Machine learning explores the study and construction of algorithms that learn and make predictions based on data. In the field of machine learning, data scientists, who specialize in analyzing data, are responsible for writing and modifying such algorithms. Initially, a data scientist writes an algorithm based on a set of data features. This is generally an iterative process in which the data scientist explores different algorithms for predictive purpose. In this process, the amount of data and the number of features chosen for analysis may change.

I thought that this interview deserved a repost here at Data Science Central. It is with the man responsible for Artificial Intelligence at Facebook: the AI director Yann Lecun; and might be of interested and appeal to the knowlegeable of AI here. IEEE Spectrum: We read about Deep Learning in the news a lot these days. What's your least favorite definition of the term that you see in these stories? Yann LeCun: My least favorite description is, "It works just like the brain."

Google is looking to sell robotics firm Boston Dynamics after concluding that it's unlikely to produce any marketable robot in the next few years, according to people familiar with the company who spoke to Bloomberg News. Boston Dynamics has become famous for its impressive (and impressively creepy) videos featuring it torturing its robotic creations with pushes, kicks, shoves and heavy weights, to demonstrate their versatility and reliability. Those creations include the quadrupedal "Big Dog" robotic mule, its lighter and quieter sibling "Spot" and the bipedal robot "Atlas". But the firm, which was acquired by Google in 2013, has failed to live up to the aspirations placed upon it. Its machines, which were largely created in response to military contracts, haven't been easy to adapt for potential commercial sale. And while Google had promised that Boston Dynamics wouldn't take any further military projects, the company still suffered a blow when the US Marine Corps rejected the Big Dog robot, saying it was too noisy for practical use.

In today's business environment, online reputation management is increasingly important. The landscape is rapidly changing and technology is leading the way. I caught up with the CEO of www.reputation.com Q: What exactly is Online Reputation Management? Online Reputation Management is making sure a business's online reputation, including publicly available opinions and reviews about your business, matches the offline experience that people have with your business.

Yesterday, news emerged that Google was using a machine-learning artificial intelligence system called "RankBrain" to help sort through its search results. Wondering how that works and fits in with Google's overall ranking system? Here's what we know about RankBrain. The information covered below comes from three sources. First, the Bloomberg story that broke the news about RankBrain yesterday (see also our write-up of it).

Zipfian Academy has graduated more than 50 alumni, placing graduates into data science roles at Facebook, Twitter, Airbnb, Tesla, Uber, Square, Coursera, and many more Silicon Valley companies. Participants in our program come from backgrounds in engineering, data analysis, statistics, and occasionally professional poker. Here, we share an interview with Alex Mentch, a graduate from our Winter 2014 Cohort. Alex hails originally from Idaho, and studied electrical engineering at Washington University in St. Louis. Looking for a career transition into data science, Alex attended our Winter 2014 cohort where he built a search engine for state legislation.

New lip-reading technology developed at the University of East Anglia could help in solving crimes and provide communication assistance for people with hearing and speech impairments. The visual speech recognition technology, created by Dr Helen L. Bear and Prof Richard Harvey of UEA's School of Computing Sciences, can be applied "any place where the audio isn't good enough to determine what people are saying," Dr Bear said. Dr Bear, whose findings will be presented at the International Conference on Acoustics, Speech and Signal Processing (ICASSP) in Shanghai on March 25, said unique problems with determining speech arise when sound isn't available – such as on CCTV footage – or if the audio is inadequate and there aren't clues to give the context of a conversation. The sounds '/p/,' '/b/,' and '/m/' all look similar on the lips, but now the machine lip-reading classification technology can differentiate between the sounds for a more accurate translation. Dr Bear said: "We are still learning the science of visual speech and what it is people need to know to create a fool-proof recognition model for lip-reading, but this classification system improves upon previous lip-reading methods by using a novel training method for the classifiers. "Potentially, a robust lip-reading system could be applied in a number of situations, from criminal investigations to entertainment.

A novel written by artificial intelligence was a finalist in Japan's Hoshi Shinichi Literary Award. The award is named after Hoshi Shinichi, a Japanese science fiction author whose books include The Whimsical Robot and Greetings from Outer Space. The unique contest accepts submissions from humans and machines, and judges for the prize, now in its third year, weren't told which novels were written by humans and which were penned by human-AI teams. This year was the first time the committee received submissions written by AI programs. The AI's novel is called The Day A Computer Writes A Novel, or Konpyuta ga shosetsu wo kaku hi in Japanese.

The competition will take a while. It doesn't start until 2017, and won't pick a winner until early 2020. DARPA will even have to create a giant wireless testbed to see how the competitors fare in relatively realistic conditions. It could be worthwhile, though, as the winner will scoop up a 2 million prize. The institution notes that there could be clear advantages to AI-based radios in the military, which could keep communications up and running on the battlefield.

The nonnegative matrix factorization (NMF) is widely used in signal and image processing, including bio-informatics, blind source separation and hyperspectral image analysis in remote sensing. A great challenge arises when dealing with a nonlinear formulation of the NMF. Within the framework of kernel machines, the models suggested in the literature do not allow the representation of the factorization matrices, which is a fallout of the curse of the pre-image. In this paper, we propose a novel kernel-based model for the NMF that does not suffer from the pre-image problem, by investigating the estimation of the factorization matrices directly in the input space. For different kernel functions, we describe two schemes for iterative algorithms: an additive update rule based on a gradient descent scheme and a multiplicative update rule in the same spirit as in the Lee and Seung algorithm. Within the proposed framework, we develop several extensions to incorporate constraints, including sparseness, smoothness, and spatial regularization with a total-variation-like penalty. The effectiveness of the proposed method is demonstrated with the problem of unmixing hyperspectral images, using well-known real images and results with state-of-the-art techniques.