Lifeboat Foundation

Many people say that overpopulation will spell the end of humanity. However, with mind uploading and the consumption of fewer resources that comes with it, I believe that humanity will not have to worry about an overpopulation issue for decades to come.

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Boston Dynamics announced that it has developed a robot arm for its “Spot” robot and also a charging station. Both will be available for purchase this spring.

The robot Spot made quite a splash on the internet last year, thanks to its YouTube videos. The four-legged yellow-bodied robot was shown marching its way autonomously and untethered through a wide variety of terrain in ways reminiscent of a dog; hence its name. The robot dog is available for sale. Those interested can purchase one directly from Boston Dynamics for $75,000. CEO Rob Playter told members of the press recently that the company has sold 260 of the robots as of last June. Those robots are currently being tested (and in some cases, used) in mining, healthcare, construction and other sectors—mostly in situations that are dangerous for people. The company has also created a host of add-ons for the robot to assist in a wide variety of applications. The company is now adding to that list by making available both a robot arm and a charging station.

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Drones or unmanned vehicles are becoming a major threat as more and more countries are developing and relying on it. The latest conflict between Azerbaijan and Armenia also saw extensive use of drones.

Microsoft and Intel have a long-standing relationship, which grows stronger today with a new collaboration on a seamless artificial intelligence (AI) and machine learning (ML) experience, from Azure in the cloud to a wide range of high-performance edge devices powered by Intel Movidius vision processing units (VPU). This will deliver a more seamless experience for developers across the intelligent cloud and intelligent edge.

Ivan Smirnov, Leading Research Fellow of the Laboratory of Computational Social Sciences at the Institute of Education of HSE University, has created a computer model that can distinguish high academic achievers from lower ones based on their social media posts. The prediction model uses a mathematical textual analysis that registers users’ vocabulary (its range and the semantic fields from which concepts are taken), characters and symbols, post length, and word length.

Every word has its own rating (a kind of IQ). Scientific and cultural topics, English words, and words and posts that are longer in length rank highly and serve as indicators of good academic performance. An abundance of emojis, words or whole phrases written in capital letters, and vocabulary related to horoscopes, driving, and military service indicate lower grades in school. At the same time, posts can be quite short—even tweets are quite informative. The study was supported by a grant from the Russian Science Foundation (RSF), and an article detailing the study’s results was published in EPJ Data Science.

Foreign studies have long shown that users’ social media behavior—their posts, comments, likes, profile features, user pics, and photos—can be used to paint a comprehensive portrait of them. A person’s social media behavior can be analyzed to determine their lifestyle, personal qualities, individual characteristics, and even their mental health status. It is also very easy to determine a person’s socio-demographic characteristics, including their age, gender, race, and income. This is where profile pictures, Twitter hashtags, and Facebook posts come in.

Cafe X Robot Coffee Bar in San Francisco employs assembly line-style robots to build your coffee orders for you. This robot barista can make two drinks in under a minute and will get your order right every time.

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Moving from one-algorithm to one-brain is one of the biggest open challenges in AI. A one-brain AI would still not be a true intelligence, only a better general-purpose AI—Legg’s multi-tool. But whether they’re shooting for AGI or not, researchers agree that today’s systems need to be made more general-purpose, and for those who do have AGI as the goal, a general-purpose AI is a necessary first step.

Humans regularly tackle and solve a variety of complex visuospatial problems. In contrast, most machine learning and computer vision techniques developed so far are designed to solve individual tasks, rather than applying a set of capabilities to any task they are presented with.

Researchers at York University in Canada have been trying to better understand the mechanisms that allow humans to actively observe their environment and solve the wide range perception tasks that they encounter every day, with the hope of informing the development of more sophisticated computer vision systems. In a paper pre-published on arXiv, they presented a new experimental setup called PESAO (psychophysical experimental setup for active observers), which is specifically designed to investigate how humans actively observe the world around them and engage with it.

“The hallmark of human vision is its generality,” Prof. John K. Tsotsos, one of the researchers who carried out the study, told TechXplore. “The same brain and visual system allow one to play tennis, drive a car, perform surgery, view photo albums, read a book, gaze into your loved one’s eyes, go online shopping, solve 1000-piece jigsaw puzzles, find lost keys, chase after his/her young daughter when she appears in danger and so much more. The reality is that as incredible as AI successes have been so far, it is humbling to acknowledge how far there still is to go.”

A pair of statisticians at the University of Waterloo has proposed a math process idea that might allow for teaching AI systems without the need for a large dataset. Ilia Sucholutsky and Matthias Schonlau have written a paper describing their idea and published it on the arXiv preprint server.

Artificial intelligence (AI) applications have been the subject of much research lately, with the development of deep learning networks, researchers in a wide range of fields began finding uses for it, including creating deepfake videos, board game applications and medical diagnostics.

Deep learning networks require large datasets in order to detect patterns revealing how to perform a given task, such as picking a certain face out of a crowd. In this new effort, the researchers wondered if there might be a way to reduce the size of the dataset. They noted that children only need to see a couple of pictures of an animal to recognize other examples. Being statisticians, they wondered if there might be a way to use mathematics to solve the problem.