A fully autonomous ship tracing the journey of the Mayflower is being built by a UK-based team, with help from tech firm IBM. The Mayflower Autonomous Ship, or MAS, will launch from Plymouth in the UK in September 2020. Its voyage will mark the 400th anniversary of the pilgrim ship which brought European settlers to America in 1620. IBM is providing artificial intelligence systems for the ship. The vessel will make its own decisions on its course and collision avoidance, and will even make expensive satellite phone calls back to base if it deems it necessary.

Tech giant Microsoft is teaming up with Nuance Communications to use technology to solve a big pain point for doctors--too much time spent on documenting and administrative tasks. The two companies are collaborating to use ambient technology combined with artificial intelligence, automation and cloud computing to create an exam room experience where the clinical documentation "writes itself," the companies said in a press release. Physician burnout continues to be a significant problem in healthcare. A recent study shows that primary care doctors now spend two hours on administrative tasks for every hour they're involved in direct patient care. Physicians reported one to two hours of after-hours work each night, mostly related to administrative tasks.

What if you could apply machine learning and other types of AI to the terabytes of transactional and sensor data being collected from the supply chain? The result could be a much more autonomous and effective form of supply chain analytics and, ultimately, a more responsive supply chain. In fact, there is a lot of interest in using AI and machine learning to enhance supply chain analytics, according to David Simchi-Levi, professor of engineering systems at MIT. Much of the focus in the supply chain is driven by the ability to integrate predictive and prescriptive analytics to, in essence, blend AI and optimization technologies. Specifically, organizations are using AI in two areas of the supply chain: to enhance predictive analytics to understand behaviors and for prescriptive analytics, where optimization technologies take input from machine learning and try to help decision-makers make better decisions, Simchi-Levi said.

MIT's Ray and Maria Stata Center (Building 32), known for its striking outward appearance, is also designed to foster collaboration among the people inside. Sitting in the famous building's amphitheater on a brisk fall day, Kristy Carpenter smiles as she speaks enthusiastically about how interdisciplinary efforts between the fields of computer science and molecular biology are helping accelerate the process of drug discovery and design. Carpenter, an MIT senior with a joint major in both subjects, said she didn't want to specialize in only one or the other -- it's the intersection between both disciplines, and the application of that work to improving human health, that she finds compelling. "For me, to be really fulfilled in my work as a scientist, I want to have some tangible impact," she says. Carpenter explains that artificial intelligence, which can help compute the combinations of compounds that would be better for a particular drug, can reduce trial-and-error time and ideally quicken the process of designing new medicines.

"The unpredictability of the outcomes of today's decision models often stems from an inability to properly capture and account for the uncertainty factors linked to these models' "behavior" in a business context. Decision intelligence provides a framework that brings together traditional and advanced techniques to design, model, align, execute, monitor and tune decision models." A growing number of companies are using DI to guide their decision-making including Google, IBM, Microsoft and many others across sectors. This intensive, one-day course will provide a hands-on, interactive, deep dive into DI for tehcnical and non-technical professionals. This is the only Decision Intelligence course currently available in New York City, being taught by one of the leading pioneers in the field, Dr. Lorien Pratt! "Curious to know what the psychology of avoiding lions on the savannah has in common with responsible AI leadership and the challenges of designing data warehouses?

Olympic skaters can launch, perform multiple aerial turns, and land gracefully, anticipating imperfections and reacting quickly to correct course. To make such elegant movements, the brain must have an internal model of the body to control, predict, and make almost-instantaneous adjustments to motor commands. So-called "internal models" are a fundamental concept in engineering and have long been suggested to underlie control of movement by the brain, but what about processes that occur in the absence of movement, such as contemplation, anticipation, planning? Using a novel combination of task design, data analysis, and modeling, MIT neuroscientist Mehrdad Jazayeri and colleagues now provide compelling evidence that the core elements of an internal model also control purely mental processes. "During my thesis, I realized that I'm interested not so much in how our senses react to sensory inputs, but instead in how my internal model of the world helps me make sense of those inputs," says Jazayeri, the Robert A. Swanson Career Development Professor of Life Sciences, a member of MIT's McGovern Institute for Brain Research, and the senior author of the study.

This article started out as an addendum to a chapter in our book, Data Visualization: A History of Visual Thinking and Graphic Communication (Friendly & Wainer, 2020). In this we claimed that much of the history of data visualization could be seen as combination of three forces: (1) important scientific problems of the day, (2) a developing abundance of data, and (3) the cognitive ability of some heroes in this history to conceive solutions to problems by visual imagination. In the book and what follows we make frequent reference to cognitive aspects of the visual understanding of phenomena and their expression in graphic displays: "inner vision", "graphic communication", "visual insight" are some of the terms we use. An early metaphor for this and an early title for our book was "A gleam in the mind's eye." We give some additional explanations and examples here. We also want to place this topic in a wider framework.

You know for sure AI has formally gone mainstream when it is a hot topic among government circles. To give a few instances -- the Central Board of Secondary Education (CBSE) has partnered with Microsoft to enable tech skills development, while the Indian army is seeking intuitive AI assistance in its weapon systems and surveillance programmes. And more recently, the Housing Ministry has ordered AI algorithms to cut down bookkeeping corruption incidences. But the most prominent development has been the proposal of Rs.7,500-crore plan by Niti Aayog to create an institutional framework for AI in the country. And while the public sector is poised to play a crucial role in the growth of India's future, the increasing adoption of AI in the corporate world is calling for experts to create responsible AI technologies.

Researchers at the San Diego Supercomputer Center at UC San Diego and UC San Diego School of Medicine have received two National Science Foundation (NSF) planning grants worth a combined $2 million under a new NSF initiative to invest in research collaborations between academia, industry, government and communities that enable capabilities beyond what is currently possible in either the private or public sectors. Called Convergence Accelerator awards, the first set of grants has been awarded to research teams, according to a recent NSF release. These projects will evaluate how employers can use sophisticated artificial intelligence tools to connect with the workers they need, while seeking ways to develop the future U.S. workforce with the universities that will educate people and the companies that will employ them. A total of 43 new awards totaling $39 million will support projects across the country. Both grants, which support one of NSF's'Big Ideas' called Harnessing the Data Revolution, are focused on the area of Open Knowledge Networks, which pool many types of information and ideas so they can be accessed and leveraged to create new understanding.

The Innovation is synonymous with risk-taking and organizations that create revolutionary products or technologies take on the greatest risk because they create new markets. Innovations create opportunities and are critical for the survival, economic growth, and success of a Business. Innovating helps in optimizing processes within an Organization. Companies that innovate set themselves in a different paradigm in terms of identifying opportunities or best methods to solve current problems. "Ideas are Dime a Dozen, People who implement them successfully are Priceless" To drive innovation and reap benefits, Leaders should be open-minded and collaborative. Feeling comfortable with uncertainty and manage changes are behavioural components enroute to innovate. Innovative leaders are curious and are optimistic since they dare to take risks. No one knows where innovation will bring the organization or individual. Innovation and Adaptability are the innate abilities of all Humans. From the Time of Birth, we innovate by learning, observing and doing.