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How Artificial Intelligence Is Reshaping Repression
He also noted that his agency planned eventually to enhance the body-camera system so as to enable “real-time facial recognition and instant alerts to the presence of persons of interest from criminal watch lists.”1 Neighboring Singapore soon followed suit, declaring its plans to launch a pilot camera-installation project with the end goal of embedding facial-recognition technology on every public lamppost.
Privacy advocates such as the Electronic Frontier Foundation have warned that this technology will enable governments to target political opponents and suppress free expression, but their protests have been to no avail.2 Meanwhile in April 2018, AI startup CloudWalk Technology, based in the Chinese city of the Guangzhou, reportedly signed a deal with Zimbabwe’s government to provide facial-recognition technology for use by state-security services and to build a national image database.
(Machine learning, which can be applied to tasks that range from winning Go matches to identifying pathogens, is an iterative statistical process in which an AI system is introduced to a set of data and “tries to derive a rule or procedure that explains the data or can predict future data.”6) The import of this technology for the world’s authoritarians and their democratic opponents alike is growing ever clearer.
In recent years, autocracies have achieved new levels of control and manipulation by applying advanced computing systems to the vast quantities of unstructured data now available online and from live video feeds and other sources of monitoring and surveillance.
A New Approach to Understanding How Machines Think
If a doctor told that you needed surgery, you would want to know why — and you’d expect the explanation to make sense to you, even if you’d never gone to medical school.
Since its ascendance roughly a decade ago, the neural-network technology behind artificial intelligence has transformed everything from email to drug discovery with its increasingly powerful ability to learn from and identify patterns in data.
But that power has come with an uncanny caveat: The very complexity that lets modern deep-learning networks successfully teach themselves how to drive cars and spot insurance fraud also makes their inner workings nearly impossible to make sense of, even by AI experts.
If a neural network is trained to identify patients at risk for conditions like liver cancer and schizophrenia — as a system called “Deep Patient” was in 2015, at Mount Sinai Hospital in New York — there’s no way to discern exactly which features in the data the network is paying attention to.
We might just drop it.” Kim and her colleagues at Google Brain recently developed a system called “Testing with Concept Activation Vectors” (TCAV), which she describes as a “translator for humans” that allows a user to ask a black box AI how much a specific, high-level concept has played into its reasoning.
One branch is interpretability for science: If you consider a neural network as an object of study, then you can conduct scientific experiments to really understand the gory details about the model, how it reacts, and that sort of thing.
But the manual says, “These are the things you need to be careful of, so as to not cut your finger.” So, given this manual, I’d much rather use the chainsaw than a handsaw, which is easier to understand, but would make me spend five hours cutting down the tree.
CES 2019: Nvidia partners with Mercedes on artificial intelligence
Mercedes and Nvidia are aiming to develop a single system with self-driving capabilities and 'smart cockpit' functions, rather than the current system involving multiple small processors known as electronic control units (ECUs), where each ECU controls separate parts of the car such as the windows, door locks, power steering, and braking.
The Mercedes announcement comes a day after Nvidia announced Drive AutoPilot, a driver-assistance system for Level 2+ autonomous vehicles that integrates Nvidia's Xavier SoC processors with new Nvidia Drive software to process data from outside and inside the vehicle.
CES 2019: Nvidia CEO Huang explains how AI changes everything Jensen Huang, fonder and CEO of computer graphics chip maker Nvidia, showed off the achievements of his company's neural network technology during a press conference Sunday night in Las Vegas at the Consumer Electronics Show.
CES 2019: Mobileye inks autonomous vehicle deals across China Mobileye is aiming to provide an autonomous public transport solution in China by 2022, while it also delivers and develops advanced driver assistance systems with Great Wall Motors.
CES 2019: Sprint pairs Curiosity IoT with 5G to power smart cities, autonomous vehicles Sprint is combining its Curiosity IoT platform and its 5G mobile network to power a smart city in South Carolina and an autonomous vehicle test track in Georgia, and to launch more precise mapping technology.
- On 15. januar 2021
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