Lifeboat Foundation: Safeguarding Humanity
Toggle light / dark theme

Blog

Category: robotics/AI – Page 1,531

Meta wants to improve its AI

Would start with scanning and reverse engineering brains of rats, crows, pigs, chimps, and end on the human brain. Aim for completion by 12/31/2025. Set up teams to run brain scans 24÷7÷365 if we need to, and partner w/ every major neuroscience lab in the world.

If artificial intelligence is intended to resemble a brain, with networks of artificial neurons substituting for real cells, then what would happen if you compared the activities in deep learning algorithms to those in a human brain? Last week, researchers from Meta AI announced that they would be partnering with neuroimaging center Neurospin (CEA) and INRIA to try to do just that.

Through this collaboration, they’re planning to analyze human brain activity and deep learning algorithms trained on language or speech tasks in response to the same written or spoken texts. In theory, it could decode both how human brains —and artificial brains—find meaning in language.

By comparing scans of human brains while a person is actively reading, speaking, or listening with deep learning algorithms given the same set of words and sentences to decipher, researchers hope to find similarities as well as key structural and behavioral differences between brain biology and artificial networks. The research could help explain why humans process language much more efficiently than machines.

Computers ace IQ tests but still make dumb mistakes. Can different tests help?

AI researchers are creating novel “benchmarks” to help models avoid real-world stumbles.

Trained on billions of words from books, news articles, and Wikipedia, artificial intelligence (AI) language models can produce uncannily human prose. They can generate tweets, summarize emails, and translate dozens of languages. They can even write tolerable poetry. And like overachieving students, they quickly master the tests, called benchmarks, that computer scientists devise for them.

That was Sam Bowman’s sobering experience when he and his colleagues created a tough new benchmark for language models called GLUE (General Language Understanding Evaluation). GLUE gives AI models the chance to train on data sets containing thousands of sentences and confronts them with nine tasks, such as deciding whether a test sentence is grammatical, assessing its sentiment, or judging whether one sentence logically entails another. After completing the tasks, each model is given an average score.

At first, Bowman, a computer scientist at New York University, thought he had stumped the models. The best ones scored less than 70 out of 100 points (a D+). But in less than 1 year, new and better models were scoring close to 90, outperforming humans. “We were really surprised with the surge,” Bowman says. So in 2019 the researchers made the benchmark even harder, calling it SuperGLUE. Some of the tasks required the AI models to answer reading comprehension questions after digesting not just sentences, but paragraphs drawn from Wikipedia or news sites. Again, humans had an initial 20-point lead. “It wasn’t that shocking what happened next,” Bowman says. By early 2021, computers were again beating people.

A new approach to reproduce human and animal movements in robots

In recent years, developers have created a wide range of sophisticated robots that can operate in specific environments in increasingly efficient ways. The body structure of many among these systems is inspired by nature, animals, and humans.

Although many existing robots have bodies that resemble those of humans or other animal species, programming them so that they also move like the animal they are inspired by is not always an easy task. Doing this typically entails the development of advanced locomotion controllers, which can require considerable resources and development efforts.

Researchers at DeepMind have recently created a new technique that can be used to efficiently train robots to replicate the movements of humans or animals. This new tool, introduced in a paper pre-published on arXiv, is inspired from previous work that leveraged data representing real-world human and animal movements, collected using technology.

Robot which can cook and taste food is here!

Machines are learning things fast and replacing humans at a faster rate than ever before. Fresh development in this direction is a robot that can taste food. And not only it can taste the food, it can do so while making the dish it is preparing! This further leads to the robot having the ability to recognise taste of the food in various stages of chewing when a human eats the food.

The robot chef was made by Mark Oleynik, a Russian mathematician and computer scientist. Researchers at the Cambride University trained the robot to ‘taste’ the food as it cooks it.

The robot had already been trained to cook egg omelets. The researchers at Cambridge University added a sensor to the robot which can recognise different levels of saltiness.

Jack in the Box to trial robots for kitchen work

Jack in the Box has become the latest American food chain to experiment with automation, as it seeks to handle staffing challenges and improve the efficiency of its service.

Jack in the Box is one of the largest quick service restaurant chains in America, with more than 2,200 branches. With continued staffing challenges impacting its operating hours and costs, Jack in the Box saw a need to revamp its technology and establish new systems – particularly in the back-of-house – that improve restaurant-level economics and alleviate the pain points of working in a high-volume commercial kitchen.

These virtual robot arms get smarter

A virtual robot arm has learned to solve a wide range of different puzzles —stacking blocks, setting the table, arranging chess pieces—without having to be retrained for each task. It did this by playing against a second robot arm that was trained to give it harder and harder challenges.

Self play: Developed by researchers at OpenAI, the identical robot arms—Alice and Bob—learn by playing a game against each other in a simulation, without human input. The robots use reinforcement learning, a technique in which AIs are trained by trial and error what actions to take in different situations to achieve certain goals. The game involves moving objects around on a virtual tabletop. By arranging objects in specific ways, Alice tries to set puzzles that are hard for Bob to solve. Bob tries to solve Alice’s puzzles. As they learn, Alice sets more complex puzzles and Bob gets better at solving them.

What is Quantum Machine Learning? Applications of Quantum Machine Learning

Quantum machine learning is a field of study that investigates the interaction of concepts from quantum computing with machine learning.

For example, we would wish to see if quantum computers can reduce the amount of time it takes to train or assess a machine learning model. On the other hand, we may use machine learning approaches to discover quantum error-correcting algorithms, estimate the features of quantum systems, and create novel quantum algorithms.

Scientists are developing a robot with electronic HAIRS

But the bizarre invention could soon become a reality, as scientists have taken a major step forward in the development of electronic skin with integrated artificial hairs.

Hairs allow for ‘natural touch’ and let us detect different sensations such as rough and smooth, as well as the direction the touch is coming from.

Researchers from Chemnitz University of Technology say the ‘e-skin’ could have a range of uses in the future, including skin replacement for humans and artificial skin for humanoid robots.

Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems

What explains the dramatic progress from 20th-century to 21st-century AI, and how can the remaining limitations of current AI be overcome? The widely accepted narrative attributes this progress to massive increases in the quantity of computational and data resources available to support statistical learning in deep artificial neural networks. We show that an additional crucial factor is the development of a new type of computation. Neurocompositional computing adopts two principles that must be simultaneously respected to enable human-level cognition: the principles of Compositionality and Continuity. These have seemed irreconcilable until the recent mathematical discovery that compositionality can be realized not only through discrete methods of symbolic computing, but also through novel forms of continuous neural computing.

/* */