Lifeboat Foundation

China announced in 2017 its ambition to become the world leader in artificial intelligence (AI) by 2030. While the US still leads in absolute terms, China appears to be making more rapid progress than either the US or the EU, and central and local government spending on AI in China is estimated to be in the tens of billions of dollars.

The move has led — at least in the West — to warnings of a global AI arms race and concerns about the growing reach of China’s authoritarian surveillance state. But treating China as a “villain” in this way is both overly simplistic and potentially costly. While there are undoubtedly aspects of the Chinese government’s approach to AI that are highly concerning and rightly should be condemned, it’s important that this does not cloud all analysis of China’s AI innovation.

The world needs to engage seriously with China’s AI development and take a closer look at what’s really going on. The story is complex and it’s important to highlight where China is making promising advances in useful AI applications and to challenge common misconceptions, as well as to caution against problematic uses.

In fact, in a recent paper in Royal Society Open Science, researchers showed that AI tasked with maximizing returns is actually disproportionately likely to pick an unethical strategy in fairly general conditions. Fortunately, they also showed it’s possible to predict the circumstances in which this is likely to happen, which could guide efforts to modify AI to avoid it.

The fact that AI is likely to pick unethical strategies seems intuitive. There are plenty of unethical business practices that can reap huge rewards if you get away with them, not least because few of your competitors dare use them. There’s a reason companies often bend or even break the rules despite the reputational and regulatory backlash they could face.

Those potential repercussions should be of considerable concern to companies deploying AI solutions, though. While efforts to build ethical principles into AI are already underway, they are nascent and in many contexts there are a vast number of potential strategies to choose from. Often these systems make decisions with little or no human input and it can be hard to predict the circumstances under which they are likely to choose an unethical approach.

Using their artificial wings made out of foam, the robots are able to stay afloat simply by mimicking the flapping motion of real birds.

Clint Brauer’s farm outside of Cheney, Kansas, could be described as Old MacDonald’s Farm plus robots. Along with 5,500 square feet of vegetable-growing greenhouses, classes teaching local families to grow their food, a herd of 105 sheep, and Warren G—a banana-eating llama named after the rapper—is a fleet of ten, 140-pound, battery-operated robots.

Brauer, the co-founder of Greenfield Robotics, grew up a farm kid. He left for the big city tech and digital world, but eventually made his way back to the family farm. Now, it’s the R&D headquarters for the Greenfield Robotics team, plus a working farm.

When Brauer returned to his agricultural roots, he did so with a purpose: to prove that food could be grown without harmful chemicals and by embracing soil- and planet-friendly practices. He did just that, becoming one of the premier farmers growing vegetables in Kansas without pesticides, selling to local markets, grocery store chains, and chefs.

The best way to prevent this is by focusing on the basics. America needs a major all-of-society push to increase the number of U.S. students being trained in both the fundamentals of math and in the more advanced, rigorous, and creative mathematics. Leadership in implementing this effort will have to come from the U.S. government and leading technology companies, and through the funding of ambitious programs. A few ideas come to mind: talent-spotting schemes, the establishment of math centers, and a modern successor to the post-Sputnik National Defense Education Act, which would provide math scholarships to promising students along with guaranteed employment in either public or private enterprises.

Forget about “AI” itself: it’s all about the math, and America is failing to train enough citizens in the right kinds of mathematics to remain dominant.

By Michael Auslin

Continue reading “Why China’s Race For AI Dominance Depends On Math” »

A new study has explored whether AI can provide more attractive answers to humanity’s most profound questions than history’s most influential thinkers.

Researchers from the University of New South Wales first fed a series of moral questions to Salesforce’s CTRL system, a text generator trained on millions of documents and websites, including all of Wikipedia. They added its responses to a collection of reflections from the likes of Plato, Jesus Christ, and, err, Elon Musk.

The team then asked more than 1,000 people which musings they liked best — and whether they could identify the source of the quotes.

TuSimple, a trucking technology company, has announced a plan for the world’s first Autonomous Freight Network (AFN) – an ecosystem consisting of autonomous trucks, digital mapped routes, strategically placed terminals, and TuSimple Connect, a proprietary autonomous operations monitoring system.

Collectively, these components will work together to create the safest and most efficient way to bring self-driving trucks to market. Partnering with TuSimple in the launch of the Autonomous Freight Network are UPS, Penske Truck Leasing, U.S. Xpress (who operate one of the largest carrier fleets in the country) and McLane, a Berkshire Hathaway company and one of the largest supply chain services leaders in the United States.

“Our ultimate goal is to have a nationwide transportation network, consisting of mapped routes connecting hundreds of terminals to enable efficient, low-cost, long-haul autonomous freight operations,” said Cheng Lu, President of TuSimple. “By launching the AFN with our strategic partners, we will be able to quickly scale operations and expand autonomous shipping lanes to provide users access to autonomous capacity anywhere and 24/7 on-demand.”

Text is backward. Clocks run counterclockwise. Cars drive on the wrong side of the road. Right hands become left hands.

Intrigued by how reflection changes images in subtle and not-so-subtle ways, a team of Cornell researchers used artificial intelligence to investigate what sets originals apart from their reflections. Their algorithms learned to pick up on unexpected clues such as hair parts, gaze direction and, surprisingly, beards – findings with implications for training machine learning models and detecting faked images.

Researchers from the UK and Switzerland have found a mathematical means of helping regulators and business police Artificial Intelligence systems’ biases towards making unethical, and potentially very costly and damaging choices.

The collaborators from the University of Warwick, Imperial College London, and EPFL – Lausanne, along with the strategy firm Sciteb Ltd, believe that in an environment in which decisions are increasingly made without human intervention, there is a very strong incentive to know under what circumstances AI systems might adopt an unethical strategy—and to find and reduce that risk, or eliminate entirely, if possible.

Artificial intelligence (AI) is increasingly deployed in commercial situations. Consider for example using AI to set prices of insurance products to be sold to a particular customer. There are legitimate reasons for setting different prices for different people, but it may also be more profitable to make certain decisions that end up hurting the company.