For artificial general intelligence (AGI) it would be efficient if multiple users trained the same giant neural network, permitting parameter reuse, without catastrophic forgetting.
PathNet is a first step in this direction. It is a neural network algorithm that uses agents embedded in the neural network whose task is to discover which parts of the network to re-use for new tasks.
Agents are pathways (views) through the network which determine the subset of parameters that are used and updated by the forwards and backwards passes of the backpropogation algorithm. During learning, a tournament selection genetic algorithm is used to select pathways through the neural network for replication and mutation. Pathway fitness is the performance of that pathway measured according to a cost function.
We demonstrate successful transfer learning; fixing the parameters along a path learned on task A and re-evolving a new population of paths for task B, allows task B to be learned faster than it could be learned from scratch or after fine-tuning. Paths evolved on task B re-use parts of the optimal path evolved on task A.
Joseph Redmon works on the YOLO (You Only Look Once) system, an open-source method of object detection that can identify objects in images and video — from zebras to stop signs — with lightning-quick speed. In a remarkable live demo, Redmon shows off this important step forward for applications like self-driving cars, robotics and even cancer detection.
A few years ago, on my personal Twitter account, I suggested that Google side benefit of owning YouTube would be having the largest archive of human activities on video to train its AI. What Redmon did here is what I had in mind at that time.
By the way, the demonstration during the TED talk is impressive.
Companies testing EE—including giants like GE, Boeing, DHL, and Volkswagen—have measured huge gains in productivity and noticeable improvements in quality. What started as pilot projects are now morphing into plans for widespread adoption in these corporations. Other businesses, like medical practices, are introducing Enterprise Edition in their workplaces to transform previously cumbersome tasks.
For starters, it makes the technology completely accessible for those who wear prescription lenses. The camera button, which sits at the hinge of the frame, does double duty as a release switch to remove the electronics part of unit (called the Glass Pod) from the frame. You can then connect it to safety glasses for the factory floor—EE now offers OSHA-certified safety shields—or frames that look like regular eyewear. (A former division of 3M has been manufacturing these specially for Enterprise Edition; if EE catches on, one might expect other frame vendors, from Warby Parker to Ray-Ban, to develop their own versions.)
Other improvements include beefed-up networking—not only faster and more reliable wifi, but also adherence to more rigorous security standards—and a faster processor as well. The battery life has been extended—essential for those who want to work through a complete eight-hour shift without recharging. (More intense usage, like constant streaming, still calls for an external battery.) The camera was upgraded from five megapixels to eight. And for the first time, a red light goes on when video is being recorded.
If Glass EE gains traction, and I believe so if it evolves into a platform for enterprise apps, Google will gain a huge amount of information and experience that can reuse on the AR contact lenses currently in the work.
It’s been pretty obvious for a few months now, but Google has finally admitted that it’s running its own investment fund targeting machine intelligence startups. The fund will go by the name Gradient Ventures and provide capital, resources and education to AI-first startups.
Google isn’t disclosing the size of the fund, but the company told us that it’s being run directly off of Google’s balance sheet and will have the flexibility to follow on when it makes sense. This is in contrast to GV (formally Google Ventures) and Capital G, which operate as independent funds.
AI is the first technology in a long time posing a real threat to Google dominance. In other words, artificial intelligence is the best bet for a newcomer to become the next Google. No surprise Google wants to spot that newcomer as early as possible.
Today we’re announcing the People + AI Research initiative (PAIR) which brings together researchers across Google to study and redesign the ways people interact with AI systems. The goal of PAIR is to focus on the “human side” of AI: the relationship between users and technology, the new applications it enables, and how to make it broadly inclusive. The goal isn’t just to publish research; we’re also releasing open source tools for researchers and other experts to use.
At the recent I/0 2017, Google stated that we were at an inflexion point with vision. In other words, it’s now more possible than ever before for a computer to look at a scene and dig out the details and understand what’s going on. Hence: Google Lens.This improvement comes courtesy of machine learning, which allows companies like Google to acquire huge amounts of data and then create systems that utilize that data in useful ways. This is the same technology underlying voice assistants and even your recommendations on Spotify to a lesser extent.