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Racing tiny cars using only Artificial Intelligence and Machine Learning

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Thirteen university students from across Canada are in Ottawa to put their artificial intelligence skills to the test. It's called the Amazon Web Services DeepRacer League, where small 1/18th scale cars are being trained to complete a racetrack as fast as possible, by themselves. "It has major components in order to do the autonomous driving," says Amanda Foo, DeepRacer Senior Technical Program Manager. They are driven by what is called reinforcement learning. "It's just like training a dog," Carleton University mechanical engineering student Masoud Karimi says.


Modern Computing: A Short History, 1945-2022

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Inspired by A New History of Modern Computing by Thomas Haigh and Paul E. Ceruzzi. But the selection of key events in the journey from ENIAC to Tesla, from Data Processing to Big Data, is mine. This was the first computer made by Apple Computers Inc, which became one of the fastest growing ... [ ] companies in history, launching a number of innovative and influential computer hardware and software products. Most home computer users in the 1970s were hobbyists who designed and assembled their own machines. The Apple I, devised in a bedroom by Steve Wozniak, Steven Jobs and Ron Wayne, was a basic circuit board to which enthusiasts would add display units and keyboards. April 1945 John von Neumann's "First Draft of a Report on the EDVAC," often called the founding document of modern computing, defines "the stored program concept." July 1945 Vannevar Bush publishes "As We May Think," in which he envisions the "Memex," a memory extension device serving as a large personal repository of information that could be instantly retrieved through associative links.


Why Synthetic Data and Deepfakes are the Future of Data Analytics?

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It's impossible to understand what's going on in the enterprise technology space without first understanding data and how data is driving innovation. Synthetic data can be employed in a privacy-safe environment, meaning that users and developers can access it without any concerns over disclosing sensitive information. The main artificial intelligence methods used to create deepfakes are based on deep learning and involve training generative neural network architectures, such as autoencoders or generative adversarial networks (GANs). A 2018 Deloitte survey, for instance, found that "data issues" such as privacy, accessing, and integration was considered the biggest challenges in implementing AI and data analytics initiatives. By generating non-identifiable datasets, however, synthetic data generation can be a vital privacy-enhancing technology that does not carry the regulatory or legal burdens associated with disclosing personal data. Synthetic data is data that you can create at any scale, whenever and wherever you need it.


The Robot Brains Podcast: Eric Horvitz of Microsoft on AI for the greater good on Apple Podcasts

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On Episode 15 of Season 2, we're joined by Eric Horvitz, Microsoft's first ever Chief Scientific Officer. His research spans theoretical and practical challenges with developing systems that perceive, learn, and reason. He's the company's top inventor since joining in 1993 with over 300 patents filed. He has been elected Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), Fellow of the National Academy of Engineering (NAE), Fellow of the American Academy of Arts and Sciences, and Fellow of the American Association for the Advancement of Science (AAAS). He was a member of the National Security Commission on AI and he also co-founded important groups like the Partnership on AI, a non-profit organization bringing together Apple, Amazon, Facebook, Google, DeepMind, IBM, and Microsoft to document the quality and impact of AI systems on things like criminal justice, the economy, and media integrity.


What Is Artificial Intelligence? - ExtremeTech

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To many, AI is just a horrible Steven Spielberg movie. But what is artificial intelligence, exactly? The answer depends on who you ask. Broadly, artificial intelligence (AI) is the combination of computer science and robust datasets, deployed to solve some kind of problem. Many definitions of artificial intelligence include a comparison to the human mind or brain, whether in form or function. Alan Turing wrote in 1950 about "thinking machines" that could respond to a problem using human-like reasoning.


AI in Medicine -- Prospective versus Retrospective

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Just like Sedol Lee was defeated by AlphaGo three or four years ago, there was an atmosphere that artificial intelligence would replace experts in medicine and replace everything in the world. The achievements of AI in the medical field were recorded one by one in an IEEE Spectrum ("AI versus Doctor"; https://ieeexplore.ieee.org/document/8048826). However, since a year or two ago, the main focus has moved to the role of artificial intelligence as an assistance tool for experts, and recently, it is not uncommon to hear that artificial intelligence is not making a profit in business. Even IBM's Watson was sold with some criticism. There may be a problem in some way, so why are we hearing these news?


Predictive analytics and algorithm: How Netflix & Spotify use it!

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Would you be amazed if food delivery apps like Zomato or Ubereats suggest what you want to eat on a specific day, keep a check on your food cravings and cheat-day plans? Well, it's a matter of a couple of years and this would be a real phenomenon. With customer expectations touching the sky to increased competition, businesses seek an edge in bringing products and services to stand apart in the market and deliver incredible customer experiences. Where customers expect businesses to read their minds and surprise them with something new, businesses are always one step ahead to do so. With the emergence of companies such as Netflix and Spotify that deliver personalized insights daily, customers expect to have recommendations catered to their needs. Customers expect other companies also to meet the expected standards and bring something unique to their table, how can businesses jump to this level of proactivity? How can you anticipate needs, trends, and behaviors?


Can You Code Empathy? with Pascale Fung

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ANJA KASPERSEN: Today I am very pleased to be joined by Pascale Fung. Pascale is a;rofessor in the Department of Electronic and Computer Engineering and Department of Computer Science and Engineering at The Hong Kong University of Science and Technology. She is known globally for her pioneering work on conversational artificial intelligence (AI), computational linguistics, and was one of the earliest proponents of statistical and machine-learning approaches for natural language processing (NLP). She is now leading groundbreaking research on how to build intelligent systems that can understand and empathize with humans. I have really been looking forward to this conversation with you. Your professional accolades are many, most of which we will touch on during our conversation. However, for our listeners to get to know you a bit better, I would like us to go back to your upbringing during what I understand to be a very tenuous political period in China. I was born, spent my childhood, ...


The Key Artificial Intelligence and Machine Learning Trends for 2022

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We've entered a new year now, and it seems likely that there will be numerous different artificial intelligence and machine learning trends for 2022. Potentially, this could influence how things look heading into the future. There are numerous potential technology trends and predictions that businesses should be aware of as we head further into 2022 – but these undeniably revolve heavily around themes of automation and artificial intelligence. As such, we've picked out some of the six current artificial intelligence and machine learning trends that you should know as follows; these might even revolutionize the way your business trades, looking ahead. One of the first big changes and trends that we have seen in recent times is the growth of AI-powered cybersecurity systems, which are undeniably becoming much more popular.


Natural Language Processing

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Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The recommendation systems (RS) are becoming an integral part of our daily lives. This means that we can obtain what we desire either through internet-accessible applications or on social media channels. Traditional views of the recommendation problem refer to it as a simple classification or prediction problem; however, recently new evidence indicates that it is essentially a sequential problem[1]. It can therefore be formulated as a Markov decision process (MDP) and reinforcement learning (RL) methods can be employed to resolve it [1]. RL algorithms play a crucial role as these algorithms are very advantageous to cope with the dynamic environment and large space [4]. Deep Reinforcement Learning (DRL), have enabled RL to be applied to the recommendation problem with massive states and action spaces. RL-based and DRL-based methods in a classified manner based on the specific RL algorithm, like Q-learning, SARSA, and REINFORCE, that is used to optimize the recommendation policy[2].