The self-driving vehicle features a bubble-like dome that resembles the space car from The Jetsons.
The Asahi Kasei AKXY2 concept car is designed to envision a future of sustainable and community-focused transportation.
Continue reading “A new futuristic concept car is designed to foster personal connection” »
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.
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.
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.
The goal of this activity was to have fun & boost everyone’s imagination to the limit. Everyone was shocked by Dall. E 2’s creative scope & infinite possibilities.
As this session was interactive & thought-provoking, it turned the usual tiresome process of learning into an energetic experience.
For those unfamiliar with Dall. E 2, it is Open AI’s newest tool that helps generate images from text inputs in seconds. The name “Dall. E 2” is a combination of the Spanish artist Salvador “Dali” & Pixar’s “Wall-E”. Dall. E 2 uses GPT 3 (Third generation Generative Pre-trained transformer) which is Open AI’s newest software release.
– 3000+)
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Using radar commonly deployed to track speeders and fastballs, researchers have developed an automated system that will allow cars to peer around corners and spot oncoming traffic and pedestrians.
The system, easily integrated into today’s vehicles, uses Doppler radar to bounce radio waves off surfaces such as buildings and parked automobiles. The radar signal hits the surface at an angle, so its reflection rebounds off like a cue ball hitting the wall of a pool table. The signal goes on to strike objects hidden around the corner. Some of the radar signal bounces back to detectors mounted on the car, allowing the system to see objects around the corner and tell whether they are moving or stationary.
“This will enable cars to see occluded objects that today’s lidar and camera sensors cannot record, for example, allowing a self-driving vehicle to see around a dangerous intersection” said Felix Heide, an assistant professor of computer science at Princeton University and one of researchers. “The radar sensors are also relatively low-cost, especially compared to lidar sensors, and scale to mass production.”
