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Category: robotics/AI – Page 1,581

This AI Uses a Scan of Your Retina to Predict Your Risk of Heart Disease

They then used QUARTZ to analyze retinal images from 7,411 more people, these aged 48 to 92, and combined this data with information about their health history (such as smoking, statin use, and previous heart attacks) to predict their risk of heart disease. Participants’ health was tracked for seven to nine years, and their outcomes were compared to Framingham risk score (FRS) predictions.

A common tool for estimating heart disease risk, the FRS looks at age, gender, total cholesterol, high density lipoprotein cholesterol, smoking habits, and systolic blood pressure to estimate the probability someone will develop heart disease within a given span of time, usually 10 to 30 years.

The QUARTZ team compared their data to 10-year FRS predictions and said the algorithm’s accuracy was on par with that of the conventional tool.

Tesla reportedly places massive order of next-gen self-driving chips with TSMC

Those who follow Elon closely will notice that he is always trying to save a buck. For example, the AI chips used in current Teslas were made by Samsung instead of TSMC to save money. (TSMC makes the best chips but they are also the most expensive.) Another example of Elon saving money would be the mass layoffs at Twitter.

Well, it looks like Elon is really trying to get Full Self-Driving working and has decided that the next generation of FSD chips will be made by TSMC. He placed an order that is so big that Tesla will be the 7th largest customer of TSMC next year. He is going for 4/5 nm chips compared to the 14 nm chips he is using today.

Tesla has reportedly placed a massive order of chips for its next-gen Full Self-Driving (FSD) computer with Taiwan’s TSMC. The order is so large that it might make Tesla one of TSMC’s biggest customers.

Back in 2016, Tesla started building a team of chip architects led by legendary chip designer Jim Keller to develop its own silicon.

The goal was to design a super powerful and efficient chip to achieve self-driving in consumer vehicles without additional hardware like in custom-built autonomous vehicles operated by Waymo and Cruise.

Scientists Created an Artificial Neuron That Actually Retains Electronic Memories

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.

The Man Who’s Building a Computer Made of Brains

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.

Amazon Robot UNVEILED, Warehouse Worker Panic Stirs

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

“What do you call a robotic arm that relies on computer vision, artificial intelligence, and suction cups to pick up items?

In Amazon’s world, it’s called a “Sparrow.”

The tech giant unveiled a robot on Thursday that’s capable of identifying individual items that vary in shape, size, and texture. Sparrow can also pick these up via the suction cups attached to its surface and place them into separate plastic crates.

Sparrow is the first robot Amazon has revealed of its kind and it has the potential to wipe out significant numbers of the company’s warehouse workers.

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

A breakthrough AI can track real-time cell changes revealing a key mystery in biology

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.

Image jungle/iStock N/A

The study was published in the journal Intelligent Computing.

This AI Supercomputer Has 13.5 Million Cores—and Was Built in Just Three Days

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.

NeurIPS: Why causal-representation learning may be the future of AI

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.

What is Galactica AI Assistance?

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.

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