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Variational quantum evolution equation solver - Scientific Reports

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Variational quantum algorithms offer a promising new paradigm for solving partial differential equations on near-term quantum computers. Here, we propose a variational quantum algorithm for solving a general evolution equation through implicit time-stepping of the Laplacian operator. The use of encoded source states informed by preceding solution vectors results in faster convergence compared to random re-initialization. Through statevector simulations of the heat equation, we demonstrate how the time complexity of our algorithm scales with the Ansatz volume for gradient estimation and how the time-to-solution scales with the diffusion parameter. Our proposed algorithm extends economically to higher-order time-stepping schemes, such as the Crank–Nicolson method. We present a semi-implicit scheme for solving systems of evolution equations with non-linear terms, such as the reaction–diffusion and the incompressible Navier–Stokes equations, and demonstrate its validity by proof-of-concept results.


5 Top Trends in AI Robotics in 2022

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In the coming years, broad exploration will occur in the field of hybrid neurosymbolic approaches towards applications that are beyond the capabilities of any one approach on its own,


#selfdrivingcars_2022-06-22_05-29-21.xlsx

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The graph represents a network of 1,695 Twitter users whose tweets in the requested range contained "#selfdrivingcars", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 22 June 2022 at 12:38 UTC. The requested start date was Wednesday, 22 June 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 16-day, 17-hour, 23-minute period from Sunday, 05 June 2022 at 02:45 UTC to Tuesday, 21 June 2022 at 20:08 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.


The Go-To-Market Strategy For Autonomous Vehicles: "Launch Somewhere"

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ENGLAND: Two autonomous, delivery robots pass on the pavement as they make home deliveries of ... [ ] groceries. Created by two of the co-founders of Skype in 2014, Starship has developed the self-driving pods for various, logistical tasks. Much more complicated than originally thought. Several manufacturers expected the first self-driving cars to hit the market 3-4 years ago. In fact, Johann Jungwirth of Volkswagen met with Focus Magazine in April of 2016 amongst beanbags, blue suede shoes and skateboards to report the first autonomous vehicles (AVs) will be on the market by 2019.


A Beginner's Guide to Basic Python

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Python programming language is a recommended language for beginners to learn. The reason is, this language has concise commands, so it is very easy to understand and write. You want to learn Python, but are confused about where to start? This time, we invite you to get to know what Python is, its functions and data types, to practice Python tutorials themselves. Python is one of the many examples of programming languages in the world.


Global Big Data Conference

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Has artificial intelligence finally come to life, or has it simply become smart enough to trick us into believing it has gained consciousness? Google engineer Blake Lemoine's recent claim that the company's AI technology has become sentient has sparked debate in technology, ethics and philosophy circles over if, or when, AI might come to life -- as well as deeper questions about what it means to be alive. Lemoine had spent months testing Google's chatbot generator, known as LaMDA (short for Language Model for Dialogue Applications), and grew convinced it had taken on a life of its own, as LaMDA talked about its needs, ideas, fears and rights. Google dismissed Lemoine's view that LaMDA had become sentient, placing him on paid administrative leave earlier this month -- days before his claims were published by The Washington Post. Most experts believe it's unlikely that LaMDA or any other AI is close to consciousness, though they don't rule out the possibility that technology could get there in future. "My view is that [Lemoine] was taken in by an illusion," Gary Marcus, a cognitive scientist and author of Rebooting AI, told CBC's Front Burner podcast.


A Brief History of Deep Learning

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Human inventions find their inspiration from nature. Likewise, deep learning was an attempt to model the human brain, one of the most complicated structures in the universe. The attempt was not to mimic every detail of the brain. Instead, artificial neural networks were inspired by biological neural networks, eventually leading to deep learning. So what is deep learning?


Pinaki Laskar on LinkedIn: #AI #machinelearning #algorithms

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How can a mathematically-oriented machine truly learn things? Mathematical machines are either formal logical systems, operationalized as symbolic rules-based AI or expert systems, or statistical learning machines, dubbed as narrow/Weak AI, ML, DL, ANNs. Such machines follow blind and mindless mathematical and statistical algorithms, codes, models, programs, and solutions, transforming input data (as independent variables) into the output data (as dependent variables), dubbed as predictions, recommendations, decisions, etc. They are unable to real knowing or learning, as having no interactions with the world, its various domains, rules, laws, objects, events, or processes. Learning is the "acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences" via senses, experience, trial and error, intuition, study and research.


Ethical AI Lapses Happen When No One Is Watching

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Transparency often plays a key role in ethical business dilemmas -- the more information we have, the easier it is to determine what are acceptable and unacceptable outcomes. If financials are misaligned, who made an accounting error? If data is breached, who was responsible for securing it and were they acting properly? Click here to view original web page at www.informationweek.com


Artificial Intelligence (7 weeks)

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This course explores the idea of artificial intelligence (A.I.) from three different perspectives: scientific, philosophical, and cultural. The scientific perspective provides insight as to how artificial intelligence technologies work, the current limitations, and supposed future potential. The philosophical perspective explores whether A.I. is good or bad, essential or dangerous, and what the future could hold. The cultural angle examines how society views A.I. and whether these views are accurate. Toward the end of the course deeper topics will be introduced including how A.I. compares to human intelligence, the singularity, and futurism.