So says the famed quote from Shakespeare's The Tempest, alleging that we can look to what has already happened as an indication of what will happen next. This idea could be interpreted as being rather bleak; are we doomed to repeat the errors of the past until we correct them? We certainly do need to learn and re-learn life lessons--whether in our work, relationships, finances, health, or other areas--in order to grow as people. Zooming out, the same phenomenon exists on a much bigger scale--that of our collective human history. We like to think we're improving as a species, but haven't yet come close to doing away with the conflicts and injustices that plagued our ancestors. What might happen over the course of this year, and what information would we use to make educated guesses about it?

Experts from the private sector and leading research labs in the US and Europe have joined forces to create a toolkit for turning AI ethics principles into practice. The preprint paper, published last week, advocates paying people for finding risks of bias in artificial intelligence (AI) systems – adapting a model used to check the security of new computer systems, in which hackers are paid'bounties' for identifying weaknesses. The paper also proposes better linking independent third-party auditing operations and government policies to foster a market in regulatory systems, and suggests that governments increase funding for researchers in academia to verify performance claims made by industry. The 80-page paper, Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims, has been put together by AI specialists from 30 organisations including Google Brain, Intel, OpenAI, Stanford University and the Leverhulme Centre for the Future of Intelligence. "In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, there is a need to move beyond [ethics] principles to a focus on mechanisms for demonstrating responsible behaviour," the executive summary reads.

Panic buying has taken hold of many Americans who are stocking up on supplies and leaving shelves empty during the coronavirus. Now, the Pentagon is harnessing the power of artificial intelligence to help predict and address shortages of water, medicines, food, medical supplies and other essentials across the country. The predictive model pulls data from the Census Bureau, Medicare, hospitals and projects how the virus is spreading, along with a number of essential items at retailers to determine where scarcities may occur. Military groups can then use this information to move essential items to specific locations, while retailers can look at the data to predictively restock their shelves. Panic buying has taken hold of many Americans who are stocking up on supplies and leaving shelves empty during the coronavirus.

Frizzle (catchphrase: "Take chances, make mistakes, get messy!") In one episode, she reduces the bus to microscopic size and takes her charges into the bloodstream of an ill classmate. You see the kids swimming around in tiny wetsuits that they happened to bring along, watching white blood cells battle infectious invaders.Well, that's what I was thinking about yesterday. The venue wasn't a school, it was the White House press briefing room. Instead of an eccentric science nerd, it was President Donald Trump talking about injecting disinfectant or UV rays into people's bodies to clear away the coronavirus.

Artificial intelligence implies automation beyond the physical. It implies the automation of the tasks that recently took a living brain to finish things like discussion, data analysis, even driving. Also, eventually, AI is nothing new; computer scientists have been talking about and building it throughout recent decades. What's changed is the availability of cheap computing, advances in algorithm coding, and an abundance of newly available data. We've quite recently had this really good synergy as the innovation and the algorithms both developed simultaneously.

This paper presents a conceptual refinement of agent cognitive architecture inspired from the beliefs-desires-intentions (BDI) and the theory of planned behaviour (TPB) models, with an emphasis on different belief facets. This enables us to investigate the impact of personality and the way that an agent weights its internal beliefs and social sanctions on an agent's actions. The study also uses the concept of cognitive dissonance associated with the fairness of institutions to investigate the agents' behaviour. To showcase our model, we simulate two historical long-distance trading societies, namely Armenian merchants of New-Julfa and the English East India Company. The results demonstrate the importance of internal beliefs of agents as a pivotal aspect for following institutional rules.

With help from artificial intelligence, we can now detect patches of floating marine plastic from satellite data. The technique may eventually help environmental researchers better monitor and manage plastic waste in the ocean. Lauren Biermann at the Plymouth Marine Laboratory in the UK, and her colleagues have devised an AI capable of identifying sea plastic in imagery taken at varying wavelengths of light. They trained it with images from the European Space Agency's Sentinel-2 satellites, which record visual information at multiple wavelengths …

I've been thinking an awful lot about the 1989 Detroit Pistons over the past few days. Rick Mahorn, the starting power forward, can't make a goddamn layup. Isaiah Thomas is missing too many shots. Do I have anyone on my team who can stop Michael Jordan, who over the past four games is averaging 83.5 points per game against a squad once known as "The Bad Boys" because of its nasty defensive disposition? Yeah, it would probably be sorry enough if I was consumed by the actual Detroit team that last played an actual NBA game at the outset of the George H.W. Bush administration. In NBA 2K20, the popular virtual hoops game that for many fans has replaced real basketball during the coronavirus pandemic, you can play using one of many fine historic NBA squads.

April 22, 2020 -- Siemens Healthineers has received clearance from the Food and Drug Administration (FDA) for its AIDAN artificial intelligence technologies on the Biograph family of positron emission tomography/computed tomography (PET/CT) systems, which includes the Biograph Horizon, Biograph mCT, and Biograph Vision. AIDAN is built on a foundation of patient-focused bed design and proprietary AI deep-learning technology to enable four new features – FlowMotion AI, OncoFreeze AI, PET FAST Workflow AI, and Multiparametric PET Suite AI. Siemens Healthineers PET/CT systems with AIDAN offer enhanced protection against cyber threats via syngo Security – a security package for general regulatory security rules that enables compliance with the Health Insurance and Accountability Act (HIPAA). FlowMotion AI Because each patient's body habitus and presentation of disease is different, tailoring PET/CT protocols to produce the highest-quality diagnostic imaging information possible for each patient can be difficult and time-consuming. The standard one-size-fits-all protocol lacks personalization and is often of suboptimal quality.

Atos, a global leader in digital transformation, is taking part in the'Covid-19 Dataset Challenge', an international competition hosted on online community Kaggle[1] asking AI researchers to apply machine learning tools and techniques to help provide answers to key questions about the virus. Atos is deeply involved with a team of 10 experts working on the project, using their AI and automation skills to digest scientific articles and help the medical community to keep up to date with the latest publications on Covid-19. Literature reviews are essential to the research process. They aim to compare and contrast the thinking, ideologies and concepts on a particular subject area. In the specific context of Covid-19, they are essential to the research process as they provide deeper insights, leading to quicker discovery of any gaps in research.