Lifeboat Foundation

If you’ve read anything about quantum computers, you may have encountered the statement, “It’s like computing with zero and one at the same time.” That’s sort of true, but what makes quantum computers exciting is something spookier: entanglement.

A new quantum device entangles 20 quantum bits together at the same time, making it perhaps one of the most entangled, controllable devices yet. This is an important milestone in the quantum computing world, but it also shows just how much more work there is left to do before we can realize the general-purpose quantum computers of the future, which will be able to solve big problems relating to AI and cybersecurity that classical computers can’t.

“We’re now getting access to single-particle-control devices” with tens of qubits, study author Ben Lanyon from the Institute for Quantum Optics and Quantum Information in Austria told Gizmodo. Soon, “we can get to the level where we can create super-exotic quantum states and see how they behave in the lab. I think that’s very exciting.”

Continue reading “A Spooky Quantum Experiment Creates What May Be the Most Entangled Controllable Device Yet” »

Each new technological breakthrough comes seemingly prepackaged with a new way for hackers to kill us all: self-driving cars, space-based weapons, and even nuclear security systems are vulnerable to someone with the right knowledge and a bit of code. Now, deep-learning artificial intelligence looks like the next big threat, and not because it will gain sentience to murder us with robots (as Elon Musk has warned): a group of computer scientists from the US and China recently published a paper proposing the first-ever trojan for a neural network.

Neural networks are the primary tool used in AI to accomplish “deep learning,” which has allowed AIs to master complex tasks like playing chess and Go. Neural networks function similar to a human brain, which is how they got the name. Information passes through layers of neuron-like connections, which then analyze the information and spit out a response. These networks can pull off difficult tasks like image recognition, including identifying faces and objects, which makes them useful for self-driving cars (to identify stop signs and pedestrians) and security (which may involve identifying an authorized user’s face). Neural networks are relatively novel pieces of tech and aren’t commonly used by the public yet but, as deep-learning AI becomes more prevalent, it will likely become an appealing target for hackers.

The trojan proposed in the paper, called “PoTrojan,” could be included in a neural network product either from the beginning or inserted later as a slight modification. Like a normal trojan, it looks like a normal piece of the software, doesn’t copy itself, and doesn’t do much of anything… Until the right triggers happen. Once the right inputs are activated in a neural network, this trojan hijacks the operation and injects its own train of “thought,” making sure the network spits out the answer it wants. This could take the form of rejecting the face of a genuine user and denying them access to their device, or purposefully failing to recognize a stop sign to create a car crash.

Continue reading “New Trojan Malware Could Mind-Control Neural Networks” »

China’s comprehensive space plans—including launches, robotic moon bases, and interplanetary manned missions—will make the country a major space power by 2050.

Fully robotic kitchens may become our chefs.

The nation’s insatiable desire to build its own hardware naturally extends to the world of robo-taxis.

Backstory: China has made no secret of wanting to design and produce huge numbers of its own chips. It’s already gunning to build the processors that power an impending wave of artificial-intelligence hardware.

The news: Bloomberg reports that domestic firms are also expected to build the chips that will be the brains behind the nation’s robotic cars. Startups like Horizon Robotics, founded by the former chief of Baidu’s Institute of Deep Learning, are scrambling to build low-power devices that process data from sensors dotted around cars.

Continue reading “China hopes to build the chips that will control millions of driverless cars” »

• Basic income will be widespread by the 2030s, according to Google futurist and director of engineering Ray Kurzweil.
• Kurzweil is known for making seemingly wild predictions. In 2016, he predicted that by 2029, medical technology will add an extra year to human life expectancies on an annual basis.
• ” We’re going to have more and more powerful technology to keep our physical bodies going. We’ll think, ‘Wow, back in 2018, people only had one body, and they couldn’t back up their mind file,’” he said onstage at TED.

As it becomes apparent that artificial intelligence will replace ever-more jobs in the coming years, a growing number of politicians, nonprofits, and Silicon Valley entrepreneurs have started thinking about how we’ll cope with a world in which not everyone can — or needs to — work.

Basic income experiments, in which people are given a regular salary just to live, no strings attached, are popping up all over Europe, Africa, and North America.

Artificial intelligence is permeating everybody’s lives through the face recognition, voice recognition, image analysis and natural language processing capabilities built into their smartphones and consumer appliances. Over the next several years, most new consumer devices will run AI natively, locally and, to an increasing extent, autonomously.

But there’s a problem: Traditional processors in most mobile devices aren’t optimized for AI, which tends to consume a lot of processing, memory, data and battery on these resource-constrained devices. As a result, AI has tended to execute slowly on mobile and “internet of things” endpoints, while draining their batteries rapidly, consuming inordinate wireless bandwidth and exposing sensitive local information as data makes roundtrips in the cloud.

That’s why mass-market mobile and IoT edge devices are increasingly coming equipped with systems-on-a-chip that are optimized for local AI processing. What distinguishes AI systems on a chip from traditional mobile processors is that they come with specialized neural-network processors, such as graphics processing units or GPUs, tensor processing units or TPUs, and field programming gate arrays or FPGAs. These AI-optimized chips offload neural-network processing from the device’s central processing unit chip, enabling more local autonomous AI processing and reducing the need to communicate with the cloud for AI processing.

Continue reading “New AI systems on a chip will spark an explosion of even smarter devices” »

Over 150 experts in AI, robotics, commerce, law, and ethics from 14 countries have signed an open letter denouncing the European Parliament’s proposal to grant personhood status to intelligent machines. The EU says the measure will make it easier to figure out who’s liable when robots screw up or go rogue, but critics say it’s too early to consider robots as persons—and that the law will let manufacturers off the liability hook.

This all started last year when the European Parliament proposed the creation of a specific legal status for robots:

so that at least the most sophisticated autonomous robots could be established as having the status of electronic persons responsible for making good any damage they may cause, and possibly applying electronic personality to cases where robots make autonomous decisions or otherwise interact with third parties independently.

Continue reading “Experts Sign Open Letter Slamming Europe’s Proposal to Recognize Robots as Legal Persons” »

Since the Cambridge Analytica scandal erupted in March, Facebook has been attempting to make a moral stand for your privacy, distancing itself from the unscrupulous practices of the U.K. political consultancy. “Protecting people’s information is at the heart of everything we do,” wrote Paul Grewal, Facebook’s deputy general counsel, just a few weeks before founder and CEO Mark Zuckerberg hit Capitol Hill to make similar reassurances, telling lawmakers, “Across the board, we have a responsibility to not just build tools, but to make sure those tools are used for good.” But in reality, a confidential Facebook document reviewed by The Intercept shows that the two companies are far more similar than the social network would like you to believe.

The recent document, described as “confidential,” outlines a new advertising service that expands how the social network sells corporations’ access to its users and their lives: Instead of merely offering advertisers the ability to target people based on demographics and consumer preferences, Facebook instead offers the ability to target them based on how they will behave, what they will buy, and what they will think. These capabilities are the fruits of a self-improving, artificial intelligence-powered prediction engine, first unveiled by Facebook in 2016 and dubbed “FBLearner Flow.”

One slide in the document touts Facebook’s ability to “predict future behavior,” allowing companies to target people on the basis of decisions they haven’t even made yet. This would, potentially, give third parties the opportunity to alter a consumer’s anticipated course. Here, Facebook explains how it can comb through its entire user base of over 2 billion individuals and produce millions of people who are “at risk” of jumping ship from one brand to a competitor. These individuals could then be targeted aggressively with advertising that could pre-empt and change their decision entirely — something Facebook calls “improved marketing efficiency.” This isn’t Facebook showing you Chevy ads because you’ve been reading about Ford all week — old hat in the online marketing world — rather Facebook using facts of your life to predict that in the near future, you’re going to get sick of your car. Facebook’s name for this service: “loyalty prediction.”

Continue reading “Does Facebook Use AI To Predict Your Future Actions For Advertisers?” »