Lifeboat Foundation

This past year, global attention has been focused on geo-strategic issues, such as the devastating war in Ukraine, which has dislocated many and caused immense suffering. Attention has also been focused on the recovery from the COVID pandemic, which was the overriding concern over the past three years. And finally, the economic destruction wrought by rapidly ramped interest rates which have targeted all sectors of the economy, particularly technology. But despite all this negativity, the business of building the future continues. There has been progress across major axes of computing, from visualization to AI and new types of processors (quantum).

With immense progress in technology, what might we look forward to in 2023?

Nearly a year after Sony and Honda shared plans to jointly make and sell electric vehicles, the two companies revealed a prototype under the brand name Afeela.

The four-door sedan was driven onstage at CES Wednesday as Kenichiro Yoshida, the CEO of Sony, talked through the company’s mobility philosophy, which prioritizes building vehicles that have autonomous capabilities and are transformed into “moving entertainment space[s].”

The first preorders of the Afeelas are scheduled in the first half of 2025, with sales to begin the same year, said Yoshida. Initial shipments will be delivered to customers in North America in the spring of 2026.

Google today is announcing a HD version of its vehicle mapping solution. Unlike Google Maps, Google’s HD map is not a consuming-facing application, but an additional layer of data that’s served to the vehicle’s L2+ or L3 assisted driving systems through Google Automotive Services.

The additional information sits on top of Google Maps’ data and delivers details such as precise lane makers and localization of objects (road signs) to help assisted driving vehicles orient themselves on the road. The driver will not be able to see or access the HD map or data directly. It’s not clear at this time if the driver will even know if the vehicle is using the HD mapping, though, presumably the vehicle’s assisted driving skills will be improved when it’s in use.

According to a Google spokesperson, the HD mapping is initially focused on high-traffic roads like freeways, but the spokesperson stopped short of saying exactly which cities or freeways. They said Google is working with automakers to determine where the HD map is most helpful.

Though it is a cornerstone of virtually every process that occurs in living organisms, the proper folding and transport of biological proteins is a notoriously difficult and time-consuming process to experimentally study.

In a new paper published in eLife, researchers in the School of Biological Sciences and the School of Computer Science have shown that AF2Complex may be able to lend a hand.

Building on the models of DeepMind’s AlphaFold 2, a machine learning tool able to predict the detailed three-dimensional structures of individual proteins, AF2Complex—short for AlphaFold 2 Complex—is a deep learning tool designed to predict the physical interactions of multiple proteins. With these predictions, AF2Complex is able to calculate which proteins are likely to interact with each other to form functional complexes in unprecedented detail.

Artificial Intelligence is the buzzword of the year with many big giants in almost every industry trying to explore this cutting-edge technology. Right from self-checkout cash registers to AI-based applications to analyse large data in real-time to advanced security check-ins at the airport, AI is just about everywhere.

Currently, the logistics industry is bloated with a number of challenges related to cost, efficiency, security, bureaucracy, and reliability. So, according to the experts, new age technologies like AI, machine learning, the blockchain, and big data are the only fix for the logistics sector which can improve the supply chain ecosystem right from purchase to internal exchanges like storage, auditing, and delivery.

AI is an underlying technology which can enhance the supplier selection, boost supplier relationship management, and more. When combined with big data analytics AI also helps in analysing the supplier related data such as on-time delivery performance, credit scoring, audits, evaluations etc. This helps in making valuable decisions based on actionable real-time insights.

This week our guest is business and technology reporter, Peter Ward. Earlier this year, Peter released his book The Price of Immortality: The Race to Live Forever, where he investigates the many movements and organizations that are seeking to extend human life, from the Church of Perpetual Life in Florida, to some of the biggest tech giants in Silicon Valley.

In this episode, we explore Peter’s findings, which takes us on a tour from cryonics to mind uploading, from supplements to gene editing, and much more. Along the way, we discuss the details of how one might actually achieve immortality, the details of senescent cells and telomeres, whether it’s better to live healthy than to live long, the scams and failures that seem to dominate the space, as well as the efforts that seem most promising.

Continue reading “The Failures and Opportunities of Immortality | Peter Ward, Feedback Loop, ep 75” »

Scientists at the recently opened SETI Post-Detection Hub at the University of St Andrews in Scotland have a daunting task ahead of them: figure out what…

One potential application: Enhancing the sensitivity of atomic magnetometers used to measure the alpha waves emitted by the human brain.

Scientists are increasingly seeking to discover more about quantum entanglement, which occurs when two or more systems are created or interact in such a manner that the quantum states of some cannot be described independently of the quantum states of the others. The systems are correlated, even when they are separated by a large distance. Interest in studying this kind of phenomenon is due to the significant potential for applications in encryption, communications, and quantum computing.

Performing computation using quantum-mechanical phenomena such as superposition and entanglement.

The first important generative models for images used an approach to artificial intelligence called a neural network — a program composed of many layers of computational units called artificial neurons. But even as the quality of their images got better, the models proved unreliable and hard to train. Meanwhile, a powerful generative model — created by a postdoctoral researcher with a passion for physics — lay dormant, until two graduate students made technical breakthroughs that brought the beast to life.

DALL·E 2 is such a beast. The key insight that makes DALL·E 2’s images possible — as well as those of its competitors Stable Diffusion and Imagen — comes from the world of physics. The system that underpins them, known as a diffusion model, is heavily inspired by nonequilibrium thermodynamics, which governs phenomena like the spread of fluids and gases. “There are a lot of techniques that were initially invented by physicists and now are very important in machine learning,” said Yang Song, a machine learning researcher at OpenAI.