Data, in particular, unstructured data has been growing at a very fast pace since mid-2000's. Eighty percent of all data generated is unstructured multimedia content which fails to get focus in organizations' big data initiatives. A good portion of this multimedia content is images and videos. Readily available smart wireless devices along with the rising popularity of sharing images and videos through the internet have contributed significantly in the massive growth of this type of content. Images and videos now reflect a good portion of human knowledge, interactions and conversations.

Google is miles and miles ahead of Apple in AI. But really that should be expected as Google has been doing it a lot longer than Apple and spent way, way more money. Plus Google has so much more data than Apple. But hopefully Apple will really get serious and close the gap over the next couple of year. Think the biggest reason Apple fell so far behind is they really did not focus on AI like Google did and now sounds like Apple is getting more serious about it. In comparisons Apple has done very poorly to Google.

Artificial intelligence and machine learning are becoming increasingly popular across various departments. Healthcare, marketing, and communications are already equipped with various types of this cutting edge tech, and now we see it used in the HR departments as well. Let us explore some of the possible applications of AI in HR. LinkedIn is a great example of using AI to attract talent. The company is using one of the most prominent types of simple machine learning – the related jobs feature.

Pattern recognition is identified as a key human skill that has supported the rise of people to become the dominant species. However, there are limits to this crucial ability, especially when confronted with masses of information that differ only slightly. Small, but significant, variations are more easily recognised by machines that can minutely inspect and compare differences without fatigue and with low margins of error. Humans are, thus, using machines to augment their pattern-recognition capability by teaching machines how to recognise patterns and correlate seemingly disparate data to gain new insights. "Specialised machine-learning algorithms are used to evaluate large quantities of data and derive and/or exploit relationships in the data," says IBM Watson Advanced Cognitive Technology and Solutions data scientist Stefan van der Stockt. "The basic idea behind machine learning is that we want to learn relationships and corelationships between the different elements of the data, whether it be recognising a face or identifying a potentially cancerous lesion on an X-ray image," says Council for Scientific and Industrial Research (CSIR) Mobile Intelligent Autonomous Systems unit principal researcher Dr Benjamin Rosman.

Guorong Wu is an Assistant Professor of Radiology and Biomedical Research Imaging Center (BRIC) in the University of North Carolina at Chapel Hill. Dr. Wu received his PhD degree from the Department of Computer Science in Shanghai Jiao Tong University in 2007. After graduation, he worked for Pixelworks and joined University of North Carolina at Chapel Hill in 2009. Dr. Wu's research aims to develop computational tools for biomedical imaging analysis and computer assisted diagnosis. He is interested in medical image processing, machine learning and pattern recognition.

Ted Dunning is chief applications architect at MapR Technologies. A long time ago in the mid 1950's, Robert Heinlein wrote a story called "A Door into Summer" in which a competent mechanical engineer hooked up some "Thorsen tubes" for pattern matching memory and some "side circuits to add judgment" and spawned an entire industry of intelligent robots. To make the story more plausible, it was set well into the future, in 1970. These robots could have a task like dishwashing demonstrated to them and then replicate it flawlessly. I don't think I have to tell you, but it didn't turn out that way.

Bing has not only grown some eyes, it has begun to learn how to use them. Microsoft's search engine has gained the ability to automatically detect objects within a photo online, the company announced on Sept. 12. The new feature, part of the Bing Visual Search toolset, supplements an existing object recognition tool called Detailed View that allows users to draw a box around an item in a picture and search the web for similar-looking items and related shopping links. Using machine learning, image recognition and other artificial intelligence (AI) technologies, Bing Visual Search can automatically detect items and selects them for the user. Bing has also developed a knack for spotting celebrities.

In the UK, the first proper machine that was tasked with playing a game was the Hollerith Electronic Computer (HEC), which is currently on display at The National Museum of Computing (TNMOC) at Bletchley Park. The machine was displayed to the public in 1953 at the Business Efficiency Exhibition in London. Raymond Bird, the electronics engineer who was tasked with developing the HEC, described the demonstration of the noughts and crosses game as a great success in showing the potential power of computers. Primary Key Associates co-founder Andrew Lea says there are three types of AI. The first is the so-called fake AI, where AI is used as a moniker for smart technology that exhibits pseudo-intelligence.

The pre-trained Inception-v3 model achieves state-of-the-art accuracy for recognizing general objects with 1,000 classes. The model extracts general features from input images in the first part and classifies them based on those features in the second part. We will use this pre-trained model and retrain it to classify houses with or without swimming pools. Upon completion of this journey, you will understand how to load and run a Jupyter Notebook with Nimbix and PowerAI, use transfer learning to leverage the TensorFlow Inception model to create a custom classifier from a set of images, then test and demonstrate the resulting classifier.

AI systems are used to recognize text and speech, to make purchase recommendations on e-commerce websites, to recognize your friends in Facebook photos, and to suggest films for you to watch on Netflix. The core element of those AI systems is something known as machine learning. The term "machine learning" sounds daunting, especially because it's related to the field of artificial intelligence, but it really just refers to the ability of computers to recognize patterns. The key, of course, is not just for a computer to recognize existing patterns that it has already been trained to learn, but also to recognize patterns that it has never seen before. The "learning" in "machine learning," then, refers to the ability of a computer to recognize an ever-growing number of patterns without the need for any form of human supervision.