Lifeboat Foundation

Scientists have been dreaming about nuclear fusion for decades. Microsoft thinks the technology is nearly ready to plug into the grid.

Microsoft just signed a jaw-dropping agreement to purchase electricity from a nuclear fusion generator. Nuclear fusion, often called the Holy Grail of energy, is a potentially limitless source of clean energy that scientists have been chasing for the better part of a century.

Continue reading “Microsoft just made a huge, far-from-certain bet on nuclear fusion” »

At its I/O developer conference, the search giant needs to rethink its AI strategy if it wants to catch Microsoft. The missing element? Experimentation.

Google has had a rough six months. Since ChatGPT launched last November — followed by the new Bing in February and GPT-4 in March — the company has failed to establish its AI credentials. Its own offering, the “experimental” chatbot Bard, compares poorly to rivals, and insider reports have portrayed a company in panic and disarray. Today, at its annual I/O conference, the company needs to convince the public (and shareholders) that it has a meaningful response. But to do that, it needs a new playbook.

AI outputs are increasingly defining the cultural moment — just not Google’s.

Continue reading “Google needs to stop throwing good money after Bard” »

The demo is clever, questionably real, and prompts a lot of questions about how this device will actually work.

Buzz has been building around the secretive tech startup Humane for over a year, and now the company is finally offering a look at what it’s been building. At TED last month, Humane co-founder Imran Chaudhri gave a demonstration of the AI-powered wearable the company is building as a replacement for smartphones. Bits of the video leaked online after the event, but the full video is now available to watch.

The device appears to be a small black puck that slips into your breast pocket, with a camera, projector, and speaker sticking out the top.

Continue reading “Humane’s new wearable AI demo is wild to watch — and we have lots of questions” »

Mandopop singer Stefanie Sun has gone viral on Bilibili, China’s largest user-generated video streaming site. But the sudden revival of interest in Sun, who hasn’t released an album since 2017, comes not from the artist having another moment of genius.

The songs that have attracted millions of views on Bilibili feature Sun’s voice cloned by artificial intelligence, raising questions about copyright protection.

Reminiscing on the golden age of Mandarin pop music, tech-savvy Chinese internet users took the liberty of mimicking Sun’s voice using singing voice conversion, a deep learning method that lets a user deliver one person’s singing in another person’s voice, and swap it into a compilation of Mandpop classics.

An innovative method decodes and finds hidden relationships in jointly recorded neural and behavioural data.

Summary: Researchers proposed the need for a legal framework to guide the conversation on whether or not human brain organoids can be considered people.

Brain organoids are grown from stem cells in a lab, mimicking the growth and structure of real brains. However, they do not fulfill the requirements to be considered natural persons, according to the researchers.

The study explores the potential juridical personhood of human brain organoids, and whether they can be considered legal entities.

Ktsimage/iStock.

So does that mean the internet will also crash by 2026? Well, it won’t if tech companies start using synthetic DNA instead of hard drives to store their data. You may not believe it, but according to Greef and his team, DNA strands can store large amounts of digital data, and in many ways, they have more advantages over modern-day data centers.

Amid all the hype and hysteria about ChatGPT, Bard, and other generative large language models (LLMs), it’s worth taking a step back to look at the gamut of AI algorithms and their uses. After all, many “traditional” machine learning algorithms have been solving important problems for decades—and they’re still going strong. Why should LLMs get all the attention?

Before we dive in, recall that machine learning is a class of methods for automatically creating predictive models from data. Machine learning algorithms are the engines of machine learning, meaning it is the algorithms that turn a data set into a model. Which kind of algorithm works best (supervised, unsupervised, classification, regression, etc.) depends on the kind of problem you’re solving, the computing resources available, and the nature of the data.

In the next section, I’ll briefly survey the different kinds of machine learning and the different kinds of machine learning models. Then I’ll discuss 14 of the most commonly used machine learning and deep learning algorithms, and explain how those algorithms relate to the creation of models for prediction, classification, image processing, language processing, game-playing and robotics, and generative AI.

Quantum dots in semiconductors such as silicon or gallium arsenide have long been considered hot candidates for hosting quantum bits in future quantum processors. Scientists at Forschungszentrum Jülich and RWTH Aachen University have now shown that bilayer graphene has even more to offer here than other materials.

The double quantum dots they have created are characterized by a nearly perfect electron-hole-symmetry that allows a robust read-out mechanism—one of the necessary criteria for quantum computing. The results were published in Nature.

The development of robust semiconductor spin qubits could help the realization of large-scale quantum computers in the future. However, current quantum dot based qubit systems are still in their infancy. In 2022, researchers at QuTech in the Netherlands were able to create 6 silicon-based spin qubits for the first time. With graphene, there is still a long way to go. The material, which was first isolated in 2004, is highly attractive to many scientists. But the realization of the first quantum bit has yet to come.