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. What will it take to make similar inroads in business?

A demo of the Orcam MyEye 2.0 was one of the highlights at the AbilityNet/RNIB TechShare Pro event in November. This small device, an update to the MyEye released in 2013, clips onto any pair of glasses and provides discrete audio feedback about the world around the wearer. It uses state-of-the-art image recognition to read signs and documents as well as recognise people and does not require internet connection. It's just one of many apps and devices that are using the power of artificial intelligence (AI) to transform the lives of people who are blind or have sight loss. Last week, we took a look Microsoft's updated free app Seeing AI and its amazing new features for people who are blind or have sight loss, including colour recognition and handwriting recognition.

IBM Palo Alto Scientific Center, 2530 Page Mill Road, Palo Alto, CA 94303 Abstract A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples-one in English and one in Chinese-are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms. We are developing a natural language interface to an expert system for message processing.

Expert system software will be a key element of an operator advisory system, a production forecasting system, and a capacity allocation system. Bell Helicopter Textron (Fort Worth, Tex.), a manufacturer of helicopters, has implemented an intelligent system to automate the procurement process. With the new system, the time required for a buyer to purchase a part has been reduced from 1 hour to approximately 10 minutes, increasing productivity by 83 percent. The U.S. Air Force Research Laboratory's Technical Library at the Phillips site on Kirtland Air Force Base, New Mexico, is using advanced pattern-recognition technology to design its virtual library information system. Knowledge-retrieval techniques will be utilized by researchers to access a myriad of information that resides in repositories throughout the state and government.

Eastman Kodak (Rochester, N.Y.), a manufacturer of imaging-related products, has developed an online neural network-based machine vision system for surface mount solder paste inspection. Caere (Los Gatos, Calif.), a provider of neural network-based optical character recognition (OCR) technology, has signed an agreement to supply IBM Ireland with OCR Readers for AN POST, Ireland's national postal service. Using a handheld wand, postal employees will be able to scan text and read bar codes from anywhere on a document. BrainTech (Scottsdale, Ariz.), a developer of neural network and fuzzy logic-based pattern recognition technologies, has signed a development agreement with Raven (Alexandria, Va.), a developer of acoustic systems for the U.S. Navy. BrainTech will integrate its pattern-matching recognition engine into Raven's new medical diagnostic systems.

"If the American public seems a bit confused about the raging debate of security versus civil liberties--Bush/Cheney versus the A.C.L.U.--it may be because the debate itself has been outpaced by technology. In our post-9/11, protowireless world, democracies and free markets are increasingly saturated with prying eyes from governments, corporations and neighbors. For better and worse, free societies are fast entering the world of total surveillance.... Allowing a computer to read your email may not sound threatening, but with advanced pattern-recognition software, scanning many messages over time could produce a powerful consumer profile. As these machines get smarter and smarter, it may soon be far more worrisome to let a machine'read' your information than to have a human reading it.... The hallmarks of the new digital tool-building age are machines that are increasingly smart, small, cheap and communicative.

Machine learning (ML) has received a lot of attention recently, and not without good reason. It has already revolutionized fields from image recognition to healthcare to transportation.

The woman in the photo seems familiar. She looks like Jennifer Aniston, the "Friends" actress, or Selena Gomez, the child star turned pop singer. She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. She was created by a machine. The image is one of the faux celebrity photos generated by software under development at Nvidia, the big-name computer chip maker that is investing heavily in research involving artificial intelligence. At a lab in Finland, a small team of Nvidia researchers recently built a system that can analyze thousands of (real) celebrity snapshots, recognize common patterns, and create new images that look much the same -- but are still a little different.

When analysts and media write about artificial intelligence (AI), most of them unfortunately only talk about machine learning. In doing so, they mention AI and machine learning in the same breath and thus equal AI with one single technology. This is wrong and a concerning progress. In particular, it is confusing the market during a time when 58 percent of organizations worldwide (according to Forrester) are still researching AI. However, AI is more than just machine learning and consists of several different components that provide intelligent solutions.

To be effective, state of the art machine learning technology needs large amounts of annotated data. There are numerous compelling applications in healthcare that can benefit from high performance automated decision support systems provided by deep learning technology, but they lack the comprehensive data resources required to apply sophisticated machine learning models. Further, for economic reasons, it is very difficult to justify the creation of large annotated corpora for these applications. Hence, automated annotation techniques become increasingly important. In this study, we investigated the effectiveness of using an active learning algorithm to automatically annotate a large EEG corpus. The algorithm is designed to annotate six types of EEG events. Two model training schemes, namely threshold-based and volume-based, are evaluated. In the threshold-based scheme the threshold of confidence scores is optimized in the initial training iteration, whereas for the volume-based scheme only a certain amount of data is preserved after each iteration. Recognition performance is improved 2% absolute and the system is capable of automatically annotating previously unlabeled data. Given that the interpretation of clinical EEG data is an exceedingly difficult task, this study provides some evidence that the proposed method is a viable alternative to expensive manual annotation.