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Our Safety Critical AI Program convenes Partners and other stakeholders to develop best practices that can help us avert likely accidents, misuses, and unintended consequences of AI technologies. As our work shows, precaution can be taken as early as the research stage to ensure the development of safe AI systems. PAI believes that AI has tremendous potential to solve major societal problems and make peoples’ lives better. At the same time, individuals and organizations must grapple with new forms of automation, wealth distribution, and economic decision-making.


And because we understand that people are central to the success of any technology transformation, our global team of experts bring the cross-functional skills to both deliver business outcomes and facilitate cultural change — empowering your workforce to use data and AI responsibly. Recommendation n°2102 about Technological convergence, artificial intelligence and human rights. Training artificial intelligence algorithms requires data to flow like water through a fire hose. Remove AI infrastructure obstacles and bottlenecks and optimize performance from edge to core to cloud. NIST contributes to the research, standards, and data required to realize the full promise of artificial intelligence as an enabler of American innovation across industry and economic sectors. Working with the AI community, NIST seeks to identify the technical requirements needed to cultivate trust that AI systems are accurate and reliable, safe and secure, explainable, and free from bias.



Through this Program, PAI works to ensure that AI systems bolster the quality of public discourse and online content around the world, which includes considering how we define quality in the first place. Graphics processing units have historically been the choice for AI projects because they can handle large datasets efficiently. However, today’s central processing units are often a better choice for AI projects. Unless running complex deep learning on extensively large datasets, CPUs are more accessible, more affordable, and more energy-efficient. Cambridge Consultants, the leader in technology-based consulting, partnered with NetApp and NVIDIA to bring deep learning to organizations worldwide. A superintelligent AI is by definition very good at attaining its goals, whatever they may be, so we need to ensure that its goals are aligned with ours.


To advance a beneficial economic future from AI, the AI, Labor, and the Economy Program gathers Partner organizations, economists, and worker representative organizations. Together, these actors work to form shared answers and recommendations for actionable steps that need to be taken to ensure AI supports an inclusive economic future. The Inclusive Research and Design Program is currently creating resources to help AI practitioners and impacted communities more effectively engage one another to develop AI responsibly.


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The most advanced companies understand that while cloud sets you up with next-level computing power and access to new kinds of data in the right quantity and quality, AI is the bridge to convert that data into business value. It’s no surprise the entire C-suite is now involved in the AI agenda and they’re asking what’s next. There are some who question whether strong AI will ever be achieved, and others who insist that the creation of superintelligent AI is guaranteed to be beneficial. At FLI we recognize both of these possibilities, but also recognize the potential for an artificial intelligence system to intentionally or unintentionally cause great harm. We believe research today will help us better prepare for and prevent such potentially negative consequences in the future, thus enjoying the benefits of AI while avoiding pitfalls. Eradicate war, disease, and poverty, and so the creation of strong AI might be the biggest event in human history.


Reactive Machines


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Researchers have made a wide range of estimates for how far we are from superhuman AI, but we certainly can’t say with great confidence that the probability is zero this century, given the dismal track record of such techno-skeptic predictions. The most extreme form of this myth is that superhuman AI will never arrive because it’s physically impossible. However, physicists know that a brain consists of quarks and electrons arranged to act as a powerful computer, and that there’s no law of physics preventing us from building even more intelligent quark blobs. Also called “lethal autonomous weapons systems” or “killer robots”, are weapons systems that use artificial intelligence to identify, select, and kill human targets without human intervention. Artificial intelligence today is properly known as narrow AI , in that it is designed to perform a narrow task (e.g. only facial recognition or only internet searches or only driving a car).


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The experimental sub-field of artificial general intelligence studies this area exclusively. Alan Turing wrote in 1950 "I propose to consider the question 'can machines think'?"He advised changing the question from whether a machine "thinks", to "whether or not it is possible for machinery to show intelligent behaviour". He devised the Turing test, which measures the ability of a machine to simulate human conversation. The only thing visible is the behavior of the machine, so it does not matter if the machine is conscious, or has a mind, or whether the intelligence is merely a "simulation" and not "the real thing".


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DeepMind unveils Gato, an AI system trained to perform hundreds of tasks, including playing Atari, captioning images and using a robotic arm to stack blocks. Baidu releases its LinearFold AI algorithm to scientific and medical teams working to develop a vaccine during the early stages of the SARS-CoV-2 pandemic. The algorithm is able to predict the RNA sequence of the virus in just 27 seconds, 120 times faster than other methods.


Myths About the Risks of Superhuman AI


These are commonly used for ordinal or temporal problems, such as language translation, natural language processing, speech recognition and image captioning. One subset of recurrent neural networks is known as long short term memory , which utilizes past data to help predict the next item in a sequence. LTSMs view more recent information as most important when making predictions, and discount data from further in the past while still utilizing it to form conclusions.

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