IBM is trying to remove some of the complications related to image recognition with new tools to automate critical machine learning tasks. A major update of the company's PowerAI tools has a feature called AI Vision, an auto tuner that makes it easy to identify and classify pictures. It will also speed up image recognition by breaking down tasks over multiple clusters. AI Vision plays a big role in automating machine learning by creating a tuned model, said Sumit Gupta, vice president of machine learning. The software abstracts machine learning, and developers don't need knowledge of low-level access to frameworks to tune, train, and deploy image recognition models.

Microsoft is doubling down on its cloud AI services for business customers with a fleet of new offerings aimed at helping companies deal with video and unique problems not solved by its off-the-shelf cognitive services. New services announced Wednesday include a new Video Indexer service that will provide customers with automated captioning, sentiment analysis, custom face recognition, object detection, optical character recognition and keyword extraction of videos they provide. The tool is built on existing Microsoft services, but gives customers an easier way to process large amounts of video for indexing and analysis rather than require manual work by humans. Also new is a custom image recognition service that allows users to take Microsoft's existing tools for detecting objects and teach them to recognize other things that aren't generally applicable. For example, manufacturers could use the service to identify different types of parts that Microsoft's off-the-shelf image recognition service couldn't recognize, according to Irving Kwong, a principal product manager in the company's artificial intelligence group.

Machine learning, AI, cognitive computing, natural language understanding, image recognition, pattern matching, autonomous devices – these are just a few of 2017's loosely defined catchall phrases. But in practice, they each refer to a significant field of study that is guaranteed to have an impact on the way people live and how business is done. If you do not have strict, well-enforced data governance policies, your various units are busy making a data mess that you will eventually have to clean up. Each unit has any number of projects in the works. Their competitors are making apps and creating systems designed to reduce friction and reduce costs wherever possible.

Machine learning, AI, cognitive computing, natural language understanding, image recognition, pattern matching, autonomous devices – these are just a few of 2017's loosely defined catchall phrases. But in practice, they each refer to a significant field of study that is guaranteed to have an impact on the way people live and how business is done. If you do not have strict, well-enforced data governance policies, your various units are busy making a data mess that you will eventually have to clean up. Each unit has any number of projects in the works. Their competitors are making apps and creating systems designed to reduce friction and reduce costs wherever possible.

Academic institutions are also getting in on the ground floor of advanced pattern recognition. At Indiana University-Purdue University Indianapolis, researchers are turning machine learning algorithms loose on pathology slides to predict relapse rates for acute myelogenous leukemia. In a small study published earlier this year, one algorithm was able to identify patients who would relapse with 100 percent accuracy.

Can a machine learn aesthetics in a way a human would? Could it then look at a set of photos, and draw on those same aesthetics to reproduce a different set? It's a big question because it has long-term implications for how AI is going to develop. Is it just what you "like"? How does it all work? When you as a human find it hard to express what you like do you think a machine going to find it easy?

For business people, AI presents a variety of challenges. On a technology level, artificial intelligence and machine learning is complicated to develop and demands rich data sets to produce meaningful results. From a business perspective, many business leaders have difficulty figuring out where to apply AI and even how to start the machine intelligence journey. Making matters worse, the constant drumbeat of AI hype from every technology vendor has created a continual barrage of noise confuses the market about the real possibilities of AI. To cut through this noise, I have invited many world-leading practitioners to share their expertise as part of the CXOTALK series of conversations with innovators.

Can machines think – and, if so, can they think critically about race and gender? Recent reports have shown that machine-learning systems are picking up racist and sexist ideas embedded in the language patterns they are fed by human engineers. The idea that machines can be as bigoted as people is an uncomfortable one for anyone who still believes in the moral purity of the digital future, but there's nothing new or complicated about it. "Machine learning" is a fancy way of saying "finding patterns in data". Of course, as Lydia Nicholas, senior researcher at the innovation thinktank Nesta, explains, all this data "has to have been collected in the past, and since society changes, you can end up with patterns that reflect the past. If those patterns are used to make decisions that affect people's lives you end up with unacceptable discrimination."

Pattern-recognition algorithms can transform horses into zebras; winter scenes can become summer; artificial intelligence algorithms can generate art; robot radiologists can analyze your X-rays with remarkable precision. We have reached the point where pattern-recognition algorithms and artificial intelligence (A.I.) are more accurate than humans at the visual diagnosis and observation of X-rays, stained breast cancer slides and other medical signs involving general correlations between normal and abnormal health patterns. Before we run off and fire all the doctors, let's better understand the A.I. landscape and the technology's broad capabilities. A.I. won't replace doctors -- it will help to empower them and extend their reach, improving patient outcomes. The challenge with artificial intelligence is that no single and agreed-upon definition exists.

Google's Cloud Vision API is easily blinded by the addition of a little noise to the images it analyses, say a trio of researchers from the Network Security Lab at the University of Washington, Seattle. Authors Hossein Hosseini, Baicen Xiao and Radha Poovendran have hit arXiv with a pre-press paper titled Google's Cloud Vision API Is Not Robust To Noise (PDF) that says "In essence, we found that by adding noise, we can always force the API to output wrong labels or to fail to detect any face or text within the image." The authors explain that if one can add different types of noise to an image, the Cloud Vision API will always incorrectly analyse the pictures presented to it. The image at the top of this story (or here for m.reg readers) shows the false results the API returned. It doesn;t need to be a lot of noise: the authors found an average of 14.25 per cent "impulse noise" got the job done.