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) …
The decision to invest in a company can rely on a lot of guesswork, but Kim Polese, co-founder and chairman of CrowdSmart, is using artificial intelligence to turn qualitative information into quantitative data--and reduce bias along the way. "When we're talking about using collective intelligence, augmented collective intelligence, what we're really talking about is using a combination of human and machine intelligence to improve the way that diligence is done," Polese said this past Wednesday at a Barron'sInvesting in Tech panel. The founder of an artificial-intelligence platform designed to predict a company's potential for success, Polese detailed how the CrowdSmart platform works, and how it could help remove bias from the diligence process. The system draws on the insights of a group of 25 or more people, selected for their different levels of expertise, to evaluate prospective investments, explained Polese, who said her career in Silicon Valley began 30 years ago at the first artificial-intelligence company to go public. "Those people are able to access all of the full diligence materials, so that might be videos, live Q&As with …
The decision to invest in a company can rely on a lot of guesswork, but Kim Polese, co-founder and chairman of CrowdSmart, is using artificial intelligence to turn qualitative information into quantitative data--and reduce bias along the way. "When we're talking about using collective intelligence, augmented collective intelligence, what we're really talking about is using a combination of human and machine intelligence to improve the way that diligence is done," Polese said this past Wednesday at a Barron'sInvesting in Tech panel. The founder of an artificial-intelligence platform designed to predict a company's potential for success, Polese detailed how the CrowdSmart platform works, and how it could help remove bias from the diligence process. The system draws on the insights of a group of 25 or more people, selected for their different levels of expertise, to evaluate prospective investments, explained Polese, who said her career in Silicon Valley began 30 years ago at the first artificial-intelligence company to go public. "Those people are able to access all of the full diligence materials, so that might be videos, live Q&As with the teams, all of the financials, and, ultimately, a brainstorming process is kicked off," Polese said.
Understanding how AI can help us enhance collective human efforts to solve complex problems is at the heart of Nesta's vision for a public interest AI. AI is increasingly being used within all fields and collective intelligence is no exception. Earlier this year, our report on the Future of Minds & Machines first drew attention to the need for more imaginative approaches to combining AI & collective intelligence (CI) in practice. By mapping case studies of collective intelligence in action, we found that most projects applied a fairly narrow range of AI methods to make sense of vast amounts of passively generated or actively crowdsourced user content. Almost all of these methods rely on big datasets and use machine-learning to find structure and patterns in "messy" data.
Their Swarm AI platform presents groups with a question and places potential answers in different corners of their screen. Users control a virtual magnet with their mouse and engage in a tug of war to drag an ice hockey puck to the answer they think is correct. The system's algorithm analyses how each user interacts with the puck – for instance, how much conviction they drag it with or how quickly they waver when they're in the minority – and uses this information to determine where the puck moves. That creates feedback loops in which each user is influenced by the choice and conviction of the others allowing the puck to end up at the answer best reflecting the collective wisdom of the group. Several academic papers and high-profile clients who use the product back up the effectiveness of the Swarm AI platform.
Plants are intelligent beings with profound wisdom to impart--if only we know how to listen. And Monica Gagliano knows how to listen. The evolutionary ecologist has done groundbreaking experiments suggesting plants have the capacity to learn, remember, and make choices. Gagliano, a senior research fellow at the University of Sydney in Australia, talks to plants. Plants summon her with instructions on how to live and work. Some of Gagliano's conversations happened in prophetic dreams, which led her to study with a shaman in Peru while tripping on psychoactive plants. Along with forest scientists like Suzanne Simard and Peter Wohlleben, Gagliano raises profound scientific and philosophical questions about the nature of intelligence and the possibility of "vegetal consciousness." But what's unusual about Gagliano is her willingness to talk about her experiences with shamans and traditional healers, along with her use of psychedelics. For someone who'd already received fierce pushback from other scientists, it was hardly a safe career move to reveal her personal experiences in otherworldly realms. Gagliano considers her explorations in non-Western ways of seeing the world to be part of her scientific work.
When it comes to artificial intelligence (AI), the dominant media narratives often end up taking one of two opposing stances: AI is the saviour or the villain. Whether it is presented as the technology responsible for killer robots and mass job displacement or the one curing all disease and halting the climate crisis, it seems clear that AI will be a defining feature of our future society. However, these visions leave little room for nuance and informed public debate. They also help propel the typical trajectory followed by emerging technologies; with inevitable regularity we observe the ascent of new technologies to the peak of inflated expectations they will not be able to fulfil, before dooming them to a period languishing in the trough of disillusionment. There is an alternative vision for the future of AI development.
For chief digital officers and technology decision-makers, it's more than curiosity that makes us wonder what the future holds. It's our job to look for patterns and trends. We want to be certain that we're making sound investments and applying digital technologies in the right way, helping our businesses stay intelligent, agile and competitive. I see five distinct trends that will shape digital transformation in 2020 and beyond. There will be tremendous opportunities for digital to transform society in big, positive ways.
'Collective intelligence' is defined as the capacity of human communities to cooperate intellectually in creation, innovation and invention. As our society becomes more and more knowledge-dependent, this collective ability becomes of fundamental importance. It is therefore vital to understand, among other things, how collective intelligence processes can be expanded by digital networks. It is one of the keys to success for modern societies. Pierre Lévy is one of the world's leading thinkers, not only in the vast area of cyberculture, but also in the fundamental field of knowledge and its processes. He was essentially the first to focus research on collective intelligence when it became a determining factor in the competitiveness, creativity and human development of knowledge-based societies. Michael Peters (MP): May I call you'Pierre'? Can you tell us something about your education, especially over the three institutions of your experience as a graduate?
Gone are those days where humans have to perform complex task, the world is gradually growing into a new system called the Artificial intelligence. With Artificial intelligence gradually growing into use, a time will come where you will walk into a banking hall and discover that all you have to do is to interract with an Artificial intelligence program and got your need sorted out swiftly. The need to solve complex issues so swiftly is the reason why the need for this system is needed. Artificial Intelligence (A.I), is simply the ability of a computer program or a machine to think and learn. This process helps to see your computer performing a very complex task and at same time, making it smart.
Department of Electrical, Electronic and Information Engineering Alma Mater Studiorum - Universit a di Bologna Bologna, Italy Abstract In this paper, we introduce the concept of collective learning (CL) which exploits the notion of collective intelligence in the field of distributed semi-supervised learning. The proposed framework draws inspiration from the learning behavior of human beings, who alternate phases involving collaboration, confrontation and exchange of views with other consisting of studying and learning on their own. On this regard, CL comprises two main phases: a self-training phase in which learning is performed on local private (labeled) data only and a collective training phase in which proxy-labels are assigned to shared (unlabeled) data by means of a consensus-based algorithm. In the considered framework, heterogeneous systems can be connected over the same network, each with different computational capabilities and resources and everyone in the network may take advantage of the cooperation and will eventually reach higher performance with respect to those it can reach on its own. An extensive experimental campaign on an image classification problem emphasizes the properties of CL by analyzing the performance achieved by the cooperating agents. 1 Introduction The notion of collective intelligence has been firstly introduced in [Engelbart, 1962] and widespread in the sociological field by Pierre L evy in [L evy and Bononno, 1997]. By borrowing the words of L evy, collective intelligence " is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills ". Moreover, " the basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities ". In this paper, we aim to exploit some concepts borrowed from the notion of collective intelligence in a distributed machine learning scenario. In fact, by cooperating with each other, machines may exhibit performance higher than those they can obtain by learning on their own. We call this framework collective learning (CL) . Distributed systems 1 have received a steadily growing attention in the last years and1 When talking about distributed systems, the word distributed can be used with different meanings.