Dublin's quarterly meetup event for product focused: Machine Learning – Natural Language Processing – Deep Learning – Machine Vision – Augmented Intelligence – Neural Networks – Cognitive – Machine Ethics, in short… Artificial Intelligence. Dublin AI is a community that brings startups, corporates and academics in the AI sphere together to understand the current state of these technologies, its value add for businesses, and what is in store for the near future. Talks aim to reveal trends, industry insights and practical approaches of applying AI technologies. While some of our talks will take a technical deep dive, the forum is open to all those with a strong interest in the field.

Bill Gates calls this market the "holy grail" of computer science. There are currently 1,031 AI start-ups listed on AngelList, with an average valuation of $5.2 million -- which equals nearly $5.4 billion in venture capital investments. The three most-followed companies on that list are robotics company Autonomous, team productivity software maker Crux, and AI social news aggregator Zero Slant. Leo is a Tech and Consumer Goods Specialist who has covered the crossroads of Wall Street and Silicon Valley since 2012. His wheelhouse includes cloud, IoT, analytics, telecom, and gaming related businesses.

Recently, I did a webinar on Machine Learning and R. I received a number of questions during the presentation. Due to time constraints, I was unable to answer all of them, so I have provided the Question and Answers here. Question: Can I Use R in SQL Server to plot non-linear regression curves? We use IC50 and others in Michaelis-Menten kinetics for bio-chemical work. R running on SQL Server provides the functionality of standard CRAN R packages with the additional capability to run the SCALER functions provided by SQL Server's implementation of R. Any other functionality performed in R can therefore also be performed on SQL Server.

Artificial intelligence is difficult to develop because real intelligence is mysterious. This mystery manifests in language, or "the dress of thought" as the writer Samuel Johnson put it, and language remains a major challenge to the development of artificial intelligence. "There's no way you can have an AI system that's humanlike that doesn't have language at the heart of it," Josh Tenenbaum, a professor of cognitive science and computation at MIT told Technology Review in August. In September, Google announced that its Neural Machine Translation (GNMT) system can now "in some cases" produce translations that are "nearly indistinguishable" from those of humans. "Machine translation is by no means solved. GNMT can still make significant errors that a human translator would never make, like dropping words and mistranslating proper names or rare terms, and translating sentences in isolation rather than considering the context of the paragraph or page."

Our brains can comprehend things so well that it makes vision seem very easy. It doesn't take any time for a human to detect an anomaly, or identify the difference between a bus and a car, or to detect and recognize a human face, but it is incredibly hard for a computer to learn how to detect and recognize an object as easy as a human brain. In the last couple of years researchers have made tremendous progress on addressing this problem. They have come up with a solution using deep convolutional neural networks, a model which can perform hard visual recognition tasks which are close to or sometimes even better than the human brain. Convolutional Neural Networks, is a black box that constructs features we would otherwise have to handcraft ourselves, hence to create one it takes very high computing power and a lot of time.

Note: This is a short version of "Deep Learning -- The Unreasonable Effectiveness of Randomness". The paper submissions for ICLR 2017 in Toulon France deadline has arrived and instead of a trickle of new knowledge about Deep Learning we get a massive deluge. This is a gold mine of research that's hot off the presses. Many papers are incremental improvements of algorithms of the state of the art. I had hoped to find more fundamental theoretical and experimental results of the nature of Deep Learning, unfortunately there were just a few.

In a speech today at Web Summit, Facebook CTO Mike Schroepfer laid out a vision for the role artificial intelligence and machine learning will play in the company's ambitions to improve global connectivity, technology accessibility, and human computer interaction. "People want to stay connected and close to other people, so whatever is the best current technology to deploy that is the business we want to be in," said Schroepfer. Large companies like Facebook play an incredibly important role in the artificial intelligence and machine learning ecosystem. Their sheer size and ability to corner the market on talent makes almost every strategic decision they make an industry-wide declaration. Despite setbacks, like the explosion of Facebook's satellite aboard a SpaceX Falcon 9 earlier this summer, the company remains steadfast in its goals to better connect the world.

At the same, the team build a new piece of software designed specifically for executing neural networks with the limited resources available on mobile phones. This AI framework is called Caffe2Go, and according to Facebook, it can execute neural nets in less than 1/20th of a second. Naturally, execution times depend on what models are being executing. But the larger point is that Facebook intends to offer the framework on both iOS and Android devices, intent on building all sorts of AI models that can operate without a tether to the data center. "With anything we can build on the server, we now have a vehicle to ship it on mobile devices--and soon," Schroepfer explains.

It is no secret that artificial intelligence (AI) is growing rapidly in sophistication. As evidenced by self-driving automobiles, computers capable of diagnosing certain medical conditions and bots that can make accurate financial trading predictions, society is poised for a major revolution. Naysayers predict there will be massive job loss as machines enter the labor pool, but history has so far proven that the economy has prospered and grown substantially due to automation. Bots are going to enter the workforce and cause displacement, that is certain. The human element will have to evolve and re-think its position to remain viable when systems become autonomous and self-driving.

Human resources departments rarely, if ever, are thought of as cutting edge when it comes to the use of technology. A closer look, however, shows the implementation of new technologies, including solutions powered by Artificial Intelligence (AI), in almost every aspect of the talent function. According to a recent Towers Watson HR Service Delivery and Technology Survey, HR professionals are overhauling structure to improve quality and efficiency with 33% of the group spending significantly more on technology in the last year. HR's investment in new technology has also spurred the creation of new data sources. Data around employee productivity, wellness, manager effectiveness, and a host of other activities is quickly dwarfing the traditional data set that HR has traditionally been using.