For decades, games have served as benchmarks for artificial intelligence (AI). In 1996, IBM famously set loose Deep Blue on chess, and it became the first program to defeat a reigning world champion (Garry Kasparov) under regular time controls. But things really kicked into gear in 2013 -- the year Google subsidiary DeepMind demonstrated an AI system that could play Pong, Breakout, Space Invaders, Seaquest, Beamrider, Enduro, and Q*bert at superhuman levels. In March 2016, DeepMind's AlphaGo won a three-game match of Go against Lee Sedol, one of the highest-ranked players in the world. And only a year later, an improved version of the system (AlphaZero) handily defeated champions at chess, a Japanese variant of chess called shogi, and Go.

Facebook has a whole set of internal tools to try and optimize its neural networks to run on mobile devices. Still, the company finds it difficult to navigate a smartphone market that is byzantine in its complexity, with thousands of different chipsets, most of poor performance, and software stacks that aren't quite up to the job. AI on mobile devices is a bit of a mess, and it's a headache for Facebook, which gets 90% of its advertising revenue off of people using its service on mobile. Those are some takeaways of a recent research paper from Facebook's AI folks, who detail how they've had to come up with all manner of tricks to get around the hardware shortcomings of mobile. That includes things like tweaking how many "threads" in an application to use to reach a common denominator across a plethora of different chip designs and capabilities. That means they can't generally "optimize" their code for a given device.

One of the most important things a business can do when its using AI products or creating custom AI projects is to make sure all the relevant data can be used as inputs. AI and machine learning are hungry for data and work best when the algorithms are using large datasets. The predictions generally become stronger the more data with important signals there is to work with. But wrangling and assembling all the data is difficult: in most projects, whether AI or analytics-focused, it's a huge undertaking to assemble all the data needed. This has never been truer than now, in the era of big data, when companies have so many data sources and formats to work with.

I compiled a list of top influencers in the field of AI and ML which I recommend to keep yourself not just updated or informed about the two fields, but rather also learn about AI or ML. I included academics, researchers, entrepreneurs, investors and authors. There are the few parameters I kept in mind. Firstly, these are the people with a huge social media following and their independent voice is not directly linked to the companies they work in or they founded. There are indeed experts in AI and ML who are not all over social media but that requires a separate list from us.

The Big Data market is likely to grow for the long haul. Let's face it, the world is undergoing an explosion of data, such as from smartphones, IoT (Internet-of-things), wearables, AI (Artificial Intelligence) and machine learning.

For retailers launching a new product or looking to break into a new market, autonomous vehicles with delivery capabilities can reduce the cost of being in a physical retail store, a mall kiosk or a pop-up shop. Additionally, autonomous vehicles can provide accurate insights into where a product is gaining traction and where it is being sold. The vehicle can then efficiently move between low and high trafficked areas. From the perspective of the lean startup methodology, autonomous vehicles applied to retail could reduce production costs and overhead and could minimize the risk of product failure before mass distribution.

Artificial Intelligence and digital marketing are beginning to go hand in hand. With the ability to collect data, analyze it, apply it and then learn from it- AI is transforming digital strategy. As it continues to advance, so will the capabilities to use it to improve digital marketing strategies and valuable customer insights for companies. Here are 3 ways AI is changing digital marketing for the better. The most important aspect of a successful digital marketing strategy is great customer experience.

How much can anyone trust a recommendation from an AI? Yaroslav Kuflinski, from Iflexion gives an explanation of explainable AI She is lying sedated on a gurney that's bumping towards the operating theater. It squeaks to a halt and a hurried member of hospital staff thrusts a form at you to sign. It describes the urgent surgical procedure your child is about to undergo--and it requires your signature if the operation is to go ahead. At this specific moment, do you think you are owed a reasonable, plain-English explanation of all the inscrutable decisions that an AI has lately been making on your daughter's behalf? in short, do we need explainable AI? There are many other examples where one or more of the actors may consider themselves entitled to an explanation of the reasoning processes behind the decisions of an AI.

Why are we so far away? In a nutshell, building a true artificial general intelligence is very hard because of one fundamental fact: intelligence is more than pattern recognition and reaction. If there's a single image that represents intelligence, it's Michelangelo's Sistine Chapel.

When one hears the term "artificial intelligence" or AI, the mind goes quickly to robots and talking computers that will one day take over the world. That's great for Hollywood, and there is applicability in manufacturing and other areas of business where intelligent machines or robots can effectively automate what were once manual processes. But when it comes to the business software we use every day at work, the application is slightly less daunting but equally as powerful. As a software salesman for over 30 years, I'm constantly crafting my elevator pitch, which is a statement that describes what my product can do in two minutes or less. The best description for business or ERP (enterprise resource planning) software is that of an application that can efficiently automate manual processes.