For designers and photographers, selecting and laying out photos is often subjective, requiring a keen sense of color and composition. Using a computer algorithm, the stock footage site Shutterstock hopes to make that process easier. It now offers a reverse image search tool that analyzes the pixels in a photo and returns images that are similar in "look and feel" to the original without requiring a user to type in keywords to search. Dragging a photo of a stained-glass cathedral window into the search box, the company demonstrates in a video, produces a series of related images that more closely match the original in color and composition. The new search engine works by using a customized convolutional neural network, a type of machine learning tool that is modeled on how the brain's visual cortex, especially that of animals, processes images.

Natasha Lomas reports in TechCrunch, "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." Lomas goes on, "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." She continues, "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?). Safe to say, the list of applications-in-waiting for machine powered lip-reading is as long as the dictionary is deep. So there's bags of future potential if only researchers can deliver the goods."

The resounding win by a Google artificial intelligence program over a champion in the complex board game Go this month was a statement not so much to professional game players as to Google's competitors. SEE ALSO: Chief of LG said Apple iPhone SE is'same old tech' Many of the tech industry's biggest companies are jockeying to become the go-to company for AI. In the industry's lingo, the companies are engaged in a "platform war." If true believers in AI are correct that this long-promised technology is ready for the mainstream, the company that controls AI could steer the tech industry for years to come. "Whoever wins this race will dominate the next stage of the information age," said Pedro Domingos, a machine learning specialist.

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A government-led effort to develop commercial guidelines for face-recognition technology is moving forward, and it has privacy advocates red in the face. The National Telecommunications and Information Administration, an agency within the U.S. Commerce Department, held a meeting in Washington Tuesday to consider a set of "best practices" for collecting and storing facial data and to discuss how face-recognition technology might apply to the Obama administration's so-called Consumer Privacy Bill of Rights publicized in 2012. The meeting is part of an ongoing process being billed as a "multistakeholder" effort to develop an enforceable code of conduct for emerging biometric technologies, but privacy groups say their warnings about potential privacy abuses are not being heard. Instead, they say the process has been hijacked by technology industry interests intent on harnessing sensitive private data for monetary gain. "Lobbyists craft purposefully vague proposals without any real safeguards for biometric data," Jeff Chester, executive director of the Center for Digital Democracy, wrote in a blog post Tuesday.

A good vendor should be able to explain both how they manage data and how they solve your specific business problem. Iyengar suggests asking some questions to see if a predictive company will be a good fit: "Ask a [ML provider] how they handle unclean data. Their answer will show you how well they know their work. You can also ask about the variety of algorithms they use, since they should have a good variety of fairly robust algorithms. They should be comfortable explaining how they deploy a model structure, what their web stack looks like, and how that will work with customer architecture."

One thing I'm missing from this awesome site are courses in these topics, both highly relevant to the IT-industry these days. I'm aware that this might be straying somewhat from Treehouse's profile of teaching web-centric skills, but seeing as Treehouse already have courses in SQL and databases, this site could really take it to the next level by delving into Machine Learning or Data Analysis. Have the Treehouse staff thought about this?

Usually when people imagine a self-aware machine, they picture a device that emerges through deliberate effort and that then makes its presence known quickly, loudly, and (in most scenarios) disastrously. Even if its inventors have the presence of mind not to wire it into the nuclear missile launch system, the artificial intelligence will soon vault past our capacity to understand and control it. If we're lucky, the new machine will simply break up with us, like the operating system in the movie Her. If not, it might decide not to open the pod bay doors to let us back into the spaceship. Regardless, the key point is that when an artificial intelligence wakes up, we'll know. But who's to say machines don't already have minds? What if they take unexpected forms, such as networks that have achieved a group-level consciousness? What if artificial intelligence is so unfamiliar that we have a hard time recognising it?

The web is full of good explanations of machine learning algorithms. And every second applicant for a data science position has finished the Coursera course on machine learning. Theory will not help you choose good values for the 16 parameters a standard implementation of a random forest takes. The default values are good to get started, but which parameters should you modify depending on your data? Choosing the right features, algorithms and parameters is an art.

Posted by A'ndrea Elyse Messer-Penn State on March 8, 2016 You are free to share this article under the Attribution 4.0 International license. Identifying an isolated leaf, especially if preserved as a fossil, can be a painstaking process for botanists. A new computer program that learns to categorize leaves into large evolutionary categories could help. Researchers "trained" a machine-learning algorithm to identify leaves based on a set of nearly 7,600 digital images of leaves that had been chemically treated to emphasize their shape and venation. The software discerned relevant patterns so well from that set of examples that it went on to identify the family of novel leaf images with greater than 70 percent accuracy (a rate 13 times better than chance) and the order with about 60 percent accuracy.