AI is making a huge comeback. It's fascinating to be part of an era where a machine (or a cluster of machines) can take on a chess champion or a Jeopardy contestant and be able to win those contests handily. The increased ease of availability of computing and huge amounts of data is helping immensely. In this seemingly futuristic battle of man versus machine, enterprises have realized that they are sitting on a wealth of data that has not been effectively used so far. Whether it's predicting buying patterns or detecting faults in consumer equipment in advance, it's clear that adapting AI techniques would yield a significant competitive advantage to enterprise solutions. The race for cognitive solutions has thus already begun. Are microservices really just "SOA done right"? Download this exclusive O'Reilly Report to find out. There are many reasons why enterprises are playing catch up. First and foremost, developers consider AI in the same realm as rocket science i.e. very hard to learn and with a significant learning curve. The traditional methods of software development break down, since a set of input(s) might yield different output(s) depending on other ambient factors, and it would be hard to do test driven development, for instance.

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I have observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow, R-caret etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. What Level of Maths Do You Need?

The next version of Microsoft's open source machine learning tools arrived today to give developers a hand in creating intelligent systems. The Microsoft Cognitive Toolkit (previously known as CNTK) provides a set of tools to help developers build systems based on deep learning, without requiring PhD-level knowledge. This revision of the Cognitive Toolkit provides a number of new features, including beta support for Keras, a popular high-level Python API for quickly coding up neural networks. TensorFlow and Theano, two other machine learning frameworks, already support Keras, and this update means it would be possible for data scientists to easily port their code between three different backends. The toolkit also includes support for compressing models to run on lower-powered devices, making it easier for companies to roll out machine learning to edge devices.

While Deep Learning has shown itself to be very powerful in applications, the underlying theory and mathematics behind it remains obscure and vague. Deep Learning works, but theoretically we do not understand much why it works. Some leading machine learning theorists like Vladimir Vapnik criticise Deep Learning for its ad-hoc approach that gives a strong flavour of brute force rather than technical sophistication. Deep Learning is not theory intensive; it is empirical based more (hence causing battle of viewpoints between empiricism and realism) and relies on clever tweakings [1].[1] This is why'Deep Learning' is viewed as a black box and why we preferred to use Theano instead of other packages as it allowed us better view inside the workings of the model (which is still not enough to fully overcome the black box criticism).

Intel just launched a new Autonomous Driving Garage facility in San Jose, at a facility that used to house Altera, the company it acquired in 2015. The autonomous tech development facility is actually one of four garages Intel maintains globally, including one in Arizona, one in Portland and one in Berlin. The Silicon Valley location, however, makes sure its biggest partners in the emerging space are close at hand. The facility officially opened at a press event on Wednesday that included a ribbon cutting ceremony, talks by Intel subject matter experts on various aspects of its self-driving program and a number of demonstrations of different parts of its business, including a ride in partner Delphi's self-driving Audi SUV, a look at BMW's latest advanced autonomous capability testing vehicle (the first in the U.S. and among the first of the fleet of 40 it's committed to producing) and a look at the company's efforts to spearhead development of fast, secure wireless infrastructure. Intel's Garage includes a literal garage -- the spacious facility was large enough to house four vehicles with plenty of room left over for media, analysts and a strong cadre of Intel staff.

A paper from Google's researchers says they simultaneously used as many as 800 of the powerful and expensive graphics processors that have been crucial to the recent uptick in the power of machine learning (see "10 Breakthrough Technologies 2013: Deep Learning"). Feeding data into deep learning software to train it for a particular task is much more resource intensive than running the system afterwards, but that still takes significant oomph. Intel has slowed the pace at which it introduces generations of new chips with smaller, denser transistors (see "Moore's Law Is Dead. It also motivates the startups--and giants such as Google--creating new chips customized to power machine learning (see "Google Reveals a Powerful New AI Chip and Supercomputer").

Artificial Intelligence ('AI') is a runaway success and we think it is going to be one of the biggest, if not the biggest driver of future economic growth. There are major AI breakthroughs on a fundamental level leading to a host of groundbreaking applications in autonomous driving, medical diagnostics, automatic translation, speech recognition and a host more. We're only at the beginning of these developments, which is going in several overlapping stages: We have described the development of specialist AI chips in an earlier article, where we already touched on the new phase emerging - the move of AI from the cloud to the device (usually the mobile phone). This certainly isn't a universal movement but involves inference (the application of the algorithms to answer queries), rather than the more computing-heavy training (where the algorithms are improved through iteration rounds with the help of massive amounts of data). Since GPUs weren't designed with AI in mind, so in principle, it isn't much of a stretch to assume that specialist AI chips will take performance higher, even if Nvidia is now designing new architectures like the Volta with AI in mind at least in part, from Medium: Although Pascal has performed well in deep learning, Volta is far superior because it unifies CUDA Cores and Tensor Cores.

Deep learning and neural networks are increasingly important concepts in computer science with great strides being made by large companies like Google and startups like DeepMind. A zero that's difficult to distinguish from a six algorithmically

The paper is here: https://arxiv.org/abs/1702.08835v2 My first pass seems to say that, when the authors report competitive data, it's on simple datasets compared to simple (sometimes exceedingly so, e.g. LeNet-5) deep networks, and when gcForest is compared to even moderately modern (e.g., AlexNet) architectures, the performance is nowhere near competitive. Is this worth reading in depth?

If you don't know, Julia is "a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments." Julia is fast, and enjoys support from and integration with the Jupyter notebook environment. Julia can call C directly without a wrapper, integrates top tier open source C and Fortran code into its Base library, and can easily call Python as well. Julia is built for parallel and cloud computing, and has particular interest from the analytics and scientific computing communities. According to KDnuggets' most recent analytics software poll, Julia placed 8th on the list of most used programming languages.