Lifeboat Foundation

“The best-kept secret in quantum computing.” That’s what Cambridge Quantum Computing (CQC) CEO Ilyas Khan called Honeywell’s efforts in building the world’s most powerful quantum computer. In a race where most of the major players are vying for attention, Honeywell has quietly worked on its efforts for the last few years (and under strict NDA’s, it seems). But today, the company announced a major breakthrough that it claims will allow it to launch the world’s most powerful quantum computer within the next three months.

In addition, Honeywell also today announced that it has made strategic investments in CQC and Zapata Computing, both of which focus on the software side of quantum computing. The company has also partnered with JPMorgan Chase to develop quantum algorithms using Honeywell’s quantum computer. The company also recently announced a partnership with Microsoft.

Computer scientists from Rice, supported by collaborators from Intel, will present their results today at the Austin Convention Center as a part of the machine learning systems conference MLSys.

Many companies are investing heavily in GPUs and other specialized hardware to implement deep learning, a powerful form of artificial intelligence that’s behind digital assistants like Alexa and Siri, facial recognition, product recommendation systems and other technologies. For example, Nvidia, the maker of the industry’s gold-standard Tesla V100 Tensor Core GPUs, recently reported a 41% increase in its fourth quarter revenues compared with the previous year.

Rice researchers created a cost-saving alternative to GPU, an algorithm called “sub-linear deep learning engine” (SLIDE) that uses general purpose central processing units (CPUs) without specialized acceleration hardware.

Some forms of autonomous vehicle watch the road ahead using built-in cameras. Ensuring that accurate camera orientation is maintained during driving is, therefore, in some systems key to letting these vehicles out on roads. Now, scientists from Korea have developed what they say is an accurate and efficient camera-orientation estimation method to enable such vehicles to navigate safely across distances.

A fast camera-orientation estimation algorithm that pinpoints vanishing points could make self-driving cars safer.

John Wallace

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Authorities in China step up surveillance and roll out new artificial intelligence tools to fight deadly epidemic.

Under the watchful eye of a microscope, busy little blobs scoot around in a field of liquid—moving forward, turning around, sometimes spinning in circles. Drop cellular debris onto the plain and the blobs will herd them into piles. Flick any blob onto its back and it’ll lie there like a flipped-over turtle.

Their behavior is reminiscent of a microscopic flatworm in pursuit of its prey, or even a tiny animal called a water bear—a creature complex enough in its bodily makeup to manage sophisticated behaviors. The resemblance is an illusion: These blobs consist of only two things, skin cells and heart cells from frogs.

Writing today in the Proceedings of the National Academy of Sciences, researchers describe how they’ve engineered so-calleds (from the species of frog, Xenopus laevis, whence their cells came) with the help of evolutionary algorithms. They hope that this new kind of organism—contracting cells and passive cells stuck together—and its eerily advanced behavior can help scientists unlock the mysteries of cellular communication.

Of course, the computers and data centers that support AI’s complex algorithms are very much dependent on electricity. While that may seem pretty obvious, it may be surprising to learn that AI can be extremely power-hungry, especially when it comes to training the models that enable machines to recognize your face in a photo or for Alexa to understand a voice command.

The scale of the problem is difficult to measure, but there have been some attempts to put hard numbers on the environmental cost.

For instance, one paper published on the open-access repository arXiv claimed that the carbon emissions for training a basic natural language processing (NLP) model—algorithms that process and understand language-based data—are equal to the CO₂ produced by the average American lifestyle over two years. A more robust model required the equivalent of about 17 years’ worth of emissions.

Scientists have been attempting to come up with an equation to unify the micro and macro laws of the Universe; quantum mechanics and gravity. We are one step closer with a paper that demonstrates that this unification is successfully realized in JT gravity. In the simplified toy model of the one dimensional domain, the holographic principle, or how information is stored on a boundary that manifests in another dimension is revealed.

How did the universe begin? How does quantum mechanics, the study of the smallest things, relate to gravity and the study of big things? These are some of the questions physicists have been working to solve ever since Einstein released his theory of relativity.

Formulas show that baby universes pops in and out of the main Universe. However, we don’t realize or experience this as humans. To calculate how this scales, theoretical physicists devised the so-called JT gravity, which turns the universe into a toy-like model with only one dimension of time or space. These restricted parameters allows for a model in which scientists can test their theories.

Scientists from Imperial College London have proposed a new regulatory framework for assessing the impact of AI, called the Human Impact Assessment for Technology (HIAT).

The researchers believe the HIAT could identify the ethical, psychological and social risks of technological progress, which are already being exposed in a growing range of applications, from voter manipulation to algorithmic sentencing.

Even in this experiment, though, the “psychology” of the algorithm in decision-making is counter-intuitive. For example, in the basketball case, the most important factor in making the decision was actually the player’s jerseys rather than the basketball.

Can You Explain What You Don’t Understand?

While it may seem trivial, the conflict here is a fundamental one in approaches to artificial intelligence. Namely, how far can you get with mere statistical associations between huge sets of data, and how much do you need to introduce abstract concepts for real intelligence to arise?