The adoption and application of Artificial Intelligence (AI) continues to accelerate at an exponential rate in modern businesses. As referenced in the 2017 Tech Trend Report, AI is nearing completion of the next layer in technological advancement, integrated into everything individuals and organizations do. This trajectory is predicted to drive cumulative worldwide spending of $40.6 billion on AI projects by 2024 – according to Raconteur. This is expected to create mass opportunity for the pioneering businesses currently investing in AI development. Moving beyond the hype in existing media coverage, this post will uncover the reality behind what AI means for businesses today, in the near future, and beyond 2017.

One of the best parts of writing the Afternoon Edition each day is that I get to explore all kinds of interesting topics. Two of those recent topics continue to be all over the news, and they are the issues of robots and automation, and artificial intelligence and the "Singularity." These topics do materially affect markets, although their effect may not be as acute as, say, a monthly employment report. And speaking of which, we got a new monthly employment report this morning, as February saw 235,000 new non-farm payroll jobs created. That's good economic news; however, I wonder how many more jobs would have been created if there weren't as many advancements in the field of automation.

Here's an analogy using a concept that we can all relate to: a supermarket. Picture the scene: Shopping list in one hand, shopping basket in the other, you're ready to tackle your weekly shopping in your local supermarket. Your items range from fruit and vegetables to washing detergent, perhaps with some free-range eggs thrown in for good measure. Quite the eclectic mix, but you know that you'll be able to find all you need under one roof. The fact that this is possible is in itself quite remarkable.

Has a digital coup begun? Is big data being used, in the US and the UK, to create personalised political advertising, to bypass our rational minds and alter the way we vote? The short answer is probably not. A series of terrifying articles suggests that a company called Cambridge Analytica helped to swing both the US election and the EU referendum by mining data from Facebook and using it to predict people's personalities, then tailoring advertising to their psychological profiles. These reports, originating with the Swiss publication Das Magazin (published in translation by Vice), were clearly written in good faith, but apparently with insufficient diligence.

Philip Hammond says he wants the UK to become a "world leader" in 5G, the next-generation mobile technology that proponents say is the key to an internet-connected world of driverless cars, smart home appliances, delivery drones and lightning-fast video on the go. The government, which has published a 70-page tome on its future 5G strategy, said in the budget it would invest up to £16m to run trials and support the technology's development, to make sure the UK is at the crest of the "next wave of mobile technology services". However, 5G, which is set to be rolled out in the UK next decade, also has its critics. They argue consumers don't need the superfast speeds the upgrade from current 4G technology promises, and many in the industry believe that logistical issues mean that 5G may not be properly rolled out in the UK for decades. In the meantime, there are still basic infrastructure issues – including rural areas with little or no broadband coverage at all – that need sorting out.

Pattern Based Heuristics 2.4 Machine Learning and Response Generation in Chatbots 2.5 Chatbot Ecosystem 2.5.1 Native Chatbots 2.5.2

For an autonomous agent, executing a poor policy may be costly or even dangerous. For such agents, it is desirable to determine confidence interval lower bounds on the performance of any given policy without executing said policy. Current methods for exact high confidence off-policy evaluation that use importance sampling require a substantial amount of data to achieve a tight lower bound. Existing model-based methods only address the problem in discrete state spaces. Since exact bounds are intractable for many domains we trade off strict guarantees of safety for more data-efficient approximate bounds. In this context, we propose two bootstrapping off-policy evaluation methods which use learned MDP transition models in order to estimate lower confidence bounds on policy performance with limited data in both continuous and discrete state spaces. Since direct use of a model may introduce bias, we derive a theoretical upper bound on model bias for when the model transition function is estimated with i.i.d. trajectories. This bound broadens our understanding of the conditions under which model-based methods have high bias. Finally, we empirically evaluate our proposed methods and analyze the settings in which different bootstrapping off-policy confidence interval methods succeed and fail.

I recently stumbled across a research paper, Using Deep Learning and Google Street View to Estimate the Demograp..., which piqued my interest in derivative uses of data, an ongoing research interest of mine. A variety of deep learning techniques were used to draw conclusions about relationships of car ownership, political affiliation and demographics. For those headline skimmers, you may be led to believe that researchers have just uncovered a vastly cheaper and more timely approach to perform the national census and make predictive claims about the population. The researchers' contention that official statistics are expensive and lagging is spot on. The principal US unemployment survey is performed in person or via telephone.

It's official: driverless cars have hit the race tracks. Roborace, the autonomous race car maker, had its two self-driving'DevBots' compete against each other at the Formula E Buenos Aires ePrix. The race didn't go without its own surprises: One car had to dodge a random dog that ended up on the race track, and the other ended up hitting a barrier, unable to finish the race. Roborace's self-driving car races will take place at Formula E events throughout 2017. All cars competing will be made identically.

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