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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.

The algorithm combines classical beam physics equations with machine-learning techniques to reduce the need for extensive data processing.

When the linear accelerator at SLAC National Accelerator Laboratory is operational, groups of approximately one billion electrons travel through metal pipes at almost the speed of light. These electron groups form the accelerator’s particle beam, which is utilized to investigate the atomic behavior of molecules, innovative materials, and numerous other subjects.

However, determining the actual appearance of a particle beam as it moves through an accelerator is challenging, leaving scientists with only a rough estimate of how the beam will behave during an experiment.

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Robert William, who was wrongfully identified by an AI algorithm and subsequently arrested, is suing the Detroit Police Department for the traumatizing experience he and his family had experienced.

Back in January of 2020, Robert Williams, a Black man, was arrested in front of his wife and children for a robbery committed at a Shinola store in 2018.

A new algorithm developed by Chinese company Baidu Research is dramatically faster than prior methods and shown to boost the antibody response of mRNA vaccines by up to 128 times.

Baidu Research is the research arm of Baidu, one of the largest technology companies in China. Established in 2014, it has since then been involved in various research activities such as natural language processing (NLP), machine learning, computer vision, robotics, and other areas of artificial intelligence.

A small team of chemists at the Russian Academy of Sciences, has found that metal atoms, not nanoparticles, play the key role in catalysts used in fine organic synthesis. In the study, reported in the Journal of the American Chemical Society, the group used multiple types of electron microscopy to track a region of a catalyst during a reaction to learn more about how it was proceeding.

Prior research has shown that there are two main methods for studying a reaction. The first is the most basic: As ingredients are added, the reaction is simply observed and/or measured. This can be facilitated through use of high-speed cameras. This approach will not work with nanoscale reactions, of course. In such cases, chemists use a second method: They attempt to capture the state of all the components before and after the reaction and then compare them to learn more about what happened.

This second approach leaves much to be desired, however, as there is no way to prove that the objects under study correspond with one another. In recent years, chemists have been working on a new approach: Following the action of a single particle during the reaction. This new method has proven to have merit but it has limitations as well—it also cannot be used for reactions that occur in the nanoworld. In this new effort, the researchers used multiple types of electron microscopy coupled with machine-learning algorithms.