A new computer model can now mimic the human ability to learn new concepts from a single example instead of the hundreds or thousands of examples it takes other machine learning techniques, researchers say. The new model learned how to write invented symbols from the animated show Futurama as well as dozens of alphabets from across the world. It also showed it could invent symbols of its own in the style of a given language. The researchers suggest their model could also learn other kinds of concepts, such as speech and gestures. Although scientists have made great advances in machine learning in recent years, people remain much better at learning new concepts than machines.

A typical human ability is the recognition of patterns in the world around us. It constitutes the basis of each natural science: the laws of physics, the description of species in biology, or the analysis of human behavior; they are all based on seeing patterns. Also in daily life pattern recognition plays an important role: reading texts, identifying people, retrieving objects, or finding the way in a city. Once patterns are established, learned from some examples or from a teacher, we are able to classify new objects or phenomena into a class of known patterns. The study of automatic pattern recognition has two sides, one purely fundamentally scientific and one applied.

Computers are good at identifying patterns in huge data sets. Humans, by contrast, are good at inferring patterns from just a few examples. In a paper appearing at the Neural Information Processing Society's conference next week, MIT researchers present a new system that bridges these two ways of processing information, so that humans and computers can collaborate to make better decisions. The system learns to make judgments by crunching data but distills what it learns into simple examples. In experiments, human subjects using the system were more than 20 percent better at classification tasks than those using a similar system based on existing algorithms.

An example of object detection and recognition application. Classifier networks are used to inspect, sort, identify, and discriminate minute details in biological or machine systems that human beings cannot discern. They are used in everything from inspecting spark plugs to face recognition. Classifier networks are becoming the basis of machine vision systems. The students' projects are designed to give them practical experience, and to ground graduate students in the field so that they are able to perform this type of research.

Since the dawn of the space age, unmanned spacecraft have flown blind with little or no ability to make autonomous decisions based on the content of the data they collect. The Autonomous Sciencecraft Experiment (ASE) is operating onboard the Earth Observing-1 mission since 2003. The ASE software uses onboard continuous planning, robust task and goal-based execution, and onboard machine learning and pattern recognition to radically increase science return by enabling intelligent downlink selection and autonomous retargeting. This software demonstrates the potential for space missions to use onboard decision-making to detect, analyze, and respond to science events, and to downlink only the highest value science data. The onboard science algorithms analyzes the images to extract static features and detect changes relative to previous observations.

Whether we realize it or not, machine learning touches our daily lives in many ways. When you upload a picture on social media, for example, you might be prompted to tag other people in the photo. That's called image recognition, a machine learning capability by which the computer learns to identify facial features. Other examples include number and voice recognition applications. From an intrusion detection perspective, analysts can apply machine learning, data mining and pattern recognition algorithms to distinguish between normal and malicious traffic.

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning). The terms pattern recognition, machine learning, data mining and knowledge discovery in databases (KDD) are hard to separate, as they largely overlap in their scope. Machine learning is the common term for supervised learning methods[dubious – discuss] and originates from artificial intelligence, whereas KDD and data mining have a larger focus on unsupervised methods and stronger connection to business use. Pattern recognition has its origins in engineering, and the term is popular in the context of computer vision: a leading computer vision conference is named Conference on Computer Vision and Pattern Recognition.

Clarifai: graphic design: 0.99% no person: 0.98% isolated: 0.97% symbol: 0.97% stripe: 0.95% Our Image Recognition API can block all images that have no relation with real estate. With restb's solution, all images containing spam, unrelated or restricted content are immediately blocked and tagged as non_related. Meanwhile the other solutions on the market will just keep giving you tags describing what's inside each image. Another big problem that property portals have is related with the publication of logos and watermarks in the images. Watermarks from your competitors or real estate agencies reduce the quality of the listings and the search experience overall. We have developed a solution that identifies all logos and watermarks that appear within an image and return the position of each one.

At the risk of calling to mind some of the thousands of books, movies, and television shows centered on the onset of a technological apocalypse, we truly are facing an artificial intelligence (AI) revolution. Competition between major tech companies like Google, Apple, Facebook, and Microsoft, combined with the ever-present exponential patterns of Moore's law, have led us to some amazing breakthroughs in the past several years when it comes to advanced pattern recognition, data analysis, and machine learning. As marketers, we owe it to ourselves (and our audiences) to stay abreast of these advancements, learn what's coming down the pipeline, and start preparing our strategies and outlooks to accommodate those developments. Are we about to enter some kind of marketing apocalypse? But we're in for some serious changes in the years to come, and it's in our best interest to stay ahead of them.

Fingerprint readers and facial recognition techniques are good for adding a base level of security to your phone without sacrificing convenience. However, they have their limits. It can be hard to switch between methods on a whim, and dedicated intruders can get through if they either make you unlock your phone or develop convincing fakes. Synaptics thinks it has a solution: It's unveiling a "biometric fusion engine" that can combine results from face and fingerprint detection before letting you into a mobile device or PC. Ideally, this makes it easier to sign in even as it adds an extra layer of security.