Publications like The Wall Street Journal, Forbes and Fortune have all called 2017 "The Year of AI." AI outperformed professional gamers and poker players in new realms. Access to deep learning education expanded through various online programs. The speech recognition accuracy record was broken multiple times, most recently by Microsoft. And research universities and organizations like Oxford, Massachusetts General Hospital and GE's Avitas Systems invested in deep learning supercomputers. These are a few of many milestones in 2017.

Machine learning, deep learning and artificial intelligence (AI) are attracting huge amounts of interest from analysts, press and IT teams. We are seeing large corporations apply data science and machine learning to make AI a reality for businesses and consumers. But is there a problem with too few companies holding too much of the market? What is the "one percent" problem around AI? Gartner predicts that 80 percent of data scientists will have deep learning skills in place by 2019, according to its research team in September 2017; meanwhile Teradata and Vanson Bourne research found that more than 42 percent of enterprises see opportunities for further implementation and process integration of AI in their operations. With so many enterprises looking at their approaches to AI and machine learning, the availability of people with the right skills and experience can become a stumbling block.

Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Timely prediction of disruptions, the sudden loss of control of the hot, charged plasma that fuels the reactions, will be vital to triggering steps to avoid or mitigate such large-scale events. Today, researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability. Researchers led by William Tang, a PPPL physicist and a lecturer with the rank of professor in astrophysical sciences at Princeton, are developing the code for predictions for ITER, the international experiment under construction in France to demonstrate the practicality of fusion energy. The new predictive software, called the Fusion Recurrent Neural Network (FRNN) code, is a form of "deep learning" -- a newer and more powerful version of modern machine learning software, an application of artificial intelligence.

Fulfilment & Logistics At Amazon, we've been making investments in ML for the last 20 years… 3. 2017, Amazon Web Services, Inc. or its Affiliates. At Amazon, we've been making investments in ML for the last 20 years… Fulfilment & Logistics Search & Discovery 5. 2017, Amazon Web Services, Inc. or its Affiliates. At Amazon, we've been making investments in ML for the last 20 years… Fulfilment & Logistics Existing Products Search & Discovery 7. 2017, Amazon Web Services, Inc. or its Affiliates. Put machine learning in the hands of every developer and data scientist ML @ AWS: Our mission 14. 2017, Amazon Web Services, Inc. or its Affiliates. What are some of the ML capabilities our customers are asking for?

Yann LeCun is Director of AI Research at Facebook, and Silver Professor of Dara Science, Computer Science, Neural Science, and Electrical Engineering at New York University, affiliated with the NYU Center for Data Science, the Courant Institute of Mathematical Science, the Center for Neural Science, and the Electrical and Computer Engineering Department.

LG is getting serious with artificial intelligence and will launch products and services that use AI under a new sub-brand called "ThinQ" starting in 2018. All its upcoming TVs, fridges, even electronic devices and services under the new brand will have features developed with deep learning techniques and will be able to communicate with one another. LG says you can expect its new offerings to use its own AI tech, DeepThinQ, as well as its partners', but it didn't elaborate further or listed possible features. The Korean company already has smart appliances out on the market under the SmartThinQ banner, but it says the new brand is meant to "highlight that LG('s) intelligent products." It said in a statement that "AI is the next frontier in technology and as a leader in home appliances and consumer electronics, [it has] a responsibility to make AI more approachable and less intimidating."

Juergen Schmidhuber, 54, is a computer scientist who works on Artificial Intelligence (AI). Considered to be one of the pioneers in improving neural networks, his techniques, the best known being Long Short-Term Memory, have been incorporated in speech translation software in smartphones. In this interview conducted in Berlin, he speaks of developments in AI, why the fear of job loss due to AI is unfounded, and his work. I would be quite biased because I'd say what's happening in my lab is the most exciting. My goal remains the same as it has been for a very long time: to build a general-purpose AI that can learn to do multiple things.

As 2017 comes to a close, I have been noodling about what deserves the title of "Technology of the year." Clearly, Artificial Intelligence (AI) is the winner! Quite a few terms are used interchangeably when discussing the subject of AI, including Deep Learning, Machine Learning, Neural Networks, Graph Theory, Random Forests, and the list goes on. AI is the broad subject, describing how intelligence is gained through machine learning using various algorithmic options like graph theory, neural networks, random forests, etc. Deep learning is a specialized form of machine learning which expands the sample data sets to multi-layer learning. I first worked on Artificial Intelligence during my final semester of engineering school.

As 2017 comes to a close, I have been noodling about what deserves the title of "Technology of the year." Clearly, Artificial Intelligence (AI) is the winner! Quite a few terms are used interchangeably when discussing the subject of AI, including Deep Learning, Machine Learning, Neural Networks, Graph Theory, Random Forests, and the list goes on. AI is the broad subject, describing how intelligence is gained through machine learning using various algorithmic options like graph theory, neural networks, random forests, etc. Deep learning is a specialized form of machine learning which expands the sample data sets to multi-layer learning. I first worked on Artificial Intelligence during my final semester of engineering school.

Scientists in Boston have developed an automated artificial intelligence (AI)-guided microscopy system that can help diagnose serious bloodstream infections (BSIs) quickly and accurately. The technology, which uses a trained convolutional neural network (CNN) to recognize the different shapes and distribution of pathogenic bacteria, could help to speed diagnosis and potentially save patient lives, as well as address the current lack of trained microbiology technologists, suggest its developers at Harvard Medical School and Beth Israel Deaconess Medical Center (BIDMC). "This marks the first demonstration of machine learning in the diagnostic area," comments James Kirby, M.D., director of the Clinical Microbiology Laboratory at BIDMC, and associate professor of pathology at Harvard Medical School. "With further development, we believe this technology could form the basis of a future diagnostic platform that augments the capabilities of clinical laboratories, ultimately speeding the delivery of patient care." The researchers report on the technology in the Journal of Clinical Investigation, in a paper entitled "Automated Interpretation of Blood Culture Gram Stains using a Deep Convolutional Neural Network."