Lifeboat Foundation

Making the most of the low light in the muddy rivers where it swims, the elephant nose fish survives by being able to spot predators amongst the muck with a uniquely shaped retina, the part of the eye that captures light. In a new study, researchers looked to the fish’s retinal structure to inform the design of a contact lens that can adjust its focus.

Imagine a contact lens that autofocuses within milliseconds. That could be life-changing for people with presbyopia, a stiffening of the eye’s lens that makes it difficult to focus on close objects. Presbyopia affects more than 1 billion people worldwide, half of whom do not have adequate correction, said the project’s leader, Hongrui Jiang, Ph.D., of the University of Wisconsin, Madison. And while glasses, conventional contact lenses and surgery provide some improvement, these options all involve the loss of contrast and sensitivity, as well as difficulty with night vision. Jiang’s idea is to design contacts that continuously adjust in concert with one’s own cornea and lens to recapture a person’s youthful vision.

The project, for which Jiang received a 2011 NIH Director’s New Innovator Award (an initiative of the NIH Common Fund) funded by the National Eye Institute, requires overcoming several engineering challenges. They include designing the lens, algorithm-driven sensors, and miniature electronic circuits that adjust the shape of the lens, plus creating a power source — all embedded within a soft, flexible material that fits over the eye.

Google has smartened up several of its products with a type of artificial intelligence called deep learning, which involves training neural networks on lots of data and then having them make predictions about new data. Google Maps, Google Photos, and Gmail, for example, have been enhanced with this type of technology. The next service that could see gains is Google Translate.

Well, let me back up. Part of Google Translate actually already uses deep learning. That would be the instant visual translations you can get on a mobile device when you hold up your smartphone camera to the words you want to translate. But if you use Google Translate to just translate text, you know that the service isn’t always 100 percent accurate.

In an interview at the Structure Data conference in San Francisco today, Jeff Dean, a Google senior fellow who worked on some of Google’s core search and advertising technology and is now the head of the Google Brain team that works on deep learning, said that his team has been working with Google’s translation team to scale out experiments with translation based on deep learning. Specifically, the work is based on the technology depicted in a 2014 paper entitled “Sequence to Sequence Learning with Neural Networks.”

Continue reading “Google Translate could become more accurate soon thanks to deep learning” »

Google, AI, and Quantum — Google believes deep learning is not suitable on Quantum. Not so sure that I agree with this position because deep learning in principle is “a series of complex algorithms that attempt to model high-level abstractions in data by using multiple processing layers with complex structures” — the beauty around quantum is it’s performance in processing of vast sets of information and complex algorithms. Maybe they meant to say at this point they have not resolved that piece for AI.

Artificial intelligence is one of the hottest subjects these days, and recent advances in technology make AI even closer to reality than most of us can imagine.

The subject really got traction when Stephen Hawking, Elon Musk and more than 1,000 AI and robotics researchers signed an open letter issuing a warning regarding the use of AI in weapons development last year. The following month, BAE Systems unveiled Taranis, the most advanced autonomous UAV ever created; there are currently 40 countries working on the deployment of AI in weapons development.

Continue reading “AI is closer than we know” »

Allen Institute working with Baylor on reconstructing neuronal connections.

The Intelligence Advanced Research Projects Activity (IARPA) has awarded an $18.7 million contract to the Allen Institute for Brain Science, as part of a larger project with Baylor College of Medicine and Princeton University, to create the largest ever roadmap to understand how the function of networks in the brain’s cortex relates to the underlying connections of its individual neurons.

The project is part of the Machine Intelligence from Cortical Networks (MICrONS) program, which seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain.

Continue reading “IARPA awards $18.7 million contract to Allen Institute to reconstruct neuronal connections” »

Another data scientist with pragmatic thinking which is badly needed today. Keeping it real with Una-May O’Reilly.

Mumbai: Una-May O’Reilly, principal research scientist at Anyscale Learning For All (ALFA) group at the Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory, has expertise in scalable machine learning, evolutionary algorithms, and frameworks for large-scale, automated knowledge mining, prediction and analytics. O’Reilly is one of the keynote speakers at the two-day EmTech India 2016 event, to be held in New Delhi on 18 March.

In an email interview, she spoke, among other things, about how machine learning underpins data-driven artificial intelligence (AI), giving the ability to predict complex events from predictive cues within streams of data. Edited excerpts:

Continue reading “Machine learning underpins data-driven AI: Una-May O’Reilly” »

I believe there are good advances in AI due to the processing performance; however, as I highlighted earlier many of the principles like complex algorithms along with the pattern & predictive analysis of large volumes of information hasn’t changed much from my own work in the early days with AI. Where I have concerns and is the foundational infrastructure that “connected” AI resides on. Ongoing hacking and attacks of today could actually make AI adoption fall really short; and in the long run cause AI to look pretty bad.

A debate in New York tries to settle the question.

By Larry Greenmeier on March 10, 2016.

Continue reading “Is Artificial Intelligence Being Oversold?” »

When I work on AI today and looking at it’s fundamental principles; it is not that much different from the work that I and another team mate many years ago did around developing a RT Proactive Environmental Response System. Sure there are some differences between processors, etc. However, the principles are the same when you consider some of the extremely complex algorithms that we had to develop to ensure that our system could proactively interrupt patterns and proactively act on it’s own analysis. We did have a way to override any system actions.

These questions originally appeared on Quora — the knowledge sharing network where compelling questions are answered by people with unique insights.

Answers by Neil Lawrence, Professor of Machine Learning at the University of Sheffield, on Quora.

Continue reading “What is the relation between Artificial Intelligence and Machine Learning?” »

US Government’s cool $100 mil in brain research. As we have been highlighting over the past couple of months that the US Government’s IARPA and DARPA program’s have and intends to step up their own efforts in BMIs and robotics for the military; I am certain that this research will help their own efforts and progress.

Intelligence project aims to reverse-engineer the brain to find algorithms that allow computers to think more like humans.

By Jordana Cepelewicz on March 8, 2016.

Continue reading “The U.S. Government Launches a $100-Million ‘Apollo Project of the Brain’” »