As the Chief Procurement Officer of BAM. Inc., Akmal considered himself more progressive than CPOs at other companies, and he had the suite of predictive AI tools to prove it. "There's more to procurement than managing constraints and winning in the margins," he had said to the CFO when making the case for the system. "We need more visibility into our supplier tiers, and we have got to be more nimble." The CFO was somewhat less than enthusiastic at first. What was a CPO doing thinking about AI anyway? After some convincing, however, the CFO signed off on the investment and the Chief Technology Officer joined the effort to bring AI to procurement at BAM, Inc.

Many boundaries are, and will continue to, shape the future of Artificial Intelligence (AI). We push on these boundaries in order to make progress, but they are both pliable and resilient--always creating new boundaries of what AI can (or should) achieve. Among these are technical boundaries (such as processing capacity), psychological boundaries (such as human trust in AI systems), ethical boundaries (such as with AI weapons), and conceptual boundaries (such as the AI people can imagine). It is within this final category while it can play a fundamental role in all other boundaries} that we find the construct of needs and the limitations that our current concept of need places on the future AI.

As the capabilities of Artificial Intelligence systems increase everyday, government officials are under more pressure than ever to develop a comprehensive and robust set of policies and laws that holds these algorithms accountable for their decisions. The question on whether these algorithms should be held accountable has gained attention over the past few years through scandals such as Google's mislabeling of images and Microsoft Tay's racist tweets. In determining whether an algorithm should be held accountable or not, it is important to break the topic down into key questions. The first is what task is the algorithm completing? What are the implications to individuals/society resulting from the algorithm's decision.

The history of AI has included several "waves" of ideas. The first wave, from the mid-1950s to the 1980s, focused on logic and symbolic hand-encoded representations of knowledge, the foundations of so-called "expert systems". The second wave, starting in the 1990s, focused on statistics and machine learning, in which, instead of hand-programming rules for behavior, programmers constructed "statistical learning algorithms" that could be trained on large datasets. In the most recent wave research in AI has largely focused on deep (i.e., many-layered) neural networks, which are loosely inspired by the brain and trained by "deep learning" methods. However, while deep neural networks have led to many successes and new capabilities in computer vision, speech recognition, language processing, game-playing, and robotics, their potential for broad application remains limited by several factors. A concerning limitation is that even the most successful of today's AI systems suffer from brittleness-they can fail in unexpected ways when faced with situations that differ sufficiently from ones they have been trained on. This lack of robustness also appears in the vulnerability of AI systems to adversarial attacks, in which an adversary can subtly manipulate data in a way to guarantee a specific wrong answer or action from an AI system. AI systems also can absorb biases-based on gender, race, or other factors-from their training data and further magnify these biases in their subsequent decision-making. Taken together, these various limitations have prevented AI systems such as automatic medical diagnosis or autonomous vehicles from being sufficiently trustworthy for wide deployment. The massive proliferation of AI across society will require radically new ideas to yield technology that will not sacrifice our productivity, our quality of life, or our values.

BRUSSELS (Reuters) - Companies working with artificial intelligence need to install accountability mechanisms to prevent it being misused, the European Commission said on Monday, under new ethical guidelines for a technology open to abuse by authoritarian regimes.

Artificial intelligence is set to play a significantly greater role in society. And that raises the issue of accountability. If we rely on machines to make increasingly important decisions, we will need to have mechanisms of redress should the results turn out to be unacceptable or difficult to understand. But making AI systems explain their decisions is not entirely straightforward. One problem is that explanations are not free; they require considerable resources both in the development of the AI system and in the way it is interrogated in practice.

Say you apply for home insurance and get turned down. You ask why, and the company explains its reasoning: Your neighborhood is at high risk for flooding, or your credit is dodgy. Now imagine you apply to a firm that uses a machine-learning system, instead of a human with an actuarial table, to predict insurance risk. After crunching your info--age, job, house location and value--the machine decides, nope, no policy for you. You ask the same question: "Why?" Nobody can answer, because nobody understands how these systems--neural networks modeled on the human brain--produce their results.