"This is a human problem," said Vivienne Ming, a longtime computer scientist and entrepreneur who calls herself a "professional mad scientist." She was talking about the problem of keeping artificial intelligence ethical. Software that learns or evolves over time while doing tasks–a rough definition of this complex category of technology–is poised to play a greater and greater role in modern society. Techonomy recently brought together a trio of leaders and technologists who are excited by its potential but committed to doing so carefully for a Tech session. These experts all agree that thus far, AI hasn't fulfilled its promise, because it hasn't been designed with sufficient ethical intention. In addition to Ming, we heard from leaders of two major companies that take AI ethics seriously–software giant Salesforce, represented by Paula Goldman, chief ethical and human use officer, and global technology services firm Wipro.

I recently participated in a podcast interview that outlined the practice of cognitive experience design, also known as CognitiveXD. In hearing the questions, I realized we needed a crisp definition that outlines what CognitiveXD is, and how we can use CognitiveXD to solve big systemic problems. Q: What is cognitive experience design? A: Cognitive experience design or #CognitiveXD is the practice of using artificial intelligence (ai) technologies to reduce the human mental effort and time required to complete a task. Q: Why do we need cognitive experience design?

When it comes to most things business, AI is making its mark as the must-have technology. Whether we are talking about customer-facing chatbots to help with engagement and conversion or AI working in the background to help make critical business decisions, AI is everywhere. And the expectations of what it can and should be able to do is often sky-high. When those expectations aren't met, however, it's not always the tech that's to blame. More likely, it's the humans who brought it on board.

The ethical landscape surrounding the use and widespread implementation of artificial intelligence (AI) is vast and complex. The forthcoming AI revolution will change the world, and our lives, in ways no other industrial revolution has done. Everyone should be forming opinions on the ethics of AI. Humans are capable of great acts of positive progress (e.g. AI will be no different.

It's been reported that an artificial intelligence (AI) has learned to adopt "highly aggressive" strategies when it feels it's about to lose a simulated game. While that certainly sounds a little scary, the study this is linked to was investigating a larger social problem far more fascinating and enlightening that mere aggression. Google's in-house AI, machine learning, and neural networking development teams are working on some truly remarkable projects at present: From AlphaGo Zero, the AI that learned 3,000 years' worth of Go tactics in a matter of days, to AutoML, a system that makes self-correcting AI "children" that are designed to perform specific tasks, their output is nothing but impressive. DeepMind, one of its acquired AI R&D teams, published a paper back in 2017, along with an accompanying blog post, describing a new test they'd run on one of their neural networks. First, they copied the AI and put both versions on two different teams, Red and Blue.

The usual argument about women and AI is that women are grossly underrepresented in this field and that we should have more women contributions and involvement. Diversity of perspective is one of the motivations for this. There are plenty of examples of how AI is designed without consideration of one half of the human population. I however will argue here about something beyond the need for diversity. I will argue that our A.I. future should be led my women and not by men. The reason for this is that women have a greater intuitive understanding of what makes us all human.

Are you promoting AI to consumers, clients or colleagues? If you're in marketing today there is a good chance that you are. But how do you sell artificial intelligence effectively given all the hype and hysteria that surrounds the technology? Here are ten evidence-based recommendations for how to communicate AI effectively from a new Syzygy study that captured people's feelings towards AI across the US, UK, and Germany (n 6000). With all the hype and hysteria around AI, people are suspicious and skeptical.

The question of how humans solve problem has been addressed extensively. However, the direct study of the effectiveness of this process seems to be overlooked. In this paper, we address the issue of the effectiveness of human problem solving: we analyze where this effectiveness comes from and what cognitive mechanisms or heuristics are involved. Our results are based on the optimal probabilistic problem solving strategy that appeared in Solomonoff paper on general problem solving system. We provide arguments that a certain set of cognitive mechanisms or heuristics drive human problem solving in the similar manner as the optimal Solomonoff strategy. The results presented in this paper can serve both cognitive psychology in better understanding of human problem solving processes as well as artificial intelligence in designing more human-like agents.

Not too long ago humanity left behind its skeuomorphic interfaces. Skeuomorphism meant using references to real world surface textures on visual interfaces to enhance their comprehensibility. We stripped our visual interfaces off their ornamentations to allow a more authentic approach to visual aesthetics. Killing skeuomorphism made us feel very smart about ourselves. We finally don't need glossy buttons to understand something is tappable!

Google shows 44m hits on AI and 9m on Cognitive Computing and the figure below from Google Trends clearly shows that the search term "Artificial Intelligence" is more popular than "Cognitive Computing", however, I'm sure we'll start to see that gap close in 2017. In our white paper "Surviving in the AI hype", we explained some of the fundamental concepts behind AI, as well as touching on Cognitive Science and Computing but in this post we want to focus in more detail on the relationship between AI and Cognitive Computing specifically. To start off, what do Intelligence and Cognition mean if we search for a definition online? Intelligence: "the ability to learn or understand or to deal with new or trying situations: reason; also: the skilled use of reason (2): the ability to apply knowledge to manipulate one's environment or to think abstractly as measured by objective criteria (as tests)." Cognition: "the mental action or process of acquiring knowledge and understanding through thought, experience, and the senses."