Lifeboat Foundation

After six decades we have finally reached controlled fusion “ignition.” Here is how it works and what it means (and doesn’t mean):

At the Lawrence Livermore National Lab (LLNL) the National Ignition Facility (NIF) starts with the Injection Laser System (ILS), a ytterbium-doped optical fiber laser (Master Oscillator) that produces a single very lower power, 1,053 nanometer (Infrared Light) beam. This single beam is split into 48 Pre-Amplifiers Modules (PAMs) that create four beams each (192 total). Each PAM conducts a two-stage amplification process via xenon flash lamps.

Self-coding and self-updating AI algorithms appear to be on the horizon. There are talks about Pitchfork AI, a top-secret Google Labs project that can independently code, refactor, and use both its own and other people’s code.

Continue reading “Google Labs unveils Pitchfork, an AI that can convert old code to new code and rewrite itself” »

Deep-learning models have proven to be highly valuable tools for making predictions and solving real-world tasks that involve the analysis of data. Despite their advantages, before they are deployed in real software and devices such as cell phones, these models require extensive training in physical data centers, which can be both time and energy consuming.

Researchers at Texas A&M University, Rain Neuromorphics and Sandia National Laboratories have recently devised a new system for training deep learning models more efficiently and on a larger scale. This system, introduced in a paper published in Nature Electronics, relies on the use of new training algorithms and memristor crossbar hardware, that can carry out multiple operations at once.

“Most people associate AI with health monitoring in smart watches, face recognition in smart phones, etc., but most of AI, in terms of energy spent, entails the training of AI models to perform these tasks,” Suhas Kumar, the senior author of the study, told TechXplore.

Search Engine Optimization (SEO) is the process of optimizing on-page and off-page factors that impact how high a web page ranks for a specific search term. This is a multi-faceted process that includes optimizing page loading speed, generating a link building strategy, as well as learning how to reverse engineer Google’s AI by using computational thinking.

Computational thinking is an advanced type of analysis and problem-solving technique that computer programmers use when writing code and algorithms. Computational thinkers will seek the ground truth by breaking down a problem and analyzing it using first principles thinking.

Since Google does not release their secret sauce to anyone, we will rely on computational thinking. We will walk through some pivotal moments in Google’s history that shaped the algorithms that are used, and we will learn why this matters.

Keith Downing is a professor of Computer Science at the Norwegian University of Science and Technology, specializing in Artificial Intelligence and Artificial Life. He has a particular interest in evolutionary algorithms, which have applications ranging from the development of the Mars Rover antenna, patented circuits, early driverless cars, to even art. For computer scientists to learn from nature, he believes there needs to be a shift in our traditional ways thinking.

About TEDx, x = independently organized event.
In the spirit of ideas worth spreading, TEDx is a program of local, self-organized events that bring people together to share a TED-like experience. At a TEDx event, TEDTalks video and live speakers combine to spark deep discussion and connection in a small group. These local, self-organized events are branded TEDx, where x = independently organized TED event. The TED Conference provides general guidance for the TEDx program, but individual TEDx events are self-organized.* (*Subject to certain rules and regulations).

Tournament selection, roulette selection, mutation, crossover — all processes used in genetic algorithms. Dr Alex Turner explains using the Knapsack Problem.

https://www.facebook.com/computerphile.
https://twitter.com/computer_phile.

Continue reading “The Knapsack Problem & Genetic Algorithms — Computerphile” »

In the cons column, quantum computers are hard to use, require a very controlled set up to operate, and have to contend with “decoherence” or losing their quantum state which gives weird results. They’re also rare, expensive, and for most tasks, way less efficient than a traditional computer.

Still, a lot of these issues can be offset by combining a quantum computer with a traditional computer, just as VTT has done. Researchers can create a hybrid algorithm that has LUMI, the traditional supercomputer, handle the parts it does best while handing off anything that could benefit from quantum computing to HELMI. LUMI can then integrate the results of HELMI’s quantum calculations, perform any additional calculations necessary or even send more calculations to HELMI, and return the complete results to the researchers.

Finland is now one of few nations in the world with a quantum computer and a supercomputer, and LUMI is the most powerful quantum-enabled supercomputer. While quantum computers are still a way from being broadly commercially viable, these kinds of integrated research programs are likely to accelerate progress. VTT is currently developing a 20-qubit quantum computer with a 50-qubit upgrade planned for 2024.

A large universal quantum computer is still an engineering dream, but machines designed to leverage quantum effects to solve specific classes of problems—such as D-wave’s computers—are alive and well. But an unlikely rival could challenge these specialized machines: computers built from purposely noisy parts.

This week at the IEEE International Electron Device Meeting (IEDM 2022), engineers unveiled several advances that bring a large-scale probabilistic computer closer to reality than ever before.

Quantum computers are unrivaled for any algorithm that relies on quantum’s complex amplitudes. “But for problems where the numbers are positive, sometimes called stochastic problems, probabilistic computing could be quite competitive,” says Supriyo Datta, professor of electrical and computer engineering at Purdue University and one of the pioneers of probabilistic computing.

Discusses the possibility of Femtotech and the technological possibilities it may unlock. Not long ago nanotechnology was a fringe topic; now it’s a flourishing engineering field, and fairly mainstream. For example, while writing this article, I happened to receive an email advertisement for the “Second World Conference on Nanomedicine and Drug Delivery,” in Kerala, India. It wasn’t so long ago that nanomedicine seemed merely a flicker in the eyes of Robert Freitas and a few other visionaries!

But nano is not as small as the world goes. A nanometer is 10–9 meters – the scale of atoms and molecules. A water molecule is a bit less than one nanometer long, and a germ is around a thousand nanometers across. On the other hand, a proton has a diameter of a couple femtometers – where a femtometer, at 10–15 meters, makes a nanometer seem positively gargantuan. Now that the viability of nanotech is widely accepted (in spite of some ongoing heated debates about the details), it’s time to ask: what about femtotech? Picotech or other technologies at the scales between nano and femto seem relatively uninteresting, because we don’t know any basic constituents of matter that exist at those scales. But femtotech, based on engineering structures from subatomic particles, makes perfect conceptual sense, though it’s certainly difficult given current technology.

Continue reading “Hugo de Garis — From Nanotech to Femtotech — There’s Plenty More Room at the Bottom” »

The software system competed against human coders in programming contests.

A novel system called AlphaCode uses artificial intelligence (AI) to create computer code, and has recently participated in programming competitions, using critical thinking, algorithms, and natural language comprehension. The AI system performed extremely well in competitions.

AlphaCode can create code quickly and efficiently

Continue reading “AlphaCode can solve complex problems and create code using AI” »