Lifeboat Foundation

Advances in artificial intelligence continue to provide entrepreneurs with exciting ways to improve their companies.

Combing through historical seismic data, researchers using a machine learning model have unearthed distinct statistical features marking the formative stage of slow-slip ruptures in the earth’s crust months before tremor or GPS data detected a slip in the tectonic plates. Given the similarity between slow-slip events and classic earthquakes, these distinct signatures may help geophysicists understand the timing of the devastating faster quakes as well.

“The machine learning model found that, close to the end of the slow slip cycle, a snapshot of the data is imprinted with fundamental information regarding the upcoming failure of the system,” said Claudia Hulbert, a computational geophysicist at ENS and the Los Alamos National Laboratory and lead author of the study, published today in Nature Communications. “Our results suggest that slow-slip rupture may well be predictable, and because slow slip events have a lot in common with earthquakes, slow-slip events may provide an easier way to study the fundamental physics of earth rupture.”

Slow-slip events are earthquakes that gently rattle the ground for days, months, or even years, do not radiate large-amplitude seismic waves, and often go unnoticed by the average person. The classic quakes most people are familiar with rupture the ground in minutes. In a given area they also happen less frequently, making the bigger quakes harder to study with the data-hungry machine learning techniques.

Newswise — Most of modern medicine has physical tests or objective techniques to define much of what ails us. Yet, there is currently no blood or genetic test, or impartial procedure that can definitively diagnose a mental illness, and certainly none to distinguish between different psychiatric disorders with similar symptoms. Experts at the University of Tokyo are combining machine learning with brain imaging tools to redefine the standard for diagnosing mental illnesses.

“Psychiatrists, including me, often talk about symptoms and behaviors with patients and their teachers, friends and parents. We only meet patients in the hospital or clinic, not out in their daily lives. We have to make medical conclusions using subjective, secondhand information,” explained Dr. Shinsuke Koike, M.D., Ph.D., an associate professor at the University of Tokyo and a senior author of the study recently published in Translational Psychiatry.

“Frankly, we need objective measures,” said Koike.

For decades, Hollywood has made millions off of our fears that artificial intelligences such as HAL in 2001: A Space Odyssey and Skynet in The Terminator could one day control us or even wipe out humanity.

Two Chinese air force J-20 stealth fighters have appeared at an air base in China’s far west as the mountain stand-off between India and Chine enters its fourth month.

The twin-engine J-20s are visible in commercial satellite imagery of Hotan air base, in the Uighur autonomous region of Xinjiang. Chinese social-media users first spotted the planes.

The J-20 deployment, however temporary, signals Beijing’s resolve as China wrestles with India for influence over a disputed region of the Himalayas. But a pair of warplanes, no matter how sophisticated, don’t represent much actual combat power.

A program that can automate website development. A bot that writes letters on behalf of nature. An AI-written blog that trended on Hacker News. Those are just some of the recent stories written about GPT-3, the latest contraption of artificial intelligence research lab OpenAI. GPT-3 is the largest language model ever made, and it has triggered many discussions over how AI will soon transform many industries.

But what has been less discussed is how GPT-3 has transformed OpenAI itself. In the process of creating the most successful natural language processing system ever created, OpenAI has gradually morphed from a nonprofit AI lab to a company that sells AI services.

The lab is in a precarious position, torn between conflicting goals: developing profitable AI services and pursuing human-level AI for the benefit of all. And hanging in the balance is the very mission for which OpenAI was founded.

Ogba Educational Clinic

Long before coronavirus appeared and shattered our pre-existing “normal,” the future of work was a widely discussed and debated topic. We’ve watched automation slowly but surely expand its capabilities and take over more jobs, and we’ve wondered what artificial intelligence will eventually be capable of.

The pandemic swiftly turned the working world on its head, putting millions of people out of a job and forcing millions more to work remotely. But essential questions remain largely unchanged: we still want to make sure we’re not replaced, we want to add value, and we want an equitable society where different types of work are valued fairly.

Continue reading “A Human-Centric World of Work: Why It Matters, and How to Build It” »

Aside from staying alive and healthy, the biggest concern most people have during the pandemic is the future of their jobs. Unemployment in the U.S. has skyrocketed, from 5.8 million in February 2020 to 16.3 million in July 2020, according to the U.S. Bureau of Labor Statistics. But it’s not only the lost jobs that are reshaping work in the wake of COVID-19; the nature of many of the remaining jobs has changed, as remote work becomes the norm. And in the midst of it all, automation has become potentially a threat to some workers and a salvation to others. In this issue, we examine this tension and explore the good, bad, and unknown of how automation could affect jobs in the immediate and near future.

Prevailing wisdom says that the wave of new AI-powered automation will follow the same pattern as other technological leaps: They’ll kill off some jobs but create new (and potentially better) ones. But it’s unclear whether that will hold true this time around. Complicating matters is that at a time when workplace safety has to do with limiting the spread of a deadly virus, automation can play a role in reducing the number of people who are working shoulder-to-shoulder — keeping workers safe, but also eliminating jobs.

Even as automation creates exciting new opportunities, it’s important to bear in mind that those opportunities will not be distributed equally. Some jobs are more vulnerable to automation than others, and uneven access to reskilling and other crucial factors will mean that some workers will be left behind.

A Christchurch company has invented the world’s first waterproof robotic hand.

Now, they are teaming up with other tech firms in the region to help them all grow.