Lifeboat Foundation

The transaction-based communications system ensures robot teams achieve their goal even if some robots are hacked.

Imagine a team of autonomous drones equipped with advanced sensing equipment, searching for smoke as they fly high above the Sierra Nevada mountains. Once they spot a wildfire, these leader robots relay directions to a swarm of firefighting drones that speed to the site of the blaze.

But what would happen if one or more leader robots was hacked by a malicious agent and began sending incorrect directions? As follower robots are led farther from the fire, how would they know they had been duped?

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The potential for AI to deliver transformative value is almost unlimited. And yet, accessing that value is by no means a given. So how do we crack the code?

As someone who’s been in the business of deploying enterprise-grade AI solutions since the earliest days of AI—from the inside, as a CIO at Verizon, and from the outside, as an advisor to an AI company ASAPP—I know that our job as CIOs is to get transformational value out of transformational technology. And yet as recently as 2,020 McKinsey reported that less than 25 percent of companies are “seeing significant bottom-line impact” from AI.

I believe that there are at least three ways we need to shift our thinking if our organizations are going to mine the full transformational potential of AI:

Advanced Nuclear Power Advocacy For Humanity — Eric G. Meyer, Founder & Director, Generation Atomic

Eric G. Meyer is the Founder and Director of Generation Atomic (https://generationatomic.org/), a nuclear advocacy non-profit which he founded after hearing about the promise of advanced nuclear reactors, and he decided to devote his life to saving and expanding the use of atomic energy.

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The availability of data can paralyze a company and its effort to bring software-centric products and services to market. To solve this issue, two-year-old data startup Rendered.ai is generating synthetic data for the satellite, medical, robotics and automotive industries.

At its most broad, synthetic data is manufactured rather than gathered from the real world. “When we use the term synthetic data what we really mean is engineered simulated datasets, and in particular, we focus on a physics-based simulation,” Rendered.ai CEO Nathan Kundtz explained in a recent interview with TechCrunch.

Kundtz received his PhD in physics from Duke University and cut his teeth in the space industry, heading the satellite antenna developer Kymeta Corporation. After leaving that company, he started working with other small space companies, when he noticed what he called a “chicken and egg” problem.

The mission will be developed with the University of Colorado’s Laboratory for Atmospheric and Space Physics and is set to be a 3.6-billion-kilometer journey.

It’s not often that messing around in the lab has produced a fundamental breakthrough, à la Michael Faraday with his magnets and prisms. Even more uncommon is the discovery of the same thing by two research teams at the same time: Newton and Leibniz come to mind. But every so often, even the rarest of events does happen. The summer of 2021 has been a banner season for condensed-matter physics. Three separate teams of researchers have created a crystal made entirely of electrons — and one of them actually did it by accident.

The researchers were working with single-atom-thick semiconductors, cooled to ultra-low temperatures. One team, led by Hongkun Park along with Eugene Demler, both of Harvard, discovered that when very specific numbers of electrons were present in the layers of these slivers of semiconductor, the electrons stopped in their tracks and stood “mysteriously still.” Eventually colleagues recalled an old idea having to do with Wigner crystals, which were one of those things that exist on paper and in theory but had never been verified in life. Wigner had calculated that because of mutual electrostatic repulsion, electrons in a monolayer would assume a tri-grid pattern.

Continue reading “Straight Out of Science Fiction: Scientists Create a Crystal Made Solely of Electrons” »

How AI could help design smells and tastes.

Artificial intelligence is already playing a role in the creation of smells and tastes. In this week’s Vergecast episode, the team explores how AI and machine learning might contribute to product design.

For more than 20 years, D-Wave has been synonymous with quantum annealing. Its early bet on this technology allowed it to become the world’s first company to sell quantum computers, but that also somewhat limited the real-world problems its hardware could solve, given that quantum annealing works especially well for optimization problems like protein folding or route planning. But as the company announced at its Qubits conference today, a superconducting gate-model quantum computer — of the kind IBM and others currently offer — is now also on its roadmap.

D-Wave believes the combination of annealing, gate-model quantum computing and classic machines is what its businesses’ users will need to get the most value from this technology. “Like we did when we initially chose to pursue annealing, we’re looking ahead,” the company notes in today’s announcement. “We’re anticipating what our customers need to drive practical business value, and we know error-corrected gate-model quantum systems with practical application value will be required for another important part of the quantum application market: simulating quantum systems. This is an application that’s particularly useful in fields like materials science and pharmaceutical research.”

Driving this revolution has been a new breed and wave of founders and startups that merge the worlds of technology and bio — importantly, not just the old world of biotech (or a narrow definition of tech in bio as only “digital health”), but something much broader, bigger, and blending both worlds. In short, biology — enabled by technology — is eating the world. This has not only changed how we diagnose, treat, and manage disease, but has been changing the way we access, pay for, and deliver care in the healthcare system. It is now entering into manufacturing, food, and several other industries as well. Bio is becoming a part of everything.

This new era of industrialized bio — enabled by AI as well as an ongoing, foundational shift in biology from empirical science to more engineered approaches — will be the next industrial revolution in human history. And propelling it forward is an enormous new driving force, the novel coronavirus SARS-CoV-2, its ever-evolving strains, and the resulting COVID-19 disease pandemic and response — which I believe is analogous to our generation’s World War II (WW2). In other words: a massive global upheaval, but that later led to unprecedented innovation and significant new players.

Continue reading “The Coming Age for Tech x Bio: The ‘Industrial Bio Complex’” »