Leisure & Entertainment


Deep Learning – what is it? Why does it matter?

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This is why in the image you can see that both models result in some errors with reds in the blue zone and blues in the red zone. The theory is that the more hidden layers you have the more you can isolate specific regions of data to classify things. GPU based processing allows for parallel execution, on large numbers of relatively cheap processors, especially when training an artificial neural network with many hidden layers and a lot of input data. That means having them able to understand images, understand speech, understand text etc.


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.


Diving deeper into the realm of AI

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Then, as high-bandwidth networking, cloud computing, and high-powered graphics-enabled microprocessors emerged, researchers began building multilayered neural networks--still extremely slow and limited compared to the human brain, but useful in practical ways. The best-known AI milestones--in which software systems beat expert human players in Jeopardy!, chess, Go, poker, and soccer--differ from most day-to-day business applications. A deep learning system is a multilayered neural network that learns representations of the world and stores them as a nested hierarchy of concepts many layers deep. Although it is the most similar duplication of the human brain scientists have developed, a deep learning neural network cannot be leveraged to solve all problems.


World's first DRIVERLESS race car Roborace hits the track

Daily Mail

These include five lidars, two radars, 18 ultrasonic sensors, two optical speed sensors, six AI cameras, GNSS positioning and a powerful Nvidia Drive PX2 'brain' processor, capable of 24 trillion AI operations per second. Roborace first revealed the stunning 4.8-metre-long (15.7 ft), two-metre-wide (6.5 ft) vehicle at March's Mobile World Congress in Barcelona. The futuristic vehicle completed a lap of the Paris ePrix circuit (pictured) ahead of the city's 2017 Formula E race, which took place on Saturday Saturday's public demonstration saw the car whip around 14 turns of the almost 2 kilometre (1.2 mile) track driven entirely by AI and sensors Mr Sverdlov said: 'This is a huge moment for Roborace as we share the Robocar with the world and take another big step in advancing driverless electric technology. Technologies guiding the vehicle include five lidars, two radars, 18 ultrasonic sensors, two optical speed sensors, six AI cameras, GNSS positioning and a powerful Nvidia Drive PX2 'brain' processor, capable of 24 trillion AI operations per second Mr Simon said: 'Roborace opens a new dimension where motorsport as we know it meets the unstoppable rise of artificial intelligence.


Applications Of Machine Learning For Designers – Smashing Magazine

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As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. As a designer, you will be facing more demands and opportunities to work with digital systems that embody machine learning. This will help with making actual design decisions and identifying the right design patterns, including situations when no directly applicable solution exists and you must transfer ideas across domains. In rare cases, machine learning might enable a computer to perform tasks that humans simply can't perform because of speed requirements or the scale of data.


Autonomous driving - do it yourself!

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ALV (Autonomous Land Vehicle) project used lidar sensors, computer vision and robotic control in order to drive a car with slow speed. On the other hand, there are approaches similar to the mentioned ALVINN and Nvidia concepts, that maps the road image directly into steering commands. OpenAI Universe makes experiments with computer games particularly easy, as it provides a complete environment for testing AI agents. Computer games are becoming complex enough to emulate the real world, therefore there are some active researches on data collection in virtual environments and evaluating models trained on this data in real traffic.


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.


The Current State of AI - IT News

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At this time machines were unable to recognise human faces or understand speech. One area that amazes me is that computer software is now as good as humans at identifying objects (people, animals, buildings, faces etc.) It took off in the 1990s when the emphasis shifted from achieving artificial intelligence to solving practical problems and evolved from the study of pattern recognition and computational learning theory. Less conventional uses for Big Data include detecting genocide in Guatemala over a 36 year period based on a sample of 80 million detailed statements.


Optimization for machine learning and monster trucks

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When he turned two, I unwisely thought he would enjoy a monster truck rally and purchased tickets, imagining father and son duo making great memories together. Prototype approaches can be created relatively quickly, requiring publishable mathematics to prove convergence, pounded home with surprisingly good results. This is why I am excited about this Hessian-free approach, because although it is currently not mainstream, and it lacks the rock star status of stochastic gradient descent (SGD) approaches, it has the potential to save the user significant user processing time. I personally love algorithm development and enjoy spending my waking hours seeking to make algorithms faster and more robust.