started worrying about the impact of AI on the job market, as machines render old skills irrelevant. Programmers might soon get a taste of what that feels like themselves.

Danny Hillis [2] has declared the end of the age of Enlightenment, our centuries-long faith in logic, determinism, and control over nature. Hillis says we’re shifting to what he calls the age of Entanglement. more> http://goo.gl/Xmk3ia