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.
Category: robotics/AI – Page 1095
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 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
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.
Age of the Androids
Posted in policy, robotics/AI
While some might view the emergence of humanoids with apprehension, a future filled with robots is likely to be a positive development for most. But as with anything, policy and society must be ready if, and when, they arrive.
There is growing corporate interest in humanoid robots to replace human labor. Tesla’s recently unveiled Bumble C robot may mark a turning point in an industry that has thus far focused on specialized machines produced in limited quantities. Should Tesla succeed, what does a mass-produced humanoid robot mean for the future of humanity?
The Santa Claus Machine
Posted in futurism, robotics/AI
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Revolutionary improvements to automation and production may one day create machines able to produce almost anything, quickly and cheaply, and far faster and more varied than modern 3D Printers. Such devices are sometimes known as Santa Claus Machines, Cornucopia Devices, or Clanking Del-Replicators, and today we will examine how likely such technology is, how far off in the future they might be, and what impact they would have on society.
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High-Risk, High-Payoff Bio-Research For National Security Challenges — Dr. David A. Markowitz, Ph.D., IARPA
Dr. David A. Markowitz, Ph.D. (https://www.markowitz.bio/) is a Program Manager at the Intelligence Advanced Research Projects Activity (IARPA — https://www.iarpa.gov/) which is an organization that invests in high-risk, high-payoff research programs to tackle some of the most difficult challenges of the agencies and disciplines in the U.S. Intelligence Community (IC).
IARPA’s mission is to push the boundaries of science to develop solutions that empower the U.S. IC to do its work better and more efficiently for national security. IARPA does not have an operational mission and does not deploy technologies directly to the field, but instead, they facilitate the transition of research results to IC customers for operational application.