Lifeboat Foundation

The human brain is incredible.

Despite consuming the equivalent of just two bananas per day, this doesn’t stop it from executing unconscionably complex tasks with impressive efficiency. But a team of researchers has designed a way to build a prototype of an artificial neuron made of unbelievably thin graphene slits housing a single layer of water molecules, according to a new study published in the journal Science.

Circa 2016 😗

Last month, Google’s AI division, DeepMind, announced that its computer had defeated Europe’s Go champion in five straight games. Go, a strategy game played on a 19×19 grid, is exponentially more difficult for a computer to master than chess—there are 20 possible moves to choose from at the start of a chess game compared to 361 moves in Go—and the announcement was lauded as another landmark moment in the evolution of artificial intelligence.

Or, at least, living neurons. His startup, Koniku, which just completed a stint at the biotech accelerator IndieBio, touts itself as “the first and only company on the planet building chips with biological neurons.” Rather than simply mimic brain function with chips, Agabi hopes to flip the script and borrow the actual material of human brains to create the chips.

Elon Musk has been touting Tesla’s FSD Beta v11 for some time now, and it’s finally here. He says it will be ready for wide rollout before the end of 2022.

Amazon unveils its newest warehouse robotic arm that utilizes artificial intelligence, which proves a terrifying possibility for Amazon warehouse workers to be easily replaced. John Iadarola and Jessica Burbank break it down on The Damage Report.

Amazon’s new robot should strike fear into its hundreds of thousands of warehouse workers — https://www.businessinsider.com/amazon-released-warehouse-ro…022-11

Continue reading “Amazon Robot UNVEILED, Warehouse Worker Panic Stirs” »

The study shows how deep learning can be used to detect cell image analysis.

Researchers have found a way to observe cell samples to study morphological changes — or the change in form and structure — of cells. This is significant because cells are the basic unit of life, the building blocks of living organisms, and researchers need to be able to observe what could influence the parameters of cells, such as size, shape, and density.

Conventionally, cell samples were observed directly through microscopes by scientists to observe and discover any changes of the cells. They would look for morphological changes in the cell structures.

Continue reading “A breakthrough AI can track real-time cell changes revealing a key mystery in biology” »

At the time, all this was theoretical. But last week, the company announced they’d linked 16 CS-2s together into a world-class AI supercomputer.

Meet Andromeda

The new machine, called Andromeda, has 13.5 million cores capable of speeds over an exaflop (one quintillion operations per second) at 16-bit half precision. Due to the unique chip at its core, Andromeda isn’t easily compared to supercomputers running on more traditional CPUs and GPUs, but Feldman told HPC Wire Andromeda is roughly equivalent to Argonne National Laboratory’s Polaris supercomputer, which ranks 17th fastest in the world, according to the latest Top500 list.

In a conversation right before the 2021 Conference on Neural Information Processing Systems (NeurIPS), Amazon vice president and distinguished scientist Bernhard Schölkopf — according to Google Scholar, the most highly cited researcher in the field of causal inference — said that the next frontier in artificial-intelligence research was causal-representation learning.

Where existing approaches to causal inference use machine learning to discover causal relationships between variables — say, the latencies of various interrelated services on a website — causal-representation learning learns the variables themselves. “These kinds of causal representations will also go toward reasoning, which we will ultimately need if we want to move away from this pure pattern recognition view of intelligence,” Schölkopf said.

Francesco Locatello, a senior applied scientist with Amazon Web Services, leads Amazon’s research on causal-representation learning, and he’s a coauthor on four papers at this year’s NeurIPS.

Large language models have advanced significantly in recent years (LLMs). Impressive LLMs have been revealed one after the other, beginning with OpenAI’s GPT-3, which generates exceptionally correct texts and ends with its open-source counterpart BLOOM. Language-related problems that were previously unsolvable had become simply a challenge for these systems.

All of this progress is made possible by the vast amount of data available on the Internet and the accessibility of powerful GPUs. As appealing as they may sound, training an LLM is an incredibly expensive procedure in terms of both data and technology needs. We’re talking about AI systems with billions of parameters, so feeding these models with enough data isn’t easy. However, once you do it, they give you a stunning performance.

Have you ever wondered where the development of “computing” gadgets began? Why did individuals devote so much time and energy to designing and constructing the first computers? We can presume it was not for the purpose of amusing people with video games or YouTube videos.

AI can also be of benefit in the diagnosis and treatment of patients. Tools have been created that help diagnose a patient as well as a human would.

AI isn’t a new technology—it’s been researched and developed since the 1950s and is currently present in many of our daily routines. Most of these applications are so common that we don’t even notice them.

Our lives often depend on the healthcare industry. So, having a technology that allows you to speed up patient registration processes and help diagnose more quickly and effectively is essential. Every health center should consider the use of AI for the benefit of its processes so it can adapt to the modern world and its accelerated pace.