Kengo Kyogoku borrows about ¥122,000 ($1,035) per month in addition to a scholarship and a part-time job, because his mother can't afford to pay his college fees at the prestigious Waseda University in Tokyo. "The amount is huge," said Kyogoku, a sophomore of communications and computer engineering. "I get depressed when I think about it. I wonder if I will have to pay it back forever. But I have no choice."

Machine learning and artificial intelligence are all the rage today in venture capital circles. We've seen spectacular exits in the past few years, from Google absorbing Deepmind in 2014 for $500 million, to Twitter buying TellApart in 2015 for $533 million, and Intel swallowing Nervana in 2016 for $400 million. But these were all IT plays. Berkeley-based Lygos is engineering and designing microbes that convert low-cost sugar into high-value, specialty chemicals. Ultimately, the ability to design and optimize microbes, or program them, is becoming faster and cheaper than ever before.

Artificial intelligence is a concept that includes automatic or machine learning, so a first approximation to both terms places us already in a context of subordination that in no way implies inferiority. Despite their specificities, both are artificial intelligence systems, and as such they pursue a single purpose: the creation of devices or algorithms that omit or replace human being by emulating their cognitive functions. In this post we will see the approach and key applications that machine learning contributes as a distinctive element, against a general context of artificial intelligence that includes this and other areas. Artificial intelligence research, indeed, focuses on many different fields, among them machine learning or, for example, deep learning, a new area of investigation of this one. In addition, recent years, it has advanced in a surprising way, acquiring a protagonism that seems to endow it with a fictitious autonomy.

Teaching machines to read isn't the same as teaching a toddler. They don't have the nuances of culture, idioms, tone and other social cues to understand what you're writing about. Waterloo's Maluuba is leading the way in teaching machines to think, reason and communicate just like we do thanks to a growing research and learning lab in Montreal that is working on those types of common sense problems that could lead to the next breakthroughs in artificial intelligence. The company, founded in 2010 by University of Waterloo students at the school's Velocity program, took another step towards that last week by releasing two sophisticated natural language understanding data sets. Instead of guarding their data sets like a secret, they decided to share them to advance innovation in artificial intelligence research and facilitate future breakthroughs.

Many technology commentators got all excited a few months ago when Microsoft launched how-old.net, a website where users could upload a photo and the site would guess the age of the person in the picture. The service was a great way to showcase the opportunity that applying artificial intelligence to a problem set introduces. Insilico hopes to deliver a similar sort of an offering, but with a far more important purpose. Insilico Medicine is an organization focused on aging research. Headquartered at the Emerging Technology Centers at the Johns Hopkins University Eastern campus in Baltimore, it has R&D resources in Belgium, Poland, Russia and China employing 39 scientists worldwide.

Machine learning is being applied in recommendation engines, marketing automation, financial fraud detection, language translation, and text-to-speech applications. Apple recently announced that the iPhone 7 would use machine learning in its camera to recognize faces, imagery, and even the lighting in a room, making Apple the latest tech company to give primacy to its use of machine learning. But machine learning is no longer exclusive to digital companies: Businesses in every industry are utilizing this technology to improve processes. The NFL uses machine learning to gather deep insights into player movements, positions, and passes to reorganize play style. In the medical sector, machine learning analyzes patients and predicts the likelihood of their returning. Even hiring and talent management in most companies is now handled by algorithms that dig out desired characteristics and, hopefully, remove biases.

There are a number of machine learning models to choose from. We can use Linear Regression to predict a value, Logistic Regression to classify distinct outcomes, and Neural Networks to model non-linear behaviors. When we build these models, we always use a set of historical data to help our machine learning algorithms learn what is the relationship between a set of input features to a predicted output. But even if this model can accurately predict a value from historical data, how do we know it will work as well on new data? Or more plainly, how do we evaluate whether a machine learning model is actually "good"?

Stream water temperature is considered both a dominant factor in determining the longitudinal distribution pattern of aquatic biota and as a general metabolic indicator for the water body, since so many biological processes are temperature dependent. Moreover, the plunging depth of stream water, its associated pollutant load, and its potential impact on lake/reservoir ecology is dependent on water temperature. Lack of detailed datasets and knowledge on physical processes of the stream system limits the use of a phenomenological model to estimate stream temperature. Rather, empirical models have been used as viable alternatives. In this study, an empirical model (artificial neural networks (ANN)), a statistical model (multiple regression analysis (MRA)), and the chaotic non-linear dynamic algorithms (CNDA) were examined to predict the stream water temperature from the available solar radiation and air temperature.

Well, here's something you might want to prepare yourself for. By the time wedding bells are ringing your new son- or daughter-in-law could very well be a robot. That's what Dr. David Levy believes, at least. You might recognize Levy's name, either because of his chess-playing prowess or his decades-long involvement with artificial intelligence research. Levy, who happens to be a very good friend of computing pioneer Clive Sinclair, says that humans and robots will be tying the knot "before, not after, the year 2050."