Lifeboat Foundation

Machine learning is transforming all areas of biological science and industry, but is typically limited to a few users and scenarios. A team of researchers at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has developed METIS, a modular software system for optimizing biological systems. The research team demonstrates its usability and versatility with a variety of biological examples.

Though engineering of biological systems is truly indispensable in biotechnology and synthetic biology, today machine learning has become useful in all fields of biology. However, it is obvious that application and improvement of algorithms, computational procedures made of lists of instructions, is not easily accessible. Not only are they limited by programming skills but often also insufficient experimentally-labeled data. At the intersection of computational and experimental works, there is a need for efficient approaches to bridge the gap between machine learning algorithms and their applications for biological systems.

Now a team at the Max Planck Institute for Terrestrial Microbiology led by Tobias Erb has succeeded in democratizing machine learning. In their recent publication in Nature Communications, the team presented together with collaboration partners from the INRAe Institute in Paris, their tool METIS. The application is built in such a versatile and modular architecture that it does not require computational skills and can be applied on different biological systems and with different lab equipment. METIS is short from Machine-learning guided Experimental Trials for Improvement of Systems and also named after the ancient goddess of wisdom and crafts Μῆτις, or “wise counsel.”

A team of researchers at DeepMind, London, working with colleagues from the University of Exeter, University College London and the University of Oxford, has trained an AI system to find a policy for equitably distributing public funds in an online game. In their paper published in the journal Nature Human Behavior, the group describes the approach they took to training their system and discuss issues that were raised in their endeavor.

How a society distributes wealth is an issue that humans have had to face for thousands of years. Nonetheless, most economists would agree that no system has yet been established in which all of its members are happy with the status quo. There have always been inequitable levels of income, with those on top the most satisfied and those on the bottom the least satisfied. In this latest effort, the researchers in England took a new approach to solving the problem—asking a computer to take a more logical approach.

The researchers began with the assumption that democratic societies, despite their flaws, are thus far the most agreeable of those tried. They then enlisted the assistance of volunteers to play a simple resource allocation game —the players of the game decided together the best ways to share their mutual resources. To make it more realistic, the players received different amounts of resources at the outset and there were different distribution schemes to choose from. The researchers ran the game multiple times with different groups of volunteers. They then used the data from all of the games played to train several AI systems on the ways that humans work together to find a solution to such a problem. Next, they had the AI systems play a similar game against one another, allowing for tweaking and learning over multiple iterations.

When robots appear to engage with people and display human-like emotions, people may perceive them as capable of “thinking,” or acting on their own beliefs and desires rather than their programs, according to research published by the American Psychological Association.

“The relationship between anthropomorphic shape, human-like behavior and the tendency to attribute independent thought and intentional behavior to robots is yet to be understood,” said study author Agnieszka Wykowska, Ph.D., a principal investigator at the Italian Institute of Technology. “As artificial intelligence increasingly becomes a part of our lives, it is important to understand how interacting with a robot that displays human-like behaviors might induce higher likelihood of attribution of intentional agency to the robot.”

The research was published in the journal Technology, Mind, and Behavior.

Mr. Shadow is a song composed with Artificial Intelligence. It was created by Flow Machines, a technology that learns different music styles and then makes up its own songs based on what it’s been fed. Although the voice in the song sounds peculiar at times, I could have easily been fooled into thinking a person made this song. You can download Flow Machines onto your apple device to make your own AI music.

This artificial intelligence software can acutely analyze facial expressions and brain waves to monitor if subjects were attentive to thought and political education by using a combination of polygraphs and facial scans. It can provide real data for organizers of ideological and political education, so they can keep improving their methods of education and enrich content. It can judge how party members have accepted thought and political education.

The Smart Political Education Bar analyses user’s brain waves and deploys facial recognition to discern the level of acceptance for ideological and political education. Making it possible to ascertain the levels of concentration, recognition, and mastery of ideological and political education so as to better understand its effectiveness.

President Xi, secretary of the Communist Party and leader of the nation of 1.4 billion, has demanded absolute loyalty to the party and has previously declared that thought and political education is an essential part of the government’s doctrine. They are using this technology to treat all party members as potential anti-CCP agents. The use of these techniques on officials demonstrates the sorry state of affairs within party ranks.

SpaceX is making a 480-foot-tall giant robot with arms that will catch giant rockets.

It is insanely cool. Sci-fi made real.

Continue reading “SpaceX Building 480-Foot-Tall Rocket Catching Robot” »

Right now, AI can’t tell the difference between a cat and a dog. AI needs thousands of pictures in order to correctly identify a dog from a cat, whereas human babies and toddlers only need to see each animal once to know the difference. But AI won’t be that way forever, says AI expert and author Max Tegmark, because it hasn’t learned how to self-replicate its own intelligence. However, once AI learns how to master AGI—or Artificial General Intelligence—it will be able to upgrade itself, thereby being able to blow right past us. A sobering thought. Max’s book Life 3.0: Being Human in the Age of Artificial Intelligence is being heralded as one of the best books on AI, period, and is a must-read if you’re interested in the subject.

Max Tegmark: I define intelligence as how good something is at accomplishing complex goals. So let’s unpack that a little bit. First of all, it’s a spectrum of abilities since there are many different goals you can have, so it makes no sense to quantify something’s intelligence by just one number like an IQ.

In a now-deleted video, researchers reportedly said the software could analyze citizens’ reactions to “thought and political education.”

https://youtube.com/watch?v=mSypDliC8g0

A Swedish researcher tasked an AI algorithm to write an academic paper about itself. The paper is now undergoing a peer-review process.

Almira Osmanovic Thunstrom has said she “stood in awe” as OpenAI’s artificial intelligence algorithm, GPT-3, started generating a text for a 500-word thesis about itself, complete with scientific references and citations.

Continue reading “AI-written academic paper submitted to peer review” »