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Transforming from Autonomous to Smart: Reinforcement Learning Basics – InFocus Blog Dell EMC Services

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With the rapid increases in computing power, it's easy to get seduced into thinking that raw computing power can solve problems like smart edge devices (e.g., cars, trains, airplanes, wind turbines, jet engines, medical devices). In chess, the complexity of the chess piece only increases slightly (rooks can move forward and sideways a variable number of spaces, bishops can move diagonally a variable number of spaces, etc. Now think about the number and breadth of "moves" or variables that need to be considered when driving a car in a nondeterministic (random) environment: weather (precipitation, snow, ice, black ice, wind), time of day (day time, twilight, night time, sun rise, sun set), road conditions (pot holes, bumpy, slick), traffic conditions (number of vehicles, types of vehicles, different speeds, different destinations). It's nearly impossible for an autonomous car manufacturer to operate enough vehicles in enough different situations to generate the amount of data that can be virtually gathered by playing against Grand Theft Auto.


Transforming from Autonomous to Smart: Reinforcement Learning Basics

@machinelearnbot

With the rapid increases in computing power, it's easy to get seduced into thinking that raw computing power can solve problems like smart edge devices (e.g., cars, trains, airplanes, wind turbines, jet engines, medical devices). In chess, the complexity of the chess piece only increases slightly (rooks can move forward and sideways a variable number of spaces, bishops can move diagonally a variable number of spaces, etc. Now think about the number and breadth of "moves" or variables that need to be considered when driving a car in a nondeterministic (random) environment: weather (precipitation, snow, ice, black ice, wind), time of day (day time, twilight, night time, sun rise, sun set), road conditions (pot holes, bumpy, slick), traffic conditions (number of vehicles, types of vehicles, different speeds, different destinations). It's nearly impossible for an autonomous car manufacturer to operate enough vehicles in enough different situations to generate the amount of data that can be virtually gathered by playing against Grand Theft Auto.


The Moral Imperative of Artificial Intelligence

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Some labor economists have viewed Polanyi's Paradox as a major barrier for AI, arguing it implies a limit on its potential to automate human jobs. Indeed, the automation of driving has been a major challenge for AI research over the past decade. Thus, the automation of driving would be hugely beneficial, saving lives and preventing injuries on a massive scale. In the balance, life saving and injury prevention must take precedence, and we have a moral imperative to develop and deploy automated driving.


Euclidean 1Q16 Letter - Deep Learning & Value Investing

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Here's a comment from a January article in Nature that describes deep learning's step forward. Traditional machine learning techniques require us to put a great deal of energy into these types of questions. While we are excited about what we might achieve, our expectations are tempered by the caveat that deep learning generally requires vastly more data than traditional approaches to machine learning. It is in this area of modeling sequences of data through time that machine learning has recently made huge steps forward.