Lifeboat Foundation

https://youtube.com/watch?v=ksMXbhftBbM

California company NDB says its nano-diamond batteries will absolutely upend the energy equation, acting like tiny nuclear generators. They will blow any energy density comparison out of the water, lasting anywhere from a decade to 28,000 years without ever needing a charge. They will offer higher power density than lithium-ion. They will be nigh-on indestructible and totally safe in an electric car crash. And in some applications, like electric cars, they stand to be considerably cheaper than current lithium-ion packs despite their huge advantages.

The heart of each cell is a small piece of recycled nuclear waste. NDB uses graphite nuclear reactor parts that have absorbed radiation from nuclear fuel rods and have themselves become radioactive. Untreated, it’s high-grade nuclear waste: dangerous, difficult and expensive to store, with a very long half-life.

Continue reading “Nano-diamond self-charging batteries could disrupt energy as we know it” »

Since OpenAI first described its new AI language-generating system called GPT-3 in May, hundreds of media outlets (including MIT Technology Review) have written about the system and its capabilities. Twitter has been abuzz about its power and potential. The New York Times published an op-ed about it. Later this year, OpenAI will begin charging companies for access to GPT-3, hoping that its system can soon power a wide variety of AI products and services.

Earlier this year, the independent research organisation of which I am the Director, London-based Ada Lovelace Institute, hosted a panel at the world’s largest AI conference, CogX, called The Ethics Panel to End All Ethics Panels. The title referenced both a tongue-in-cheek effort at self-promotion, and a very real need to put to bed the seemingly endless offering of panels, think-pieces, and government reports preoccupied with ruminating on the abstract ethical questions posed by AI and new data-driven technologies. We had grown impatient with conceptual debates and high-level principles.

And we were not alone. 2020 has seen the emergence of a new wave of ethical AI – one focused on the tough questions of power, equity, and justice that underpin emerging technologies, and directed at bringing about actionable change. It supersedes the two waves that came before it: the first wave, defined by principles and dominated by philosophers, and the second wave, led by computer scientists and geared towards technical fixes. Third-wave ethical AI has seen a Dutch Court shut down an algorithmic fraud detection system, students in the UK take to the streets to protest against algorithmically-decided exam results, and US companies voluntarily restrict their sales of facial recognition technology. It is taking us beyond the principled and the technical, to practical mechanisms for rectifying power imbalances and achieving individual and societal justice.

Continue reading “The term ‘ethical AI’ is finally starting to mean something” »

An artificial intelligence algorithm by Heron Systems defeated an F-16 pilot in a simulated dogfight competition Thursday. Photo by SSgt. Christine Groening/U.S. Air force.

In June 2019, Facebook’s AI lab, FAIR, released AI Habitat, a new simulation platform for training AI agents. It allowed agents to explore various realistic virtual environments, like a furnished apartment or cubicle-filled office. The AI could then be ported into a robot, which would gain the smarts to navigate through the real world without crashing.

In the year since, FAIR has rapidly pushed the boundaries of its work on “embodied AI.” In a blog post today, the lab has announced three additional milestones reached: two new algorithms that allow an agent to quickly create and remember a map of the spaces it navigates, and the addition of sound on the platform to train the agents to hear.

“We have for the first time used deep learning to find disease-related genes. This is a very powerful method in the analysis of huge amounts of biological information, or ‘big data’,” said Sanjiv Dwivedi, first author of the newly published research.

The plan in the next big war will probably be to let waves of AI fighters wipe out all the enemies targets, Anti aircraft systems, enemy fighters, enemy air fields etc…, however many waves that takes. And, then human pilots come in behind that.

An artificial intelligence algorithm defeated a human F-16 fighter pilot in a virtual dogfight sponsored by the Defense Advanced Research Projects Agency Thursday.

Researchers at Oxford University, in collaboration with DeepMind, University of Basel and Lancaster University, have created a machine learning algorithm that interfaces with a quantum device and ‘tunes’ it faster than human experts, without any human input. They are dubbing it “Minecraft explorer for quantum devices.”

Classical computers are composed of billions of transistors, which together can perform complex calculations. Small imperfections in these transistors arise during manufacturing, but do not usually affect the operation of the computer. However, in a quantum computer similar imperfections can strongly affect its behavior.

In prototype semiconductor quantum computers, the standard way to correct these imperfections is by adjusting input voltages to cancel them out. This process is known as tuning. However, identifying the right combination of voltage adjustments needs a lot of time even for a single quantum device. This makes it virtually impossible for the billions of devices required to build a useful general-purpose quantum computer.

Quantifications are produced by several disciplinary houses in a myriad of different styles. The concerns about unethical use of algorithms, unintended consequences of metrics, as well as the warning about statistical and mathematical malpractices are all part of a general malaise, symptoms of our tight addiction to quantification. What problems are shared by all these instances of quantification? After reviewing existing concerns about different domains, the present perspective article illustrates the need and the urgency for an encompassing ethics of quantification. The difficulties to discipline the existing regime of numerification are addressed; obstacles and lock-ins are identified. Finally, indications for policies for different actors are suggested.

Newswise — Most of modern medicine has physical tests or objective techniques to define much of what ails us. Yet, there is currently no blood or genetic test, or impartial procedure that can definitively diagnose a mental illness, and certainly none to distinguish between different psychiatric disorders with similar symptoms. Experts at the University of Tokyo are combining machine learning with brain imaging tools to redefine the standard for diagnosing mental illnesses.

“Psychiatrists, including me, often talk about symptoms and behaviors with patients and their teachers, friends and parents. We only meet patients in the hospital or clinic, not out in their daily lives. We have to make medical conclusions using subjective, secondhand information,” explained Dr. Shinsuke Koike, M.D., Ph.D., an associate professor at the University of Tokyo and a senior author of the study recently published in Translational Psychiatry.

“Frankly, we need objective measures,” said Koike.