The simplest approach gave a baseline accuracy of 32%. By the next morning, one team already had a 53% accurate model. Extrapolating the first four days to our 60-day contest, you might expect the winning accuracy to get close to 100%. But in fact, this is what happened: The winning entry -- submitted by Chenglong Chen -- was just 6% more accurate than the best model submitted a week into the contest. As the Kaggle competition went on, more and more teams entered and existing teams refined and resubmitted their entries: Given that over 1,000 smart data scientists worked on this task, it's fair to say that 71% accuracy on this task is very close to the best possible accuracy with today's technology.

Artificial intelligence is changing the world of work and education, with some groups saying AI could eliminate the need for standardized tests. AI trends raise questions, including what skills should be taught to students for future jobs and how best to prepare educators for the classroom.

The most common approach to address'Machine Learning' these days is to think of it as just another arduous subject, one which young computer scientists choose in their last two years of college. A huge percentage of people are not able to realise that this'subject' is actually one of the most important strategies that humans use throughout their lifetime. Let me start with a simple example. During childhood, every kid is curious about how the surroundings around him or her work – anyone with any experience of young children will know you have to be very careful about their activities! You can't just let it go near a burning flame, near sharp knives or let it eat just anything off the floor.

One dating app is on a mission to end "catfishing," the term for someone having a fictional persona online and trying to get into a relationship. Badoo is a name many Americans have never heard of, but the dating service is trying to grow its presence in the U.S. It's part of the reason why the brand acquired LuLu, a dating app originally for anonymous ranking men and gained popularity in U.S. colleges, and brought on its founder Alexandra Chong as president last month. Soon, she and Badoo will be opening an office in New York, adding to the locations in Moscow and London. "They're not known as the biggest," Chong told International Business Times while sitting on the rooftop of the Soho House earlier this week. "Tinder was the one that caught the millennials."

Here's what happened yesterday at the first meeting. Rachel started by going through the syllabus. So, what is data science? This is an ongoing discussion, but Michael Driscoll's answer is pretty good: Data science, as it's practiced, is a blend of Red-Bull-fueled hacking and espresso-inspired statistics. But data science is not merely hacking, because when hackers finish debugging their Bash one-liners and Pig scripts, few care about non-Euclidean distance metrics.

For human lip readers, context is key in deciphering words stripped of the full nuance of their audio cues. But a technology model for lip-reading developed at the University of East Anglia in the UK has been shown to be able to interpret mouthed words with a greater degree of accuracy than human lip readers, thanks to the application of machine learning tech to classify the visual aspect of sounds. And the kicker is the algorithm doesn't need to know the context of what you're discussing to be able to identify the words you're using. While the model remains a piece of research at this stage, there are scores of potential applications for technology that could automagically transform visual cues into accurate speech -- whether it's helping people who have audio impairments, or enhancing audio-less security video footage with additional speech data -- or even to try to figure out exactly what charged word one footballer spat at another in the heat of a match… Such a tech could also be applied as a fallback for poor audio quality on a mobile or video call. Or even perhaps to power a front-facing camera-based mobile'voice' assistant which you wouldn't actually have to speak to but could just discreetly mouth commands at (how cool would that be?).

Here is the link to coursera course for NLP Pick the software from the The Stanford NLP (Natural Language Processing) Group and input some text to view its parse tree, named entities, part of speech tags, etc.

Few days back I went to a bar with couple of friends and found that one of my friends is working with the bartender to create a perfect cocktail. The scene was such that it got me thinking. The bartender's action could very well be used to explain how an analytics lead could get his machine learning deployed and what best practices are needed. A good bartender keeps his vocabulary updated with what all liquor and additives at his disposal, so that he could create a variety that specifically targets your experience. Similarly, having an open mindset will help in picking the tool that could best serve the problem and not the bias?

New lip-reading technology developed at the University of East Anglia (UEA) 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.

Researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have created an algorithm that can predict how memorable or forgettable an image is almost as accurately as humans -- and they plan to turn it into an app that subtly tweaks photos to make them more memorable. For each photo, the "MemNet" algorithm -- which you can try out online by uploading your own photos -- also creates a heat map that identifies exactly which parts of the image are most memorable. "Understanding memorability can help us make systems to capture the most important information, or, conversely, to store information that humans will most likely forget," says CSAIL graduate student Aditya Khosla, who was lead author on a related paper. "It's like having an instant focus group that tells you how likely it is that someone will remember a visual message." Team members picture a variety of potential applications, from improving the content of ads and social media posts, to developing more effective teaching resources, to creating your own personal "health-assistant" device to help you remember things.