In a world of smart machines that can drive cars, beat humans at chess, advise on medical diagnoses and perform a host of other tasks, imagine the next likely step as a smart machine as a people manager. As business investment in smart machines grows, "robobosses" will increasingly make workplace decisions that previously could only have been made by human managers. By 2018, more than three million workers globally will be supervised by robobosses, according to Frances Karamouzis, vice president and distinguished analyst at Gartner. These smart machine managers will look at data derived from worker performance in new ways because of their ability to dispassionately discover previously undetected relationships and correlations, test their hypotheses, and then elevate them to production status. "Supervisor duties are increasingly shifting toward monitoring worker accomplishments through measurements of performance that are directly tied to output and customer evaluation," explained Ms. Karamouzis.

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As you read this essay, you understand each word based on your understanding of previous words. You don't throw everything away and start thinking from scratch again. Traditional neural networks can't do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It's unclear how a traditional neural network could use its reasoning about previous events in the film to inform later ones. Recurrent neural networks address this issue.

Today on In Case You Missed It: A University of Washington robot hand has an algorithm in it that knows what works and what doesn't when handling things, and can improve itself over time. Samsung Brasil made a smart surfboard for a professional surfer that shows water conditions and incoming texts, and researchers hacked movies to include the visual style of art masters. Mass transit fiends will want to know how the hyperloop test in Nevada went. As always, please share any great tech or science videos you find by using the #ICYMI hashtag on Twitter for @mskerryd.

The amount of data produced every day grows tremendously in most real-life domains, including medical imaging, genomics, text categorisation, computational biology, and many others. Although it appears beneficial at the first glance (more data could mean more possibilities of extracting and revealing useful underlying knowledge), handling massively large datasets became a challenging issue and attracts research attention, especially in the era of big data. This big data revolution affected many research fields, including statistics, machine learning, parallel computing, and computer systems in general [1]. Storing and analysing the acquired historical information should allow predicting the label of an incoming (unseen) feature vector, containing some quantified features of a given data example. If the labels are categorical, then we are to tackle the classification task (it's regression otherwise).

When you get right down to it, developing vaccines is about data and luck. Scientists start with a set of variables--what drugs a virus responds to, how effectively, and for whom--and then it's a whole lot of trial and error until they stumble upon a cure. One of the most exciting possibilities in medical research right now is how technology like machine learning could help researchers rapidly process those enormous sets of data, more quickly leading to cures. This is already starting to happen: In a study published Wednesday in the journal Macromolecules, researchers from IBM and Singapore's Institute of Bioengineering and Nanotechnology reveal a breakthrough that could help prevent deadly virus infections. With the help of IBM super computer Watson, they hope their finding will soon make its way into vaccines.

MasterCard Inc. says new machine-learning technology has helped it quickly control three separate cyberattacks that targeted automated bank tellers, limiting the damage to about 100,000 each. The transaction-monitoring system, which also employs data visualization tools, caught the three attacks during the first two months of 2016, according to MasterCard. The company declined to identify the banks. The Safety Net system, rolled out globally late last year, analyzes more than 1.3 billion transactions per day involving MasterCard debit and credit accounts at banks, merchants and ATMs, using algorithms that assess customer behavior in real-time. In the three attacks this year, directed against two U.S. banks and one bank in South America, Safety Net identified anomalies such as large cash withdrawals or transactions outside the usual geographic area for a given account.

A fire has destroyed a 90-foot-long lifelike robotic dinosaur that was set to be part of an exhibit at a New Jersey theme park. Exhibit creator Guy Gsell tells The Record newspaper (http://bit.ly/1T90hJZ The Field Station: Dinosaurs exhibit is moving to the park this month after operating in Secaucus (sih-KAW'-kuhs) since 2012. Its creator says a welder was putting finishing touches on the Argentinosaurus when a spark started the blaze. The dinosaur was burned to its skeleton.

While CRISPR, nanobots and head transplants are making headlines as medical breakthroughs, a number of new technologies are also making progress tackling some of the toughest age-old diseases still plaguing millions of people in the poorest parts of the world. In low income countries, over 75% of the population dies before the age of 70 due to infectious diseases including HIV/AIDS, lung infections, tuberculosis, diarrheal diseases, malaria, and increasingly, cardiovascular diseases. Over a third of deaths in low income countries are among children under age 14 primarily due to pneumonia, diarrheal diseases, malaria and neonatal complications. In the developed world, those living in extreme poverty, such as homeless populations, also die on average at age 48. Over the last year, artificial intelligence, robotics and biotechnology have all generated a number of new solutions that have the potential to dramatically reduce these problems.

Let's suppose you've agreed to a rather odd travel program, where you're going to be suddenly transported to a randomly selected country, and your job is to figure out where you end up. So, here you are in some new country, and all countries are equally likely. You make a list of places and probabilities that you're in those places (all equally likely at about 1/200 for 200 countries). You look around and appear to be in a restaurant. Some countries have more restaurants (per capita/per land area) than others, so you decrease the odds that you're in Algeria or Sudan and increase the odds that you're in Singapore or other high-restaurant-density places. That is, you just multiply the probability that you were in a country with the probability of finding oneself in a restaurant in that country, given that one were already in the country, to obtain the new probability. After a few moments, the waitress brings you sushi, so you decrease the odds for Tajikistan and Paraguay and correspondingly increase the odds on Japan, Taiwan, and such places where sushi restaurants are relatively common. You pick up the chopsticks and try the sushi, discovering that it's excellent. Japan is now by far the most likely place, and though it's still possible that you're in the United States, it's not nearly as likely (sadly for the US). Those "probabilities" are getting really hard to read with all those zeros in front. All that matters is the relatively likelihood, so perhaps you scale that last column by the sum of the whole column. Now it's a probability again, and it looks something like this: Now that you're pretty sure it's Japan, you make a new list of places inside Japan to see if you can continue to narrow it down. You write out Fukuoka, Osaka, Nagoya, Hamamatsu, Tokyo, Sendai, Sapporo, etc., all equally likely (and maybe keep Taiwan too, just in case). Now the waitress brings unagi. You can get unagi anywhere, but it's much more common in Hamamatsu, so you increase the odds on Hamamatsu and slightly decrease the odds everywhere else. By continuing in this manner, you may eventually be able to find that you're eating at a delicious restaurant in Hamamatsu Station -- a rather lucky random draw.