Over the years, society has become more dependent on digital technologies. Today, nearly every person, business, and government agency uses the internet to transmit and store data. As a result of that dependence, there is no shortage of hackers who try to access that data. We see this at every level. Celebrities have had their phones hacked and their personal photographs stolen and dispersed online.

When millions of people across the world tuned in to watch DeepMind's machine beat the human Go world champion Lee Sedol, they also witnessed a historic victory for open-source. DeepMind used a scientific computing framework called Torch extensively in the development and execution of AlphaGo's neural networks. Torch was first released back in 2002 under a BSD open-source license with algorithms that are still commonly used by data scientists such as multi-layer perceptrons, support vector machines and K-nearest neighbours. Torch also supported ensembles -- a popular technique that combines the output of multiple algorithms, usually with a weighted average. It's not just open-source software that contributed to the growth of machine learning.

Consequently, there is an equivalence between parameter averaging and update-based data parallelism, when parameters are updated synchronously (this last part is key). This equivalence also holds for multiple averaging steps and other updaters (not just simple SGD). Update-based data parallelism becomes more interesting (and arguably more useful) when we relax the synchronous update requirement. That is, by allowing the updates Wi,j to be applied to the parameter vector as soon as they are computed (instead of waiting for N 1 iterations by all workers), we obtain asynchronous stochastic gradient descent algorithm. These benefits are not without cost, however. By introducing asynchronous updates to the parameter vector, we introduce a new problem, known as the stale gradient problem.

New times are marked by the sign of the digital age, globalization and the huge amount of mass data generated daily. Big Data is rigged to great challenges and better opportunities. Beyond the famous 5 Vs that characterize it (volume, velocity, variety, veracity and value), Big Data have great possibilities in the most unimaginable fields. And it does, especially, because new technologies have emerged to respond, with unprecedented efficiency, to the needs of storage and analysis of big data. There are many technologies and concepts that are part of this universe of big data, whose growth is unstoppable, as the Internet of Things (IoT), data exchange machine to machine (M2M), the increasingly complex environment of IT or predictive machine learning. The Big Data challenges, in effect, require capable approaches and systems to collect, store, make efficient searches and, finally, carry out analysis, whose results could be conveniently displayed.

This year the excitement is about Salesforce's introduction of Einstein – artificial intelligence (AI) built into the core of the Salesforce Platform. With Einstein, Salesforce is adding AI capabilities to Sales, Service and Marketing Clouds. This is a very significant new capability, allowing learning from all the valuable data that exists in the Salesforce Clouds. And learning is the most critical part of this offering because it leads to action recommendations, outcome predictions and if requested automation, which is what every organization is looking for today to get closer to their customers. Informatica is a long-standing partner with Salesforce and has teamed with us to help 5,000 customers on their journey to the Cloud.

A brief and concise tutorial on how to visualize different aspects such as the loss of your neural network using tensorboard. We are going to work with a fully-connected neural network using the MNIST dataset. I'm going to use the network I have introduced in an earlier post. It achieves on the test-set an accuracy of 90%. This is not bad but we have no clue what is actually going on or how our model looks like.

Philip Koopman, professor of Carnegie Mellon Univ., believes the biggest hole in a Federal Automated Policy published late Sept. is in the regulators' failure to tangle head-on with fundamental difficulties in testing Machine Learning -- a problem already known to the scientific/engineering community. Representativeness of data Carmakers are building a fake city, for example, in Michigan to test autonomous vehicles. What's important, though, is whether the test data represents real-world driving conditions? A highly autonomous vehicle is designed to operate only in a certain designated area such as "driving only in downtown Pittsburgh." In DoT lingo, this concept is the "Operational Design Domain."

Consider a case where you search for a gadget in Google Image search. Your screen will be deluged with numerous images of it drawn from infinite quarters across world wide web. The logic behind surfacing these images is quite simple. If the image uploaded has a name, title and'alt' tag mentioned, that image will get picked by google crawlers. Another possibility is, even though the image might not have any such parameters, but if the web page content(where images reside) matches the search term, the image in that web page shows up. This works fluently if it was not been abused by black strategies.

This year legal tech companies and law firms that have developed legal AI applications will have a dedicated category for the 2nd'Global Annual Achievement Awards for Artificial Intelligence'. Those developing legal AI technology can join the competition under the: 'Best use of AI in Legal Tech' category, which is defined as an: 'Application of AI in the Legal Tech Industry. This can be applications developed by law firms themselves, technology companies or consultancies providing products and services to the legal sector.' The legal tech category is one of 20 main AI prize categories this year following a successful first year. The awards are organised by UK-based Home.AI, a forum for AI and machine learning across the entire sector, including legal tech, fin tech and more specific areas such as Natural Language Processing and AI in robotics.

Artificial Intelligence (AI) is fast becoming a buzzword in the technology industry. If you are following up technology news, you will get to read eye catching AI news almost every day. Large companies are realising that they will be at a loss if they remain behind in acquiring competence in AI. Prominent technology companies like Google, Facebook, Microsoft and IBM are investing billions of dollars in developing AI teams and technologies. Venture capitalists are investing in a number of start-ups promising to make AI products.