Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron (previously Deep Science), aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week in AI, a new study reveals how bias, a common problem in AI systems, can start with the instructions given to the people recruited to annotate data from which AI systems learn to make predictions. The coauthors find that annotators pick up on patterns in the instructions, which condition them to contribute annotations that then become over-represented in the data, biasing the AI system toward these annotations. Many AI systems today "learn" to make sense of images, videos, text, and audio from examples that have been labeled by annotators.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron (previously Deep Science), aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week in AI, a new study reveals how bias, a common problem in AI systems, can start with the instructions given to the people recruited to annotate data from which AI systems learn to make predictions. The coauthors find that annotators pick up on patterns in the instructions, which condition them to contribute annotations that then become over-represented in the data, biasing the AI system toward these annotations. Many AI systems today "learn" to make sense of images, videos, text, and audio from examples that have been labeled by annotators.

Research in the field of machine learning and AI, now a key technology in practically every industry and company, is far too voluminous for anyone to read it all. This column, Perceptron (previously Deep Science), aims to collect some of the most relevant recent discoveries and papers -- particularly in, but not limited to, artificial intelligence -- and explain why they matter. This week in AI, a new study reveals how bias, a common problem in AI systems, can start with the instructions given to the people recruited to annotate data from which AI systems learn to make predictions. The co-authors find that annotators pick up on patterns in the instructions, which condition them to contribute annotations that then become over-represented in the data, biasing the AI system toward these annotations. Many AI systems today "learn" to make sense of images, videos, text and audio from examples that have been labeled by annotators.

We don't usually give much thought to good old trusty kitchen sinks. And perhaps that's why there hasn't been much change in their design in the last century or so. Only notable additions to our kitchen counter include mixing taps and better handles. So in the 21st century where even beds have become an IoT device now, it wouldn't be a farfetched bet to say there's a lot of room for improvement in kitchen sinks.And that's probably how the computer engineering and robotics Ph.D. student Jake Ammons got the idea to develop a robotic sink faucet for his graduate-level course on Architectural Robotics at Clemson University. Source: Jake AmmonsSEE ALSO: RISE OF THE MACHINES: ONE OF THESE ADVANCED ROBOTS MAY SOON TAKE OVER THE WORLDThe contraption built in four weeks uses a common vinyl hose as its "continuum manipulator."

It seems the more I read, the more confused I get - models, algorithms, surrogates; my head is spinning. Assume the dataset is in perfect condition - pure as the driven snow, no correlated features, no null in sight, nothing; and it has "enough" observations. To simplify, let's say we are looking at binary classification. Let's also say that we want to try four different algorithms: for example - logistic regression, naive Bayes, gradient boosted tree and multilayer perceptron. And, finally, let's assume that (since all this is for educational purposes), we have no issues with time, efficiency, computing power, computing budget and whatnot; we don't care if this is an overkill or if we're going after a fly with an elephant gun: we want to throw everything, including the kitchen sink, at the problem so we can extract every last ounce of performance when it's time to make predictions on totally unseen data.

If you know anything about home design, I'm guessing you know the name Kohler. Their kitchen and bath collections are some of the most visually stunning out there, and at CES 2018 they unveiled a whole new facet to their faucets (and showers and toilets and baths). Kohler Konnect marks the brand's foray into the smart home world, and they're making a big splash with a lineup of connected bath and kitchen products. Bathroom humor aside, Kohler is a strong contender for anyone who wants to enjoy all the luxuries of design and technology in new and innovative ways. With Kohler Konnect and a lineup of seven new connected bathroom and kitchen products, your hands-free smart technology reaches into new areas of your home.

With 100 million users active on its platform every month, Pinterest is increasingly relying on machine learning to help guide the company to new online discoveries. People come to Pinterest to explore, save, and share images and posts from around the internet. Finding content they like naturally keeps them engrossed in the platform: The company says 30% of engagement and 25% of in-Pinterest purchases are driven by the platform's recommendations of related content. To get those recommendations right, the company relies on cutting-edge, data-driven techniques and lots of experimentation. "A lot of what I"m doing here is trying to shape what direction we go in approaching the discovery problem," says Pinterest's lead discovery science engineer Mohammad Shahangian.

A colleague recently asked me if deep learning lives up to the hype. Is it really revolutionary or just the same old thing dressed up with a new name? As with most fads, the truth is somewhere in between. Let's start by dismissing the hype. I've heard deep learning referred to as the breakthrough that will lead to "Strong AI" (universal, general purpose AI) and seems to have gotten some rich (Elon Musk), smart (Stephen Hawking) guys (Steve Wozniak) all riled up about the end of humanity.