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Identifying artificial intelligence 'blind spots'

The AI systems powering driverless cars, for example, are trained extensively in virtual simulations to prepare the vehicle for nearly every event on the road.

Consider a driverless car that wasn't trained, and more importantly doesn't have the sensors necessary, to differentiate between distinctly different scenarios, such as large, white cars and ambulances with red, flashing lights on the road.

If the car is cruising down the highway and an ambulance flicks on its sirens, the car may not know to slow down and pull over, because it does not perceive the ambulance as different from a big white car.

In a pair of papers—presented at last year's Autonomous Agents and Multiagent Systems conference and the upcoming Association for the Advancement of Artificial Intelligence conference—the researchers describe a model that uses human input to uncover these training 'blind spots.'

The researchers then combine the training data with the human feedback data, and use machine-learning techniques to produce a model that pinpoints situations where the system most likely needs more information about how to act correctly.

The researchers' approach first puts an AI system through simulation training, where it will produce a 'policy' that essentially maps every situation to the best action it can take in the simulations.

For driverless cars, for instance, a human would manually control the car while the system produces a signal if its planned behavior deviates from the human's behavior.

'At that point, the system has been given multiple contradictory signals from a human: some with a large car beside it, and it was doing fine, and one where there was an ambulance in the same exact location, but that wasn't fine.

'Because the agent is getting all these contradictory signals, the next step is compiling the information to ask, 'How likely am I to make a mistake in this situation where I received these mixed signals?'' Intelligent aggregation The end goal is to have these ambiguous situations labeled as blind spots.

If the system performed correct actions nine times out of 10 in the ambulance situation, for instance, a simple majority vote would label that situation as safe.

In the end, the algorithm produces a type of 'heat map,' where each situation from the system's original training is assigned low-to-high probability of being a blind spot for the system.

We analyzed 16,625 papers to figure out where AI is headed next

This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear.

“If somebody had written in 2011 that this was going to be on the front page of newspapers and magazines in a few years, we would’ve been like, ‘Wow, you’re smoking something really strong,’” says Pedro Domingos, a professor of computer science at the University of Washington and author of The Master Algorithm.

We downloaded the abstracts of all 16,625 papers available in the “artificial intelligence” section through November 18, 2018, and tracked the words mentioned through the years to see how the field has evolved.

Through our analysis, we found three major trends: a shift toward machine learning during the late 1990s and early 2000s, a rise in the popularity of neural networks beginning in the early 2010s, and growth in reinforcement learning in the past few years.

In the ’80s, knowledge-based systems amassed a popular following thanks to the excitement surrounding ambitious projects that were attempting to re-create common sense within machines.

Instead, as our analysis of key terms shows, researchers tested a variety of methods in addition to neural networks, the core machinery of deep learning.Some of the other popular techniques included Bayesian networks, support vector machines, and evolutionary algorithms, all of which take different approaches to finding patterns in data.

During the annual ImageNet competition, intended to spur progress in computer vision, a researcher named Geoffrey Hinton, along with his colleagues at the University of Toronto, achieved the best accuracy in image recognition by an astonishing margin of more than 10 percentage points.

Every decade, in other words, has essentially seen the reign of a different technique: neural networks in the late ’50s and ’60s, various symbolic approaches in the ’70s, knowledge-based systems in the ’80s, Bayesian networks in the ’90s, support vector machines in the ’00s, and neural networks again in the ’10s.

But characteristically, the research community has competing ideas about what will come next—whether an older technique will regain favor or whether the field will create an entirely new paradigm.

7 Takeaways From Recent Surveys About The State-Of-Artificial Intelligence (AI)

Recent surveys show increased adoption of AI, some measurable impact, people-related challenges for enterprises trying to implement AI, and lots of issues related to data, the lifeblood of AI: what role does data play in the lives of organizations, how secure they keep it and their policies regarding data privacy.

Attitudes about data are crucial for successful AI adoption Data privacy is about communicating with the people whose data you collect Investing in data security should come before investing in AI Public attitudes and understanding of AI will probably influence its adotpion Sources

The Hidden Automation Agenda of the Davos Elite

“On one hand,” he said, profit-minded executives “absolutely want to automate as much as they can.” “On the other hand,” he added, “they’re facing a backlash in civic society.” For an unvarnished view of how some American leaders talk about automation in private, you have to listen to their counterparts in Asia, who often make no attempt to hide their aims.

Richard Liu, the founder of the Chinese e-commerce company, said at a business conference last year that “I hope my company would be 100 percent automation someday.” One common argument made by executives is that workers whose jobs are eliminated by automation can be “reskilled” to perform other jobs in an organization.

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