A few months ago, my company, CrowdFlower, ran a machine learning competition on Kaggle. It perfectly highlighted the biggest opportunity (and challenge) with machine learning: What do you do with an 80% accurate algorithm? We uploaded data collected on our platform and Kaggle sent it out to over 1,000 data scientists, who competed to see who could build the best search model. The simplest approach gave a baseline accuracy of 32%. By the next morning, one team already had a 53% accurate model.

Sometimes the simplest methods are best... Random sampling is easy to understand, hard to screw up, and unlikely to introduce bias into your process. Building a training pipeline using a random sample (without replacement) of your dataset is a good way to work faster. Once you have a pipeline you're satisfied with, you can then run it again over your entire dataset to estimate the gain in performance from using the entire dataset. If your training pipeline is robust, your results should not change too much, and although your performance might rise, it will tend to do so very slowly as you add more data. The basic intuition here is that the strongest signals in your data will show up even with relatively small samples of the data, almost by definition (if they didn't, they wouldn't be strong!).

The IBM Watson AI XPRIZE, a Cognitive Computing Competition, was announced on the TED Stage on Feb 17, 2016. It is a 5 million competition challenging teams from around the world to develop and demonstrate how humans can collaborate with powerful cognitive technologies to tackle some of the world's grand challenges. Every year leading up to TED2020, teams will go head-to-head at World of Watson, IBM's annual conference, competing for interim prizes and the opportunity to advance to the next year's competition. The three finalist teams will take the TED stage in 2020 to deliver jaw-dropping, awe-inspiring TED Talks demonstrating what they have achieved. Ideas will be evaluated by a panel of expert judges for technical validity and ultimately, the TED and XPRIZE communities will choose the winner based on the audacity of their mission and the awe-inspiring nature of the teams' TED Talks in 2020.

Spam is a kind of messaging where the cost of sending is usually negligible and the receiver and the ISP pays the cost in terms of bandwidth usage. An example of a manual approach to detecting spam is using knowledge engineering. If the subject line of an email contains words'Buy viagra' its spam These rules can be configured by the user himself or by the email provider and if correctly thought out and executed this technique can be effectively be used to combat spam. This is a blog post about one such implementation. However, a manual rules based approach doesn't scale because of active human spammers circumventing any manual rules.

Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks. Neural networks have been around for decades, just that no one used to call them deep networks back then. Now we have all sorts of different flavors of neural networks - deep belief networks (DBNs), convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and more.

Welcome to robot nursery school," Pieter Abbeel says as he opens the door to the Robot Learning Lab on the seventh floor of a sleek new building on the northern edge of the UC-Berkeley campus. The lab is chaotic: bikes leaning against the wall, a dozen or so grad students in disorganized cubicles, whiteboards covered with indecipherable equations. Abbeel, 38, is a thin, wiry guy, dressed in jeans and a stretched-out T-shirt. He moved to the U.S. from Belgium in 2000 to get a Ph.D. in computer science at Stanford and is now one of the world's foremost experts in understanding the challenge of teaching robots to think intelligently. But first, he has to teach them to "think" at all. "That's why we call this nursery school," he jokes. He introduces me to Brett, a six-foot-tall humanoid robot made by Willow Garage, a high-profile Silicon Valley robotics manufacturer that is now out of business. The lab acquired the robot several years ago to experiment with. Brett, which stands for ...

In recent years Artificial Intelligence (AI) has rapidly gone from an obscure academic research field, to an ever more useful and ubiquitous applied discipline. We increasingly rely on AI for more and more of our everyday tasks, and whole lines of work are being thoroughly transformed by its advances. AI's increasing ubiquity is not making it any easier to understand. AI concepts and techniques are still domain of advanced undergraduate or graduate school level courses. There are a few popular AI books out there, but most of them don't get "under the hood" of how AI actually works.

Google's Go-playing software defeated a human champion. Google's Go-playing software defeated a human champion. An artificially intelligent computer system built by Google has just beaten the world's best human, Lee Sedol of South Korea, at an ancient strategy game called Go. Go originated in Asia about 2,500 years ago and is considered many, many times more complex than chess, which fell to AI back in 1997. Google's programmers didn't explicitly teach AlphaGo to play the game. Instead, they built a sort of model brain called a neural network that learned how to play Go by itself. The Google program, known as Alpha Go, actually learned the game without much human help.

On tap at the brewpub. A nice dark red color with a nice head that left a lot of lace on the glass. Aroma is of raspberries and chocolate. Not much depth to speak of despite consisting of raspberries. The bourbon is pretty subtle as well. I really don't know that find a flavor this beer tastes like. I would prefer a little more carbonization to come through. It's pretty drinkable, but I wouldn't mind if this beer was available.

Scatterings of completed buildings, sporting new plantings of drought-tolerant grasses, are already occupied; other buildings are going up quickly, including a new fire station. There's Nissan's new Silicon Valley research center, a well-financed medical device startup called Spiracur, a digital cash startup called Quisk, and a biotech startup incubator. And there is Baidu's Silicon Valley AI Lab--my destination along this dusty road crowded with construction vehicles. It's good to spend time in a new research lab; there's not only fresh paint and hip decor--like living walls of plants--there are fresh, excited faces, and empty desks waiting to be filled. In mid-2014, I spent a morning on just the other side of nearby Moffett Field watching a far more somber group of researchers moving out of a suddenly closed division of Microsoft Research.