Facial recognition technology used to identify criminals can also identify lemurs with a near perfect level of accuracy. The system, called LemurFaceID, is a facial recognition system that works by identifying individual lemurs based on photographs of them. The system can facilitate the long-term research of known individual lemurs, which could help with understanding how lemurs evolve and how to best conserve them. The LemurFaceID system scored 98.7 per cent accuracy after 100 trials of using the system. Amateur photographers took photos of the lemurs in Ranomafana National Park (RNP), a rainforest in southeastern Madagascar.

Mark Cuban, chairman of AXS TV and owner of the Dallas Mavericks, listens to testimony during a Senate Judiciary Subcommittee hearing Dec. 7, 2016 in Washington, DC. (Photo: Mark Wilson, Getty Images) Add Mark Cuban to the list of tech visionaries exhorting the need to address the advance of robotics and artificial intelligence. "Automation is going to cause unemployment and we need to prepare for it," Cuban posted on Twitter with a link to an essay about the rise of robots in the workplace. Automation is going to cause unemployment and we need to prepare for it. Employment and jobs have been a hot topic recently with President Trump's emphasis on getting U.S. corporations to focus on jobs at home and his plans of tightening of trade and immigration policies to foster job growth. Automakers including Fiat Chrysler, GM and Ford, as well as Intel and Walmart have recently announced plans that the companies say will create new jobs.

TensorFlow is the machine-learning library open sourced by Google in November 2015. It gained over 11,000 stars on GitHub in its first week after launch, and has built up quite a community since then: at the time of this writing, TensorFlow has over 45,000 stars, 13,000 commits and 21,000 forks. This is the second installment in our interview series with Jeff Dean, Google Senior Fellow and lead of the Google Brain research team. In our first installment, we talked about the landscape of machine learning: its past, present and future. In this installment, we'll cover TensorFlow: why we built it originally, how to use it, and what its future may hold.

Prior to that, Siri made adding an event to your calendar or quickly looking up the weather as easy as a simple push of a button on your smart phone. At what point does "Siri, tell me the weather" or "Alexa, play music" become "I'll have a cheeseburger and fries" or "What aisle can I find the peanut butter on?" or "Does this shirt come in small"? As we move away from assistants who perform simple automated tasks, 2017 has the potential to turn voice assistant technology into a virtual customer service functionality. The convergence of chatbots and virtual voice assistants has the potential to completely change the retail experience. For years, we have heard rumors about quick service retail (or QSR) replacing humans with touch screen tablets.

Earlier this week we looked at how deep nets can learn intuitive physics given an input of objects and the relations between them. If only there was some way to look at a 2D scene (e.g., an image from a camera) and build a 3D model of the objects in it and their relationships… Today's paper choice is a big step in that direction, learning the 3D structure of objects from 2D observations. The 2D projection of a scene is a complex function of the attributes and positions of the camera, lights and objects that make up the scene. If endowed with 3D understanding agents can abstract away from this complexity to form stable disentangled representations, e.g., recognizing that a chair is a chair whether seen from above or from the side, under different lighting conditions, or under partial occlusion. Moreover, such representations would allow agents to determine downstream properties of these elements more easily and with less training, e.g., enabling intuitive physical reasoning… The approach described is this paper uses an unsupervised deep learning end-to-end model and "demonstrates for the first time the feasibility of learning to infer 3D representations of the world in a purely unsupervised manner."

While we mostly hear about Artificial Intelligence systems like IBM's Watson, which won Jeopardy! Over the last five years or so, Machine Learning, a type of AI, has been a quickly rising tide that's now starting to permeate nearly every corner of technology. From self-driving cars to online advertising, cybersecurity, and video surveillance, companies are training computers to do many of the things human workers have been doing but better, or at least cheaper. Neural networks, computer systems that aim to simulate the way neurons are interconnected in the human brain, are trained to do these tasks the same way babies learn about the world – by observation, repetition, trial, and error, assisted instead of parents by computer scientists – although babies are still much, much better at it. A neural net learns to understand spoken language, for example, by listening to a lot of recorded speech, such as movie dialogue; it learns to identify objects by looking at tons of images.

Utrip, a Seattle startup that uses machine learning to help travelers plan their trips, just closed a $4 million funding round. Investors in the Series A round include Plug and Play, Tiempo Capital, Acorn Ventures, and executives from companies such as Apple and Costco, participating as angel investors. The cash will go toward Utrip's machine learning and data science operations, which fuel the platform's recommendation engine. "One of the things that our travelers love about Utrip is the depth with which we curate destinations and go beyond those top 10 lists that are available everywhere to offer experiences that are really unique and local and authentic for that destination," said Utrip CEO Gilad Berenstein. "That's one big priority, continuing to build out our machine learning capabilities as well as our human expert network, our chefs, artists, historians, etcetera."

A vast majority of recorded video data is never viewed. Adding more cameras increases the amount of video data collected, but if it's not viewed, the useful information is missed. There is an imbalance between the amount of video data collected and the human attention available to effectively mine that data. As security technology continues to evolve, exciting developments are taking place, with the inclusion of artificial intelligence (AI). Powerful tools are being created that will allow us to modify our approach to video search.

Opinions expressed by Forbes Contributors are their own. The author is a Forbes contributor. The opinions expressed are those of the writer. The Tsubame3.0 is currently being built in Japan. The Tokyo Institute of Technology Global Scientific Information (Tokyo Tech) and Computing Center (GSIC) have begun the development and construction of a supercomputer that's expected to meet the demands of artificial intelligence (AI) and big data applications and process in the petaFLOPS range, which is a quadrillion floating point operations per second (FLOPS).

Did you ever consider all of the examples of machine learning in your personal life? Google's page ranking system, photo tagging on Facebook, and customized product recommendations from Amazon are all driven by machine learning under the hood. How do these same techniques improve productivity for your business? Search is the new data and content curation. Download the Five-Minute Guide to Machine Learning to find out how self-learning technologies drive increasingly relevant answers and better context for cognitive search.