Remember the movie "The Imitation Game"? The tragic story of a brilliant man who decrypted secret German Enigma messages, indirectly shortened World War II, saved millions of lives, and was later charged for homosexuality, forced to undergo chemical treatment, and ended his life shortly after? The real Alan Turing accomplished many more brilliant miracles than this. He also published papers on theories of artificial intelligence (AI). In fact, the title "The Imitation Game" had little to do with the movie. It was a game he mentioned in one of his papers where humans will one day engineer a machine to imitate humans so well that a human on the other side of the room will be fooled he was communicating with another human. Turing was a pioneer in the field of computer science. Only after his death would he be known as the father of AI.
Last September, the world welcomed Juggalos (or Juggalettes, depending on which you prefer) to The Resistance when they marched on Washington en masse to protest the policies of the Trump administration. As if they weren't already doing the absolute most, the die-hard fans of the rap group Insane Clown Posse have become accidental heroes for people concerned about facial recognition tech: According to Twitter user @tahkion, a computer science blogger for WonderHowTo, Juggalo makeup outmatches the machine learning algorithms that govern facial recognition technology. In a series of follow-up tweets, @tahkion explained that facial recognition works by pinpointing the areas of contrast on a human face--for instance, where a nose is located, or where the chin becomes the neck. As it happens, juggalo makeup often involves applying black paint below the mouth, but above the chin. That makes facial recognition vulnerable to misidentifying the placement of the jaw.
It's no secret that in recent years the public's trust in the media has been upended with the staggering amount of "fake news" circulating the internet, but one news company, in particular, has set out to correct this issue by incorporating machine learning methods into the journalistic process. Knowhere collects a lot of data in order to write completely unbiased news: "We need a human and A.I. collaboration to scan the quantity of information that is published out there, and re-establish trust in what we're writing," explains Alexandre Elkrief, the president and co-founder of Knowhere. For Knowhere's team of journalists, the initial step to publishing any news story is to first decide what that story is. From there, they are able to quantify the stories they collect to learn which news reports are being written about most at any given time, on any given day. This allows them to decide what to write about and, subsequently, what to publish next.
In the last few years there have been numerous developments involving Artificial Intelligence (AI) and Machine Learning (ML), and the ways in which both are being used are constantly expanding. On the video editing front software and tech giants have been heavily investing in AI and ML, and as a result it has fueled a wide range of innovations. Several years ago IBM made headlines when it created a movie trailer using its Watson supercomputer. Using ML, Watson'learned' from other movie trailers and subsequently curated and identified video footage that could be used in the trailer for the horror film, Morgan. Since then AI and ML have been used frequently to curate videos for editing.
One of the most contentious aspects of AI is the meaning of'intelligence.' No one debates the meaning of the word'strength,' or belittles the idea that machines can be stronger than humans, or even tries to re-define mechanical strength to mean some mysterious physico-spiritual capability that is unique to humans. The debate around the meaning of intelligence when it crops up in any conversation on AI is extremely baffling - until we take into account the fragile psychology of humans. Somehow, we've convinced ourselves that cognitive abilities are the sole province of the human brain, while we grudgingly cede the physical realm to the machines. Every encroachment on human cognitive abilities is fiercely contested.
Could behavioural economics and machine learning help to better understand consumers' movie preferences? A team of researchers from the University of Cambridge, the University of West England, and the Alan Turing Institute dove deeper into this question, in a fascinating study that combines behavioural economics, business and AI. Marco Del Vecchio, Alexander Kharlamov, Glenn Parry, and Ganna Pogrebna used their diverse skillsets to develop tools that could help the media industry to better understand what content viewers really want to see. Currently, the motion picture, media and entertainment industry selects content offerings based on top-down decisions, typically informed by expertise, experience, surveys and focus groups. "Our main motivation was to understand whether and to what extent we can put viewer perceptions at the heart of the equation," the researchers said.
Neural networks show impressive results working with image data. Today, well-trained technology out-performs the human brain when it comes to classifying millions of images or recognizing patterns in the photos taken by Kepler telescope. As a result, AI-enabled image analysis and processing have made their way to diverse areas, far beyond photography or social media. EBay, for example, launched a computer vision feature that allows to search products using image instead of keywords or description. Opting for Image Search, a customer can simply take a picture of the product and use it to find a similar one in the marketplace.
Today, Artificial Intelligence (AI) and Machine Learning (ML) are two popular terms that tech companies cannot stop talking about. Everyone from Google and Microsoft, to Apple, Samsung and Amazon are going big on AI. Besides smartphones, smart speakers, voice assistants, apps, connected cars, security surveillance, healthcare and customer support are other areas where AI is used. Machine Learning (and deep learning) has been going on for years, and now with the data that exists, tech companies are putting it to the best use. On device machine learning combined with artificial intelligence can help in anticipating things in advance.
Don't fall behind when it comes to applying machine learning in your facility. Employing analytics with the mass of data collected in your facility can help you cut costs across the board. There are five steps key to getting the most out of machine learning, according to Ash Awad, Chief Market Officer at McKinstry, a design, build, operate and maintain firm. Follow this blueprint to optimize your building through data analytics. Related: Machine Learning 101: Is Predictive Analytics Possible in Your Facility?