Men and women have "distinct brain organization patterns" according to a new Stanford Medicine study. The findings were published in the "Proceedings of the National Academy of Sciences" journal on Tuesday. According to Stanford Medicine's statement on the study, it was conducted utilizing a new artificial intelligence model to scan around 1,500 brains. The AI was then instructed to determine whether the brain scan came from a man or a woman, predicting correctly with a 90% accuracy rate. "A key motivation for this study is that sex plays a crucial role in human brain development, in aging, and in the manifestation of psychiatric and neurological disorders," Vinod Menon, PhD, professor of psychiatry and behavioral sciences and director of the Stanford Cognitive and Systems Neuroscience Laboratory, said.

Men's brains tend to be larger than women's, which makes them difficult to compare Are men's and women's brains all that different? A new way of investigating this question has concluded that they are – but it takes artificial intelligence (AI) to distinguish between them. The question of whether we can measure differences between men's and women's brains has long been contentious, with previous research coming up with contradictory results. One problem is that men tend to have slightly larger brains than women, probably because they generally have larger bodies, and some previous studies that compared the size of different small regions of the brain failed to adjust for the overall brain volume. However, even doing so hasn't previously resulted in clear-cut findings.

"We coach our teams that success and innovation does not come from rebuilding something that somebody has already built, but instead from leveraging it and taking the next leap with additional features upon it to create high impact business outcomes," says Menon. At JPMorgan Chase, technologists are encouraged, where possible, to see the bigger picture and solve for the larger pattern rather than just the singular problem at hand. To reduce redundancies and automate tasks, Menon and her team focus on data and measurements that indicate where emerging technologies like AI and machine learning could enhance processes like onboarding or transaction processing at scale. AI/ML have become commonplace across many industries with private banking being no exception, says Menon. At a base level, AI/ML can extract data from documents, classify information, analyze data smartly and detect issues and outliers across a wide range of use cases.

Artificial intelligence and machine learning (AI/ML) are key to enabling drug discovery and development, and Pfizer is leading the biopharma industry into the next wave of innovation. The company is rapidly scaling up and recruiting talent for a collaborative effort intended to get transformative medicines to patients faster. The mandate is "uncompromising and extremely high-quality science," Sandeep Menon, chief scientific officer, AI digital sciences, SVP and head of early clinical development told BioSpace. The vision is three-fold: uncover disease biology with AI; use these insights to design the right molecules; determine the right patient population for clinical trial success. "We're building the next generation of tools to use across the preclinical and clinical development spectrum," said Jared Christensen, vice president and head of early clinical development, clinical AI/ML and quantitative sciences.

NASA inaugurated its 23rd class of new astronauts on Monday, which includes 10 individuals who are set to walk on the moon and maybe even Mars. Deemed the'Artemis Generation,' this group consists of several former US military, an ex-SpaceX medical director and a bioengineer who also participated in the 2020 Tokyo Olympics as a track cyclist. The name is a reference to NASA's Artemis program, which aims to send the first woman and the first person of color to moon as early as 2025. The astronaut candidates for 2021 are: Nichole Ayers, Marcos Berríos, Guaynabo, Christina Birch, Deniz Burnham, Luke Delaney, Andre Douglas, Jack Hathaway, Anil Menon, Christopher Williams and Jessica Wittner. This is NASA first new class in four years and the group is set to begin the two-year training process in January 2022.

Millennials expect everything at their fingertips-- including love. Their expectations regarding an ideal partner are evolving fast and so are social and cultural expectations. Keen to make their own choices based on the connection they share with a person, they are in no hurry to settle down or compromise until they feel comfortable with their choice of partner. "Around 67 per cent (of individuals) would rather find a meaningful relationship in the serendipity of a dating app than have friends and family arrange a set-up," says Sitara Menon, senior marketing manager of dating app OkCupid. With the proliferation of Internet, new ways and means are in place to find love.

Analytics India Magazine has published its annual list of the 50 Most Influential AI Leaders In India for the year 2021. The magazine has selected AI professionals on the basis of the expertise they hold in the field of AI and Data Science. The list of leading AI (Artificial Intelligence) professionals recognizes the exceptional work these professionals have accomplished over the last year.

Multiagent systems provide an ideal environment for the evaluation and analysis of real-world problems using reinforcement learning algorithms. Most traditional approaches to multiagent learning are affected by long training periods as well as high computational complexity. NEAT (NeuroEvolution of Augmenting Topologies) is a popular evolutionary strategy used to obtain the best performing neural network architecture often used to tackle optimization problems in the field of artificial intelligence. This paper utilizes the NEAT algorithm to achieve competitive multiagent learning on a modified pong game environment in an efficient manner. The competing agents abide by different rules while having similar observation space parameters. The proposed algorithm utilizes this property of the environment to define a singular neuroevolutionary procedure that obtains the optimal policy for all the agents. The compiled results indicate that the proposed implementation achieves ideal behaviour in a very short training period when compared to existing multiagent reinforcement learning models.

Hawkes processes are a popular means of modeling the event times of self-exciting phenomena, such as earthquake strikes or tweets on a topical subject. Classically, these models are fit to historical event time data via likelihood maximization. However, in many scenarios, the exact times of historical events are not recorded for either privacy (e.g., patient admittance to hospitals) or technical limitations (e.g., most transport data records the volume of vehicles passing loop detectors but not the individual times). The interval-censored setting denotes when only the aggregate counts of events at specific time intervals are observed. Fitting the parameters of interval-censored Hawkes processes requires designing new training objectives that do not rely on the exact event times. In this paper, we propose a model to estimate the parameters of a Hawkes process in interval-censored settings. Our model builds upon the existing Hawkes Intensity Process (HIP) of in several important directions. First, we observe that while HIP is formulated in terms of expected intensities, it is more natural to work instead with expected counts; further, one can express the latter as the solution to an integral equation closely related to the defining equation of HIP. Second, we show how a non-homogeneous Poisson approximation to the Hawkes process admits a tractable likelihood in the interval-censored setting; this approximation recovers the original HIP objective as a special case, and allows for the use of a broader class of Bregman divergences as loss function. Third, we explicate how to compute a tighter approximation to the ground truth in the likelihood. Finally, we show how our model can incorporate information about varying interval lengths. Experiments on synthetic and real-world data confirm our HIPPer model outperforms HIP and several other baselines on the task of interval-censored inference.

For years scientists have sought to create the ultimate cancer-screening test--one that can reliably detect a malignancy early, before tumor cells spread and when treatments are more effective. A new method reported today in Nature Communications brings researchers a step closer to that goal. By using a blood test, the international team was able to diagnose cancer long before symptoms appeared in nearly all the people it tested who went on to develop cancer. "What we showed is: up to four years before these people walk into the hospital, there are already signatures in their blood that show they have cancer," says Kun Zhang, a bioengineer at the University of California, San Diego, and a co-author of the study. "That's never been done before."