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   Technology StocksNVIDIA Corporation (NVDA)


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To: Selectric II who wrote (2549)6/14/2023 9:35:51 PM
From: Zen Dollar Round
   of 2616
 
Yep. I was just joking, obviously there would be huge liability issues for putting any weaponry or anything else in one of those robots that could injure a human.

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From: Frank Sully6/18/2023 2:31:13 PM
   of 2616
 
NVIDIA: The Bull Case Is Much More Powerful than You May Think

seekingalpha.com

I particularly like what the author says about "Full Stack" solutions, a phrase often heard but rarely explained. To the Moon, Alice!

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From: Glenn Petersen6/28/2023 9:54:25 AM
1 Recommendation   of 2616
 
Nvidia dips on report U.S. considering new A.I. chip export restrictions for China

PUBLISHED WED, JUN 28 20238:38 AM EDT
UPDATED 8 MIN AGO
Rohan Goswami @IN/ROHANGOSWAMICNBC/ @ROGOSWAMI
CNBC.com

KEY POINTS

- The federal government is weighing further restrictions on exporting powerful computing chips to China, the kind that power A.I. -models, The Wall Street Journal reported Wednesday.

-- The restrictions would impact Nvidia and AMD, both of which make powerful processors used in A.I. applications.

-- The Biden administration has already tightened controls, forcing Nvidia to create a weaker version of its flagship A100 for China, but even that weakened chip wouldn’t be allowed under the rules under consideration.

Shares of Nvidia dipped 3.7% and Advanced Micro Devices fell about 3% in premarket trading after The Wall Street Journal reported the federal government is weighing new restrictions on exports of sophisticated chips used in artificial intelligence computing to China.

The export restrictions under consideration would be imposed by the Commerce Department and would come after the U.S. government already limited the computing power of chips made for Chinese use. Nvidia and AMD had been impacted by the prior limitation.

Other chipmakers also fell in premarket trading on the news. Marvell dipped more than 2%, and Broadcom and Qualcomm both dropped about 1%.

Nvidia responded to the earlier restrictions by building a lower-spec chip for the Chinese market. But under the new controls being considered, even that chip, the A800, would be export restricted without licensing, the Journal reported.

The restrictions would also apply to companies that offer cloud-based computing solutions, the Journal reported, which have been used by some companies to skirt export controls.

Competition between the U.S. and China over hardware and software technology has amplified in recent years. Cybersecurity threats from Chinese state-backed threats have been identified by top U.S. officials as one of the top national security threats facing the United States. Sensitive technology has allegedly been stolen from American companies to benefit Chinese domestic competitors, whether through outright industrial espionage or through joint-venture projects, which require American companies to partner with Chinese firms to do business within China.

Against this backdrop, tightened chip export controls would likely further inflame trade tensions between the two countries. U.S. officials have tried to mitigate potential impacts, but a tightening of export controls would likely jeopardize those efforts. Gina Raimondo, who as secretary of Commerce would lead the enforcement of any export controls, met with her Chinese counterpart in Beijing earlier this year.

Nvidia declined to comment, as did the Bureau of Industry and Security, which is the Commerce Department’s export control unit. AMD did not immediately respond to a request for comment.

Nvidia dips on report U.S. mulling new AI chip restrictions for China (cnbc.com)

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From: Frank Sully6/30/2023 12:32:21 PM
   of 2616
 
What is Robotics Simulation?

NVIDIA blog. Note there are a half-dozen short embedded videos which are worth watching.

blogs.nvidia.com

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From: Glenn Petersen7/12/2023 5:19:57 AM
1 Recommendation   of 2616
 
Nvidia in talks to be an anchor investor in Arm IPO

World’s most valuable semiconductor group discusses acquiring stake in SoftBank-owned chip designer ahead of New York listing

Tabby Kinder in San Francisco, Qianer Liu in Hong Kong, Nicholas Megaw in New York, Kana Inagaki in Tokyo and Tim Bradshaw in London
Financial Times

3 HOURS AGO

Chip designer Arm is in talks to bring in Nvidia as an anchor investor, while the SoftBank-owned company presses ahead with plans for a New York listing as soon as September, several people briefed on the talks said.

Nvidia, the world’s most valuable semiconductor company, was forced last year to abandon its planned $66bn acquisition of Arm after the deal was challenged by regulators.

The Silicon Valley-based chipmaker is one of several existing Arm partners, including Intel, that the UK-based company is hoping will take a long-term stake at the initial public offering stage, according to the people.

The prospective investors are still negotiating with Arm over its valuation. One person familiar with the discussions said Nvidia wanted to come in at a share price that would put Arm’s total value at $35bn to $40bn, while Arm wants to be closer to $80bn.

The aim of bringing in large anchor investors as Arm launches an IPO in New York would be to help to support the stock as SoftBank, which bought Arm for £24bn ($32bn) in 2016, sells down its stake.

Many private tech companies and their advisers are watching closely to see if Arm can succeed in launching its IPO in 2023 after a year-long slump in new listings.

Securing the advance support of a few anchor investors is a popular tactic during difficult IPO markets, serving as a way to ensure demand and reassure other potential investors.

Arm and Nvidia declined to comment. A person close to the situation said the talks had not been concluded and might not lead to an investment.

Arm is expected to be the most valuable company to go public in the US since automaker Rivian, which listed with an initial market capitalisation of $70bn in late 2021.

It is widely considered to be a less risky prospect than many IPO candidates given its previous record as a public company.

People close to SoftBank said its founder Masayoshi Son has been personally involved in seeking anchor investors for Arm. Son has been focusing on expanding the chip designer’s revenue ahead of its IPO. SoftBank declined to comment.

Arm and Nvidia have contacted regulators in the US to smooth over any potential concerns about what is likely to be a small minority investment, in the low hundreds of millions of dollars, according to people close to the discussions.

When Nvidia first announced plans to take over Arm in September 2020, competition authorities in the US and Europe objected. They said the deal might restrict rivals’ access to Arm’s intellectual property, which can be found in the vast majority of smartphones and a growing portion of the automotive market, as well as give Nvidia access to competitively sensitive information.

Nvidia is already an Arm customer but its talks to invest in the company point to bigger ambitions to expand from its core business in “graphics processing units” — dedicated chips for accelerating specialised tasks, including 3D rendering and training artificial intelligence — into the “central processing units” that handle most other computing functions.

One person familiar with the plans said Arm was keen to bring in Nvidia in its bid as a way to position AI as central to the UK group’s growth plans. “AI will be every third word in the offering document,” the person said. “Nvidia is so important as its involvement implies AI.”

The move would bring Nvidia into closer competition with Intel, whose CPUs have long dominated the PC and data centre markets.

Nvidia, which became the first chipmaker to hit a $1tn valuation in May, recently produced its first CPU using Arm’s designs as part of a so-called superchip, dubbed Grace Hopper and designed for artificial intelligence and high-performance computing.

Global listing volumes plummeted last year as investors were put off by falling stock prices, rising market volatility and the uncertain economic outlook. However, activity has begun to pick up in recent weeks, thanks in part to a broader upswing in stock prices led by Nvidia.\

Additional reporting by Richard Waters

Nvidia in talks to be an anchor investor in Arm IPO | Financial Times (archive.ph)

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From: The Ox7/13/2023 1:32:10 PM
   of 2616
 
AI-Fueled Productivity: Generative AI Opens New Era of Efficiency Across Industries

blogs.nvidia.com

A watershed moment on Nov. 22, 2022, was mostly virtual, yet it shook the foundations of nearly every industry on the planet.

On that day, OpenAI released ChatGPT, the most advanced artificial intelligence chatbot ever developed. This set off demand for generative AI applications that help businesses become more efficient, from providing consumers with answers to their questions to accelerating the work of researchers as they seek scientific breakthroughs, and much, much more.

Businesses that previously dabbled in AI are now rushing to adopt and deploy the latest applications. Generative AI — the ability of algorithms to create new text, images, sounds, animations, 3D models and even computer code — is moving at warp speed, transforming the way people work and play.

By employing large language models (LLMs) to handle queries, the technology can dramatically reduce the time people devote to manual tasks like searching for and compiling information.

The stakes are high. AI could contribute more than $15 trillion to the global economy by 2030, according to PwC. And the impact of AI adoption could be greater than the inventions of the internet, mobile broadband and the smartphone — combined.

The engine driving generative AI is accelerated computing. It uses GPUs, DPUs and networking along with CPUs to accelerate applications across science, analytics, engineering, as well as consumer and enterprise use cases.

Early adopters across industries — from drug discovery, financial services, retail and telecommunications to energy, higher education and the public sector — are combining accelerated computing with generative AI to transform business operations, service offerings and productivity.

Click to view the infographic: Generating the Next Wave of AI TransformationGenerative AI for Drug DiscoveryToday, radiologists use AI to detect abnormalities in medical images, doctors use it to scan electronic health records to uncover patient insights, and researchers use it to accelerate the discovery of novel drugs.

Traditional drug discovery is a resource-intensive process that can require the synthesis of over 5,000 chemical compounds and yields an average success rate of just 10%. And it takes more than a decade for most new drug candidates to reach the market.

Researchers are now using generative AI models to read a protein’s amino acid sequence and accurately predict the structure of target proteins in seconds, rather than weeks or months.

Using NVIDIA BioNeMo models, Amgen, a global leader in biotechnology, has slashed the time it takes to customize models for molecule screening and optimization from three months to just a few weeks. This type of trainable foundation model enables scientists to create variants for research into specific diseases, allowing them to develop target treatments for rare conditions.

Whether predicting protein structures or securely training algorithms on large real-world and synthetic datasets, generative AI and accelerated computing are opening new areas of research that can help mitigate the spread of disease, enable personalized medical treatments and boost patient survival rates.

Generative AI for Financial ServicesAccording to a recent NVIDIA survey, the top AI use cases in the financial services industry are customer services and deep analytics, where natural language processing and LLMs are used to better respond to customer inquiries and uncover investment insights. Another common application is in recommender systems that power personalized banking experiences, marketing optimization and investment guidance.

Advanced AI applications have the potential to help the industry better prevent fraud and transform every aspect of banking, from portfolio planning and risk management to compliance and automation.

Eighty percent of business-relevant information is in an unstructured format — primarily text — which makes it a prime candidate for generative AI. Bloomberg News produces 5,000 stories a day related to the financial and investment community. These stories represent a vast trove of unstructured market data that can be used to make timely investment decisions.

NVIDIA, Deutsche Bank, Bloomberg and others are creating LLMs trained on domain-specific and proprietary data to power finance applications.

Financial Transformers, or “FinFormers,” can learn context and understand the meaning of unstructured financial data. They can power Q&A chatbots, summarize and translate financial texts, provide early warning signs of counterparty risk, quickly retrieve data and identify data-quality issues.

These generative AI tools rely on frameworks that can integrate proprietary data into model training and fine-tuning, integrate data curation to prevent bias and use guardrails to keep conversations finance-specific.

Expect fintech startups and large international banks to expand their use of LLMs and generative AI to develop sophisticated virtual assistants to serve internal and external stakeholders, create hyper-personalized customer content, automate document summarization to reduce manual work, and analyze terabytes of public and private data to generate investment insights.

Generative AI for RetailWith 60% of all shopping journeys starting online and consumers more connected and knowledgeable than ever, AI has become a vital tool to help retailers match shifting expectations and differentiate from a rising tide of competition.

Retailers are using AI to improve customer experiences, power dynamic pricing, create customer segmentation, design personalized recommendations and perform visual search.

Generative AI can support customers and employees at every step through the buyer journey.

With AI models trained on specific brand and product data, they can generate robust product descriptions that improve search engine optimization rankings and help shoppers find the exact product they’re looking for. For example, generative AI can use metatags containing product attributes to generate more comprehensive product descriptions that include various terms like “low sugar” or “gluten free.”

AI virtual assistants can check enterprise resource planning systems and generate customer service messages to inform shoppers about which items are available and when orders will ship, and even assist customers with order change requests.

Fashable, a member of NVIDIA Inception’s global network of technology startups, is using generative AI to create virtual clothing designs, eliminating the need for physical fabric during product development. With the models trained on both proprietary and market data, this reduces the environmental impact of fashion design and helps retailers design clothes according to current market trends and tastes.

Expect retailers to use AI to capture and retain customer attention, deliver superior shopping experiences, and drive revenue by matching shoppers with the right products at the right time.

Generative AI for TelecommunicationsIn an NVIDIA survey covering the telecommunications industry, 95% of respondents reported that they were engaged with AI, while two-thirds believed that AI would be important to their company’s future success.

Whether improving customer service, streamlining network operations and design, supporting field technicians or creating new monetization opportunities, generative AI has the potential to reinvent the telecom industry.

Telcos can train diagnostic AI models with proprietary data on network equipment and services, performance, ticket issues, site surveys and more. These models can accelerate troubleshooting of technical performance issues, recommend network designs, check network configurations for compliance, predict equipment failures, and identify and respond to security threats.

Generative AI applications on handheld devices can support field technicians by scanning equipment and generating virtual tutorials to guide them through repairs. Virtual guides can then be enhanced with augmented reality, enabling technicians to analyze equipment in a 3D immersive environment or call on a remote expert for support.

New revenue opportunities will also open for telcos. With large edge infrastructure and access to vast datasets, telcos around the world are now offering generative AI as a service to enterprise and government customers.

As generative AI advances, expect telecommunications providers to use the technology to optimize network performance, improve customer support, detect security intrusions and enhance maintenance operations.

Generative AI for EnergyIn the energy industry, AI is powering predictive maintenance and asset optimization, smart grid management, renewable energy forecasting, grid security and more.

To meet growing data needs across aging infrastructure and new government compliance regulations, energy operators are looking to generative AI.

In the U.S., electric utility companies spend billions of dollars every year to inspect, maintain and upgrade power generation and transmission infrastructure.

Until recently, using vision AI to support inspection required algorithms to be trained on thousands of manually collected and tagged photos of grid assets, with training data constantly updated for new components. Now, generative AI can do the heavy lifting.

With a small set of image training data, algorithms can generate thousands of physically accurate images to train computer vision models that help field technicians identify grid equipment corrosion, breakage, obstructions and even detect wildfires. This type of proactive maintenance enhances grid reliability and resiliency by reducing downtime, while diminishing the need to dispatch teams to the field.

Generative AI can also reduce the need for manual research and analysis. According to McKinsey, employees spend up to 1.8 hours per day searching for information — nearly 20% of the work week. To increase productivity, energy companies can train LLMs on proprietary data, including meeting notes, SAP records, emails, field best practices and public data such as standard material data sheets.

With this type of knowledge repository connected to an AI chatbot, engineers and data scientists can get instant answers to highly technical questions. For example, a maintenance engineer troubleshooting pitch control issues on a turbine’s hydraulic system could ask a bot: “How should I adjust the hydraulic pressure or flow to rectify pitch control issues on a model turbine from company X?” A properly trained model would deliver specific instructions to the user, who wouldn’t have to look through a bulky manual to find answers.

With AI applications for new system design, customer service and automation, expect generative AI to enhance safety and energy efficiency, as well as reduce operational expenses in the energy industry.

Generative AI for Higher Education and ResearchFrom intelligent tutoring systems to automated essay grading, AI has been employed in education for decades. As universities use AI to improve teacher and student experiences, they’re increasingly dedicating resources to build AI-focused research initiatives.

For example, researchers at the University of Florida have access to one of the world’s fastest supercomputers in academia. They’ve used it to develop GatorTron — a natural language processing model that enables computers to read and interpret medical language in clinical notes that are stored in electronic health records. With a model that understands medical context, AI developers can create numerous medical applications, such as speech-to-text apps that support doctors with automated medical charting.

In Europe, an industry-university collaboration involving the Technical University of Munich is demonstrating that LLMs trained on genomics data can generalize across a plethora of genomic tasks, unlike previous approaches that required specialized models. The genomics LLM is expected to help scientists understand the dynamics of how DNA is translated into RNA and proteins, unlocking new clinical applications that will benefit drug discovery and health.

To conduct this type of groundbreaking research and attract the most motivated students and qualified academic professionals, higher education institutes should consider a whole-university approach to pool budget, plan AI initiatives, and distribute AI resources and benefits across disciplines.

Generative AI for the Public SectorToday, the biggest opportunity for AI in the public sector is helping public servants to perform their jobs more efficiently and save resources.

The U.S. federal government employs over 2 million civilian employees — two-thirds of whom work in professional and administrative jobs.

These administrative roles often involve time-consuming manual tasks, including drafting, editing and summarizing documents, updating databases, recording expenditures for auditing and compliance, and responding to citizen inquiries.

To control costs and bring greater efficiency to routine job functions, government agencies can use generative AI.

Generative AI’s ability to summarize documents has great potential to boost the productivity of policymakers and staffers, civil servants, procurement officers and contractors. Consider a 756-page report recently released by the National Security Commission on Artificial Intelligence. With reports and legislation often spanning hundreds of pages of dense academic or legal text, AI-powered summaries generated in seconds can quickly break down complex content into plain language, saving the human resources otherwise needed to complete the task.

AI virtual assistants and chatbots powered by LLMs can instantly deliver relevant information to people online, taking the burden off of overstretched staff who work phone banks at agencies like the Treasury Department, IRS and DMV.

With simple text inputs, AI content generation can help public servants create and distribute publications, email correspondence, reports, press releases and public service announcements.

The analytical capabilities of AI can also help process documents to speed the delivery of vital services provided by organizations like Medicare, Medicaid, Veterans Affairs, USPS and the State Department.

Generative AI could be a pivotal tool to help government bodies work within budget constraints, deliver government services more quickly and achieve positive public sentiment.

Generative AI – A Key Ingredient for Business Success Across every field, organizations are transforming employee productivity, improving products and delivering higher-quality services with generative AI.

To put generative AI into practice, businesses need expansive amounts of data, deep AI expertise and sufficient compute power to deploy and maintain models quickly. Enterprises can fast-track adoption with the NeMo generative AI framework, part of NVIDIA AI Enterprise software, running on DGX Cloud. NVIDIA’s pretrained foundation models offer a simplified approach to building and running customized generative AI solutions for unique business use cases.

Learn more about powerful generative AI tools to help your business increase productivity, automate tasks, and unlock new opportunities for employees and customers.

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To: The Ox who wrote (2555)7/13/2023 1:37:14 PM
From: The Ox
   of 2616
 
NVDA up 317% since Oct 13th, 2022.

$108 to $450...

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To: The Ox who wrote (2556)7/26/2023 4:01:21 PM
From: Frank Sully
   of 2616
 
Correction: The Ox - up three-fold is 200%

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To: Frank Sully who wrote (2557)7/26/2023 4:03:26 PM
From: The Ox
   of 2616
 
108 x 3.17 = 342
342+108 = 450

Seems to me that's 317% <grin>

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To: The Ox who wrote (2558)7/26/2023 4:31:52 PM
From: Frank Sully
   of 2616
 
Sorry, The Ox, I was wrong.

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