This year, we have seen all the hype around AI Deep Learning. With recent innovations, deep learning demonstrated its usefulness in performing tasks such as image recognition, voice recognition, price forecasting, across many industries. It's easy to overestimate deep learning's capabilities and pretend it's the magic bullet that will allow AI to obtain General Intelligence. In truth, we are still far away from that. However, deep learning has a relatively unknown partner: Reinforcement Learning.

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The quest to understand what's happening inside the minds and brains of animals has taken neuroscientists down many surprising paths: from peering directly into living brains, to controlling neurons with bursts of light, to building intricate contraptions and virtual reality environments. In 2013, it took the neurobiologist Bob Datta and his colleagues at Harvard Medical School to a Best Buy down the street from their lab. At the electronics store, they found what they needed: an Xbox Kinect, a gaming device that senses a player's motions. The scientists wanted to monitor in exhaustive detail the body movements of the mice they were studying, but none of the usual laboratory techniques seemed up to the task. So Datta's group turned to the toy, using it to collect three-dimensional motor information from the animals as they explored their environment.

AI algorithm has been able to forecast the movement of the Apple stock price (AAPL) with an accuracy of up to 96%. The I Know First predictive AI algorithm has been able to forecast the movement of the Apple stock price (AAPL) with an accuracy of up to 96%, says an evaluation report released by the Tel Aviv-based company on July 23 2019. The assessment covers the forecasts for the period from January 1 until July 23 2019, with time horizons ranging from 3 days to 3 months. The algorithm, which was initially designed to help investors identify undervalued stocks, has demonstrated a consistent accuracy rate above 65%. Three-month forecasts have proven to be the most accurate ones, with demonstrating precision of nearly 100%.

The struggle is real, as they say, when it comes to getting machine learning into production. That was one of the big messages of 2019 as enterprises completed successful machine learning pilots but found it much more difficult to put their efforts into production let alone scale them across the whole organization. Even though everyone seems to be working on it, machine learning deployed in production grew at a slower rate between 2018 and 2019, according to Gartner's annual CIO survey. Gartner VP analyst and fellow Rita Sallam is forecasting that enterprises that may have experimented with open source technologies in their pilot efforts will likely turn to commercial artificial intelligence and machine learning platforms to pull together those open source efforts into their enterprise deployment efforts. What's more, enterprises are likely to turn to the AI and ML platforms offered by public cloud providers such as Amazon AWS, Google, and Microsoft Azure.

You will learn ALL (almost all) that's out there with this investigative research. These will be discussed separately ... plus muchmore ... but for now let's take a test ride. See if you're up to the rest of this investigative research. Warning ‒ you haven't seen anything yet. New developments happen almost daily (wait until you study the section on Technological Singularity ... you might want a relaxant!)

The retail sector is the poster child for the use of artificial intelligence. Self-driving delivery robots, automated warehouses, intelligent chatbots, personalized recommendations, and deep supply chain analytics have been making significant impact on the bottom line -- if you're Amazon.com. Other retailers, however, are struggling to adapt. In fact, only 19 percent of large retailers in the U.S., UK, Canada and Europe have deployed AI and are using it in production, according to Gartner. Get the latest insights with our CIO Daily newsletter.

Early BA tools proved their worth by analyzing historical data to determine what happened, such as whether sales declined in a certain market, or which types of patients had higher-than-average hospital readmission rates. The latest generation of BA solutions adds predictive algorithms to ferret out additional insights -- ones that help organizations make better decisions about the future. As one example of data in action, universities can use next-generation BA tools such as IBM Cognos to identify which students are at a higher risk of dropping out. That analysis could explore whether reduced involvement with clubs, fraternities, sororities and other activities means weaker ties to the school. Some schools also are using involvement data to predict which alumni are more likely to be receptive to donation appeals and to identify the most effective channels for reaching them.

"The essence of general intelligence is the capacity to imagine oneself" -- myself Recognize that to gain the perspective that comes from seeing things through another's eyes, you must suspend judgement for a time -- only by empathizing can you properly evaluate another point of view. Moravec's paradox is the observation made by many AI researchers that high-level reasoning requires less computation than low-level unconscious cognition. This is an empirical observation that goes against the notion that greater computational capability leads to more intelligent systems. However, we have today computer systems that have super-human symbolic reasoning capabilities. Nobody is going to argue that a man with an abacus, a chess grandmaster or a champion Jeopardy player has any chance at besting a computer.

The objective of this usecase is to predict the major risk factors that may result in the development of heart diseases in the future by using machine learning algorithms. Heart diseases are one of the main causes of death worldwide. Many factors like age, family history, high BP, high cholesterol levels, unhealthy lifestyles, lack of physical activity, etc can be attributed to the increasing cases of cardiovascular diseases. While some of these factors can be controlled, some others like age, hereditary, etc cannot be controlled by individuals. These factors are not constant among the individuals and keep varying.