Lifeboat Foundation

Our brain is constantly working to make sense of the world around us and finding patterns in it, even when we are asleep the brain is storing patterns. Making sense of the brain itself, however, has remained an intricate pursuit.

Christoff Koch, a well-known neuroscientist, famously called the human brain the “most complex object in our observable universe” [1]. Aristotle, on the other hand, thought it was the heart that gave rise to consciousness and that the brain functioned as a cooling system both practically and philosophically [2]. Theories of the brain have evolved since then, generally shaped by knowledge gathered over centuries. Historically, to analyze the brain, we had to either extract the brain from deceased people or perform invasive surgery. Progress over the past decades has led to inventions that allow us to study the brain without invasive surgeries. A few examples of imaging techniques that do not require surgery include macroscopic imaging techniques such as functional magnetic resonance imaging (fMRI) or approaches with a high temporal resolution such as electroencephalogy (EEG). Advances in treatments, such as closed-loop electrical stimulation systems, have enabled the treatment of disorders like epilepsy and more recently depression [3, 4]. Existing neuroimaging approaches can produce a considerable amount of data about a very complex organ that we still do not fully understand which has led to an interest in non-linear modeling approaches and algorithms equipped to learn meaningful features.

Continue reading “Deep Learning in Neuroimaging” »

Waves break once they swell to a critical height, before cresting and crashing into a spray of droplets and bubbles. These waves can be as large as a surfer’s point break and as small as a gentle ripple rolling to shore. For decades, the dynamics of how and when a wave breaks have been too complex to predict.

Now, MIT engineers have found a new way to model how waves break. The team used machine learning along with data from wave-tank experiments to tweak equations that have traditionally been used to predict wave behavior. Engineers typically rely on such equations to help them design resilient offshore platforms and structures. But until now, the equations have not been able to capture the complexity of breaking waves.

The updated model made more accurate predictions of how and when waves break, the researchers found. For instance, the model estimated a wave’s steepness just before breaking, and its energy and frequency after breaking, more accurately than the conventional wave equations.

The West Japan Rail Company has released video of its new humanoid heavy equipment robot. Mounted on the end of a crane, this gundam-style robot torso mimics the arm and head motions of a human pilot, who sees through the robot’s eyes via VR goggles.

The key objectives here, according to the company, are “to improve productivity and safety,” enabling workers to lift and naturally manipulate heavy equipment around the rail system without exposing them to the risk of electric shocks or falling.

The robot’s large torso is mounted to a hydraulic crane arm, which rides around the rail system on a specially braced rail car, putting down stabilizing legs when it’s time to get to work.

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World Economic Forum.

They’re autonomous, self-cleaning and powered entirely by solar energy.

ECHO, the robot, belongs to the Woods Hole Oceanographic Institution and rolls around the tundra collecting data used to study marine ecosystems.

The small robot takes readings and collects data like a normal researcher, but his existence allows researchers to collect real-time information year round and minimize the impact their presence could have on the animals’ lives.

Researchers say the penguins seem to be getting along swimmingly with the robot.

Eleuther AI developed GPT Neo, an open-source model, an autoregressive transformer using the mesh library.

Elon talks about x-risks and making us a multi-planetary species, amongst other things.

What’s on Elon Musk’s mind? In this exclusive conversation with head of TED Chris Anderson, Musk details how the radical new innovations he’s working on — Tesla’s intelligent humanoid robot Optimus, SpaceX’s otherworldly Starship and Neuralink’s brain-machine interfaces, among others — could help maximize the lifespan of humanity and create a world where goods and services are abundant and accessible for all. It’s a compelling vision of a future worth getting excited about. (Recorded at the Tesla Texas Gigafactory on April 6, 2022)

Continue reading “[Exclusive] Elon Musk: A future worth getting excited about | TED | Tesla Gigafactory interview” »

One key aspect of intelligence is the ability to quickly learn how to perform a new task when given a brief instruction. For instance, a child may recognise real animals at the zoo after seeing a few pictures of the animals in a book, despite any differences between the two. But for a typical visual model to learn a new task, it must be trained on tens of thousands of examples specifically labelled for that task. If the goal is to count and identify animals in an image, as in “three zebras”, one would have to collect thousands of images and annotate each image with their quantity and species. This process is inefficient, expensive, and resource-intensive, requiring large amounts of annotated data and the need to train a new model each time it’s confronted with a new task. As part of DeepMind’s mission to solve intelligence, we’ve explored whether an alternative model could make this process easier and more efficient, given only limited task-specific information.

Today, in the preprint of our paper, we introduce Flamingo, a single visual language model (VLM) that sets a new state of the art in few-shot learning on a wide range of open-ended multimodal tasks. This means Flamingo can tackle a number of difficult problems with just a handful of task-specific examples (in a “few shots”), without any additional training required. Flamingo’s simple interface makes this possible, taking as input a prompt consisting of interleaved images, videos, and text and then output associated language.

Similar to the behaviour of large language models (LLMs), which can address a language task by processing examples of the task in their text prompt, Flamingo’s visual and text interface can steer the model towards solving a multimodal task. Given a few example pairs of visual inputs and expected text responses composed in Flamingo’s prompt, the model can be asked a question with a new image or video, and then generate an answer.

0:00 PeopleLens AI Helps The Blind.
1:40 Brain Fingerprints Detect Autism.
4:52 AI Predicts Cancer Tumor Regrowth.

Learn more about the future of decentralized AI here:
SingularityNET AGIX Website — https://singularitynet.io
Developer Documentation — https://dev.singularitynet.io/
Publish AI Services — https://publisher.singularitynet.io/
AGIX Community Telegram — https://t.me/singularitynet
AGIX Price Chat Telegram — https://t.me/AGIPriceTalk

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