Let's say you're scrolling through your favorite social media app and you come across a series of pictures of a man in a tuxedo and a woman in a long white dress. An automated image captioning system might describe that scene as "a picture of a man and a woman," or maybe even "a bride and a groom." But a person might look at the pictures and think, "Wow, my friends got married! As image captioning tools get increasingly good at correctly recognizing the objects in an image, a group of researchers is taking the technology one step further. They are working on a system that can automatically describe a series of images in the same kind of way that a human would, by focusing not just on the items in the picture but also what's happening and how it might make a person feel. "Captioning is about taking concrete objects and putting them together in a literal description," said Margaret Mitchell, a Microsoft researcher who is leading the research project. "What I've been calling visual ...

Last November, Google opened up its in-house machine learning software TensorFlow, making the program that powers its translation services and photo analytics (among many other things) open-source and free to download. Now, the company is giving TensorFlow the machine learning equivalent of smart pills, releasing a distributed version of the software that allows it to run across multiple machines -- up to hundreds at a time. This sounds like an obvious way to improve TensorFlow, and, well, it is. Machine learning software only gets to be clever by analyzing large amounts of data; looking for common properties and trends like facial features in photographs, for example. Letting TensorFlow run these sorts of operations on networks of computers simultaneously rather than individual machines means users can make smarter systems, and improve them faster.

Robot cage fights are pretty sweet--who doesn't like seeing machines armed with spinning sawblades flamethrowers fight to the death--but medieval robot wars are so much more fun. Think about it: Hand-built robots catapulting rocks at castles, trying to breach their defenses. George R. R. Martin couldn't come up with something that crazy. But some 1,500 high school kids in Southern California did, coming together for an epic battle last month during the Los Angeles regional championship of the "Super Bowl of Smarts." Now, the thought of a robot war set in the Middle Ages probably brings to mind all the worst stereotypes of geeks and nerds and dweebs.

Summary: In which we attempt to answer the question, how does someone in school or recently out enter the exciting world of data science. There is no question that comes up more frequently than'how do I become a data scientist'. I've actually written several articles on this topic (and will reference them liberally in this post) but they lacked the global perspective that potential new entrants to data science want. I'm going to try to resolve here. I thought about changing the title to "Doing Data Science" instead of becoming a Data Scientist to focus on the activity and not just the job title.

Your math teacher lied to you. Sometimes getting your sums wrong is a good thing. So says Joseph Bates, cofounder and CEO of Singular Computing, a company whose computer chips are hardwired to be incapable of performing mathematical calculations correctly. Ask it to add 1 and 1 and you will get answers like 2.01 or 1.98. Pentagon research agency DARPA funded the creation of Singular's chip because that fuzziness can be an asset when it comes to some of the hardest problems for computers, such as making sense of video or other messy real world data. "Just because the hardware is sucky doesn't mean the software's result has to be," says Bates.

The PhD intern recruited at the beginning of the year for 6 months (who has become full-time staff now in the Data Science team) had no knowledge of machine learning at all. Also little statistics knowledge as his area he did his PhD thesis was on partial differential equations applying to option pricing & financial markets. I requested him to send his thesis. His lack of machine learning & statistic's knowledge swayed some team members from him, but I put more weight in his favour after reading his thesis. Anyone who understands partial differential equations can also self taught to understand machine learning & that's fact.

The data consists of a sample of Australian Sign Language signs performed by volunteers. There are 95 unique signs, each recorded 27 times on different days. The data was recorded using two Fifth Dimension Technologies (5DT) gloves (one for each hand) and two Ascension Flock-of-Birds magnetic position trackers. Together, this produced 22 channels of data, 11 for each hand. These channels included x, y and z position, roll, pitch and yaw movements and finger bend measurements for each finger.

Google just expanded its free software for creating smart, self-learning computers. The company added the ability to run this software, known as TensorFlow, across a network of many computers -- the same way that Google uses it for its own operations. This means that anyone with access to a bunch of computer servers can create their own smart computer that can basically program itself. Set the computer program up with whatever it is you want it to learn. Give it a bunch of data to study and then the computer knows how to do things that, until now, only humans could do like talk, recognize pictures, draw, etc.

DUBAI – The teaching methods of Kazuya Takahashi, 35, using Lego blocks and speaking entirely in English, may not be the norm in the Japanese education system. But on a global level, the educator, who teaches at the Kogakuin junior high and high schools in Hachioji, western Tokyo, is considered ahead of the game and has won recognition for his efforts to promote global citizenship. His methods may provide clues as to where education should be heading in Japan, a nation often criticized for focusing more on cramming knowledge rather than encouraging critical thinking. At the Global Education and Skills Forum in Dubai, which ran for two days from March 12, Takahashi gave a presentation as one of the 10 finalists for the Global Teacher Prize, known in the industry as the Nobel Prize in education. The event was attended by around 1,600 people from 110 nations.

We consider a statistical inverse learning problem, where we observe the image of a function $f$ through a linear operator $A$ at i.i.d. random design points $X_i$, superposed with an additive noise. The distribution of the design points is unknown and can be very general. We analyze simultaneously the direct (estimation of $Af$) and the inverse (estimation of $f$) learning problems. In this general framework, we obtain strong and weak minimax optimal rates of convergence (as the number of observations $n$ grows large) for a large class of spectral regularization methods over regularity classes defined through appropriate source conditions. This improves on or completes previous results obtained in related settings. The optimality of the obtained rates is shown not only in the exponent in $n$ but also in the explicit dependency of the constant factor in the variance of the noise and the radius of the source condition set.