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) …
But if the narrative of the present is one of "prediction machines," referencing the book of the same title by Ajay Agrawal, Joshua Gans, and Avi Goldfarb, the narrative of the future will belong to "decision machines." If the narrative of the present is one of managers who are valued for showing judgment in decision making -- don't tell me whether someone will do well on the job, or whether a new product will win in the marketplace, but tell me instead who I should hire, which products I should bet on -- then the narrative of the future will be one in which we are valued for our ability to judge and shape the decision-making capabilities of machines. Artificial intelligence (AI) is the pursuit of machines that are able to act purposefully to make decisions towards the pursuit of goals. Machines need to be able to predict to decide, but decision making requires much more. Decision making requires bringing together and reconciling multiple points of view.
Rational models of causal induction have been successful in accounting for people's judgments about the existence of causal relationships. However, these models have focused on explaining inferences from discrete data of the kind that can be summarized in a 2 2 contingency table. This severely limits the scope of these models, since the world often provides non-binary data. We develop a new rational model of causal induction using continuous dimensions, which aims to diminish the gap between empirical and theoretical approaches and real-world causal induction. This model successfully predicts human judgments from previous studies better than models of discrete causal inference, and outperforms several other plausible models of causal induction with continuous causes in accounting for people's inferences in a new experiment.
Chris Dixon, who invested early in companies ranging from Warby Parker to Kickstarter, once wrote that the next big thing always starts out looking like a toy. That's certainly true of artificial intelligence, which started out playing games like chess, go and playing humans on the game show Jeopardy! Yet today, AI has become so pervasive we often don't even recognize it anymore. Besides enabling us to speak to our phones and get answers back, intelligent algorithms are often working in the background, providing things like predictive maintenance for machinery and automating basic software tasks. As the technology becomes more powerful, it's also forcing us to ask some uncomfortable questions that were once more in the realm of science fiction or late-night dorm room discussions.
Artificial Intelligence (AI) is acquiring increasing importance in many applications that support decision-making in various areas, including healthcare, consumption, and risk classification of individuals. The growing impact of AI on people's lives naturally raises the question about its ethical and moral components. Are AI decisions ethically acceptable? How can we ensure that AI remains ethical over time? Should we dominate AI and impose specific behavioural rules, possibly limiting its enormous potential, or should we allow AI to develop its own ethics, possibly ultimately subjugating us to intellectual slavery?
Some industry experts argue that machine learning (ML) will reverse an increasing trend toward passive investment funds. But although ML offers new tools that could help active investors outperform the indexes, it is unclear whether it will deliver a sustainable business model for active asset managers. Let's start with the positives A form of artificial intelligence, ML enables powerful algorithms to analyze large data sets in order make predictions against defined goals. Instead of precisely following instructions coded by humans, these algorithms self-adjust through a process of trial and error to produce increasingly more accurate prescriptions as more data comes in. ML is particularly adaptable to securities investing because the insights it garners can be acted on quickly and efficiently.
Minority Report was a classic Steven Spielberg sci-fi film. Employing tech-noir, the film exhibited a dystopian plot showcasing the dire pitfalls and consequences of predictive law enforcement. The movie conceived a futuristic technology, mixing psychics and premonitions, to pre-empt crime, with a suspect apprehended using a special department labelled, quite literally, "PreCrime". Similar themes surrounding the deployment of intelligent machines to aid in law enforcement and criminal justice, which in turn go awry, have consistently featured in popular culture. These seemingly grandiose notions of artificial intelligence (AI) are rapidly finding themselves at play in real life.
It will take time, but at some point every application will have its share of "AI Inside." Today, however, we're far from that point, and false advertising of AI capabilities isn't helping, something Arvind Narayanan, Associate Professor of Computer Science at Princeton, has called out as "snake oil" in a recent presentation. It's not that there aren't real, useful ways to employ AI today, he stresses, but rather that "Much of what's being sold as'AI' today is snake oil--it does not and cannot work." To help parse good from bad AI advertising, where does Narayanan believe we're making real progress in AI, and where should we myth bust? As with any new technology, aspirations to embrace it always outpace actual production usage, and AI is no different.
Traditionally, the way one evaluates the performance of an Artificial Intelligence (AI) system is via a comparison to human performance in specific tasks, treating humans as a reference for high-level cognition. However, these comparisons leave out important features of human intelligence: the capability to transfer knowledge and make complex decisions based on emotional and rational reasoning. These decisions are influenced by current inferences as well as prior experiences, making the decision process strongly subjective and apparently biased. In this context, a definition of compositional intelligence is necessary to incorporate these features in future AI tests. Here, a concrete implementation of this will be suggested, using recent developments in quantum cognition, natural language and compositional meaning of sentences, thanks to categorical compositional models of meaning.
WITF-FM, a public radio, television, and online news broadcaster in central Pennsylvania, includes the following statement above select online news coverage: "WITF strives to provide nuanced perspectives from the most authoritative sources. We are on the lookout for biases or assumptions in our own work, and we invite you to point out any we may have missed." It's not uncommon for news organizations to invite comments and feedback from their audience; in fact, most encourage it. But WITF has gone above and beyond a general invitation for engagement. This statement highlights the potential for bias in their own reporting -- and their attempt to avoid it.
Some say allowing artificial intelligence (AI) to determine guilt or innocence in a courtroom is a step too far. But for those who are sceptical about the neutrality of human judgment, or have witnessed an unfair justice system in action, AI and legal robots could be the answer to providing a fair and impartial jury. We already automate so much else in society, so why not extend this smart automation to juries? After all, lawyers rely on technology to scan documents for keywords or evaluate collected data. And people can now use legal chatbots to determine if it's worthwhile to pursue their case in court.