What will it take for AI to become mainstream in business? We're in the midst of a breakthrough decade for artificial intelligence (AI): More sophisticated neural networks paired with sufficient voice recognition training data brought Amazon Echo and Google Home into scores of households. Deep learning's improved accuracy in image, voice, and other pattern recognition have made Bing Translator and Google Translate go-to services. And enhancements in image recognition have made Facebook Picture Search and the AI in Google Photos possible. Collectively, these have put machine recognition capabilities in the hands of consumers in a big way.

Editor's note: This post is part of our Trainspotting series, a deep dive into the visual and audio detection components of our Caltrain project. You can find the introduction to the series here. SVDS has previously used real-time, publicly available data to improve Caltrain arrival predictions. However, the station-arrival time data from Caltrain was not reliable enough to make accurate predictions. Using a Raspberry PiCamera and USB microphone, we were able to detect trains, their speed, and their direction. When we set up a new Raspberry Pi in our Mountain View office, we ran into a big problem: the Pi was not only detecting Caltrains (true positive), but also detecting Union Pacific freight trains and the VTA light rail (false positive).

Machine Learning means a machine which is learning on itself and is a method of automated data analysis. It is the science that enables computers to analyze data and automatically build models from that data. The machine can feed on data and adapt itself to make more precise predictions and act accordingly. Machine Learning has been there all the time. Do you remember simple pattern recognition algorithms?

Ensure that the product offerings on your pages have schema.org

Google is Trying to Turn Image Search into a Shopping Tool (Recode) Google has added a new shopping feature in Image Search called "style ideas" that shows users perusing fashion merchandise what specific items look like paired with others. Yext Shares Up Sharply In Initial Day of Trading, Portending Well for Local (Street Fight) Yext's shares jumped nearly 22% in the company's initial day of trading, with the price rising as high as $14.25 per share before settling to $13.41 at close. The strong opening was a hopeful message from Wall Street for the local marketing industry. Instagram's Snapchat Clone is Now More Popular than Snapchat (Quartz) In about eight months, Instagram Stories, the function of Facebook's image-sharing social network that allows users to post images and short videos that disappear after 24 hours, has amassed 200 million daily users, it announced today. Street Culture: Why Telecommuting Makes Sense for Many Tech Startups (Street Fight) "If you have the right team, the right employees, then they don't have to be there physically," says Kristen Stiles, co-founder and CEO of babysitter-finding app Sitter.me. "If you don't trust your employees to work at home, you shouldn't have hired them in the first place."

AI has achieved recent performance breakthroughs across numerous cognitive applications (Figure 7), from image classification to pattern recognition and ontological reasoning. This progress is due largely to convergent advances across three enablers: computing power, training data and learning algorithms. Solutions are currently trained on millions of image data, a 100-fold increase compared with a decade ago. Computing and storage costs have declined commensurately by an average of 35% year on year. In the near future, AI will build on adoption enablers to unlock faster, smarter and more intuitive applications, although progress will probably be confined to broad adoption of narrow, context-aware intelligence across domains.

Google's latest AI toy may be its most clever: an automated drawing bot that analyzes what you're doodling in real time to suggest a more polished piece of clip art to replace it. Called AutoDraw, the software is another of Google's ongoing creative machine learning demonstrations that it releases as part of its AI Experiments series. It uses the underlying technology behind the company's experimental image recognition software to identify potential objects and pairs that with a database of neat and simplistic hand-drawn images. The company bills AutoDraw as a "drawing tool for the rest of us," and by us it means aesthetically impaired individuals who couldn't doodle themselves out of a paper bag. "AutoDraw pairs the magic of machine learning with drawings from talented artists to help you draw stuff fast," says the narrator in Google's AutoDraw teaser video.

At a demonstration of Amazon Web Services' new artificial intelligence image recognition tool last week, the deep learning analysis calculated with near certainty that a photo of speaker Glenn Gore depicted a potted plant. "It is very clever, it can do some amazing things but it needs a lot of hand holding still. AI is almost like a toddler. They can do some pretty cool things, sometimes they can cause a fair bit of trouble," said AWS' chief architect in his day two keynote at the company's summit in Sydney. Where the toddler analogy falls short, however, is that a parent can make a reasonable guess as to, say, what led to their child drawing all over the walls, and ask them why.

The relationship may have faded long ago, but the intimate images you shared have not. If you're lucky, your ex deleted them. If you're not, the photos have sprouted up online. Victims of such non-consensual posts, often referred to as "revenge porn," now have some help in preventing their spread: On Wednesday, Facebook announced new artificial intelligence tools designed to keep such content, once flagged, off its site for good. "It's wrong, it's hurtful, and if you report it to us, we will now use AI and image recognition to prevent it from being shared across all of our platforms," Mark Zuckerberg, the social network's founder and chief executive, said in a Facebook post.

This paper describes a new entropy-style of equation that may be useful in a general sense, but can be applied to a cognitive model with related processes. The model is based on the human brain, with automatic and distributed pattern activity. Methods for carrying out the different processes are suggested. The main purpose of this paper is to reaffirm earlier research on different knowledge-based and experience-based clustering techniques. The overall architecture has stayed essentially the same and so it is the localised processes or smaller details that have been updated. For example, a counting mechanism is used slightly differently, to measure a level of 'cohesion' instead of a 'correct' classification, over pattern instances. The introduction of features has further enhanced the architecture and the new entropy-style equation is proposed. While an earlier paper defined three levels of functional requirement, this paper re-defines the levels in a more human vernacular, with higher-level goals described in terms of action-result pairs.