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Physics can assist with key challenges in artificial intelligence

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IMAGE: In an article published today in the journal Scientific Reports, researchers from Bar-Ilan University show how two challenges in current research and applications in the field of artificial intelligence are… view more Credit: Prof. Ido Kanter, Bar-Ilan University Current research and applications in the field of artificial intelligence (AI) include several key challenges. These include: (a) A priori estimation of the required dataset size to achieve a desired test accuracy. For example, how many handwritten digits does a machine have to learn before being able to predict a new one with a success rate of 99%? Similarly, how many specific types of circumstances does an autonomous vehicle have to learn before its reaction will not lead to an accident? This type of realization of fast on-line decision making is representative of many aspects of human activity, robotic control and network optimization.


Physics can assist with key challenges in artificial intelligence

#artificialintelligence

Current research and applications in the field of artificial intelligence (AI) include several key challenges. These include: (a) A priori estimation of the required dataset size to achieve a desired test accuracy. For example, how many handwritten digits does a machine have to learn before being able to predict a new one with a success rate of 99%? Similarly, how many specific types of circumstances does an autonomous vehicle have to learn before its reaction will not lead to an accident? This type of realization of fast on-line decision making is representative of many aspects of human activity, robotic control and network optimization.


Physics Can Assist with Key Challenges in Artificial Intelligence

#artificialintelligence

Current research and applications in the field of artificial intelligence (AI) include several key challenges. These include: (a) A priori estimation of the required dataset size to achieve a desired test accuracy. For example, how many handwritten digits does a machine have to learn before being able to predict a new one with a success rate of 99 percent? Similarly, how many specific types of circumstances does an autonomous vehicle have to learn before its reaction will not lead to an accident? This type of realization of fast online decision making is representative of many aspects of human activity, robotic control, and network optimization.


Power-law scaling to assist with key challenges in artificial intelligence

#artificialintelligence

Power-law scaling, a central concept in critical phenomena, is found to be useful in deep learning, where optimized test errors on handwritten digit examples converge as a power-law to zero with database size. For rapid decision making with one training epoch, each example is presented only once to the trained network, the power-law exponent increased with the number of hidden layers. For the largest dataset, the obtained test error was estimated to be in the proximity of state-of-the-art algorithms for large epoch numbers. Power-law scaling assists with key challenges found in current artificial intelligence applications and facilitates an a priori dataset size estimation to achieve a desired test accuracy. It establishes a benchmark for measuring training complexity and a quantitative hierarchy of machine learning tasks and algorithms.


Key Challenges when Implementing Artificial Intelligence

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What’s the bigger challenge in implementing Artificial Intelligence within your enterprise? Risk or uncertainty? Hang on a minute, you say, aren’t they sort of the same thing? They’re related, but I’m going to argue uncertainty is emotional. We’ll play a game, You may be familiar with it from your undergraduate studies. Imagine I’ve got a jar with 100 balls in it. 50 red and 50 black. The jar is opaque and you can’t see inside. Pick a color; red or black. I’ll draw out a ball from the jar. If it’s your color I pay you $10,000. If it’s not your color, you get nothing. And I’m only going to let you play this game with me once.


5 Key Challenges In Today's Era of Big Data

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Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes.


How supply chain analytics can improve demand fulfilment

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The ever-increasing demand clusters for a given geography and supplying them with the optimal resources in the current fast-paced retail and service industry leads these industries to rework their supply chain channels with the application of advanced Machine Learning and Neural Network algorithms. Using supply chain analytics, a business can make accurate forecasts of what the overall demand will be. Apart from this,it is also important for them to successfully fulfill the demand across different segments and timely supply those desired signals. Demand fulfilment from limited number of Big Demand Centers is not enough; the business needs to improve their localized fulfillment centers or warehouses across geography to improve their overall fulfillment rate and customer satisfaction. Due to increase in the market challenges and competition, the traditional business models can no longer keep up with supply interruptions.


5 Key Challenges In Today's Era of Big Data

#artificialintelligence

Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes.


5 Key Challenges In Today's Era of Big Data

#artificialintelligence

Digital transformation will create trillions of dollars of value. While estimates vary, the World Economic Forum in 2016 estimated an increase in $100 trillion in global business and social value by 2030. Due to AI, PwC has estimated an increase of $15.7 trillion and McKinsey has estimated an increase of $13 trillion in annual global GDP by 2030. We are currently in the middle of an AI renaissance, driven by big data and breakthroughs in machine learning and deep learning. These breakthroughs offer opportunities and challenges to companies depending on the speed at which they adapt to these changes.


Key Challenges That Healthcare AI Needs to Overcome in 2020 - Dataconomy

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The promise of artificial intelligence (AI) is finally being realized across a wide variety of industries. AI is now viewed as a crucial technology to adopt for enterprises to thrive in today's business environment. Healthcare, in particular, has been one of the industries that AI advocates expect to be revolutionized by AI. Potential use cases paint a clear picture of how healthcare stakeholders stand to benefit from AI in the months ahead. Patient care standards are projected to improve, diagnostic capabilities are expected to expand, and facilities should become far more efficient.