Visual aesthetics are very personal, often subconscious, and hard to express. In a world with an overload of photographic content, a lot of time and effort is spent manually curating photographs, and it's often hard to separate the good images from the visual noise. The question we put forward at EyeEm is: can a machine learn personalized aesthetics embodied in a set of chosen photos, and recreate them in a different set? The incapacity to name is a good symptom of disturbance. Does this photograph draw your attention?

AMLG: Gary I'm super excited to have you today, thanks for coming on the show. We first met a few years ago in New York when I was running a tech meetup, the Singularity society, and you kindly came and spoke. You've been a professor of psychology at NYU for many years where your work has focused on language, biology, and the human mind. You've spent decades studying how children learn, and then in 2015 you founded this startup called Geometric Intelligence, focused on mining cognitive psychology for insights into building better machine learning techniques. Just this past December you were acquired by Uber to run their newly founded AI labs -- congratulations on that exit. So your algorithms offer an alternative approach to what is now a very popular branch of machine learning, called deep learning. Let's talk about deep learning -- it's a sexy buzzword which is thrown into about every startup pitch I see these days, and many corporate presentations, so I'm sure listeners have heard the term. What it really is is a rebranding of an old technique of using neural nets, which dates back to the 50s. Neural nets basically mimic the human neocortex, and by feeding in massive amounts, gigabytes of data and using tons of computational power, the algorithms are able to recognize patterns. Part of the reason why this technique is back in vogue is the combination of increasingly powerful computers combined with the massive training datasets that companies are building up. So there's been a flurry of activity, and the Googles and Facebooks of the world are throwing resources at the technique. As just one example, Facebook, using the over 400 billion photos people have uploaded, has built something called DeepFace, an image recognition tool that's now better than humans at recognizing whether two different images are of the same person. Gary you are well known as a critic of this technique, you've said that it's over-hyped. That there's some low hanging fruit that deep learning's good at -- specific narrow tasks like perception and categorization, and maybe beating humans at chess, but you felt that this deep learning mania was taking the field of AI in the wrong direction, that we're not making progress on cognition and strong AI. Or as you've put it, "we wanted Rosie the robot, and instead we got the roomba."

Move over, managers, there's a new boss in the office: artificial intelligence. The same technology that enables a navigation app to find the most efficient route to your destination or lets an online store recommend products based on past purchases is on the verge of transforming the office -- promising to remake how we look for job candidates, get the most out of workers and keep our best workers on the job. These applications aim to analyze a vast amount of data and search for patterns--broadening managers' options and helping them systematize processes that are often driven simply by instinct. And just like shopping sites, the AIs are designed to learn from experience to get an ever-better idea of what managers want. A company can provide a job description, and AI will collect and crunch data from a variety of sources to find people with the right talents, with experience to match--candidates who might never have thought of applying to the company, and whom the company might never have thought of seeking out.

Providing a broad but in-depth introduction to neural network and machine learning in a statistical framework, this book provides a single, comprehensive resource for study and further research. All the major popular neural network models and statistical learning approaches are covered with examples and exercises in every chapter to develop a practical working understanding of the content. Each of the twenty-five chapters includes state-of-the-art descriptions and important research results on the respective topics. The broad coverage includes the multilayer perceptron, the Hopfield network, associative memory models, clustering models and algorithms, the radial basis function network, recurrent neural networks, principal component analysis, nonnegative matrix factorization, independent component analysis, discriminant analysis, support vector machines, kernel methods, reinforcement learning, probabilistic and Bayesian networks, data fusion and ensemble learning, fuzzy sets and logic, neurofuzzy models, hardware implementations, and some machine learning topics. Applications to biometric/bioinformatics and data mining are also included.

This weekend's arrival of "Ghost in the Shell," the live-action adaptation of the landmark Japanese anime film, is being met with criticism from the Media Action Network for Asian Americans (MANAA), which is condemning what it calls the "whitewashed" casting of Scarlett Johansson in the lead role. The organization's complaint joins the backlash that erupted following the announcement in early 2015 that Johansson had signed on to star in Paramount and DreamWorks' version of Masamune Shirow's manga series that spawned the classic animated film. Many fans of the franchise called the casting of Johansson yet another example of Hollywood "whitewashing" because she plays an Asian heroine named Motoko Kusanagi. When asked about the ongoing flap, Johansson didn't agree with the critics' point of contention. "I think this character is living a very unique experience, in that she is a human brain in an entirely machinate body," Johansson said on ABC's "Good Morning America" earlier this week.

Things change pretty much on a daily basis in the world of SEO. Since the announcement of Google's AI machine learning algorithm – RankBrain – in 2015, one of the most discussed topics in SEO galleries is: With Google admitting RankBrain being one of the top three ranking factors, these discussions have become even more worthwhile. In past 3-4 months, we also saw a spike in the number of SERPed members asking the same question. And, multiple posts claiming 2017 as the year of AI and Voice Search, we think it is the right time to dive deeper to understand more about it. To get more clarity on this topic, we decided to go straight to the big guns and find out what they think about it. The responses from each expert are compiled below. Fasten your seat belts and get ready for an awesome ride. Albert Mora is the CEO and co-founder of Seolution, an SEO agency for Shopify e-commerce sites. He has been doing SEO from 1997 and has around 20 years of experience. Follow Albert on Twitter here. Since the beginning of the Internet, artificial intelligence has played a relevant role in the operation of search engines. Logically, the algorithms have been evolving, but the fundamental underlying principle remains the same: search engines want to deliver quality search results to the users. For this reason, if you want a long term sustainable SEO results, you must think about the users first, not about the search engines. Alex has more than 15 years of experience in Digital Marketing, and he is working online since 2002.

Above the Trend Line: machine learning industry rumor central, is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items such as people movements, funding news, financial results, industry alignments, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz. Our intent is to provide our readers a one-stop source of late-breaking news to help keep you abreast of this fast-paced ecosystem. We're working hard on your behalf with our extensive vendor network to give you all the latest happenings. Be sure to Tweet Above the Trend Line articles using the hashtag: #abovethetrendline.

Google's April Fools' Day jokes have come to be expected over the years, and this year they haven't missed a step. Open up Google Maps today and you'll be greeted with a pleasant surprise: the option to play Ms. Pac-Man on any set of streets in the world. While Google also offered Pac-Man back in 2015, given that gameplay is only available for one day at a time, the novelty of exploring the world through such a hungry, two-dimensional lens has far from worn off. I spent a couple of hours playing early this morning, and chose five of my favorite locations around the world to play, included here with direct links to the maps so you can try them out yourself. How could I leave Lombard Street off of this list?

The end of the world as we know it is near. And that's a good thing, according to many of the futurists who are predicting the imminent arrival of what's been called the technological singularity. The technological singularity is the idea that technological progress, particularly in artificial intelligence, will reach a tipping point to where machines are exponentially smarter than humans. It has been a hot topic of late. Well-known futurist and Google engineer Ray Kurzweil (co-founder and chancellor of Singularity University) reiterated his bold prediction at Austin's South by Southwest (SXSW) festival this month that machines will match human intelligence by 2029 (and has said previously the Singularity itself will occur by 2045).

Want to receive a weekly deep dive into all things auto, transportation, & logistics tech? Click here to subscribe to our auto tech newsletter. Momentum in auto tech is at an all-time high, with investors funding private startups in the field at a record pace. Of course, much of the buzz has revolved around autonomous driving software, with startups like Zoox seeing $200M funding rounds, tech corporates looking to capitalize, and major automakers working feverishly to catch up. Validating the reliability of fully autonomous vehicles will be no small feat, with RAND estimating that tens or hundreds of billions of test miles might have to be driven to properly gauge their safety. While many players are meeting this challenge head-on, a number of other startups are also developing autonomous tech for more focused applications.