Create new proteins in minutes. Two years ago researchers cracked the code on using A.I. to predict the shape of proteins. Creating new proteins can be a critical scientific endeavor: In the past, humans have been able to make insulin analogs for diabetics and immune cells that fight cancer. But all of that is hard. Building a new protein requires determining how a sequence of amino acids will fold up into a final molecular structure, to figure out how the protein actually functions.

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Mariana Diaz Garcia, works with the UNICRI under the framework of the Knowledge Center Security through Research, Technology and Innovation (SIRIO). Extremist groups have used a variety of technologies to recruit members, spread their ideology, and plan and execute attacks. The internet has long been used by terrorists and other violent extremists as a communication and propaganda tool. They now exist across a variety of platforms and occupy different online ecosystems. Terrorist and violent extremist content continues to circulate on well-known sites like Facebook, Twitter, and Instagram despite ongoing content moderation efforts.

In December 2022 a team of researchers from the Johns Hopkins University has published a remarkable paper: SELF-INSTRUCT: Aligning Language Model with Self Generated Instructions. It describes a method that serves as an innovative approach to instruction-tuning of pretrained language models (LMs) by utilizing instructional signals from the model itself. Informally, it describes a pipeline that allows a Language Model to generate data that will serve to train the model itself. In a few words, the Self-instruct process is an iterative bootstrapping algorithm. It commences with a seed set of manually-written instructions, which is relatively small (175 in the paper). These instructions form the foundation for guiding the overall generation.

Summary: A new AI model that mimics the neural architecture and connections of the hippocampus is able to alter its synaptic connections as it moves a car-like virtual robot. HBP researchers at the Institute of Biophysics of the National Research Council (IBF-CNR) in Palermo, Italy, have mimicked the neuronal architecture and connections of the brain's hippocampus to develop a robotic platform capable of learning as humans do while the robot navigates around a space. The simulated hippocampus is able to alter its own synaptic connections as it moves a car-like virtual robot. Crucially, this means it needs to navigate to a specific destination only once before it is able to remember the path. This is a marked improvement over current autonomous navigation methods that rely on deep learning, and which have to calculate thousands of possible paths instead.

When it comes to making money with AI, there are several potential avenues for monetization: Consulting: Businesses that need help implementing GPT models or integrating them into their operations may be willing to pay for consulting services. Products: Companies can develop and sell software products that incorporate GPT models, such as chatbots or virtual assistants. Services: Companies can offer services that utilize GPT models, such as content generation or language translation. Licensing: Companies that have developed GPT models may choose to license their technology to other businesses for a fee. Research: Companies can also monetize AI research by publishing papers or presenting at conferences, which can lead to increased recognition and opportunities for consulting or licensing.

Abstract: There has been a longstanding belief that generation can facilitate a true understanding of visual data. In line with this, we revisit generatively pre-training visual representations in light of recent interest in denoising diffusion models. While directly pre-training with diffusion models does not produce strong representations, we condition diffusion models on masked input and formulate diffusion models as masked autoencoders (DiffMAE). Our approach is capable of (i) serving as a strong initialization for downstream recognition tasks, (ii) conducting high-quality image inpainting, and (iii) being effortlessly extended to video where it produces state-of-the-art classification accuracy. Abstract: Fully supervised models often require large amounts of labeled training data, which tends to be costly and hard to acquire.

A team of engineering and health researchers has developed a tool that improves the ability of electronic devices to detect when a human patient is coughing, which has applications in health monitoring. The new tool relies on an advanced artificial intelligence (AI) algorithm that helps the AI better identify uncertainty when faced with unexpected data in real-world situations. The paper, "Robust Cough Detection with Out-of-Distribution Detection," is published in the IEEE Journal of Biomedical and Health Informatics. "When AI is being trained to identify the sound of coughing, this is usually done with'clean' data--there is not a lot of background noise or confusing sounds," says Edgar Lobaton, corresponding author of a paper on the work and an associate professor of electrical and computer engineering at North Carolina State University. "But the real world is full of background noise and confusing sounds. So previous cough detection technologies often struggled with'false positives'--they would say that someone was coughing even if nobody was coughing. "We've developed an algorithm that helps us address this problem by allowing an AI to express uncertainty.

Abstract: he latent space model is one of the well-known methods for statistical inference of network data. While the model has been much studied for a single network, it has not attracted much attention to analyze collectively when multiple networks and their latent embeddings are present. We adopt a topology-based representation of latent space embeddings to learn over a population of network model fits, which allows us to compare networks of potentially varying sizes in an invariant manner to label permutation and rigid motion. This approach enables us to propose algorithms for clustering and multi-sample hypothesis tests by adopting well-established theories for Hilbert space-valued analysis. After the proposed method is validated via simulated examples, we apply the framework to analyze educational survey data from Korean innovative school reform.

"In recent years, the emergence of artificial intelligence (AI) has revolutionized various industries, including the world of art. With AI-powered algorithms and machine learning, artists are now exploring new frontiers in the realm of visual art." That was how ChatGPT, an AI chatbot, suggested we start this story. We asked it for an introduction to an article in which four artists and professors of the practice at SMFA at Tufts sat down to weigh the pros and cons of AI art generators. Contrary to ChatGPT's rosy view, they say it's early to call AI art a revolution, and they question just how meaningful those "new frontiers" will be.