In 1997 a remarkable event caught everybody's attention - the then champion of the world Garry Kasparov lost a tournament to a supercomputer Deep Blue. It was called "a beginning of a new era of computers" by many and now it seems that time keeps justifying those loud statements... Being a sub-set of artificial intelligence, machine learning involves algorithms allowing computers to autonomously learn from input data. A fundamental distinction from "usual" software programs, such as Photoshop or, say, Excel, is that in machine learning computers don't have to be explicitly programmed but can change and improve their algorithms by themselves. The history of machine learning goes back to the 1950th. The first learning program was created by Arthur Samuel in 1952 and it was the game of checkers.

Not to mention, technological disruption that (a) erodes, (b) replaces, or (c) reverses most of the current best practices every 6-12 months. "RankBrain has become the third-most important signal contributing to the result of a search query", according to senior research scientist at Google, Greg Corrado. It's an artificial intelligence engine that uses pattern matching at scale to process millions of search queries daily. And with an 80% accuracy rating, it outperforms engineers by a wide margin. There's no wonder that it's taking over the search giant then. Part of that success comes from the ability to literally guess – based on millions of datapoints in fractions of a second – the searcher's intent behind a few random keystrokes.

Google announced today that it has acquired Paris-based Moodstocks, a startup that has developed machine learning technology to bolster the image recognition features on smartphones. "We continue to pursue our machine learning and research efforts," wrote Vincent Simonet, head of the research and development team for France Google, "and Moodstocks is the latest proof of our commitment to this area." Today, we're thrilled to announce that we've reached an agreement to join forces with Google in order to deploy our work at scale. We expect the acquisition to be completed in the next few weeks. Our focus will be to build great image recognition tools within Google, but rest assured that current paying Moodstocks customers will be able to use it until the end of their subscription. The terms of the deal were not disclosed.

Last year, the United States Customs and Border Protection rolled out a recognition pilot program that uses biometric recognition tools like face and iris scanners. The program will snag "imposters" using a fake passport at airports, and what's more, reduce wait times at security checkpoints. But what might identify individuals even more conclusively and speed travelers on their way more swiftly is another kind of biometrics, based on the ear. Scientists have taken note that the curves of the cartilage, the protrusions of the auricle, and the hollow of the concha cava are all, like fingerprints, features distinctive to each person. The way noise bounces within their folds allows the ears to guarantee a highly accurate identification of who we are, Steve Beeby, professor of Electronic Systems and Devices at University of Southampton, told the Telegraph back in 2009.

Image recognition is the process of identifying and detecting an object or a feature in a digital image or video. This concept is used in many applications like systems for factory automation, toll booth monitoring, and security surveillance. Specific image recognition applications include classifying digits using HOG features and an SVM classifier (Figure 1). Cross correlation can be used for pattern matching and target tracking as shown in Figure 2. An effective approach for image recognition includes using a technical computing environment for data analysis, visualization, and algorithm development.

Objectively, cognitive computing creates automated IT systems which are capable of determining and resolving problems without assistance. Mostly used in Artificial Intelligence (AI) applications, it is a subset of Artificial Intelligence. A CC system encompasses the following characteristics: Machine Learning Natural Language Processing Spatial and contextual awareness Semantic Understanding Sophisticated pattern recognition Common Sense Vision-based sensing and image recognition Emotional Intelligence Reasoning and decision automation Robotic Control Algorithms that learn and adapt Neural Networks Noise Filtering So basically, you can say that a cognitive computing system might be trained via neural networks. It is basically all about making machine intelligent. Enforcing artificial intelligence into a machine, also known as "Machine Learning".

Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2] As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.[3] Capabilities still classified as AI include advanced Chess and Go systems and self-driving cars. AI research is divided into subfields[4] that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[5] General intelligence is among the field's long-term goals.[6] Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it."[7] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity.[8] Attempts to create artificial intelligence has experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973 and the collapse of the Lisp machine market in 1987. In the twenty-first century AI techniques became an essential part of the technology industry, helping to solve many challenging problems in computer science.[9]

Google has acquired Moodstocks, a Paris-based startup that specialises in smartphone image recognition as part of its continued efforts to boost its own artificial intelligence (AI) research, development and capabilities. Announced in a blog post on 6 July, Vincent Simonet, head of Google's research and development (R&D) centre in Paris, says the tech giant's latest purchase is proof of its commitment to the promising sector. "Many Google services use machine learning to make them simpler and more useful in everyday life such as Google Translate, Smart Reply Inbox, or the Google app," Simonet wrote in French. "We have made great strides in terms of visual recognition: Now you can search in Google Pictures such as'party' or'beach' and the application will offer you good pictures without you needing to categorise them manually. But there is still much to do in this area. And this is where Moodstocks comes in."

DUBLIN--(BUSINESS WIRE)--Research and Markets has announced the addition of the "Artificial Intelligence and Machine Learning in Big Data and IoT: The Market for Data Capture, Analytics, and Decision Making 2016 - 2021" report to their offering. More than 50% of enterprise IT organizations are experimenting with Artificial Intelligence (AI) in various forms such as Machine Learning, Deep Learning, Computer Vision, Image Recognition, Voice Recognition, Artificial Neural Networks, and more. AI is not a single technology but a convergence of various technologies, statistical models, algorithms, and approaches. Machine Learning is a sub-field of computer science that evolved from the study of pattern recognition and computational learning theory in AI. Every large corporation collects and maintains a huge amount of human-oriented data associated with its customers including their preferences, purchases, habits, and other personal information.

It's a good time to be a machine learning startup. Two weeks after Twitter bought London-based Magic Pony, Google has purchased French firm Moodstocks. The acquisition was made for an unknown sum, and seems primarily a grab for talent. Moodstocks' engineers and researchers will move to Google's Paris R&D site, and the startup's primary commercial product -- an image recognition API for smartphones -- will be phased out. "Ever since we started Moodstocks, our dream has been to give eyes to machines by turning cameras into smart sensors able to make sense of their surroundings," said Moodstocks in a statement.