Discover trends in motion control, from servo advancements to motor miniaturization and robotics, driving high-speed automation and boosting efficiency across industries.
Category: robotics/AI – Page 350
SERHANT. secures $45M to further develop its AI platform
Ryan Serhant’s eponymous brokerage has been in rapid growth mode this year following the success of the Netflix show “Owning Manhattan,” and now investors want in on the action.
SERHANT. announced Monday that it secured $45 million in a seed funding round led by real estate venture capital firm Camber Creek and participation from Left Lane Capital.
The investment — which is going to SERHANT. Technologies, the umbrella company that includes the brokerage — will be used to develop the company’s AI platform known as S.MPLE. The company believes S.MPLE will optimize workflows and help scale other parts of its business, including the brokerage.
How DNA molecules and enzymes can control robot swarms
Scientists developed a DNA-based molecular controller that autonomously directs the assembly and disassembly of molecular robots, a key approach with potential applications in medicine and nanotechnology.
Who Will Dominate the AI Race? Nvidia’s Llama 3.1 vs GPT 4.o । OpenAI vs Nvidia । Technology Now
The AI race is heating up! In this video, we delve into the competition between Nvidia’s Llama-3.1 and OpenAI’s GPT-4. Discover how these two AI giants are revolutionizing the field of large language models (LLMs) and reshaping AI performance benchmarks. From Nvidia’s groundbreaking Llama-3.1 Nemotron to GPT-4’s advanced video generation capabilities, we analyze their strengths, use cases, and potential to lead the AI revolution.
Topics covered:
Nvidia Llama-3.1 vs. OpenAI GPT-4: Performance benchmarks.
How to use Nvidia Llama-3.1 Nemotron-70B
AI in video generation: OpenAI’s GPT-4 and Nvidia AI animation.
Nvidia AI benchmarks, GPUs, and requirements.
OpenAI vs. Nvidia: Who’s winning the AI race?
Llama GPU requirements and running Llama without a GPU
Stay tuned to learn which of these tech titans might dominate the future of AI innovation!
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Nvidia AI animation and OpenAI video generation tools.
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OpenAI H100 and Nvidia Llama: A performance comparison.
Nvidia AI performance benchmarks: 2024 updates.
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AI has use in every stage of real estate development, HPI execs say
What do motion detectors, self-driving cars, chemical analyzers and satellites have in common? They all contain detectors for infrared (IR) light. At their core and besides readout electronics, such detectors usually consist of a crystalline semiconductor material.
Such materials are challenging to manufacture: They often require extreme conditions, such as a very high temperature, and a lot of energy. Empa researchers are convinced that there is an easier way. A team led by Ivan Shorubalko from the Transport at the Nanoscale Interfaces laboratory is working on miniaturized IR detectors made of colloidal quantum dots.
The words “quantum dots” do not sound like an easy concept to most people. Shorubalko explains, “The properties of a material depend not only on its chemical composition, but also on its dimensions.” If you produce tiny particles of a certain material, they may have different properties than larger pieces of the very same material. This is due to quantum effects, hence the name “quantum dots.”
IIoT: Driving The Future Of Manufacturing With AI And Edge Computing
While the technology itself is impressive, its true potential lies in how leaders manage its adoption. Fostering a culture of innovation and continuous learning is crucial for success in this new industrial era. Leaders must ensure that their workforce is not only comfortable with automation but is also empowered to collaborate with AI-driven systems. Upskilling and reskilling employees to work alongside AI will create a workforce capable of leveraging technology to enhance operational efficiency.
It’s also essential for business leaders to prioritize cybersecurity and data privacy. The increased connectivity that comes with IIoT introduces new vulnerabilities, and safeguarding company and customer data must be a top priority.
AI, edge computing and IIoT represent a fundamental shift in the way industries operate. The future of manufacturing is not just automated. It is also intelligent, with systems that learn, predict and adapt in real time. For leaders, the challenge is not only implementing these technologies; it’s also fostering an environment of innovation where technology, data and human expertise work together to achieve operational excellence.
OpenAI CEO Sam Altman discusses the future of generative AI
On September 12 2024, Sam Altman, Chief Executive Officer of OpenAI, participated in a fireside chat for University of Michigans students, faculty and staff. The ChatGPT developer head spoke about the future of AI and its implications for education, as well as the challenges and opportunities presented by rapid technological advancements. Altman also shared insights into OpenAI’s new reasoning model, Strawberry, a model he describes as capable of complex reasoning and problem-solving.
“You all are going to create things that astonish us. The story of human history is that we build better tools, and then people do even more amazing stuff with them, and they themselves, you know, add their layer of scaffolding. And we’re on this steadily increasing curve of possibility.”
https://news.engin.umich.edu/2024/09/.…
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Photonic processor could enable ultrafast AI computations with extreme energy efficiency
The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of traditional electronic computing hardware.
Photonic hardware, which can perform machine-learning computations with light, offers a faster and more energy-efficient alternative. However, there are some types of neural network computations that a photonic device can’t perform, requiring the use of off-chip electronics or other techniques that hamper speed and efficiency.
Building on a decade of research, scientists from MIT and elsewhere have developed a new photonic chip that overcomes these roadblocks. They demonstrated a fully integrated photonic processor that can perform all the key computations of a deep neural network optically on the chip.
Tesla’s GEN-3 Teslabot Stuns with Human-Like Dexterity and Precision
Tesla hasn’t unveiled its next generation human robot in the form of the app named GEN-3 Teslabot, bringing with it significant advancements in the field of humanoid robotics, merging state-of-the-art engineering with a design inspired by human anatomy. This next-generation robot demonstrates exceptional dexterity and precision, setting a new benchmark for what humanoid robots can accomplish. From catching a tennis ball mid-air to envisioning tasks like threading a needle, the Teslabot is poised to reshape how robots interact with and adapt to the world around them.
Wouldn’t it be great if robots didn’t just assemble cars or vacuum your living room but perform tasks requiring the finesse of human hands—threading a needle, playing a piano, or even catching a tennis ball mid-air. It sounds like science fiction, doesn’t it? Yet, Tesla’s latest innovation, the GEN-3 Teslabot, is bringing us closer to that reality. With its human-inspired design and new engineering, this robot is redefining what we thought machines could do.
But what makes the Teslabot so extraordinary? It’s not just the flashy demonstrations or its sleek design. It’s the way Tesla has managed to replicate human dexterity and precision in a machine, giving it the potential to tackle tasks we once thought only humans could handle. From its 22 degrees of freedom in the hand to its vision-driven precision, it’s a glimpse of what’s to come. Let’s dive into the details of Tesla’s GEN-3 Teslabot and explore how it’s pushing the boundaries of what’s possible.