For every great advance in artificial intelligence (AI), offering a chance for machines to truly think and be more useful than ever before, there is always a negative story supporting the fear that humans may one day be subservient to their creations. A recent online beauty pageant with 600,000 entrants using an AI judging process was blasted by critics when almost all the winners were white. Of course the AI system needs to learn from how someone defines beauty before it can start teaching itself from additional examples. In this case the vast majority of images that were initially used to define beauty were images of white people. Skin tone was not a factor in the judging process, but with the desirable attributes mostly defined as those more common in white people it's hardly a surprise that the AI system effectively learned how to be racist.

Whilst I can't fully refute either scenario, I can speculate on the potential for near-future political applications of AI which have the potential to be both disruptive and unintuitively desirable. Automatic, near-instant, ubiquitous fact checking UK fact-checking organisation Full Fact recently released a report that covered the state of various fact checking technologies in use and in development. It covered areas like overall existing semantic data standards in use and their challenges, and trends seem to be that natural language and contextual language recognition is going from strength to strength. The techniques in play include quickly responding to previously debunked claims, data mining statistics on demand and drawing confidence-based correlations from less reliable but plentiful data like news and social media reports. The convergent goal of these various projects appears to be moving fact checking from a post-hoc arm-chair analyst world, to a force that for example, in the middle of a debate or news report, is avalible to instantly challenge, rate or question any given factual statement within seconds of it being said.

I've been putting out more JavaScript courses, playing with new technologies, and I got really deep into React and Redux. Been having a lot of fun with those. Redux introduces a more functional approach to JavaScript state management, which is pretty amazing. I've enjoyed building it into my projects, and it has really simplified things like unit testing for your app's state management. I really appreciate the simplicity of Reducers in Redux. If you're not familiar with Redux, the reducers are regular reducer functions for the purposes of app state management. Using reducers for state management has been really amazing and has totally transformed the way I think about building apps, so I've been really happy with it. I've also played with Angular 2 just a little and I've been playing with TypeScript. I have mixed feelings about both of those so far. Angular 2, compared to React and Redux, feels like it has a lot more overhead and not a lot of benefit. Like writing unit tests for the views in Angular 2 is much more complicated than writing unit tests for things like Pure Components in React. Maybe as I use it more I'll start to "feel" it a little more. In TypeScript, I really like the IDE(Integrated development environment) type hinting that it gives you, and I like the Type Inference capability so you don't have to manually annotate everything, which is fantastic ... an amazing feature. I like that more than the type annotations available with Tern.js, but the problem is that since it needs to see how the types flow through the program, sometimes it will infer types that are a little too strict and you have to go in and manually loosen up the type annotations. Sometimes, it's really hard to do that especially if you use any kind of complicated functional programming techniques, which I tend to do once in a while.

Artificial intelligence interesting facts - is the topic for endless discussions. It is out of the discussion; we are futurists. And fantasies about the future, about the amazing things, such as "thinking computers" are always fascinating. But at the same time, there is something frightening about the issue. Artificial Intelligence interesting facts are on this subject as follows.

New York City's firefighters have a new ally that could help keep them safe: drones. They've begun testing a small, fire-engine red unmanned aerial vehicle that can take both standard and infrared images and videos. The 85,000 machine will send footage to commanders in real-time, giving them the information they need to quickly come up with the best possible response. The fire department typically sends out tactical personnel equipped with gadgets to take photos of a scene before conjuring up a course of action. However, fire department director Timothy E. Herlocker realized the advantages of deploying drones for the job after a gas explosion in 2014.

A recent study shows how artificial intelligence (machine learning) could help improve the accuracy of lung diseases diagnosis. The presence of artificial intelligence (AI) in the broad and complex field of medicine has continually improved over time. As a matter of fact, AI's potential to enhance the accuracy and efficiency when it comes to disease diagnostics is simply undeniable. Following the influences of artificial intelligence in the various subspecialties of medicine, a team of researchers developed an AI-based algorithm process that can learn and perform predictive data analysis. According to Tech 2, Belgium's University of Leuven professor and senior study author Wim Janssens explained that the AI algorithm process can provide experts more accurate lung diseases diagnosis by simulating physicians' complex reasoning in a "more standardized and objective way." Based on the study, researchers successively probed how the concept of machine learning could assist them in analyzing the complete lung function tests.

Prominent researcher Andrew Ng has stated that "AI is the new electricity. " Indeed, AI is already a silent force behind the scenes in many of our interactions with the digital world -- from the news in your feed to the next show to suggest after a binge session of Stranger Things (seriously, where did my eight hours go?). Tensorflow, Scikit), AI is on an unprecedented path towards democratization. However, democratization is the first step towards commoditization. So if AI does not automatically make a product amazing, what is it for?

Chanda Kochhar, managing director & chief executive officer, ICICI Bank, said by the end of FY17, the bank would more than double the number of transactions cleared by the bots. "Currently, about 10 per cent of our internal transactions are being carried out via the software robots and by the end of this year, we believe, this will go up to 20 per cent of our transactions. This has helped us in improving productivity and efficiency and will help us in handling larger volumes as we continue to grow." The lender started experimenting with software bots at the beginning of this financial year. Now, over a million banking transactions across 200 business processes are carried out by the technology on a daily basis.

By Shray Ambe This post was written as part of a class assignment from students who took a neuroethics course with Dr. Rommelfanger in Paris of Summer 2016. My name is Shray Ambe and I am a rising senior at Emory University. I am a Neuroscience and Behavioral Biology major who is pursuing a career in the medical field. Outside of the classroom, I am involved in organizing the booth for Emory's Center for The Study of Human Health at the Atlanta Science Festival Expo every year and also enjoy volunteering at the Emory Autism Center and the Radiology Department at Emory University Hospital. At the 2016 Neuroethics Network in Paris, France, bioethicist and philosopher John Harris gave a lecture titled "How Smart Do We Want Machines to Be?" During his lecture, Harris discussed the potential impacts of artificial intelligence (AI) and stated "it doesn't matter how smart they are; obviously the smarter the better."

While serving a nine-month stint at Google, Sergey Levine watched as the company's AlphaGo program defeated the world's best human player of the ancient Chinese game Go in March. Levine, a robotics specialist at the University of California, Berkeley, admired the sophisticated feat of machine learning but couldn't help focusing on a notable shortcoming of the powerful Go-playing algorithms. "They never picked up any of the pieces themselves," he jokes. One way that the creators of AlphaGo trained the program was by feeding 160,000 previous games of Go to a powerful algorithm called a neural network, much the way similar algorithms have been shown countless labeled pictures of cats and dogs until they learn to recognize the animals in unlabeled photos. So roboticists have instead turned to a different technique: the scientist gives a robot a goal, such as screwing a cap onto a bottle, but relies on the machine to figure out the specifics itself.