Lifeboat Foundation

Despite growing excitement around the transformative potential of quantum computing, leaders in many industries are still unfamiliar with the technology that’s likely to prove more disruptive than Artificial Intelligence and blockchain. This ignorance seems particularly acute in industries that deal with physical systems and commodities. In an informal survey of two dozen executives in transportation, logistics, construction and energy, only eight had heard of quantum computing and only two could explain how it works.

In many ways this lack of awareness is understandable. Quantum computing’s value to our digital infrastructure is obvious, but its value to our physical infrastructure is perhaps less evident. Yet, the explosion of power and speed that quantum computers will unleash could indeed have a profound impact on physical systems like our transportation and utility networks. For companies, municipalities and nation states to stay competitive and capture the full benefit of the quantum revolution, leaders must start thinking about how quantum computing can improve our infrastructure.

Unlike classical computers, in which a bit of information can be either a zero or a one, quantum computers are able to take advantage of a third state through a phenomenon known as superposition. Superposition, which is a property of physics at the quantum scale, allows a quantum bit or qubit to be a zero, a one or a zero and a one simultaneously. The result is an astronomical increase in computational capacity over existing transistor-based hardware. Google, for example, has found that its quantum machines can run some algorithms 100 million times faster than conventional processors.

Continue reading “Quantum Computing Can Reshape Our Physical Infrastructure If We Let It” »

A team of Italian researchers successfully ran a perceptron algorithm on a real, working quantum computer using IBM’s cloud-access Q Experience system.

A new show looks back over a half century of this surprisingly robust genre.

• By Elizabeth Bailey on November 19, 2018

The brain has always been considered the main inspiration for the field of artificial intelligence(AI). For many AI researchers, the ultimate goal of AI is to emulate the capabilities of the brain. That seems like a nice statement but its an incredibly daunting task considering that neuroscientist are still struggling trying to understand the cognitive mechanism that power the magic of our brains. Despite the challenges, more regularly we are seeing AI research and implementation algorithms that are inspired by specific cognition mechanisms in the human brain and that have been producing incredibly promising results. Recently, the DeepMind team published a paper about neuroscience-inspired AI that summarizes the circle of influence between AI and neuroscience research.

You might be wondering what’s so new about this topic? Everyone knows that most foundational concepts in AI such as neural networks have been inspired by the architecture of the human brain. However, beyond that high level statement, the relationship between the popular AI/deep learning models we used everyday and neuroscience research is not so obvious. Let’s quickly review some of the brain processes that have a footprint in the newest generation of deep learning methods.

Attention is one of those magical capabilities of the human brain that we don’t understand very well. What brain mechanisms allow us to focus on a specific task and ignore the rest of the environment? Attentional mechanisms have become a recent source of inspiration in deep learning models such as convolutional neural networks(CNNs) or deep generative models. For instance, modern CNN models have been able to get a schematic representation of the input and ignore irrelevant information improving their ability of classifying objects in a picture.

Continue reading “Five Functions of the Brain that are Inspiring AI Research” »

Anything that prevents the next big data leak is good news.

“We’re going to get these massive pools of sequenced genomic data,” Metzl said. “The real gold will come from comparing people’s sequenced genomes to their electronic health records, and ultimately their life records.” Getting people comfortable with allowing open access to their data will be another matter; Metzl mentioned that Luna DNA and others have strategies to help people get comfortable with giving consent to their private information. But this is where China’s lack of privacy protection could end up being a significant advantage.

To compare genotypes and phenotypes at scale—first millions, then hundreds of millions, then eventually billions, Metzl said—we’re going to need AI and big data analytic tools, and algorithms far beyond what we have now. These tools will let us move from precision medicine to predictive medicine, knowing precisely when and where different diseases are going to occur and shutting them down before they start.

But, Metzl said, “As we unlock the genetics of ourselves, it’s not going to be about just healthcare. It’s ultimately going to be about who and what we are as humans. It’s going to be about identity.”

Continue reading “Designer Babies, and Their Babies: How AI and Genomics Will Impact Reproduction” »

Machine learning algorithms don’t yet understand things the way humans do — with sometimes disastrous consequences.

The constant figures in other situations, making physicists wonder why. Why does nature insist on this number? It has appeared in various calculations in physics since the 1880s, spurring numerous attempts to come up with a Grand Unified Theory that would incorporate the constant since. So far no single explanation took hold. Recent research also introduced the possibility that the constant has actually increased over the last six billion years, even though slightly. If you’d like to know the math behind fine structure constant more specifically, the way you arrive at alpha is by putting the 3 constants h, c, and e together in the equation — As the units c, e, and h cancel each other out, the.

As multiple research groups around the world race to build a scalable quantum computer, questions remain about how the achievement of quantum supremacy will be verified.

Quantum supremacy is the term that describes a quantum computer’s ability to solve a computational task that would be prohibitively difficult for any classical algorithm. It is considered a critical milestone in quantum computing, but because the very nature of quantum activity defies traditional corroboration, there have been parallel efforts to find a way to prove that quantum supremacy has been achieved.

Researchers at the University of California, Berkeley, have just weighed in by giving a leading practical proposal known as random circuit sampling (RCS) a qualified seal of approval with the weight of complexity theoretic evidence behind it. Random circuit sampling is the technique Google has put forward to prove whether or not it has achieved quantum supremacy with a 72-qubit computer chip called Bristlecone, unveiled earlier this year.