Click the arrow above to listen to the full conversation between computer scientist Nicolas Sabouret and St. Pete Catalyst publisher Joe Hamilton in the Catalyst studio. The term "artificial intelligence" has been so tainted by science fiction that for most it carries images of dystopian futures, homicidal robots and epic Steven Spielberg movies. But according to AI expert Nicolas Sabouret, the first thing people should know when about artificial intelligence when putting it in layman's terms, is that artificial intelligence is not really about intelligence at all. "What machines do is computation, something they do very well," Sabouret said in advance of his artificial intelligence presentation for the St. Petersburg Conference on World Affairs Nov. 6. "Artificial intelligence is the science that makes machines do things by computation that human beings do with their intelligence. Sabouret used chess as an example in explaining how artificial intelligence works through computation. There are more combinations of games in chess than the number of atoms in the entire universe, he said. With so many possibilities, machines are far better able to discover new paths or types of games than human beings could think of. "Still it doesn't mean the machine has created something new.

Ever since computers were invented, there has been an exponential growth in their ability and potential to perform various tasks. In order to use computers across diverse working domains, humans have developed computer systems while increasing their speed, and reducing size with respect to time. Artificial Intelligence pursues the stream of developing the computers or machines to be as intelligent as humans themselves. In this article we will scrape the top layer about the concepts of artificial intelligence that will help understand related concepts like Artificial Neural Networks, Natural Language Processing, Machine Learning, Deep Learning, Genetic algorithms etc. Along with this, we will also learn about its implementation in Python.

Wayve, a U.K.-based startup that's developing artificial intelligence (AI) that teaches cars to drive autonomously using reinforcement learning, simulation, and computer vision, has raised $20 million in a series A round of funding led by Palo Alto venture capital (VC) firm Eclipse Ventures, with participation from Balderton Capital, Compound Ventures, Fly Ventures, and First Minute Capital. Several notable angel investors also participated in the round, including Uber's chief scientist Zoubin Ghahramani and Pieter Abbeel, a UC Berkeley robotics professor and pioneer of deep reinforcement learning. Founded out of Cambridge, U.K., in 2017, Wayve's core premise is that the big breakthrough in self-driving cars will come from better AI brains rather than more sensors or "hand-coded" rules. The company said that it trains its autonomous driving system using simulated environments and then transfers that knowledge into the real world, where it emulates how humans adapt to conditions in real time. Wayve's systems learn from each safety driver intervention to understand why the driver had to intervene, bypassing HD maps, lidar, and other sensors that have become synonymous with the burgeoning autonomous vehicle movement.

When society uses artificial intelligence (AI) to help build judgments about individuals, fairness and equity are critical considerations. On Nov. 12, Brookings Fellow Nicol Turner-Lee sat down with Solon Barocas of Cornell University, Natasha Duarte of the Center for Democracy & Technology, and Karl Ricanek of the University of North Carolina Wilmington to discuss artificial intelligence in the context of societal bias, technological testing, and the legal system. Artificial intelligence is an element of many everyday services and applications, including electronic devices, online search engines, and social media platforms. In most cases, AI provides positive utility for consumers--such as when machines automatically detect credit card fraud or help doctors assess health care risks. However, there is a smaller percentage of cases, such as when AI helps inform decisions on credit limits or mortgage lending, where technology has a higher potential to augment historical biases.

The National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign is excited to announce the creation of the Center for Artificial Intelligence Innovation (CAII), which seeks to continue the groundbreaking work already being done in the domains of Deep Learning, Machine Learning, and Artificial Intelligence. "We are truly excited to announce the formation of this Center, which brings together a number of efforts that have been underway for some time along with new areas of focus to propel AI research and application forward," said NCSA's Director, Bill Gropp. By leveraging the expertise and resources that exist at NCSA and Illinois, the CAII seeks to become a hub for innovation on campus, regionally, nationally and internationally. The CAII at Illinois will complement existing initiatives across campus by operating as a central nexus for AI research with applications in both academia and industry. Whether it be in astrophysics, automotive, agriculture, infrastructure, big data or a variety of other fields, NCSA has made a concerted commitment to artificial intelligence, and the CAII will only help to both further and formalize that commitment while simultaneously building the foundations for the next generation of innovators.

Though creating an autonomous robot that can tidy a room seems like enough of an achievement, Tokyo-based Preferred Networks goes one step further. By integrating natural language processing (NLP) into their technology, their robots respond to commands and adjust their actions. Jun Hatori, a software engineer at Preferred Networks, stopped to talk with AI Podcast host Noah Kravitz about the company's latest developments.

Headstart, a platform that leverages data science to help companies reduce unconscious bias in the hiring process, has raised $7 million in a seed round of funding led by AI-focused Silicon Valley VC firm FoundersX, with participation from Founders Factory. Founded out of London in 2017, Headstart is one of a growing number of startups that promise to help companies increase their diversity during recruitment drives. This is achieved through combining machine learning with myriad data sources to find the best candidates based on specific objective criteria. "The machine -- the algorithms and models -- does this without emotion, fatigue, or overt subjective, conscious or subconscious opinion or feeling," Headstart cofounder and chairman Nicholas Sherekdemain told VentureBeat. This internal data is reviewed for built-in bias, so if there is a clear leaning toward a specific demographic this can be addressed in subsequent hiring campaigns.

Headstart, a platform that leverages data science to help companies reduce unconscious bias in the hiring process, has raised $7 million in a seed round of funding led by AI-focused Silicon Valley VC firm FoundersX, with participation from Founders Factory. Founded out of London in 2017, Headstart is one of a growing number of startups that promise to help companies increase their diversity during recruitment drives. This is achieved through combining machine learning with myriad data sources to find the best candidates based on specific objective criteria. "The machine -- the algorithms and models -- does this without emotion, fatigue, or overt subjective, conscious or subconscious opinion or feeling," Headstart cofounder and chairman Nicholas Sherekdemain told VentureBeat. This internal data is reviewed for built-in bias, so if there is a clear leaning toward a specific demographic this can be addressed in subsequent hiring campaigns.

We live in a world where value-added products and services affect our purchasing decisions. Most consumers want to know they are getting the most for their time and effort. The same is true in education. They want to know that their child is getting the best education possible. Value-added modeling has been a part of teacher performance reviews for years.

Technology has advanced enormously in the 30 years since the introduction of the first Children Act, which shaped the UK's system of child safeguarding. Today a computer-generated analysis – "machine learning" that produces predictive analytics – can help social workers assess the probability of a child coming on to the at-risk register. It can also help show how they might prevent that happening. But with technological advances come dilemmas unimaginable back in 1989. Is it right for social workers to use computers to help promote the welfare of children in need?