Lifeboat Foundation

Even soft objects like balloons are now within reach.

Recently, a team of researchers from Facebook AI and Tel Aviv University proposed an AI system that solves the multiple-choice intelligence test, Raven’s Progressive Matrices. The proposed AI system is a neural network model that combines multiple advances in generative models, including employing multiple pathways through the same network.

Raven’s Progressive Matrices, also known as Raven’s Matrices, are multiple-choice intelligence tests. The test is used to measure abstract reasoning and is regarded as a non-verbal estimate of fluid intelligence.

In this test, a person tries to finish the missing location in a 3X3 grid of abstract images. According to the researchers, there have been various similar researches, where the main focus entirely on choosing the right answer out of the various choices. However, in this research, the researchers focussed on generating a correct answer given the grid, without seeing the choices.

If Facebook’s AI research objectives are successful, it may not be long before home assistants take on a whole new range of capabilities. Last week the company announced new work focused on advancing what it calls “embodied AI”: basically, a smart robot that will be able to move around your house to help you remember things, find things, and maybe even do things.

Robots That Hear, Home Assistants That See

Continue reading “Facebook Wants to Make Smart Robots to Explore Every Nook and Cranny of Your Home” »

Intel, in its latest acquisition spree, has acquired Israel-based Cnvrg.io. The deal, like most of the deals in the past, is aimed at strengthening its machine learning and AI operations. The 2016-founded startup provides a platform for data scientists to build and run machine learning models that can be used to train, run comparisons and recommendations, among others. Co-founded by Yochay Ettun and Leah Forkosh Kolben, Cnvrg was valued at around $17 million in its last round.

According to a statement by Intel spokesperson, Cnvrg will be an independent Intel company and will continue to serve its existing and future customers after the acquisition. However, there is no information on the financial terms of the deal or who will join Intel from the startup.

The deal comes merely a week after Intel’s announcement of acquiring San Francisco-based software optimisation startup SigOpt, which it did to leverage SigOpt’s technologies across its products to accelerate, amplify and scale AI software tools. SigOpt’s software technologies combined with Intel hardware products could give it a major competitive advantage providing differentiated value for data scientists and developers.

Japanese researches control Gundam robot using their mind.

Japanese scientists have created a device that controls a mini toy Gundam robot using the human mind, turning one of the anime’s most exciting technological concepts into reality.

Continue reading “Japanese researchers have created a mind-controllable Gundam robot” »

Real-time health monitoring and sensing abilities of robots require soft electronics, but a challenge of using such materials lie in their reliability. Unlike rigid devices, being elastic and pliable makes their performance less repeatable. The variation in reliability is known as hysteresis.

Guided by the theory of contact mechanics, a team of researchers from the National University of Singapore (NUS) came up with a new sensor material that has significantly less hysteresis. This ability enables more accurate wearable health technology and robotic sensing.

The research team, led by Assistant Professor Benjamin Tee from the Institute for Health Innovation & Technology at NUS, published their results in the prestigious journal Proceedings of the National Academy of Sciences on 28 September 2020.

DiCarlo and Yamins, who now runs his own lab at Stanford University, are part of a coterie of neuroscientists using deep neural networks to make sense of the brain’s architecture. In particular, scientists have struggled to understand the reasons behind the specializations within the brain for various tasks. They have wondered not just why different parts of the brain do different things, but also why the differences can be so specific: Why, for example, does the brain have an area for recognizing objects in general but also for faces in particular? Deep neural networks are showing that such specializations may be the most efficient way to solve problems.

Neuroscientists are finding that deep-learning networks, often criticized as “black boxes,” can be good models for the organization of living brains.

Hello World.

I’m Imagination.

Continue reading “AI Camera Mistakes Soccer Ref’s Bald Head For Ball” »

Researchers at Stanford University have developed a CRISPR-based “lab on a chip” to detect COVID-19, and are working with automakers at Ford to develop their prototype into a market-ready product.

This could provide an automated, hand-held device designed to deliver a coronavirus test result anywhere within 30 minutes.

In a study published this week in the Proceedings of the National Academy of Sciences, the test spotted active infections quickly and cheaply, using electric fields to purify fluids from a nasal swab sample and drive DNA-cutting reagents within the system’s tiny passages.