When you hear "artificial intelligence" or "machine learning," what comes to mind? A complicated technology that demands deep domain experience or a degree to use? This was once the way technology worked; only a select few had access. But innovation has a funny way of changing things. What might seem out of reach today can become widely accessible tomorrow -- just look at the GPS system, or drones. Machine learning has made it so that marketing automation platforms can be predictive -- able to learn, think and act without explicit instructions.

For all of the visions of robots taking over the world, stealing jobs and outpacing humans in every facet of existence, we haven't seen many cases of AI drastically changing industries or even our day-to-day lives just yet. For this reason, media and AI deniers alike question whether true broad-scale AI even exists. Some go as far as to conclude that it doesn't. The answer is a bit more nuanced than that. Current AI applications can be broken down into three loose categories: Transformative AI, DIY (Do It Yourself) AI, and Faux AI.

The consumer in-store retail experience is undergoing a critical evolution, and it's clearly upended the retail industry: in the first four months of 2017 alone, there have been fourteen retail bankruptcies -- almost as many as in all of 2016. Other companies, such as J.C. Penney, Macy's, and Sears, have announced massive store closures. While putting together Firebrand Group's Future of Artificial Intelligence report for our clients (you can get an excerpt, focused on retail innovation, here), my team and I reviewed nearly one hundred brands engaging in some form of retail innovation. Starbucks already allows people to order remotely and go into their retail locations to pick up drinks, and is presently deploying an AI assistant into their app. Called My Starbucks Barista, the feature will allow users to place orders with one tap of a button, then speaking to a virtual barista.

As artificial intelligence and machine learning applications become pervasive, questions arise with regards to rationalising jobs and managing human talent. Businesses must decide how best to balance the efficiencies of automating tasks using intelligent machines while at the same time managing the risks of automating too quickly or slowly. A useful tool in deciding how jobs may be affected in the era of robotics and cognitive machines is the'Return on Improved Performance' (ROIP) curve shown in the graph. Using it, companies can make the important distinction between two categories of role: 'proficiency' and'pivotal'. Let's take the airline industry as an example.

A week ago, Microsoft held its Build developer conference in its backyard in Seattle. This week, Google did the same in an amphitheater right next to its Mountain View campus. While Microsoft's event felt like it embodied the resurgence of the company under the leadership of Satya Nadella, Google I/O -- and especially its various, somewhat scattershot keynotes -- fell flat this year. The two companies have long been rivals, of course, but now -- maybe more than ever -- they are on a collision course that has them compete in cloud computing, machine learning and artificial intelligence, productivity applications and virtual and augmented reality. Both opened their respective shows. But while Pichai used his time mostly to announce new stats and a new product or two, Nadella instead used his time on stage to talk about the opportunities and risks of the inevitable march of technological progress that went way beyond saying that his company is now'AI first.' "Let us use technology to bring more empowerment to more people," Nadella said of one of the core principles of what he wants his company to focus on.

Several organizations are already using drones to fight poaching, but the Lindbergh Foundation is taking it one step further. The environmental non-profit has joined forces with Neurala in order to use the company's deep learning neural network AI to boost the capabilities of the drones in its Air Shepherd program. Neurala taught its technology what elephants, rhinos and poachers look like, so it can accurately pinpoint and mark them in videos. It will now put the AI to work sifting through all the footage the foundation's drones beam back in real time, including infrared footage taken at night. The AI's job is to pore over these videos and quickly identify the presence of poachers to prevent them from even reaching the animals' herds. It's the perfect addition to the Air Shepherd program that aims to use cutting edge software and drones to stop poaching in Africa.

This article comes from GitHub. A curated list of resources dedicated to bayesian deep learning. To read the original article, click here.

More overlooked machine learning and/or machine learning-related projects? OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. From Intel comes a(nother) deep learning framework, optimized for distribution over Apache Spark. BigDL is a distributed deep learning library for Apache Spark; with BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

Deep learning models are indisputably the state of the art for many problems in machine perception. Using neural networks with many hidden layers of artificial neurons and millions of parameters, deep learning algorithms exploit both hardware capabilities and the abundance of gigantic datasets. In comparison, linear models saturate quickly and under-fit. But what if you don't have big data? Say you have a novel research problem, such as identifying cancerous moles given photographs.

AI was very popular 30 years ago, then disappeared, and is now making a big come back because of new robotic technologies: driver-less cars, automated diagnostic, IoT (including vacuum cleaning and other household robots), automated companies with zero employee, soldier robots, and much more. Will AI replace data scientists? I think so, though data scientists will be initially replaced by "low intelligence" yet extremely stable and robust systems. There has been a lot of discussions about the automated statistician. I am myself developing data science techniques such as Jackknife regression that are simple, robust, suitable for black-box, machine-to-machine communications or other automated use, and easy to understand and pilot by the layman, just like a Google driver-less car can be "driven" by an 8 years old kid.