Science fiction author Rick Wilber is best known for writing about baseball, including a series of alternate history stories about real-life catcher-turned-spy Moe Berg. Wilber's preoccupation with the game is understandable given his upbringing. "My father was a major league baseball player," Wilber says in Episode 412 of the Geek's Guide to the Galaxy podcast. "Then he was a minor league manager--AAA manager--for many years. So I grew up in dugouts and clubhouses of major league teams through the 1950s into the '60s."

We all enjoy sharing jokes with friends, hoping a witty one might elicit a smile--or maybe even a belly laugh. A lawyer opened the door of his BMW, when, suddenly, a car came along and hit the door, ripping it off completely. When the police arrived at the scene, the lawyer was complaining bitterly about the damage to his precious BMW. "Officer, look what they've done to my Beeeeemer!" he whined. "You lawyers are so materialistic, you make me sick!" retorted the officer.

Alfredo joined Element AI as a Research Engineer in the AI for Good lab in London, working on applications that enable NGOs and non-profits. He is one of the primary co-authors of the first technical report made in partnership with Amnesty International, on the large-scale study of online abuse against women on Twitter from crowd-sourced data. He's been a Machine Learning mentor at NASA's Frontier Development Program, helping teams apply AI for scientific space problems. More recently, he led the joint-research with Mila Montreal on Multi-Frame Super-Resolution, which was awarded by the European Space Agency for their top performance on the PROBA-V Super-Resolution challenge. His research interests lie in computer vision for satellite imagery, probabilistic modeling, and AI for Social Good.

The rapid development of artificial intelligence has the potential to remake how the federal government delivers a broad range of core services to citizens in a profound way. When implemented correctly, these technologies can help the government to render decisions faster, using better data, at a far lower cost in vital areas ranging from awarding disability benefits to granting patents to adjudicating immigration applications and healthcare insurance benefits. Pursuing AI transformation will also position the government to better respond to sudden demand surges for relief as a result of pandemics or other unforeseen future emergencies. Right now, AI strategies are at an early adoption stage in most administrative agencies, with just 45 percent of surveyed agencies having an AI use case according to a recent report, Government by Algorithm, issued by Stanford University and NYU to the Administrative Conference of the United States. Out of those agencies that have implemented AI, only 12 percent were considered to be highly sophisticated applications, according to Stanford's computer scientists.

Dascena, a machine learning diagnostic algorithm company that is targeting early disease intervention to improve patient care outcomes, announced it raised $50 million in Series B funding led by Frazier Healthcare Partners with participation from Longitude Capital, existing investor Euclidean Capital, and an undisclosed investor. This round of funding will enable Dascena to advance a suite of machine learning algorithms to inform patient care strategies and improve outcomes. And Dascena algorithms have been validated through eighteen peer-reviewed publications in several studies funded by the National Institutes of Health and the National Science Foundation. According to a randomized controlled trial of hospitalized patients in the intensive care unit (ICU), Dascena's InSight algorithm resulted in a 58% reduction in patient mortality and a 21% reduction in length of hospital stay. And data from this prospective study of InSight were published in the BMJ Open Respiratory Research in 2017.

Self-supervised learning could lead to the creation of AI that's more human-like in its reasoning, according to Turing Award winners Yoshua Bengio and Yann LeCun. Bengio, director at the Montreal Institute for Learning Algorithms, and LeCun, Facebook VP and chief AI scientist, spoke candidly about this and other research trends during a session at the International Conference on Learning Representation (ICLR) 2020, which took place online. Supervised learning entails training an AI model on a labeled data set, and LeCun thinks it'll play a diminishing role as self-supervised learning comes into wider use. Instead of relying on annotations, self-supervised learning algorithms generate labels from data by exposing relationships among the data's parts, a step believed to be critical to achieving human-level intelligence. "Most of what we learn as humans and most of what animals learn is in a self-supervised mode, not a reinforcement mode. It's basically observing the world and interacting with it a little bit, mostly by observation in a test-independent way," said LeCun.

I recently presented the plenary session at the ePhMRA annual conference in Warsaw. Covering how AI is transforming pharma sales and marketing, I provided examples Eularis had completed for pharma client projects. Several of the attendees sent me emails afterwards wanting to know more about the specific examples I gave, which were as varied as our client product needs. It was interesting to learn how few of these types of applications they were familiar with, and I thought the readers of this blog would want to know about them, too. I've written about many of these topics before, and I'm including those links at the end of each section in case you are interested in digging deeper into a specific topic.

Artificial intelligence is being increasingly used to classify employees, and there's a growing fear ... [ ] it might be used to classify people in other respects. These are only a small handful of the most well-known uses of artificial intelligence, yet there is one that, despite being on the margins for much of AI's recent history, is now threatening to grow significantly in prominence. This is AI's ability to classify and rank people, to separate them according to whether they're "good" or "bad" in relation to certain purposes. At the moment, Western civilization hasn't reached the point where AI-based systems are used en masse to categorize us according to whether we're likely to be "good" employees, "good" customers, "good" dates and "good" citizens. Nonetheless, all available indicators suggest that we're moving in this direction, and that this is regardless of whether Western nations consciously decide to construct the kinds of social credit system currently being developed by China.

Recent reports indicate Artificial Intelligence (AI) investment will reach an all-time high with even'traditional industries' jumping on board. It is also indicated that these industries will gain the greatest impact from the implementation of AI versus industries that are already highly data-driven or using advanced technology. The study suggests that, although AI technologies currently account for US$12.4 billion (around £9.40 billion) of global investment, this number will skyrocket in the next three years, with 40 percent of executives expected to increase their AI investments by 20 percent or more. Even, in current times as well we can see millions of dollars of investment in AI. Therefore, we have enlisted the top 10 AI investments and funding of April 2020.

Digital transformation is all about how companies decide to embrace new technology and change to optimize their business. Digital progress needs to be used for the best of companies, employees and customers. In the US alone, out of 10 companies 8 have started a digital transformation program. In this post, let's have a look at how the hospitality industry could be impacted. From the moment you start thinking about a hotel or a restaurant, AI powered algorithm can choose for you which one will be more suited for you.