Amazon just dropped Nova multimodal AI models that beat gpt-4o and sonnet 3.5 đ€Ż
New state-of-the-art foundation models from Amazon deliver frontier intelligence and industry-leading price performance.
Category: robotics/AI – Page 150
As described in that paper and henceforth, a transformer is a deep learning neural network architecture that processes sequential data, such as text or time-series information.
Now, MIT-birthed startup Liquid AI has introduced STAR (Synthesis of Tailored Architectures), an innovative framework designed to automate the generation and optimization of AI model architectures.
The STAR framework leverages evolutionary algorithms and a numerical encoding system to address the complex challenge of balancing quality and efficiency in deep learning models.
Froilan Mendoza is Founder & Chief Technology Officer of Fulcrum Solutions.
Small businesses are the backbone of the U.S. economy. They represent 99.9% of all businesses in the country, account for 43.5% of GDP and employ almost half of the U.S. workforce. Yet small business owners have always had to overcome obstacles to survive and succeed. Lack of capital is responsible for 38% of small business failures. Labor costs make up 70% of their expenses, and a national labor shortage of 2 million workers is exacerbating the difficulty of hiring and keeping talent.
The good news is that AI is leveling the playing field for small businesses, giving them easy-to-use tools to optimize their processes and scale their organizations without huge teams or budgets. A 2024 study from the U.S. Chamber of Commerce found that 98% of small businesses are already using an AI-enabled tool, and 91% of owners say that AI will fuel future business growth. The use of generative AI tools, such as chatbots and image creators, grew by 40% in the last year.
Thereâs a common popular science demonstration involving âsoap boats,â in which liquid soap poured onto the surface of water creates a propulsive flow driven by gradients in surface tension. But it doesnât last very long since the soapy surfactants rapidly saturate the water surface, eliminating that surface tension. Using ethanol to create similar âcocktail boatsâ can significantly extend the effect because the alcohol evaporates rather than saturating the water.
That simple classroom demonstration could also be used to propel tiny robotic devices across liquid surfaces to carry out various environmental or industrial tasks, according to a preprint posted to the physics arXiv. The authors also exploited the so-called âCheerios effectâ as a means of self-assembly to create clusters of tiny ethanol-powered robots.
As previously reported, those who love their Cheerios for breakfast are well acquainted with how those last few tasty little âOâ s tend to clump together in the bowl: either drifting to the center or to the outer edges. The âCheerios effect is found throughout nature, such as in grains of pollen (or, alternatively, mosquito eggs or beetles) floating on top of a pond; small coins floating in a bowl of water; or fire ants clumping together to form life-saving rafts during floods. A 2005 paper in the American Journal of Physics outlined the underlying physics, identifying the culprit as a combination of buoyancy, surface tension, and the so-called â meniscus effect.â
Discover trends in motion control, from servo advancements to motor miniaturization and robotics, driving high-speed automation and boosting efficiency across industries.
Ryan Serhantâs eponymous brokerage has been in rapid growth mode this year following the success of the Netflix show âOwning Manhattan,â 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 â which is going to SERHANT. Technologies, the umbrella company that includes the brokerage â will be used to develop the companyâs 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.
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.
The AI race is heating up! In this video, we delve into the competition between Nvidiaâs Llama-3.1 and OpenAIâs GPT-4. Discover how these two AI giants are revolutionizing the field of large language models (LLMs) and reshaping AI performance benchmarks. From Nvidiaâs groundbreaking Llama-3.1 Nemotron to GPT-4âs 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: OpenAIâs GPT-4 and Nvidia AI animation.
Nvidia AI benchmarks, GPUs, and requirements.
OpenAI vs. Nvidia: Whoâs 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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GPT-4 video generation: Is OpenAI leading the AI race?
Nvidia AI vs OpenAI: Benchmarks and features compared.
Llama-3.1 vs GPT-4: Pros, cons, and use cases.
Nvidia AI animation and OpenAI video generation tools.
Best GPU for running Nvidia Llama models.
OpenAI H100 and Nvidia Llama: A performance comparison.
Nvidia AI performance benchmarks: 2024 updates.
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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 âquantum dotsâ do not sound like an easy concept to most people. Shorubalko explains, âThe properties of a material depend not only on its chemical composition, but also on its dimensions.â 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 âquantum dots.â
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.
Itâs 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; itâs also fostering an environment of innovation where technology, data and human expertise work together to achieve operational excellence.