Either a comprehensive list, or some combination of these AI technologies, can help implement AI systems or AI like systems/applications or increase an application's "intelligent" behavior somewhat.

Computer science professor makes MIT's "35 Innovators Under 35" list IRIS.TV and Vemba Partner to Provide Publishers With Turnkey AI / Machine Learning ... Why are robots so racist? HBO's new Westworld trailer shows AI ethical questions made flesh Is Artificial Intelligence Going To Take Your Job?

Manulife's Lab of Forward Thinking is partnering with indico data solutions, a Boston-based company specializing in deep Learning, to better analyze unstructured financial data, the insurer announced today. Indico's platform will enable the Canadian insurer to evaluate data from news articles and analyst reports and recommend investment decisions to portfolio managers. Deciphering natural language and extracting insights is one of indico's platform's core strengths, according to the companies. "Indico will help us accelerate our use of deep learning to improve the decision-making capabilities of our analysts, portfolio managers and researchers," said Greg Framke, executive vice president and chief information officer, Manulife, in a statement. "By introducing new capabilities that we know will add to our user experience and overall impact, we will improve the customer experience."

Human-robot strike teams, autonomous land mines, and covert swarms of minuscule robotic spies: the US Department of Defense's idea of the future of war seems like a sci-fi movie. According to Engadget, it took a while for the U.S. military to perfect its defense strategies against cyberattacks and it seems that when it comes to artificial intelligence (AI), U.S. military faces a similar deficit. Other countries, especially U.S. rivals such as China and Russia, implement less restrictive policies that deal with killer robots and other lethal AI autonomous weapons. This is one of the reasons U.S. cannot afford to be left behind. The nation's military may need deadly AI technology before it's too late.

SugarCRM Inc. co-founder Clint Oram recalled discussing the potential of CRM systems to predict sales with machine learning more than 15 years ago. "Sitting around as a product manager, kicking around the ideas that are coming out today, the limiting factor back then was technology was really hard to deploy and extremely expensive," Oram said. Software as a service, mobile and social all became prominent." Back then, CRM systems were mostly on premises and operated as Excel replacements and static data-entry systems. Then, cloud-based CRM entered the scene and opened the door to modern tools underpinned by machine learning and artificial intelligence technologies. Now, as industry leaders like Salesforce acquire data intelligence companies, there's a move toward creating cloud-based CRM systems that act as digital assistants, rather than data input tools. "Gone are the days where CRM is just a database," said Jon Lee, co-founder and CEO of ProsperWorks Inc., based in San Francisco. "There has to be some inherent value.

Can you believe it, we are only a little over a month away from Dreamforce'16, which is lining up to be even bigger than last year when Salesforce reported over 160,000 registered attendees! Whether you plan to attend or catch some of Dreamforce online, there is undoubtedly a depth and breadth of topics, sessions and keynotes to interest anyone, in any role, in any industry. But with so much you could focus on, how do you decide? At NeuraFlash, we are expert implementers and a Consulting Partner focused on Salesforce and A.I. We are interested in areas of Advanced Intelligence combined with Salesforce. While creating the best customer and employee experiences possible, we aim to make Salesforce users more efficient, capitalize on opportunities and get the most business value from the data captured through Salesforce.

Implicit biases are a well-documented and pernicious feature of human languages. These associations, which we're often not even aware of, can be relatively harmless: We associate flowers with positive words and insects with negative ones, for example. New research from computer scientists at Princeton suggests that computers learning human languages will also inevitably learn those human biases. In a draft paper, researchers describe how they used a common language-learning algorithm to infer associations between English words. The results demonstrated biases similar to those found in traditional psychology research and across a variety of topics.

They're becoming more like colleagues or co-workers -- members of the team who can collaborate with us to solve problems. As the relationship between humans and machines evolves, it promises to turn design into a team sport where humans and machines are playing side-by-side, together. This new way of working will transform design in exciting ways, expanding the possibilities of what we can make and how we can shape the world around us. One of the most influential tools moving the relationship between humans and machines in this new collaborative direction is generative design. This technology allows a designer to feed various criteria and constraints into a computer that harnesses the computing power of the cloud to rapidly generate hundreds of design options that meet those criteria.

Technological revolutions have long animated economic history. The concept of "creative destruction"--in which technological advancement destroys certain sectors of the economy while giving rise to new ones--has roots in some of the earliest economic thought.1 This process hinges on the idea that machines serve to supplement human labor, primarily labor dedicated to repetitive physical and cognitive tasks. At the moment, machines can solve intensive well-defined tasks but for the most part cannot be expected to define problems nor identify and traverse particularly complex systems without human oversight. Robots: A Retrospective The most primitive economies are essentially brawn-based. Human labor is largely priced by the ability to perform physical tasks associated with farming and building. A number of studies (e.g., Thomas and Strauss, 1997) show how in modern-day less-developed economies, men make more than woman as a function of body mass and thus perceived brawn, and that men with more brawn made more than those with less.

Studies in artificial intelligence started as a US defense project in the 1960s with the goal of understanding how humans process information. This concept would then be simulated and adapted within "logical systems." Although development slowed a couple of decades later, innovations in technology have propelled advances in artificial intelligence in recent years. These advances are now making our lives easier and safer. AI has already enabled several task-specific systems even outside of military functions to aid human activities with faster and more accurate execution.