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Category: information science

By translating a key human physical dynamic skill — maintaining whole-body balance — into a mathematical equation, the team was able to use the numerical formula to program their robot Mercury, which was built and tested over the course of six years. They calculated the margin of error necessary for the average person to lose one’s balance and fall when walking to be a simple figure — 2 centimeters.

“Essentially, we have developed a technique to teach autonomous robots how to maintain balance even when they are hit unexpectedly, or a force is applied without warning,” Sentis said. “This is a particularly valuable skill we as humans frequently use when navigating through large crowds.”

Sentis said their technique has been successful in dynamically balancing both bipeds without ankle control and full humanoid robots.

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If simulating the brain is proving tricky, why don’t we try decoding it?

“There’s a good reason the first flying machines weren’t mechanical bats: people tried that, and they were terrible.” — Dan Robitzski

In the current AI Spring, many people and corporations are betting big that the capabilities of deep learning algorithms will continue to improve as the algorithms are fed more data. Their faith is backed by the miracles performed by such algorithms: they can see, listen and do a thousand other things that were previously considered too difficult for AI.

Our guest for the third episode of the AGI Podcast, Pascal Kaufmann, is amongst those who believe such faith in deep learning is misplaced.

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In this video, we’ll be discussing big data – more specifically, what big data is, the exponential rate of growth of data, how we can utilize the vast quantities of data being generated as well as the implications of linked data on big data.

[0:30–7:50] — Starting off we’ll look at, how data has been used as a tool from the origins of human evolution, starting at the hunter-gatherer age and leading up to the present information age. Afterwards, we’ll look into many statistics demonstrating the exponential rate of growth and future growth of data.

[7:50–18:55] — Following that we’ll discuss, what exactly big data is and delving deeper into the types of data, structured and unstructured and how they will be analyzed both by humans and machine learning (AI).

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https://youtu.be/TbjQGooiJNs

James Hughes : “Great convo with Yuval Harari, touching on algorithmic governance, the perils of being a big thinker when democracy is under attack, the need for transnational governance, the threats of automation to the developing world, the practical details of UBI, and a lot more.”

In this episode of the Waking Up podcast, Sam Harris speaks with Yuval Noah Harari about his new book 21 Lessons for the 21st Century. They discuss the importance of meditation for his intellectual life, the primacy of stories, the need to revise our fundamental assumptions about human civilization, the threats to liberal democracy, a world without work, universal basic income, the virtues of nationalism, the implications of AI and automation, and other topics.

Yuval Noah Harari has a PhD in History from the University of Oxford and lectures at the Hebrew University of Jerusalem, specializing in world history. His books have been translated into 50+ languages, with 12+ million copies sold worldwide. Sapiens: A Brief History of Humankind looked deep into our past, Homo Deus: A Brief History of Tomorrow considered far-future scenarios, and 21 Lessons for the 21st Century focuses on the biggest questions of the present moment.

Twitter: @harari_yuval

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This video is the second in a two-part series discussing big data. In this video, we’ll be discussing how we can utilize the vast quantities of data being generated as well as the implications of linked data on big data.

[0:33–4:43] — Starting off we’ll look at, what exactly big data is and delving deeper into the types of data, structured and unstructured and how they will be analyzed both by humans and machine learning (AI).

[4:43–11:37] — Following that we’ll discuss, how this data will be put to use and the next evolution of data, linked data, and how it will change the world and the web!

[11:37–12:37] — To conclude we’ll briefly overview the role cloud computing will play with big data!

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Recently, there has been an explosion of interest in applying artificial intelligence (AI) to medicine. Whether explicitly or implicitly, much of this interest has centered on using AI to automate decision-making tasks that are currently done by physicians. This includes two seminal papers in the Journal of the American Medical Association demonstrating that AI-based algorithms have similar or higher accuracy than physicians: one in diagnostic assessment of metastatic breast cancer compared to pathologists and the other in detecting diabetic retinopathy compared to ophthalmologists.

While promising, these applications of AI in medicine raise a number of novel regulatory and policy issues around efficacy, safety, health workforce, and payment. They have also triggered concerns from the medical and patient communities about AI replacing doctors. And, except in narrow domains of practice, general AI systems may fall far short of the hype.

We posit that the applications of AI to “augment” physicians may be more realistic and broader reaching than those that portend to replace existing health care services. In particular, with the right support from policy makers, physicians, patients, and the technology community, we see opportunities for AI to be a solution for—rather than a contributor to—burnout among physicians and achieving the quadruple aim of improving health, enhancing the experience of care, reducing cost, and attaining joy in work for health professionals.

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Machine-learning algorithms are helping to unravel the quantum behaviour of a type of superconductor that has baffled physicists for decades.

Researchers used artificial intelligence to spot hidden order in images of a bizarre state in high-temperature superconductors.

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This video is the first in a two-part series discussing big data. In this video, we’ll be discussing the importance of data and the role, it has played in advancing humankind as well as the exponential rate of growth of data.

[0:29–4:19] — Starting off we’ll look at, how data has been used as a tool from the origins of human evolution, starting at the hunter-gatherer age and leading up to the present information age.

[4:19–7:48] — Following that we’ll discuss, the many statistics demonstrating the exponential rate of growth and future growth of data.

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