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10 Fascinating Facts About Ex Machina

Frontiers | How Organisms Come to Know the World: Fundamental Limits on Artificial General Intelligence

Artificial intelligence has made tremendous advances since its inception about seventy years ago. Self-driving cars, programs beating experts at complex games, and smart robots capable of assisting people that need care are just some among the successful examples of machine intelligence. This kind of progress might entice us to envision a society populated by autonomous robots capable of performing the same tasks humans do in the near future. This prospect seems limited only by the power and complexity of current computational devices, which is improving fast. However, there are several significant obstacles on this path. General intelligence involves situational reasoning, taking perspectives, choosing goals, and an ability to deal with ambiguous information. We observe that all of these characteristics are connected to the ability of identifying and exploiting new affordances-opportunities (or impediments) on the path of an agent to achieve its goals. A general example of an affordan

Frontiers | Artificial Intelligence Is Stupid and Causal Reasoning Will Not Fix It

Artificial Neural Networks have reached ‘Grandmaster’ and even ‘super-human’ performance’ across a variety of games, from those involving perfect-information, such as Go ((Silver et al. (2016)); to those involving imperfect-information, such as ‘Starcraft’ (Vinyals et al. (2019)). Such technological developments from AI-labs have ushered concomitant applications across the world of business, where an ‘AI’ brand-tag is fast becoming ubiquitous. A corollary of such widespread commercial deployment is that when AI gets things wrong - an autonomous vehicle crashes; a chatbot exhibits ‘racist’ behaviour; automated credit-scoring processes ‘discriminate’ on gender etc. - there are often significant financial, legal and brand consequences, and the incident becomes major news. As Judea Pearl sees it, the underlying reason for such mistakes is that “. all the impressive achievements of deep learning amount to just curve fitting”. The key, Pearl suggests (Pearl and

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