The first event will feature Yann LeCun, Facebook's director of AI research and a member of the New York University faculty. LeCun will inaugurate the series on Tuesday, February 20, 2018, from 10 to 11 am in Downtown Brooklyn, at NYU Tandon's Pfizer Auditorium, 5 MetroTech Center. Registration is free and open to all. His address, "Obstacles to Deep Learning and AI," will explore a new frontier: predictive models that capture the "common sense" exhibited by humans and animals, who often learn by observation and occasional action. LeCun's pioneering work in the application of neural networks to computer vision and other AI areas led to products and services deployed across most technology companies.

It's not hard to understand why people are worried about artificial intelligence taking their jobs. Seems like every month there's a new warning, a new report estimating just how many workers will be displaced. Not surprisingly, millennials are the most worried about losing their jobs: 34 percent fear getting laid off or seeing their job outsourced to a robot. Of the other age groups in the workforce, 27 percent of Generation X and baby boomers combined are worried about losing their jobs to AI. Are millennials the most worried because they grew up more engrossed in technology, they're closer to the issue, and they work the menial jobs that will be displaced? Or is it because they spend more time online reading about what AI will do to their jobs?

Of course as an introductory book, we can only leave it at that: an introductory book. For the mathematically trained, they may cure the curiosity this text generates with other texts designed with mathematical analysis in mind. For the enthusiast with less mathematical-background, or one who is not interested in the mathematics but simply the practice of Bayesian methods, this text should be sufficient and entertaining. The choice of PyMC as the probabilistic programming language is two-fold. As of this writing, there is currently no central resource for examples and explanations in the PyMC universe.

Every time a new tool or app is invented, a new word follows. So, let's tackle two that have been flying around our heads for the past few years: Machine Learning (ML) and Deep Learning (DL). Techies, business gurus, and marketers love these words and throw them around whether or not they understand the differences. Side Note: We know that this topic is old news, it's discussed continuously. Which is why we had to write about it, clearly it's not being fully understood because all the current content out there is either too simple or too complicated.

For as popular as the term "machine learning" has come to be, it's surprising to me how often it's equated to robots taking over the world. Phrases like "neural nets" and "deep learning" tap into our sense of fantasy, but when we jump from new tech to robot takeover, we miss the beauty and power of what machine learning actually is, and the groundbreaking new developments that are pushing industries forward. With this in mind, I sat down with our team's data scientist, Hillary Green-Lerman, to shed light on the buzzword. I asked the questions Wikipedia failed to fully answer: what is machine learning, who should be learning it and how soon can I visit Westworld? "Machine Learning is about using the data you already have to make predictions. This sounds really fancy, but most of the time, the'prediction' is really just a label," Hillary told us.

Spotlight is a well-implemented python framework for constructing a recommender system. It contains two major types of models, factorization model and sequence model. The former one makes use of the idea behind SVD, decomposing the utility matrix (the matrix that records the interaction between users and items) into two latent representation of user and item matrices, and feeding them into the network. The latter one is built with time-series model such as Long Short-term Memory (LSTM) and 1-D Convolutional Neural Networks (CNN). Since the backend of Spotlight is PyTorch, make sure you have installed proper version of PyTorch before using it.

HL7 is holding its annual conference on genomics February 20-21 in Washington, D.C. The theme this year is focused on the intersection of clinical genomics and artificial intelligence. To preview the meeting, I recently interviewed Grant Wood, a member of the HL7 Clinical Genomics Work Group and a senior strategist at Intermountain Healthcare's Clinical Genetics Institute, who is chairing the meeting. HCI: Why did HL7 choose to focus on the confluence of clinical genomics and AI this year? Wood: AI and machine learning are getting a lot of hype now.

"After having worked with machine learning as CTO of HistoWiz, a biotech company that used computer vision and machine learning to detect cancer cells in tissue samples, it soon became clear to us that one could use similar machine learning technology in other industries, including aquaculture. We looked at several industries, but growing up I had a family friend who was a professor of aquaculture at Cornell, and former business partners who had also invested in aquaculture. After making a trip to the AquaNor conference in Norway, I was able to connect with great Norwegians from the aquaculture industry, some of whom would later sign on full time. Having a network from Princeton and Silicon Valley, I was also able to quickly on-board people to help out with the engineering," says Bryton Shang, Founder and CEO of Aquabyte.

Can deep learning be done at the edge of the network, in real time, without a team of data scientists in attendance? That's the promise of Boston-based startup Neurala Inc. and its twist on deep learning, a technology it's dubbed lifelong deep neural networks or L-DNNs. L-DNNs are designed to "overcome the catastrophic forgetting" problem encountered with traditional deep neural nets, technology that uses a hierarchy of algorithms and layers of processing to produce an outcome. Deep neural nets learn sequentially. To teach a deep neural net to recognize a new object, data scientists have to start the entire training process over, which requires time and computational power via the cloud.

For centuries, pig-rearing in the country was predominantly a backyard occupation. But since the 1980s, China has swiftly modernized its pork industry to meet the demands of a newly-rich middle class. Now, half of the world's pigs -- some 700 million animals -- live and die in China, most in huge farms. And to help manage this porcine horde, the country's farmers are turning to a decidedly untraditional tool: artificial intelligence. Earlier this month, Chinese tech giant Alibaba signed a deal with pig farming corporation Dekon Group and pig feed manufacturer Tequ Group to develop and deploy AI-powered pig-tracking systems.