Deep learning is part of the broader family of machine learning methods. It was introduced with the objective of moving machine learning closer to its main goal--that of artificial intelligence. The human brain has evolved over many, many years and is one of our most important organs. The brain perceives every smell, taste, touch, sound and sight. Many decisions are taken by the brain every nano second, without our knowledge.

From multi-billion mergers and acquisitions, to competition heating up in the wireless arena, to the fate of Net Neutrality hanging in the balance, 2017 was a busy year in the telecom industry. Here are ten of the biggest headline-making news stories.

Many of the technology trends that drove us into 2017 will continue into 2018: connected devices, digital transformation, the internet of things (IoT), machine learning, artificial intelligence, and automation. These hot-button issues will remain part of the technology vocabulary in 2018 and beyond. Where I see a substantive difference is in the union of the technologies. AI and IoT are transformative by themselves; now imagine digital transformation in a connected and automated world empowered by an artificial intelligence of things. Going into 2018, I see two common technology characteristics: intelligence and automation.

At the 2017 Advanced Process Control (APC 2017) conference, several companies presented implementations of deep learning to find transistor defects, align lithography steps, and apply predictive maintenance. The application of neural networks to semiconductor manufacturing was a much-discussed trend at the 2017 APC meeting in Austin, starting out with a keynote speech by Howard Witham, Texas operations manager for Qorvo Inc. Witham said artificial intelligence has brought human beings to "a point in history, for our industry and the world in general, that is more revolutionary than a small, evolutionary step." People in the semiconductor industry "need to take what's out there and figure out how to apply it to your own problems, to figure out where does the machine win, and where does the brain still win?" At Seagate Technology, a small team of engineers stitched together largely packaged or open source software running on a conventional CPU to create a convolution neural network (CNN)-based tool to find low-level device defects. In an APC paper entitled Automated Wafer Image Review using Deep Learning, Sharath Kumar Dhamodaran, an engineer/data scientist based at Seagate's Bloomington, Minn.

"We're definitely right in the eye of the storm of the hype cycle," Rasu Shrestha, M.D., chief innovation officer at University of Pittsburgh Medical Center, told SearchHealthIT on the busy "technical exhibition," or show, floor. "Having said that, that hype is being driven by an immense amount of hope. Could AI and machine learning solve for the complexities of healthcare?" Langlotz acknowledged that radiology AI has already been through a number of hype-bust cycles in recent decades, but his work and that of colleagues at the Mayo Clinic and The Ohio State University, among others, shows that AI and machine learning have made dramatic progress. Luciano Prevedello, M.D., division chief for medical imaging informatics at The Ohio State University Wexner Medical Center, said at the same deep learning session that "from 2014 to 2015 is when the algorithms started surpassing the human ability to classify" medical image data.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. To close out 2017, we recently asked some of the leading experts in Big Data, Data Science, Artificial Intelligence, and Machine Learning for their opinion on the most important developments of 2017 and key trends they 2018. This post, the first in this series of such year-end wrap-ups, considers what happened in Machine Learning & Artificial Intelligence this year, and what may be on the horizon for 2018. "What were the main machine learning & artificial intelligence related developments in 2017, and what key trends do you see in 2018?"

Recent news on Sophia the robot getting citizenship in the Saudi Arabia has widely attracted daily news and social media. Despite the debates and agitations on a robot getting recognition as humans, experts view this event as a phenomenal milestone in the research of AI. The current level of Artificial Intelligence is achieved through years of research in Machine Learning, Deep Learning and other related fields. With a lot of hype and investments around, Deep Learning technology – a subdivision of Machine Learning is now successfully applied in our daily life from speech recognition apps in smartphones to YouTube recommendations. One of the pioneers of the Deep Learning, Andrew Ng feels that AI is the new form of electricity where every AI application in future electronic devices will be fuelled by Deep Learning models.

The Facebook AI Research (FAIR) Residency Program is a one-year research training program with Facebook's AI Research group, designed to give you hands-on experience of machine learning research. The program will pair you with a senior researcher or engineer in FAIR, who will act as your mentor. Together, you will pick a research problem of mutual interest and then devise new deep learning techniques to solve it. We also encourage collaborations beyond the assigned mentor. The research will be communicated to the academic community by submitting papers to top academic venues (NIPS, ICML, ICLR, CVPR, ICCV, ACL, EMNLP etc.), as well as open-source code releases.

Jeff Hawkins has a principle that intuitively makes a lot of sense, yet is something that Deep Learning research has not emphasized enough. This is the notion of embodied learning. That is, biological systems learn from interacting with the environment. Hawkins is of the opinion that the brain learns by interacting with its environment. The classic Deep Learning training procedure is one of the crudest teaching methods that one can possibly imagine.

Many years ago I was with Garry Kasparov for an event in London's Home House, and there we had dinner with a young lad, a former child prodigy in chess, one who had reached master level (Elo 2300) at the age of 13 and captained a number English junior chess teams. It was an interesting encounter with the boy enthusiastically describing a computer game he was developing. After he left I said to Garry: "That's a cocky young fellow!" "But very smart," Garry replied. And we left it at that. More than twenty years later I had occasion to contact him again.