Anyone have any thoughts on when it's best to use se ML v. Scorecards? This blog compares predicted probabilities vs. observed proportions at the feature/predictor level. The example finds that the XGBoost model is consistently under-estimating good credit risk across all bins of this predictor while the risk Scorecard demonstrates less discrepancy between the estimated and observed outcome.

This is related to a theorem that I have proved and its relation (or not) to an existing result. Essentially, I have shown that PAC-learning is undecidable in the Turing sense. The arxiv link to the paper is https://arxiv.org/abs/1808.06324 I am told that this is provable as a corollary of existing results. I was hinted that the fundamental theorem of statistical machine learning that relates the VC dimension and PAC-learning could be used to prove the undecidability of PAC-learning.

When Facebook chief executive Mark Zuckerberg promised Congress that AI would help solve the problem of fake news, he revealed little in the way of how. New research brings us one step closer to figuring that out. In an extensive study that will be presented at a conference later this month, researchers from MIT, Qatar Computing Research Institute (QCRI), and Sofia University in Bulgaria tested over 900 possible variables for predicting a media outlet's trustworthiness--probably the largest set ever proposed. The researchers then trained a machine-learning model on different combinations of the variables to see which would produce the most accurate results. The best model accurately labeled news outlets with "low," "medium," or "high" factuality just 65% of the time.

Nowadays, AI is all around us. Google exemplifies this by utilizing Machine Learning within Gmail to filter spam messages. Netflix and Amazon also utilize Deep Learning to predict your next movie or purchase. You might be hearing these terms appear more frequently and it would make sense due to the advent of rapid Big Data and analytics growth in the industrial and business workforce. You've hear these buzzwords consistently enough to peak your interest -- but what is the real meaning of Artificial Intelligence, Machine Learning, and Deep Learning? John McCarthy first coined the term'Artificial Intelligence' in 1956 but AI has turned out to be more famous today due to advance algorithms and enhancements in computing power and capacity.

It's how we've always made sense of the world: Our ancestors wouldn't have survived if they hadn't realized that plants tend to flourish after rainfall or that sabertooth tigers tended to eat them. But sometimes we're just a little too good at finding meaning in the noise, occasionally unable to separate real patterns from those of our own imagining. These days, your pattern matching skills will help you find Waldo, but they are also why celebrities' faces keep popping up on tortillas. At their most paranoid and byzantine, these pattern-matching misfires are called conspiracy theories: unfounded, deeply held alternative explanations for how things are--often invoking some shadowy, malevolent force masterminding the coverup. It's an unfounded, deeply held alternative explanations for how things are--often invoking some shadowy, malevolent force masterminding the coverup. Conspiracy theories thrive on the internet, but that's certainly not where they were born. The Flat Earth Society has existed since the 1800s, and people have been speculating about which people are secretly living or dead at least since 68 AD, when Romans weren't convinced their arsonist emperor Nero had actually committed suicide. But conspiracies and the digital world do mesh well, probably because they scratch similar itches in our not-quite-domesticated psyches.

While most TV watchers should be perfectly happy with a regular remote control, there's a certain nerdy pleasure in bringing a full-sized keyboard and trackpad into the living room. This combo is better for typing out search terms and passwords, of course, but it also helps with browsing the web on the big screen, it makes home-theater PCs considerably more usable, and it lets you use extensive shortcuts with media-center software such as Kodi. The $70 Logitech K600 keyboard is aimed specifically at these kinds of uses, with thumb-friendly TV navigation buttons, a built-in trackpad, and preset key mappings for various smart TV platforms. But for all its clever couch-friendly ideas, it's also missing some features that would make living-room use even easier. And no matter how useful it might be, using a keyboard from the couch will always feel a little bit silly.

Artificial intelligence (AI) tends to divide public opinion; some favor the developments made in tech, while others worry we've gone too far. Tony Emerson, head of global entertainment at Microsoft quashed fears and answered questions from The Drum editor Stephen Lepitak during the Edinburgh Television Festival, when he spoke about the company's decision to invest in entertainment. He admitted that the company wasn't actively involved in the sector, but revealed that it works closely with and provides services to entertainment organizations, which explains the launch of its Video Indexer tool. Video Indexer promises to bolster the entertainment industry by streamlining processes and freeing up manpower. Humans have felt continually threatened by the potential of AI, but Emerson clarified that technology will always act as an assistant to human creativity and will only help to relieve labor-intensive jobs.

The machines haven't taken over. However, they are seeping their way into our lives, affecting how we live, work and entertain ourselves. From voice-powered personal assistants like Siri and Alexa, to more underlying and fundamental technologies such as behavioral algorithms, suggestive searches and autonomously-powered self-driving vehicles boasting powerful predictive capabilities, there are several examples and applications of artificial intellgience in use today. However, the technology is still in its infancy. What many companies are calling A.I. today, aren't necessarily so.

Five Thirty Eight: Technology Is Biased Too. Growing up, one of my favorite movies was Steven Spielberg's Minority Report. I was fascinated by the idea that a crime could be prevented before it occurred. More interesting to me at the time was the futuristic role that'super intelligent' technology – something depicted as more sophisticated and advanced than humans – could play in doing this accurately. Recently, the role that pre-crime and artificial intelligence can play in our world has been explored in episodes of the popular Netflix TV show Black Mirror, focusing on the debate between free will and determinism. Working in counter-terrorism, I know that the use of artificial intelligence in the security space is fast becoming a reality.

Summary: Researchers have created a new deep learning system that can determine if a news outlet is accurate or biased based on only 150 articles published. The algorithm can also detect the political leanings of a news site. Researchers say fake news articles are more likely to use language that is hyperbolic, subjective and emotional.