Lifeboat Foundation

Algorithms with learning abilities collect personal data that are then used without users’ consent and even without their knowledge; autonomous weapons are under discussion in the United Nations; robots stimulating emotions are deployed with vulnerable people; research projects are funded to develop humanoid robots; and artificial intelligence-based systems are used to evaluate people. One can consider these examples of AI and autonomous systems (AS) as great achievements or claim that they are endangering human freedom and dignity.

We need to make sure that these technologies are aligned to humans in terms of our moral values and ethical principles to fully benefit from the potential of them. AI and AS have to behave in a way that is beneficial to people beyond reaching functional goals and addressing technical problems. This will allow for an elevated level of trust for technology that is needed for a fruitful pervasive use of AI/AS in our daily lives.

In the popular TV show Sherlock, visual depictions of our hero’s deductive reasoning often look like machine algorithms. And probably not by accident, given that this version of Conan Doyle’s detective processes tremendous amounts of observed data—the sort of minutiae that the average person tends to pass over or forget—more like a computer than a human.

Sherlock’s intelligence is both strength and limitation. His way of thinking is often bounded by an inability to intuitively understand social and emotional contexts. The show’s central premise is that Sherlock Holmes needs his friend John Watson to help him synthesize empirical data into human truth.

In Sherlock we see the analog for modern AI: highly performant learning machines that can achieve metacognitive results with the assistance of fully cognitive human partners. Machine intelligence does not by its nature make human intelligence obsolete. Quite the opposite, really—machines need human guidance.

Continue reading “Why the Rise of AI Makes Human Intelligence More Valuable Than Ever” »

Happy Pi Day from Carnegie Mellon University! Professor of mathematical sciences Po-Shen Loh explains why Euler’s Equation is the most beautiful equation in math. The video was filmed as part of a pi and pie discussion with CMU alumna, baker and blogger Quelcy Kogel (A 2007). For more: https://youtu.be/2sC1-DXT9Oo

Are robots coming for your job?

Although technology has long affected the labor force, recent advances in artificial intelligence and robotics are heightening concerns about automation replacing a growing number of occupations, including highly skilled or “knowledge-based” jobs.

Just a few examples: self-driving technology may eliminate the need for taxi, Uber and truck drivers, algorithms are playing a growing role in journalism, robots are informing consumers as mall greeters, and medicine is adapting robotic surgery and artificial intelligence to detect cancer and heart conditions.

Historian Yuval Noah Harari makes a bracing prediction: just as mass industrialization created the working class, the AI revolution will create a new unworking class.

The most important question in 21st-century economics may well be: What should we do with all the superfluous people, once we have highly intelligent non-conscious algorithms that can do almost everything better than humans?

This is not an entirely new question. People have long feared that mechanization might cause mass unemployment. This never happened, because as old professions became obsolete, new professions evolved, and there was always something humans could do better than machines. Yet this is not a law of nature, and nothing guarantees it will continue to be like that in the future. The idea that humans will always have a unique ability beyond the reach of non-conscious algorithms is just wishful thinking. The current scientific answer to this pipe dream can be summarized in three simple principles:

(Phys.org)—Scientists have built tiny logic machines out of single atoms that operate completely differently than conventional logic devices do. Instead of relying on the binary switching paradigm like that used by transistors in today’s computers, the new nanoscale logic machines physically simulate the problems and take advantage of the inherent randomness that governs the behavior of physical systems at the nanoscale—randomness that is usually considered a drawback.

The team of researchers, Barbara Fresch et al., from universities in Belgium, Italy, Australia, Israel, and the US, have published a paper on the new nanoscale logic machines in a recent issue of Nano Letters.

“Our approach shows the possibility of a new class of tiny analog computers that can solve computationally difficult problems by simple statistical algorithms running in nanoscale solid-state physical devices,” coauthor Francoise Remacle at the University of Liege told Phys.org.

Big data budgets now rival CRM software.

Right now it’s easiest to think about an artificial intelligence algorithm as a specific tool, like a hammer. A hammer is really good at hitting things, but when you need a saw to cut something in half, it’s back to the toolbox. Need a face recognized? Train an facial recognition algorithm, but don’t ask it to recognize cows.

Alphabet’s AI research arm, DeepMind, is trying to change that idea with a new algorithm that can learn more than one skill. Having algorithms that can learn multiple skills could make it far easier to add new languages to translators, remove bias from image recognition systems, or even have algorithms use existing knowledge to solve new complex problems. The research published in Proceedings of the National Academy of Sciences this week is preliminary, as it only tests the algorithm on playing different Atari games, but this research shows multi-purpose algorithms are actually possible.

The problem DeepMind’s research tackles is called “catastrophic forgetting,” the company writes. If you train an algorithm to recognize faces and then try to train it again to recognize cows, it will forget faces to make room for all the cow-knowledge. Modern artificial neural networks use millions of mathematic equations to calculate patterns in data, which could be the pixels that make a face or the series of words that make a sentence. These equations are connected in various ways, and are so dependent on some equations that they’ll begin to fail when even slightly tweaked for a different task. DeepMind’s new algorithm identifies and protects the equations most important for carrying out the original task, while letting the less-important ones be overwritten.

Continue reading “Artificial intelligence has a multitasking problem, and DeepMind might have a solution” »

Mathematics professor Po-Shen Loh has created Expii, a free education tool that democratizes learning by turning your smartphone into a tutor.