Lifeboat Foundation

Year 2018 face_with_colon_three

Computers are shrinking rapidly. You can build a pretty capable little machine powered by a device like the Raspberry Pi, but that’s still huge compared with IBM’s latest machine. The company that started out selling massive mainframe computers has developed the world’s smallest computer (Opens in a new window). Each one is smaller than a grain of salt, but it packs more computing power than you’d expect.

The micro-computer is a complete system-on-a-chip (SoC) with a processor, memory, storage, and a communication module. The CPU contains several hundred thousand transistors, and IBM says it’s capable of performance on par with an x86 CPU from 1990. That’s not very fast compared with even the slowest modern computers, but it’s impressive for something you can’t see without a magnifying glass. It makes more sense when you look at the impressive developments in other SoC designs. The latest Qualcomm Snapdragon chips are about 1 square centimeter and have more processing power than supercomputers from the early 90s.

Continue reading “IBM Creates World’s Smallest Computer for Blockchain Technology” »

To build the supercomputer that powers OpenAI’s projects, Microsoft says it linked together thousands of Nvidia graphics processing units (GPUs) on its Azure cloud computing platform. In turn, this allowed OpenAI to train increasingly powerful models and “unlocked the AI capabilities” of tools like ChatGPT and Bing.

Scott Guthrie, Microsoft’s vice president of AI and cloud, said the company spent several hundreds of millions of dollars on the project, according to a statement given to Bloomberg. And while that may seem like a drop in the bucket for Microsoft, which recently extended its multiyear, multibillion-dollar investment in OpenAI, it certainly demonstrates that it’s willing to throw even more money at the AI space.

CAMBRIDGE, Mass. — Researchers at MIT have achieved a significant breakthrough in quantum computing, bringing the potential of these incredible thinking machines closer to realization. Quantum computers promise to handle calculations far too complex for current supercomputers, but many hurdles remain. A primary challenge is addressing computational errors faster than they arise.

In a nutshell, quantum computers find better and quicker ways to solve problems. Scientists believe quantum technology could solve extremely complex problems in seconds, while traditional supercomputers you see today could need months or even years to crack certain codes.

What makes these next generation supercomputers different from your everyday smartphone and laptop is how they process data. Quantum computers harness the properties of quantum physics to store data and perform their functions. While traditional computers use “bits” (either a 1 or a 0) to encode information on your devices, quantum technology uses “qubits.”

A team led by Northwestern University researchers has developed the first artificial intelligence (AI) to date that can intelligently design robots from scratch.

To test the new AI, the researchers gave the system a simple prompt: Design a robot that can walk across a flat surface. While it took nature billions of years to evolve the first walking species, the new algorithm compressed evolution to lightning speed—designing a successfully walking robot in mere seconds.

But the AI program is not just fast. It also runs on a lightweight personal computer and designs wholly novel structures from scratch. This stands in sharp contrast to other AI systems, which often require energy-hungry supercomputers and colossally large datasets. And even after crunching all that data, those systems are tethered to the constraints of human creativity—only mimicking humans’ past works without an ability to generate new ideas.

The advance brings quantum error correction a step closer to reality.

In the future, quantum computers may be able to solve problems that are far too complex for today’s most powerful supercomputers. To realize this promise, quantum versions of error correction codes must be able to account for computational errors faster than they occur.

However, today’s quantum computers are not yet robust enough to realize such error correction at commercially relevant scales.

While the Quantum Computer race is heating up with companies such as Atlantic Quantum Innovations joining the race, Google has published a plan to make Quantum Computers usable for everyday consumers by 2029. This is in hopes of revolutionizing Healthcare, finding room temperature superconductors, enabling with like artificial general intelligence through quantum AI and increasing supercomputer performance a million times. In this video, we’re exploring all of these secret projects and other Quantum Computing Companies.
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TIMESTAMPS:
00:00 CPU’s, GPU’s and now QPU’s.
01:14 Google’s Secret Project.
04:36 Other Quantum Computer Companies.
07:17 Fastest Quantum Computer today.
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#google #quantum #future

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00:00 — Introduction.
00:38 — Landauer Limit.
02:51 — Quantum Computing.
04:21 — Human Brain Power?
07:03 — Turing Complete Universal Computation?
10:07 — Diminishing Returns.
12:08 — Byzantine Generals Problem.
14:38 — Terminal Race Condition.
17:28 — Metastasis.
20:20 — Polymorphism.
21:45 — Optimal Intelligence.
23:45 — Darwinian Selection “Survival of the Fastest“
26:55 — Speed Chess Metaphor.
29:42 — Conclusion & Recap.

Continue reading “My predictions about Artificial Super Intelligence (ASI)” »

HBP researchers have employed highly advanced methods from computing, neuroinformatics and artificial intelligence in a truly integrative approach to understanding the brain as a multi-level system.

The EU-funded Human Brain Project (HBP) comes to an end in September and celebrates its successful conclusion today with a scientific symposium at Forschungszentrum Jülich (FZJ). The HBP was one of the first flagship projects and, with 155 cooperating institutions from 19 countries and a total budget of 607 million euros, one of the largest research projects in Europe. Forschungszentrum Jülich, with its world-leading brain research institute and the Jülich Supercomputing Centre, played an important role in the ten-year project.

“Understanding the complexity of the human brain and explaining its functionality are major challenges of brain research today”, says Astrid Lambrecht, Chair of the Board of Directors of Forschungszentrum Jülich. “The instruments of brain research have developed considerably in the last ten years. The Human Brain Project has been instrumental in driving this development — and not only gained new insights for brain research, but also provided important impulses for information technologies.”

Continue reading “Human Brain Project celebrates successful conclusion” »

Now, scientists have a mathematical model that closely matches how the human brain processes visual information.

Scientists have confirmed that human brains are naturally wired to perform advanced calculations, much like a high-powered computer, to make sense of the world through a process known as Bayesian inference.

In a study published in the journal Nature Communications, researchers from the University of Sydney, University of Queensland and University of Cambridge developed a specific mathematical model that closely matches how human brains work when it comes to reading vision. The model contained everything needed to carry out Bayesian inference.