One day in 2005, a mother in Evanston, Ill., joined Flickr. Then she more or less forgot her account existed. Years later, their faces are in a database that's used to test and train some of the most sophisticated artificial intelligence systems in the world. The pictures of Chloe and Jasper Papa as kids are typically goofy fare: grinning with their parents; sticking their tongues out; costumed for Halloween. None of them could have foreseen that 14 years later, those images would reside in an unprecedentedly huge facial-recognition database called MegaFace.

The Rutgers-led study, published in the journal Genome Medicine, used artificial intelligence to examine genetic signatures of Crohn's in 111 people. The method revealed previously undiscovered genes linked to the disease, and accurately predicted whether thousands of other people had the disease. "Our method is not a clinical diagnosis tool, but it generates interesting observations that need to be followed up," said senior author Yana Bromberg, an associate professor in the Department of Biochemistry and Microbiology at Rutgers University-New Brunswick. "Further experimental work could reveal the molecular reasons behind some forms of Crohn's disease and, potentially, lead to better treatments of the disease." Crohn's affects up to 780,000 people in the United States, the study notes.

Artificial intelligence (AI) is one of the key drivers of digital health. Digital health and AI applications in medicine and biology are emerging worldwide, not only in resource-rich but also resource-limited regions. AI predates to the mid-20th century, but the current wave of AI builds in part on machine learning (ML), big data, and algorithms that can learn from massive amounts of online user data from patients or healthy persons. There are lessons to be learned from AI applications in different medical specialties and across developed and resource-limited contexts. A case in point is congenital heart defects (CHDs) that continue to plague sub-Saharan Africa, which calls for innovative approaches to improve risk prediction and performance of the available diagnostics.

Surrounded by enemy fire, trapped in a valley between mountains and unable to use certain sensors, drones, fire-control and radar applications, a forward-positioned Army infantry unit suddenly finds itself with no radio, sensors, electronics... or GPS. Their communications are jammed, disabled and rendered useless, making them isolated and vulnerable to lethal air and ground attacks. Does this outnumbered infantry unit have any options with which to avoid destruction? How can they get air support or armored vehicle reinforcement? This very realistic possible threat scenario, increasingly becoming more ominous with modern technical advances, is precisely why the Army is moving quickly to modernize its arsenal of electronic weapons -- and further integrate them with cyber systems.

In this inteview, we discuss the internet of cows, how AI and machine learning are used on a dairy farm, the type of data that comes from every cow and how that data is used to drive decisions. We also discuss the black box of AI, and what's ahead for Artificial Intelligence and the future of mankind. Peter Schooff: Hello, this is Peter Schooff, editor of Data Decisioning and today I am very pleased to be joined by Bharath Sudarsan, the founder and director of artificial intelligence for SomaDetect. And that's pretty much what we're going to focus on in this podcast: exactly how AI and Internet of Things is contributing to companies at their operational level. In fact, I think I just recently saw that you guys referred to the IoT as the Internet of Cows, and I definitely want to hear about that.

Economic intersectionality can refer to the compounded effects of any combination of characteristics associated with economic disadvantage. In this article, we focus on differing levels of automation-based challenges for African American men and women of various ages and education levels in rural and urban America. We project that African Americans in the 13 community archetypes we analyzed may have a higher rate of job displacement than workers in other segments of the US population due to rising automation and gaining a smaller share of the net projected job growth between 2017 and 2030. By 2030, the employment outlook for African Americans--particularly men, younger workers (ages 18–35), and those without a college degree--may worsen dramatically. Additionally, we find that African Americans are geographically removed from future job growth centers and more likely to be concentrated in areas of job decline.

IBM this week launched a new supply chain service based on its blockchain platform and open-source software from recently-acquired Red Hat that allows developers and third-party apps to integrate legacy corporate data systems onto a distributed ledger. Through the use of open APIs, the new Sterling Supply Chain Suite allows distributors, manufacturers and retailers to integrate their own data and networks – as well as those of their suppliers – onto a Hyperledger-based blockchain to track and trace products and parts. Among the data that can be integrated are IoT sensor systems for real-time shipment position location. "This is the first move from IBM in what we anticipate to be a significant investment in the reinvention of supply chains by global organizations in the coming decades," an IBM spokesperson said via email. The "As a Service" model delivers services, not products; flexibility, not rigidity; and costs that align to business outcomes.

When I was in grad school it was called statistics. A few years later I told people I did machine learning and after seeing the confused look on their face I changed that to data science which excited them. More years passed, and without changing anything I do, I now practice AI, which seems scary to some people and somehow involves ML. We'll touch upon key concepts and see a little bit of code in action to get a sense of what is happening in ML, AI or whatever else we want to call the field.

Multi-agent systems have been used to predict online trading behaviors, disaster response protocols, and social structure modeling. They can help us understand dimensionality, discreteness, determinism, and episodicity. Syria is not the only model that can be constructed using MAAI. An even more contentious example is voting. While there has been a lot of talk about the potential role of deepfakes in the 2020 election in America--even though, currently, most videos are currently used to pornographically degrade women--MAAI might play an even bigger role.

Facebook today introduced Captum, a library for explaining decisions made by neural networks with deep learning framework PyTorch. Captum is designed to implement state of the art versions of AI models like Integrated Gradients, DeepLIFT, and Conductance. Captum allows researchers and developers to interpret decisions made in multimodal environments that combine, for example, text, images, and video, and allows them to compare results to existing models within the library. Developers can also use Captum to understand feature importance or perform a deep dive on neural networks to understand neuron and layer attributions. The tool will also launch with Captum Insights, a visualization tool for visual representations of Captum results.