Lifeboat Foundation

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?” »

Google today announced a pair of new artificial intelligence experiments from its research division that let web users dabble in semantics and natural language processing. For Google, a company that’s primary product is a search engine that traffics mostly in text, these advances in AI are integral to its business and to its goals of making software that can understand and parse elements of human language.

The website will now house any interactive AI language tools, and Google is calling the collection Semantic Experiences. The primary sub-field of AI it’s showcasing is known as word vectors, a type of natural language understanding that maps “semantically similar phrases to nearby points based on equivalence, similarity or relatedness of ideas and language.” It’s a way to “enable algorithms to learn about the relationships between words, based on examples of actual language usage,” says Ray Kurzweil, notable futurist and director of engineering at Google Research, and product manager Rachel Bernstein in a blog post. Google has published its work on the topic in a paper here, and it’s also made a pre-trained module available on its TensorFlow platform for other researchers to experiment with.

The first of the two publicly available experiments released today is called Talk to Books, and it quite literally lets you converse with a machine learning-trained algorithm that surfaces answers to questions with relevant passages from human-written text. As described by Kurzweil and Bernstein, Talk to Books lets you “make a statement or ask a question, and the tool finds sentences in books that respond, with no dependence on keyword matching.” The duo add that, “In a sense you are talking to the books, getting responses which can help you determine if you’re interested in reading them or not.”

Continue reading “Google’s latest AI experiments let you talk to books and test word association skills” »

Before I started working on real-world robots, I wrote about their fictional and historical ancestors. This isn’t so far removed from what I do now. In factories, labs, and of course science fiction, imaginary robots keep fueling our imagination about artificial humans and autonomous machines.

Real-world robots remain surprisingly dysfunctional, although they are steadily infiltrating urban areas across the globe. This fourth industrial revolution driven by robots is shaping urban spaces and urban life in response to opportunities and challenges in economic, social, political, and healthcare domains. Our cities are becoming too big for humans to manage.

Continue reading “Robot Cities: Three Urban Prototypes for Future Living” »

In the last year, the business and consumer markets alike have seen the release of advanced technologies that were once considered the stuff of science fiction. Smart gadgets that control every facet of your home, self-driving vehicles, facial and biometric identification systems and more have begun to emerge, giving us a glimpse of the high-tech reality we’re moving towards.

To find out which futuristic technologies are on the horizon, we asked a panel of YEC members the following question:

Researchers proposed implementing the residential energy scheduling algorithm by training three action dependent heuristic dynamic programming (ADHDP) networks, each one based on a weather type of sunny, partly cloudy, or cloudy. ADHDP networks are considered ‘smart,’ as their response can change based on different conditions.

“In the future, we expect to have various types of power supplies to every household including the grid, windmills, solar panels and biogenerators. The issues here are the varying nature of these power sources, which do not generate electricity at a stable rate,” said Derong Liu, a professor with the School of Automation at the Guangdong University of Technology in China and an author on the paper. “For example, power generated from windmills and solar panels depends on the weather, and they vary a lot compared to the more stable power supplied by the grid. In order to improve these power sources, we need much smarter algorithms in managing/scheduling them.”

The details were published on the January 10th issue of IEEE/CAA Journal of Automatica Sinica, a joint bimonthly publication of the IEEE and the Chinese Association of Automation.