Researchers hope the experiments will pave the way for people with paralysis to explore virtual worlds or more intuitively control electric wheelchairs in this one. Peter Janssen at KU Leuven in Belgium and colleagues implanted three rhesus macaque ( Macaca mulatta) monkeys with BCIs. Crucially, each animal got three implants, each consisting of 96 electrodes, positioned in the primary motor, dorsal and ventral premotor cortex. The first area is commonly used in BCI research and relates to physical movement, but the latter two are thought to be involved in planning movement in a higher, more abstract way. Electrical signals from the implants were then interpreted by an AI model and used to control VR avatars as the monkeys watched a 3D monitor.

Agent-based models (ABMs) are gaining increasing traction in several domains, due to their ability to represent complex systems that are not easily expressible with classical mathematical models. This expressivity and richness come at a cost: ABMs can typically be analyzed only through simulation, making their analysis challenging. Specifically, when studying the output of ABMs, the analyst is often confronted with practical questions such as: (i) how many independent replications should be run? (ii) how many initial time steps should be discarded as a warm-up? (iii) after the warm-up, how long should the model run? (iv) what are the right parameter values? Analysts usually resort to rules of thumb and experimentation, which lack statistical rigor. This is mainly because addressing these points takes time, and analysts prefer to spend their limited time improving the model. In this paper, we propose a methodology, drawing on the field of Statistical Model Checking, to automate the process and provide guarantees of statistical rigor for ABMs written in NetLogo, one of the most popular ABM platforms. We discuss MultiVeStA, a tool that dramatically reduces the time and human intervention needed to run statistically rigorous checks on ABM outputs, and introduce its integration with NetLogo. Using two ABMs from the NetLogo library, we showcase MultiVeStA's analysis capabilities for NetLogo ABMs, as well as a novel application to statistically rigorous calibration. Our tool-chain makes it immediate to perform statistical checks with NetLogo models, promoting more rigorous and reliable analyses of ABM outputs.

Our galaxy's centre may contain an exceptional cosmic spinning top – a black hole that seems to be spinning almost as fast as possible. Michael Janssen at Radboud University in the Netherlands and his colleagues were studying the black hole at the Milky Way's centre, Sagittarius A*, using the data gathered by a network of observatories collectively known as the Event Horizon Telescope (EHT). To deal with the complexity of the data, they turned to artificial intelligence. First, they used well-known mathematical models to simulate about a million black holes – which was itself a computational feat that required millions of hours of supercomputer time. Then they used these simulations to train a type of AI called a neural network, enabling it to determine a black hole's traits based on observational data. Finally, they fed the AI the data about Sagittarius A* that EHT had collected throughout 2017.

Big data and large-scale machine learning have had a profound impact on science and engineering, particularly in fields focused on forecasting and prediction. Yet, it is still not clear how we can use the superior pattern matching abilities of machine learning models for scientific discovery. This is because the goals of machine learning and science are generally not aligned. In addition to being accurate, scientific theories must also be causally consistent with the underlying physical process and allow for human analysis, reasoning, and manipulation to advance the field. In this paper, we present a case study on discovering a new symbolic model for oceanic rogue waves from data using causal analysis, deep learning, parsimony-guided model selection, and symbolic regression. We train an artificial neural network on causal features from an extensive dataset of observations from wave buoys, while selecting for predictive performance and causal invariance. We apply symbolic regression to distill this black-box model into a mathematical equation that retains the neural network's predictive capabilities, while allowing for interpretation in the context of existing wave theory. The resulting model reproduces known behavior, generates well-calibrated probabilities, and achieves better predictive scores on unseen data than current theory. This showcases how machine learning can facilitate inductive scientific discovery, and paves the way for more accurate rogue wave forecasting.

In support of the Alberta Technology and Innovation Strategy (ATIS) and in partnership with AltaML, a leading Canadian artificial intelligence company, the AI lab (named GovLab.ai) AltaML will work alongside government staff and post-secondary students and graduates as they work to develop smart products and models that leverage AI to solve complex, real-world problems. The lab will create opportunities for Alberta's public and private sectors to create intellectual property while accelerating Alberta's recovery and economic diversification. "Alberta is a world leader in AI and machine learning research. With the launch of GovLab.ai, Ultimately this will help Alberta's government offer better services, better results and better value to Albertans."

When starting a vaccine program, scientists generally have anecdotal understanding of the disease they're aiming to target. When Covid-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease. Scientists at Janssen Research & Development, developers of the Johnson & Johnson Covid-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company's research efforts into a potential vaccine. "Data science and machine learning can be used to augment scientific understanding of a disease," says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. "For Covid-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms."

When starting a vaccine program, scientists generally have anecdotal understanding of the disease they're aiming to target. When COVID-19 surfaced over a year ago, there were so many unknowns about the fast-moving virus that scientists had to act quickly and rely on new methods and techniques just to even begin understanding the basics of the disease. Scientists at Janssen Research & Development, developers of the Johnson & Johnson-Janssen COVID-19 vaccine, leveraged real-world data and, working with MIT researchers, applied artificial intelligence and machine learning to help guide the company's research efforts into a potential vaccine. "Data science and machine learning can be used to augment scientific understanding of a disease," says Najat Khan, chief data science officer and global head of strategy and operations for Janssen Research & Development. "For COVID-19, these tools became even more important because our knowledge was rather limited. There was no hypothesis at the time. We were developing an unbiased understanding of the disease based on real-world data using sophisticated AI/ML algorithms."

LOS ANGELES, CA - FEBRUARY 06: Fei-Fei Li speaks onstage during The 2018 MAKERS Conference at ... [ ] NeueHouse Hollywood on February 6, 2018 in Los Angeles, California. After the COVID-19 pandemic is over and the economy reopens, many students will resume work on their careers. But for many young people, their priorities are going to shift. After seeing the pain and suffering caused by a single invisible enemy, some will naturally prioritize biomedical research over other easier and more lucrative trades, like law and finance. And some will choose to pursue possibly the most impactful area, which lies on the borderline of computer science and biomedicine - Artificial Intelligence (AI) for drug discovery.

The novel coronavirus, also known as COVID-19 made its first appearance in December 2019 in China. It is believed to have originated from bats or pangolins in the meat markets that occupy the streets of Wuhan. Since then, the number of those infected has increased worldwide as the coronavirus spreads rapidly across the globe. Along with the growing fear rate in the United States with new travel advisories, and more deaths reported in the U.S. Pharmaceutical companies are gearing up to have a new vaccine on the market to combat the virus as the number of COVID-19 cases increase in the United States. The U.S. government is working diligently with pharmaceutical companies round the clock to produce a vaccine to treat the vastly growing coronavirus.

Looking to broaden its use of artificial intelligence to help lock down its R&D work, Johnson & Johnson's biotech unit Janssen has penned a new deal with AI specialist nference. The multiyear deal, financial details of which were not disclosed, will see the Big Pharma "leverage the nference artificial intelligence (AI) platform to create a unified data science-powered connective fabric across the Janssen R&D organization." In practical terms, this will see nference uncovering and prioritizing new targets and disease subsets as well as boosting effectiveness by matching the right patients to the right drugs. Further, nference will encourage efficiencies by identifying the optimal sites and investigators for pushing on with clinical trials across hospitals. To do this, nference said it has developed a "holistic data science kernel" that will synthesize some of the Janssen R&D databases with "real-time insights gleaned by the core nference AI platform from the world's public biomedical knowledge bases."