Machine learning is perhaps the hottest buzzword in cybersecurity today. The artificial intelligence technology is deployed by cybersecurity firms in an effort to keep pace with the evolution of cyberattacks, as machine learning algorithms are able to improve predictability the more it is used. But according to Guy Caspi, CEO of cybersecurity company Deep Instinct, machine learning is no longer enough in an age of unprecedented evolution and volume of cybercrime. G DATA researchers recently found that last year a new malware specimen surfaced every 4.6 seconds. In the first quarter of 2017, it reduced to every 4.2 seconds, meaning millions and millions of new malware surfaced every year.

Last year, Movidius announced its Fathom Neural Compute Stick -- a USB thumb drive that makes its image-based deep learning capabilities super accessible. But then in September of last year, Intel bought Movidius, delaying the expected winter rollout of Fathom. However, Intel has announced that the deep neural network processing stick is now available and going by its new name, the Movidius Neural Compute Stick. "Designed for product developers, researchers and makers, the Movidius Neural Compute Stick aims to reduce barriers to developing, tuning and deploying AI applications by delivering dedicated high-performance deep-neural network processing in a small form factor," said Intel in a statement. The Compute Stick contains a Myriad 2 Vision Processing Unit that uses only around one watt of power.

Yes, most faculty, graduate students, and a lot of engineering teams in industry have already abandoned everything else and shifted to deep learning. Most new graduate students in applied areas such as computer vision that I meet, know nothing about probabilistic graphical models for instance, and their proposed solution to any problem is a CNN/LSTM/GAN. It is a huge deal to have an algorithm that can absorb large amounts of data - which is what deep learning methods enable. The (re-)discovery of such an algorithm (deep neural network training) has thus made possible many new applications, which were not possible just a few years ago. How excited are you about the steam engine today?

The world of Banking and Finance is changing more than ever, with Artificial Intelligence (AI) being the front-runner in bringing about a sea-change in the banking industry. Various AI solutions have already been implemented in banking across various areas like core banking, operations efficiency, customer facing services and analytics. With the onset of AI, banking will no longer be just apps, websites or physical branches but a whole new experience. Although, it is only recently that we have witnessed the application of AI, the history of AI goes back to the 1950s where a paper was published by Alan Turing about the possibility of machines with true intelligence. This was only the inception of Artificial Intelligence as a concept but no use case or AI process was implemented until the late 1990s.

Machine Learning is no longer just a buzzword, it is all around us: from protecting your email, to automatically tagging friends in pictures, to predicting what movies you like. Computer vision is one of today's most exciting application fields of Machine Learning, with Deep Learning driving innovative systems such as self-driving cars and Google's DeepMind. OpenCV lies at the intersection of these topics, providing a comprehensive open-source library for classic as well as state-of-the-art computer vision and Machine Learning algorithms. In combination with Python Anaconda, you will have access to all the open-source computing libraries you could possibly ask for. Machine Learning for OpenCV begins by introducing you to the essential concepts of statistical learning, such as classification and regression.

Today, machine learning is found in almost every product and service by Apple. They use deep learning to extend battery life between charges on their devices and detect fraud on the Apple store, recognize the locations and faces in your photos, and help Apple choose news stories for you. The concept of AI (Artificial Intelligence) has been the subject of many discussions lately. According to some predictions, AI will have the ability to learn by itself, outclassing the capabilities of the human brain, and even manage to fight for equal rights by the year 2100. Even though these are (still) just speculations and predictions, companies like Apple are developing and implementing machine learning technology, which is still in its infancy.

Using machine learning to fight fraud may just be starting to see its day in the sun, but if industry trends are to be believed, it's fast becoming the soul of the global fraud fighting machine. From offering fraud protection to identity verification, machine learning is increasingly being used by companies small and large to build out their next generation of products. In the last month alone, the digital identity space has seen numerous players roll out upgrades to their tools and services, which are now being powered by deep learning algorithms. In Cyprus, for example, AU10TIX announced the launch of its new OCR reader that utilizes deep learning algorithms to decipher complicated language fonts and extract content from difficult-to-read ID documents. Back in the states, Calif.-based Sift Science debuted its account takeover (ATO) prevention tool.

According to a report from Markets and Markets, the global Artificial Intelligence (AI) space is expected to surge to $16 billion over the next five years, growing at a CAGR of nearly 63 percent from 2016 to 2022. AI is the development of smart systems that can perform tasks which normally require human intelligence. Machine and deep learning are subsets of AI that mimic activities in neural networks of the brain where thinking occurs. Deep learning software can be programmed to recognize patterns in the digital representations of sounds, images and other data. While popular images of AI involve robots bent on annihilating humanity, the most common real-world applications are taking place at the consumer level.

The first time I watched the DARPA challenge for self-driving cars I thought this was a breakthrough I want to be involved in. But to my surprise, the challenge was in 2005 -- so long ago and yet nothing came to the market for a long time. Moreover, Artificial Neural Networks -- the algorithms to process all this data was developed in the 1960's (!!). So what happened all of a sudden and what has changed to make self-driving cars (and AI) go forward? The answer is Nvidia happened, aka GPU chip designs (Graphics Processing Units) -- even if at the time the big breakthrough happened, Nvidia was not aware they made this huge contribution.

In a previous post, I introduced the Meta Meta-Model of Deep Learning. However, I did not introduce its details. A word of warning for the reader, the concepts in this section is in flux and in undergoing a lot of changes. Therefore, this article is just a reflection of my current understanding of the language of Deep Learning Meta Meta-Model. That's definitely a mouth full, so to make life simpler for everyone, I just call this the Deep Learning Canonical Patterns.