Lifeboat Foundation

In many ways, the human eye is nothing like a digital camera. Our eyes don’t have a fixed frame rate or resolution; there’s no consistent color reproduction, and we have literal, sizable blind spots. But, these optic inconsistencies — found in every biological eye — are the product of natural selection, and offer a number of benefits which scientists working in digital vision can take advantage of.

Case in point is a new type of 3D-printed lens created by researchers from the University of Stuttgart in Germany. Each lens is made from plastic and is no bigger than a grain of salt. But, their size is only one aspect of their cleverness. The real innovation here is that the lenses mimic the action of the “fovea,” a key physiological feature of the eyes of humans and eagles, that allows for for speedier image processing.

Continue reading “Tiny 3D-printed lenses will help robots see like eagles” »

Team OB just won the Robotics for Good Award in Dubai. Over 1,600 technologies for good applied and after competing against the top 10 best assistive technologies the judges chose our bionic hands! Now we have the funding to push our hands through the final stages of medical testing and finally get them to everyone who needs one.

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Taxation and redistribution.

Bill Gates, the co-founder of Microsoft and world’s richest man, said in an interview Friday that robots that steal human jobs should pay their fair share of taxes.

“Right now, the human worker who does, say, $50,000 worth of work in a factory, that income is taxed and you get income tax, Social Security tax, all those things,” he said. “If a robot comes in to do the same thing, you’d think that we’d tax the robot at a similar level.”

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For those interested in life extension and bionic / cyborg type enhancements, this CMU Robotics Institute Seminar gives an overview of the background and current developments in artificial vision. José Alain Sahel MD is a world leading ophthalmologist with a lengthy bio and numerous honors and appointments.

In the future, if you’re going blind, these sight restoration technologies may be used to remediate your vision loss.

Three major ideas are covered. 1) Implanting arrays of tiny 3-color LEDs under a failed retina to stimulate still-okay cells, and 2) using gene therapy to express a novel photoreceptor, borrowed from algae, to restore a form of sight to failed cells. These can be done together. Lots of studies in mice, primates, and humans. Some coverage is also given to 3) directly implanting electronics in the brain to send complete images to vision centers, but this is still at an early stage.

Continue reading “Artificial Vision, Artificial Retina, Optogenetics, José Alain Sahel MD, CMU RI Seminar” »

Potentially describing how general artificial intelligence will look like.

Since scientists started building and training neural networks, Transfer Learning has been the main bottleneck. Transfer Learning is the ability of an AI to learn from different tasks and apply its pre-learned knowledge to a completely new task. It is implicit that with this precedent knowledge, the AI will perform better and train faster than de novo neural networks on the new task.

DeepMind is on the path of solving this with PathNet. PathNet is a network of neural networks, trained using both stochastic gradient descent and a genetic selection method.

Continue reading “DeepMind just published a mind blowing paper: PathNet” »

Ford is investing $1 billion in a secretive artificial intelligence startup headed by former Google and Uber execs to advance its self-driving car efforts.

The startup, Argo AI, was founded by Bryan Salesky, the former director of hardware for Google’s self-driving-car efforts, and Peter Rander, Uber’s engineering lead at its autonomous cars center.

The $1 billion investment will be spread out over five years as Ford looks to commercialize its self-driving technology by 2021.

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Google and Kaggle today announced a new machine learning challenge that asks developers to find the best way to automatically tag videos.

The challenge, which comes with a $30,000 prize for the first-place finisher (and $25,000, $20,000, $15,000 and $10,000 for the next four teams), asks developers to classify and tag videos from Google’s updated YouTube-8M V2 data set. This data set features a total of 7 million YouTube videos that add up to 450,000 hours of video. YouTube-8M already includes labels, too, and developers can use this as their training data. The challenge then is to tag 700,000 previously unseen videos.

Continue reading “Google teams up with Kaggle to host $100,000 video classification challenge” »

Wide models are great for memorization, deep models are great for generalization — why not combine them to create even better models? In this talk, Heng-Tze Cheng explains Wide and Deep networks and gives examples of how they can be used.

Check out our blog post, paper, YouTube video, TensorFlow tutorials: https://goo.gl/MwVlVa

Continue reading “Wide & Deep Learning: Memorization + Generalization with TensorFlow (TensorFlow Dev Summit 2017)” »