Lifeboat Foundation

Many A.I. experts are concerned that Facebook, Google and a few other big companies are hoarding talent in the field. The internet giants also control the massive troves of online data that are necessary to train and refine the best machine learning programs.

Several start-ups hope to use the technology introduced by Bitcoin to give broader access to the data and algorithms behind artificial intelligence.

The pace of AI change continues to accelerate, so for example, say hello to Google’s children.

Google Brain’s researchers created the AutoML, an AI algorithm capable of generating its own AIs, thereby eliminating the need to hire human experts.

IBM researchers provide mathematical proof to Shor’s Algorithm.

Maybe they’re not alien doppelgangers — mirror images of us.

But extraterrestrial life—should it exist—might look “eerily similar to the life we see on Earth,” says Charles Cockell, professor of astrobiology at the University of Edinburgh in Scotland.

Indeed, Cockell’s new book (The Equations of Life: How Physics Shapes Evolution, Basic Books, 352 pages) suggests a “universal biology.” Alien adaptations, significantly resembling terrestrial life—from humanoids to hummingbirds—may have emerged on billions of worlds.

Continue reading “Extraterrestrials Might Look Like Us, Says Astrobiologist” »

The Pentagon is pushing algorithmic warfare, but big tech’s involvement assumes the US military is a benevolent force.

Can the origin of life be explained with quantum mechanics? And if so, are there quantum algorithms that could encode life itself?

We’re a little closer to finding out the answers to those big questions thanks to new research carried out with an IBM supercomputer.

Encoding behaviours related to self-replication, mutation, interaction between individuals, and (inevitably) death, a newly created quantum algorithm has been used to show that quantum computers can indeed mimic some of the patterns of biology in the real world.

Continue reading “Scientists Just Created Quantum Artificial Life For The First Time Ever” »

It is important to know why a program does what it does. This is not a mystery, technology is a tool and that tool is only as good as the human who created it.

You always have to know why a program, makes the decisions that it makes. No program or Algorithm will be perfect, that is the main issue that Lisa Haven brings forward. You also have to make sure of the reason for the error whether it is innocent or intentional or even criminal.

Continue reading “A Secret Algorithm That Could Ruin Your Life” »

Quantum computing isn’t going to revolutionize AI anytime soon, according to a panel of experts in both fields.

Different worlds: Yoshua Bengio, one of the fathers of deep learning, joined quantum computing experts from IBM and MIT for a panel discussion yesterday. Participants included Peter Shor, the man behind the most famous quantum algorithm. Bengio said he was keen to explore new computer designs, and he peppered his co-panelists with questions about what a quantum computer might be capable of.

Quantum leaps: The panels quantum experts explained that while quantum computers are scaling up, it will be a while—we’re talking years here—before they could do any useful machine learning, partly because a lot of extra qubits will be needed to do the necessary error corrections. To complicate things further, it isn’t very clear what, exactly, quantum computers will be able to do better than their classical counterparts. But both Aram Harrow of MIT and IBM’s Kristian Temme said that early research on quantum machine learning is under way.

Continue reading “Quantum machine learning is a big leap away, at least for now” »

When moving through a crowd to reach some end goal, humans can usually navigate the space safely without thinking too much. They can learn from the behavior of others and note any obstacles to avoid. Robots, on the other hand, struggle with such navigational concepts.

MIT researchers have now devised a way to help robots navigate environments more like humans do. Their novel motion-planning model lets robots determine how to reach a goal by exploring the environment, observing other agents, and exploiting what they’ve learned before in similar situations. A paper describing the model was presented at this week’s IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Popular motion-planning algorithms will create a tree of possible decisions that branches out until it finds good paths for navigation. A robot that needs to navigate a room to reach a door, for instance, will create a step-by-step search tree of possible movements and then execute the best path to the door, considering various constraints. One drawback, however, is these algorithms rarely learn: Robots can’t leverage information about how they or other agents acted previously in similar environments.