"WOULD it not be of great satisfaction to the king to know, at a designated moment every year, the number of his subjects?" A military engineer by the name of Sébastien le Prestre de Vauban posed this question to Louis XIV in 1686, pitching him the idea of a census. All France's resources, the wealth and poverty of its towns and the disposition of its nobles would be counted, so that the king could control them better. These days, such surveys are common. But they involve a lot of shoe-leather, and that makes them expensive.

It seems like everyone wants to invest in artificial intelligence (AI). And it's not just the tech giants: USAA is using AI to protect its users from identity theft and Under Armour has connected its health app, MyFitnessPal, to IBM Watson so users can get a more thorough read of their health. AI is already a $15 billion dollar industry, according to the MIT Technology Review, with more than 2,600 companies developing their own tech, and the value of AI is reported to rise to over $70 billion by 2020. Because of AI's business opportunities, hundreds of designers in digital agencies, people who were taught to create products and services that live on the Internet, are starting to build physical products that interact with us, respond to our moods, and make decisions for us. It's a challenge that requires every skill they've learned, plus many they haven't. Still, designers know the basics: The principles of user-centric design lay the groundwork for building a great AI system.

Machine learning can benefit many different spaces, but the influencer marketing space is the newest to utilize AI in marketing matches. Brands have quickly found that influencer marketing is the fastest way to directly reach customers. Influencer marketing subverts ad blockers and "commercial aversion" that leads consumers to shy away from platforms that are ad-heavy. But it's not always easy to find the influencers who are best for your brand. Former director of FameBit (which has since been acquired by Google) Rob Ilas has been concocting a way to make "Soulmates" out of brands and influencers using deep machine learning, AI, and social listening.

You are overly personifying AI. You asked "what kind of intelligence likes to be enslaved", and you said none. But you are basically defining intelligence to be human in that argument, whether you realize that's what you're doing or not. From there, you conclude that, if machines are to be superintelligent, they have to be humans, but faster, and therefore wouldn't like being enslaved. But there's no justification for thinking that an AI would be like humans in the first place. An AGI can be roughly characterized as an entity which can solve a broad class of problems at the same or greater efficiency flexibility as humans.

Join us as Dr. Huining (Henry) Cao explores the impact of artificial intelligence on financial markets. Artificial intelligence (AI) is changing driverless car technology, speech recognition, image processing, automatic translation, big data analysis and many other sectors in marketing and finance. One striking example is Google's AlphaGo, a recent algorithm based on AI that has defeated the best human players in Go and Texas Poker, previously thought the last strongholds of human intelligence. Much of the progress comes from the connectionism approach, which models behavior as the emergent processes of interconnected networks of simple units. A major representation of the connectionist approach uses artificial neural networks to simulate human perception, cognition and decision making.

Organizations create style guides to capture the rationale of their design decisions and help other teams build great experiences. You might have read gov.UK;s service manual or the U.S. Digital Services Playbook.

The electrical grid has become a network of billions of linked devices with highly complex energy and information flows. Add to this the elevated role of the consumer as a producer and you are looking at a massive volume, velocity and variety of data from smart meters, transformers, and substations that remains largely untapped. One extremely interesting solution to the impending challenges is cognitive computing, which is gaining traction among industry experts as a way to better manage data and improve both operating efficiencies and customer service. According to GTM Research, applying cognitive computing is expected to deliver an estimated $121 billion global return on investment (ROI) on grid analytics by 2020. There are three key areas where energy companies are already engaging with cognitive systems to experience real benefits.

This week, we heard the car service was using secret software to evade government regulators and a video showed its chief executive in a verbal altercation with one of the company's drivers. Previously, the company's self-driving cars raised safety concerns in San Francisco when, because of faulty and incomplete technology, they reportedly barreled through red lights and crossed over bike lanes. Uber has recently been accused of sexual harassment, intellectual property theft, and other questionable behavior. Silicon Valley is gaining a reputation for being obsessed with making money at any cost, i.e. Theranos, which made false claims and risked lives.

Machine Learning (ML), a branch of Computer Science that focuses on drawing insights and conclusions by examining data sets, is an increasingly popular discipline today in resolving enterprise business issues. However the field is vast and consists of numerous algorithms and approaches. Data sets are also often complex and require to be pre-processed before an ML algorithm can be'trained' to learn from such data. For a particular problem domain and data set, defining the pre-processing technique and selecting the ML algorithm (or set of algorithms) is still largely'an art rather than a science' depending on the knowledge and skills of the expert/data scientist in question. With time this will change and scientific guiding principles/best practices will emerge to pre-process data and to select appropriate algorithms for a particular problem domain - as the discipline matures.

IDC sees widespread adoption of cognitive systems and artificial intelligence (AI) across a broad range of industries will drive worldwide revenues from nearly $8.0 billion in 2016 to more than $47 billion in 2020. According to a new Worldwide Semiannual Cognitive/Artificial Intelligence Systems Spending Guide from International Data Corporation (IDC), the market for cognitive/AI solutions will experience a compound annual growth rate (CAGR) of 55.1% over the 2016-2020 forecast period. "Near-term opportunities for cognitive systems are in industries such as banking, securities and investments, and manufacturing," said Jessica Goepfert, program director, Customer Insights and Analysis at IDC. "In these segments, we find a wealth of unstructured data, a desire to harness insights from this information, and an openness to innovative technologies. For instance, cognitive technologies are being used in the banking industry to detect and combat fraud – consistently a top industry pain point. Meanwhile, in manufacturing, executives cite improving product quality as a top initiative. In this case, cognitive systems recognize and know how to respond to dynamic fluctuations in product specs by adapting the production to stay within quality targets."