Leisure & Entertainment


"Scientists are still suspicious of AI" - Globes English

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In March 2016, Google's Alphago artificial intelligence (AI) program stunned the world by beating the human world champion Go player in front of 200 million spectators. This was living proof of the potential in AI technology and the level of maturity reached by neural network and deep learning technologies. Those astounded by the success included quite a few engineers and managers who have been leading the AI revolution in the world in recent years. One of these was Intel VP Naveen Rao, general manager of the company's Artificial Intelligence Products Group, which was founded last year. "When I studied at college in the 1990s, we regarded artificial intelligence as'creative work'," Rao relates.


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.


Deep learning boosted AI. Now the next big thing in machine intelligence is coming

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Inside a simple computer simulation, a group of self-driving cars are performing a crazy-looking maneuver on a four-lane virtual highway. Half are trying to move from the right-hand lanes just as the other half try to merge from the left. It seems like just the sort of tricky thing that might flummox a robot vehicle, but they manage it with precision. I'm watching the driving simulation at the biggest artificial-intelligence conference of the year, held in Barcelona this past December. What's most amazing is that the software governing the cars' behavior wasn't programmed in the conventional sense at all.


Deep learning boosted AI. Now the next big thing in machine intelligence is coming

#artificialintelligence

Inside a simple computer simulation, a group of self-driving cars are performing a crazy-looking maneuver on a four-lane virtual highway. Half are trying to move from the right-hand lanes just as the other half try to merge from the left. It seems like just the sort of tricky thing that might flummox a robot vehicle, but they manage it with precision. I'm watching the driving simulation at the biggest artificial-intelligence conference of the year, held in Barcelona this past December. What's most amazing is that the software governing the cars' behavior wasn't programmed in the conventional sense at all.


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.