Last December, the world ushered in a new era of popular music: human and artificial intelligence (A.I.) collaboration. Musical eras are often defined by their dominant modes of production -- analog, electronic, digital -- each bringing about new styles and ways of listening. This era is marked by the release of the first A.I.-human collaborated album, Hello World, by the music collaborative Skygge. Skygge, led by composer and producer Benoît Carré and musician and tech researcher François Pachet, translates to "shadow" in Danish and was inspired by the Hans Christian Andersen story of the same name. We now know that algorithms can learn human bias, but can they also create highly creative and emotionally engaging music?

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. After all, decisions and choices previously made by humans are being increasingly delegated to algorithms, which can advise, and decide, how data is interpreted and what actions should result.

When in industry more than 50% of new roles are driven towards a specific skill set and when projections from various recruiting companies shows the world being short of certain skilled people and employers are scrambling to find certain type of resources in the market and are willing to pay a premium to get them on-board then it is a clear sign that we're in a hype cycle. The skill here is Data Science and resources Data Scientists. Those who have been in the industry long enough can recognize this. A decade back industry was going crazy for a similar skill known as Business Analysts, who are now found dime a dozen in the market (apologies if I've hurt someone's sentiments, but you can't escape the truth). I know certain organizations where the certain preference is being given to Data Modelers, Data Analysts & data Scientists instead of sitting Business Analysts.

Say you're staring at the image of a small circle in the center of a larger circle: The larger one looks green, but the smaller one appears gray. Except your friend looks at the same image and sees another green circle. So is it green or gray? It can be maddening and fun to try to decipher what is real and what is not. In this instance, your brain is processing a type of optical illusion, a phenomenon where your visual perception is shaped by the surrounding context of what you are looking at.

Keeping in mind the overall trustworthiness of the website itself--and checking its Wikipedia page, if it has one--is a good step for regular people, too. For example, in August, Facebook and a cybersecurity firm announced they'd uncovered "inauthentic" news coming out of Iran. One of the websites associated with Iran was called the Liberty Front Press; they called themselves "independent" but appeared to actually be pro-Iran. And tellingly, the site does not appear to have a Wikipedia page. Of course, the MIT research group aren't the only ones using AI to analyze language like this: a Google-made AI system called Jigsaw automatically scores the toxicity of reader comments, and Facebook has turned to AI to help augment its efforts to keep hate speech at bay in Myanmar.

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.