Lifeboat Foundation

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’” »

Don’t let the title mislead you — Quantum is not going to require AI to operate or develop it’s computing capabilities. However, what is well known across Quantum communities is that AI will greatly benefit from the processing capabilities & performance of Quantum Computing. There has been a strong interest in marrying the 2 together. However, Quantum maturity gap and timing has not made that possible until recently resulting from the various discoveries in microchip development, programming language (Quipper) development, Q-Dots Silicon wafers, etc.

Researchers at the University of Vienna have created an algorithm that helps plan experiments in this mind-boggling field.

The reach of #BigData: Connecting the world for more collaboration. With Jay Walker from TEDMED. http://bit.ly/1R4cYUQ

Glad to see this article get published because it echoes many of the concerns established around China and Russia governments and their hackers having their infrastructures on Quantum before US, Europe, and Canada. Computer scientists at MIT and the University of Innsbruck say they’ve assembled the first five quantum bits (qubits) of a quantum computer that could someday factor any number, and thereby crack the security of traditional encryption schemes.

Shor’s algorithm performed in a system less than half the size experts expected.