Deep learning is a vast field that's generating massive interest these days. It's popularly used in research but has slowly gained market penetration in the industry in the last few years. But what essentially is deep learning? Deep learning refers to neural networks with lots of layers. It's still quite a buzzword, but the technology behind it is real and quite sophisticated.

During the spring semester of my junior year in college, I had the opportunity to study abroad in Copenhagen, Denmark. I had never been to Europe before that, so I was incredibly excited to immerse into a new culture, meet new people, travel to new places, and, most important, encounter a new language. Now although English is not my native language (Vietnamese is), I have learned and spoken it since early childhood that it has become second-nature. Danish, on the other hand, is an incredibly complicated language with very different sentence structure and grammatical made-ups. Before my trip, I tried to learn a bit of Danish using the app Duolingo; however, I only got a hold of simple phrases such as Hello (Hej) and Good Morning (God Morgen).

Backpropagation over deep neural networks has as much to do with the way the brain learns as modern jet airplanes have to do with the way birds fly. Both jets and birds fly, but they do so using entirely different principles. Jets do things birds cannot (fly at 500 miles per hour carrying many passengers), birds do things jets cannot (take off instantly). Each neuron is sending out "da dit da" messages like Morse code to neighboring neurons. The transfer functions are entirely different from RLUs or sigmoid.

Real time strategy games are among the most challenging for artificial intelligence development and research. The need to manage resources and agent co-ordination in this genre still presents real challenges to even the most state of the art techniques in AI. My recent series on the AI of Total War highlights the continued efforts by series developers Creative Assembly to improve and expand the suite of AI systems required to craft the epic battles and nuanced diplomacy players comes to expect from that franchise. Today I want to look at this same issue from a research perspective, with a particular focus on the franchise that is arguably most synonymous with the genre: Blizzard's StarCraft. I want to take a look at the challenges this series presents to AI research and the significant efforts made in developing new AI techniques that adopts StarCraft as a test-bed. It's important that this be re-iterated at time when mass media rhetoric suggests the recent interest by the likes of Google DeepMind is the first real exploration of the problem.

Apache Spark has become the de-facto data processing and AI engine in enterprises today due to its speed, ease of use, and sophisticated analytics. As the first Unified Analytics engine to unify data with AI, Spark allows data engineering and data science teams to simplify data preparation and model training -- enabling innovative AI use cases that leverage advanced analytics like machine learning, graph analytics, and deep learning. Join Bill Chambers, author of the book "Spark: The Definitive Guide," and Matei Zaharia, Chief Technologist and Co-founder of Databricks and the orginal creator of Apache Spark, in this Data Science Central webinar as they break down the basic operations and common functions of Spark and walk through sample use cases where Spark has helped accelerate AI innovation. In this webinar, we will cover: A gentle overview of big data and Spark Expert guidance on how to use, deploy and maintain Spark The fundamentals of monitoring, tuning, and debugging Spark An exploration into machine learning techniques and scenarios for employing MLlib, Spark's scalable machine-learning library Speakers: Bill Chambers, Product Manager -- Databricks Matei Zaharia, Co-founder and Chief Technologist -- Databricks Hosted by: Bill Vorhies, Editorial Director -- Data Science Central

These and many other fascinating findings and insights are from a recent McKinsey Global Institute (MGI) Discussion Paper, Notes from the AI frontier: Applications and value of deep learning. Titled Notes from the AI Frontier: Insights From Hundreds Of Use Cases (36 pp., PDF, no opt-in) the discussion paper draws on MGI research and the firm's applied experience with artificial intelligence (AI) of McKinsey Analytics, assessing the practical applications and the economic potential of advanced AI techniques. The discussion paper's findings are based on intensive MGI analytics collated and integrated with more than 400 use cases across 19 industries and nine business functions.

Early results from our partnership with the Radiotherapy Department at University College London Hospitals NHS Foundation Trust suggest that we are well on our way to developing an artificial intelligence (AI) system that can analyse and segment medical scans of head and neck cancer to a similar standard as expert clinicians. This segmentation process is an essential but time-consuming step when planning radiotherapy treatment. The findings also show that our system can complete this process in a fraction of the time. More than half a million people are diagnosed each year with cancers of the head and neck worldwide. Radiotherapy is a key part of treatment, but clinical staff have to plan meticulously so that healthy tissue doesn't get damaged by radiation: a process which involves radiographers, oncologists and/or dosimetrists manually outlining the areas of anatomy that need radiotherapy, and those areas that should be avoided.

By "higher-level" they mean higher abstraction level, which is what we're after. So we have our 7 lines of code for a multi-layer neural net. This is magnificent -- 5 lines of code to define our neural net structure (input 2 hidden output regression), 2 lines to train it. Our notebook code is here. Let's go through this in detail, you'll notice that the data and learning intent is identical to our earlier example.

The initial focus on NVIDIA's recently launched GeForce RTX 2080 Ti and GeForce RTX 2080 graphics cards has been on how well they perform in games, especially when cranking up the resolution to 4K (3840x2160). That will continue to be a point of interest, though it's not the only one. A fresh set of benchmarks making the rounds highlight how the new cards perform in deep learning workloads. Before we get to the numbers, let's talk about why this matters. As you might already know, the GeForce RTX series pushes consumer graphics cards into new territory.

CA Technologies announced its participation in scientific research to discover how Internet of Things (IoT) applications can use a type of AI known as'deep learning' to imitate human decisions. The research will also explore how to prevent that AI-based decisions are not producing biased results. This three-year research project is named ALOHA (adaptive and secure deep learning on heterogeneous architectures). It is funded by the European Union as part of the Horizon 2020 research and innovation programme, and coordinated by the University of Cagliari in Italy. "The future of all technologies will include AI and deep learning in some way," said Otto Berkes, CTO, CA Technologies.