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If we can't design autonomous robots, maybe they can design themselves – TechCrunch

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Elon Musk's recent announcement of an upcoming Tesla Bot -- complete with a human form, "human-level hands" and a characteristically optimistic delivery date -- has garnered a healthy serving of criticism for good reason. Among other capabilities, Musk says, the robot will eventually be capable of running errands such as going to the grocery store alone. Boston Dynamics, which has developed the most advanced humanoid robot ever created, has spent more than a decade working on its Atlas platform. While progress has been impressive, with Atlas running, jumping and even dancing in front of tens of millions of YouTube viewers, the company is quick to acknowledge that the robot is a long way from performing complex tasks autonomously. One of the best examples of evolutionary robotics potential -- and unfulfilled promise -- goes as far back as 2010 to a study published in the PLOS Biology journal.


If we can't design autonomous robots, maybe they can design themselves – TechCrunch

#artificialintelligence

Elon Musk's recent announcement of an upcoming Tesla Bot -- complete with a human form, "human-level hands" and a characteristically optimistic delivery date -- has garnered a healthy serving of criticism for good reason. Among other capabilities, Musk says, the robot will eventually be capable of running errands such as going to the grocery store alone. Boston Dynamics, which has developed the most advanced humanoid robot ever created, has spent more than a decade working on its Atlas platform. While progress has been impressive, with Atlas running, jumping and even dancing in front of tens of millions of YouTube viewers, the company is quick to acknowledge that the robot is a long way from performing complex tasks autonomously. One of the best examples of evolutionary robotics potential -- and unfulfilled promise -- goes as far back as 2010 to a study published in the PLOS Biology journal.


Robot Evolution: Ethical Concerns

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Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.



Artificial intelligence - Wikipedia, the free encyclopedia

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Artificial intelligence (AI) is intelligence exhibited by machines. In computer science, an ideal "intelligent" machine is a flexible rational agent that perceives its environment and takes actions that maximize its chance of success at some goal.[1] Colloquially, the term "artificial intelligence" is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".[2] As machines become increasingly capable, facilities once thought to require intelligence are removed from the definition. For example, optical character recognition is no longer perceived as an exemplar of "artificial intelligence" having become a routine technology.[3] Capabilities still classified as AI include advanced Chess and Go systems and self-driving cars. AI research is divided into subfields[4] that focus on specific problems or on specific approaches or on the use of a particular tool or towards satisfying particular applications. The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects.[5] General intelligence is among the field's long-term goals.[6] Approaches include statistical methods, computational intelligence, soft computing (e.g. machine learning), and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics. The AI field draws upon computer science, mathematics, psychology, linguistics, philosophy, neuroscience and artificial psychology. The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it."[7] This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been explored by myth, fiction and philosophy since antiquity.[8] Attempts to create artificial intelligence has experienced many setbacks, including the ALPAC report of 1966, the abandonment of perceptrons in 1970, the Lighthill Report of 1973 and the collapse of the Lisp machine market in 1987. In the twenty-first century AI techniques became an essential part of the technology industry, helping to solve many challenging problems in computer science.[9]