According to study results by Fivetran, 86% of companies struggle to trust AI to make all business decisions without human participation. In contrast, 90% of enterprises rely on manual data procedures. The companion paper, "Achieving AI: A Study of AI Opportunities and Obstacles," explains the problems businesses confront in today's AI ecosystem. The paper investigates how, even though 87% of businesses identify AI as the future of business and aim to expand their investment in it, a lack of trust in machine-led decision-making is a significant obstacle caused by technical challenges and a lack of education. Only 14% of respondents believe their companies are "advanced" in AI maturity.

A group of roboticists at the University of Tokyo have created a flexible, flying "drone-robot" that could see a multitude of uses. The Dual-rotor embedded multilink Robot with the Ability of multi-deGree-of-freedom aerial transformatiON, is (thankfully) better known by its acronym, DRAGON. As illustrated in the video below, it can change its shape mid-flight and fly through tight spaces. The current version of DRAGON consists of four modules, each equipped with a set of maneuverable thrusters. Battery-powered hinged joints link the modules.

Over the last few months, I've had the chance to engage with customers and industry analysts about a range of topics in the field of Artificial Intelligence, and I've been struck by how effective the Sentient Enterprise is in addressing the most common questions and misconceptions about AI. Examining customer case studies is one of the best way to share knowledge and insights around how enterprises are driving business outcomes from AI technology. Case studies are practical, relatable and authentic; and we are fortunate to have some great reference accounts that allow us to publicly share their AI and deep learning success stories. For context, most of our AI case studies start with Rapid Analytic Consulting Engagements (RACE) based on an agile and experimental process to find and test new insights and produce results in weeks, not months. So, the starting point for telling these stories is identifying the business outcome we want to achieve, and then jumping into a range of deep neural net taxonomies, augmenting current platforms with requisite software and GPU enablers, and measuring the final results.

When we say #ThinkBeyond, what we really mean is: Think anew. The irony of the word'intelligence' in our industry is that it has come to mean an empty set of quickly expiring, low-confidence, weakly-attributed, slow-to-operationalize, cloud-dependent, noise. Even so, it has become a virtual currency between C-level intelligence communities, traded almost like challenge coins, in a quid-pro-quo manner. This dynamic defeats the whole purpose of'intelligence' to begin with, much like when monetized search results killed the dream of free information for the world during the early 2000's. What we need to do in the industry is to shift focus to a different kind of intelligence that has wisdom as its goal.

Artificial Intelligence is a great help to humanity. Unfortunately, the scope for misuse is also huge. Given tendencies among corporate and state entities to establish dominance, we analyze ethical concerns and advocate the need for democratization. "It is not enough to be electors only. It is necessary to be law-makers; otherwise those who can be law-makers will be the masters of those who can only be electors."

Much of the focus on artificial intelligence has been on the impact that task automation will have on jobs. While PwC expects that the nature of jobs will change and that some will be susceptible to automation, their newest research, Sizing the prize, shows that AI-driven products and services will also generate significant economic value, offsetting job gains, as well as boosting productivity and average wage levels. Organisations still need to develop approaches to embed AI responsibly into our workplaces and to secure the right talent to make the most of the opportunities created. MARGINALIA spoke with PwC's AI Programme Leader, Rob McCargow (pictured right), to explore the key findings from their new report. McCargow is deeply involved in the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems, and so shares important ethical considerations and advice around how to maximise AI efforts in a way that benefit the enterprise, its people, and the society.

Is there a retribution gap? In an interesting and carefully argued paper John Danaher argues that in respect of robots, there is. For human beings in normal life he argues that a fairly broad conception of responsibility works OK. Often enough we don't even need to distinguish between causal and moral responsibility, let alone worrying about the six or more different types identified by hair-splitting philosophers. However, in the case of autonomous robots the sharing out of responsibility gets more difficult.