Lifeboat Foundation

During its ongoing Think 2023 conference, IBM today announced an end-to-end solution to prepare organisations to adopt quantum-safe cryptography. Called Quantum Safe technology, it is a set of tools and capabilities that integrates IBM’s deep security expertise. Quantum-safe cryptography is a technique to identify algorithms that are resistant to attacks by both classical and quantum computers.

Under Quantum Safe technology, IBM is offering three capabilities. First is the Quantum Safe Explorer to locate cryptographic assets, dependencies, and vulnerabilities and aggregate all potential risks in one central location. Next is the Quantum Safe Advisor which allows the creation of a cryptographic inventory to prioritise risks. Lastly, the Quantum Safe Remidiator lets organisations test quantum-safe remediation patterns and deploy quantum-safe solutions.

In addition, the company has also announced IBM Safe Roadmap, which will serve as the guide for industries to adopt quantum technology. IBM Quantum Safe Roadmap is the company’s first blueprint to help companies in dealing with anticipated cryptographic standards and requirements and protect systems from vulnerabilities.

An algorithm that allows more precise forecasts of the positions and velocities of a beam’s distribution of particles as it passes through an accelerator has been developed by researchers with the Department of Energy (DOE) and the University of Chicago.

Traveling at nearly light speed, the linear accelerator at the DOE’s SLAC National Accelerator Laboratory fires bursts of close to one billion electrons through long metallic pipes to generate its particle beam. Located in Menlo Park, California, the facility, originally called the Stanford Linear Accelerator Center, has used its 3.2-kilometer accelerator since its construction in 1962 to propel electrons to energies as great as 50 gigaelectronvolts (GeV).

The powerful particle beam generated by SLAC’s linear accelerator is used in the study of everything from innovative materials to the behavior of molecules on the atomic scale, despite how the beam itself remains somewhat mysterious since researchers have a hard time gauging its appearance as it passes through an accelerator.

Did humanity miss the party? Are SETI, the Drake Equation, and the Fermi Paradox all just artifacts of our ignorance about Advanced Life in the Universe? And if we are wrong, how would we know?

A new study focusing on black holes and their powerful effect on star formation suggests that we, as advanced life, might be relics from a bygone age in the Universe.

Universe Today readers are familiar with SETI, the Drake Equation, and the Fermi Paradox. All three are different ways that humanity grapples with its situation. They’re all related to the Great Question: Are We Alone? We ask these questions as if humanity woke up on this planet, looked around the neighbourhood, and wondered where everyone else was. Which is kind of what has happened.

Microsoft will also contribute its algorithm knowledge to make Builder.ai’s Natasha, an AI assistant, sound more human.

Microsoft Corporation has invested an undisclosed amount in Builder.ai, a no-code builder startup, as it looks to diversify its bets in the artificial intelligence (AI) game. Builder.ai lets users with no technical knowledge or experience in coding build their own apps and manage them.

Microsoft is already ahead in the AI game thanks to its partnership with OpenAI, the maker of the popular chatbot ChatGPT.

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Summary: Researchers have created a device that emulates the human eye’s ability to see color by using narrowband perovskite photodetectors and a neuromorphic algorithm.

The photodetectors, sensitive to red, green, and blue light, mimic our cone cells, while the neuromorphic algorithm simulates our neural network to process information into high-quality images. Unlike modern cameras that require external filters, this technology could improve resolution and reduce manufacturing costs.

The device also generates electricity as it absorbs light, potentially leading to battery-free camera technology.

Marketing and business. In marketing, businesses can use art and science to create compelling, memorable and emotionally resonant campaigns. By leveraging scientific knowledge of consumer behavior and psychology and using art to create visually striking and emotionally engaging advertisements, businesses can create marketing campaigns that are both effective and memorable.

Artificial intelligence (AI). AI design involves scientific research to develop algorithms and models that simulate human intelligence and decision-making. However, AI design requires artistic input to create user interfaces, visualizations and other interactive features that engage users and make AI more accessible. For example, an AI-powered virtual assistant may require an appealing visual interface to help users interact with the system more efficiently, such as graphic design, animation and other visual arts, to create a user-friendly pleasing interface. Moreover, AI-powered applications and systems can incorporate creative design and artistry elements to enhance functionality and appeal (generative AI algorithms can generate music or art or analyze and interpret cultural trends and patterns.)

The intersection of scientific research and art is exciting for generating novel business ideas. By bringing together different perspectives and skill sets, it is possible to create a more comprehensive and innovative approach to problem-solving. Whether through interdisciplinary research or using art as a tool for communication, the possibilities for collaboration are endless. The art of innovation is not about creating something out of nothing but finding new and unexpected ways to combine existing elements. By embracing the intersection of science and art, we can unlock a wealth of possibilities for the future of business by leading in new business ideas leveraging on new technologies, materials and creative solutions to problems. The integration of these domains enables the formation of groundbreaking and lucrative business concepts that can revolutionize industries and enhance individuals’ well-being.

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

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.

Optical AI chips have struggled to implement a crucial algorithm used to train neural networks–backpropagation. But in a new paper in the journal Science, a team from Stanford University has described the first ever implementation of the training approach on a photonic chip.