If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
After releasing what may well have been the most comprehensive report on the State of AI in 2019, Air Street Capital and RAAIS founder Nathan Benaich and AI angel investor and UCL IIPP visiting professor Ian Hogarth are back for more. In the State of AI Report 2020, Benaich and Hogarth outdid themselves. While the structure and themes of the report remain mostly intact, its size has grown by nearly 30%. This is a lot, especially considering their 2019 AI report was already a 136 slide long journey on all things AI. The State of AI Report 2020 is 177 slides long, and it covers technology breakthroughs and their capabilities, supply, demand, and concentration of talent working in the field, large platforms, financing, and areas of application for AI-driven innovation today and tomorrow, special sections on the politics of AI, and predictions for AI.
Full-stack AI solution SingularityNET is switching the Ethereum blockchain for peer-reviewed rival Cardano. SingularityNET is a decentralised AI marketplace which has the ultimate goal of forming the basis for the emergence of the world's first true Artificial General Intelligence (AGI). One of the brightest and most respected minds in AI leads the SingularityNET project, Dr Ben Goertzel. "Current speed and cost issues with the Ethereum blockchain have increased the urgency of exploring alternatives for SingluarityNET's blockchain underpinning," says Goertzel. "The ambitious Ethereum 2.0 design holds promise but the timing of rollout of different aspects of this next-generation Ethereum remains unclear, along with many of the practical particulars."
After releasing what may well have been the most comprehensive report on the State of AI in 2019, Air Street Capital and RAAIS founder Nathan Benaich and AI angel investor, and UCL IIPP Visiting Professor Ian Hogarth are back for more. In the State of AI Report 2020 released today, Benaich and Hogarth outdid themselves. While the structure and themes of the report remain mostly intact, its size has grown by nearly 30 percent. This is a lot, especially considering their 2019 AI report was already a 136 slide long journey on all things AI. The State of AI Report 2020 is 177 slides long, and it covers technology breakthroughs and their capabilities, supply, demand and concentration of talent working in the field, large platforms, financing and areas of application for AI-driven innovation today and tomorrow, special sections on the politics of AI, and predictions for AI.
Let's go back to a simpler time. It is the early or late 90s. You are eight years old, waking up early to catch the latest action-filled episodes of your Saturday morning cartoons; TV shows that portray what technology may look like in the future. In Japan, popular anime shows like Outlaw Star, Mobile Suit Gundam, and Cowboy Bebop. These shows would pull viewers in, giving us a taste of the future for breakfast. They would show us worlds where humans and cyborgs were almost unidentifiable from each other, where trips to space were as simple as catching a bus, or where artificial intelligence and robotics were used to better humanity (and used for epic battles in space).
I am Imtiaz Adam, and this article is an introduction to AI key terminologies and methodologies on behalf of myself and DLS (www.dls.ltd). This article has been updated in September 2020 to take into account advances in the field of AI with techniques such as NeuroSymbolic AI, Neuroevolution and Federated Learning. AI deals with the area of developing computing systems which are capable of performing tasks that humans are very good at, for example recognising objects, recognising and making sense of speech, and decision making in a constrained environment. Narrow AI: the field of AI where the machine is designed to perform a single task and the machine gets very good at performing that particular task. However, once the machine is trained, it does not generalise to unseen domains. This is the form of AI that we have today, for example Google Translate.
For several years, there has been a lot of discussion around AI's capabilities. Many believe that AI will outperform humans in solving certain areas. As the technology is in its infancy, researchers are expecting human-like autonomous systems in the next coming years. OpenAI has a leading stance in the artificial intelligence research space. Founded in December 2015, the company's goal is to advance digital intelligence in a way that can benefit humanity as a whole.
In a lecture held by Nobel Laureate Richard Feynman (1918–1988) on September 26th, 1985, the question of artificial general intelligence (also known as "strong-AI") comes up. Do you think there will ever be a machine that will think like human beings and be more intelligent than human beings? Below is a structured transcript of Feynman's verbatim response. With the advent of machine learning via artificial neural nets, it's fascinating to hear Feynman's thoughts on the subject and just how close he gets, even 35 years ago. Estimated reading time is 8 minutes.
AI will take your job, AI can sort out even the messiest data, AI will take over the world, AI is new. AI has been touted in the recent past, with it comes myths that often lead to misunderstanding of the technology. Eliezer Yudkowsk says that "By far the greatest danger of Artificial Intelligence is that people conclude too early that they understand it." In this article, we take a look at the top myths about Artificial intelligence we get to see what is true and what is not. "understanding is much deeper than knowledge there are very many people who know artificial intelligence but very few understand AI" AI dates back to the 19th century when an English mathematician and writer, Lady Ada Lovelace predicted that "a machine might compose elaborate and scientific pieces of music of any degree of complexity or extent" this was later advanced in the 1940s when a Bombe machine was created by Alan Turing.
Many people claim current technological progress as happening at a faster and faster pace (exponential even), with no end in sight. The merits and detriments of technology can be argued ad nauseum, but I won't be getting into that in this post (I generally view technology itself as neutral -- it can be used to improve human life or terribly misused to oppress, control, and kill). What I am going to briefly explore here is the question: is current progress in AI exponential? And if so, what implications does that have for estimates on the arrival of human level or superhuman level AI? Before I dive in, it's worth asking (if you didn't study mathematics): why does it matter if something is changing exponentially? Frequently people think the word "exponential" means "really fast", which is sometimes true, but doesn't capture much of the meaning of the concept.