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From: Frank Sully8/2/2021 3:16:53 PM
   of 2381
 
Nvidia AI development hub now available to North American customers

Monthly subscription pricing to the Nvidia Base Command Platform starts at $90,000, with a three-month minimum.

By Jonathan Greig | August 2, 2021 -- 13:00 GMT (06:00 PDT)

Nvidia announced on Monday that its new hosted AI development hub -- the Nvidia Base Command Platform -- is now available to North American customers after debuting in May.

Nvidia said in a statement that the platform "provides enterprises with instant access to powerful computing infrastructure wherever their data resides."

The tool is available to be rented for a monthly subscription price of $90,000. There is a three-month minimum to all subscriptions, Nvidia explained.

Manuvir Das, head of Enterprise Computing at Nvidia, said the Base Command Platform makes it easy for enterprises to instantly access the power of an Nvidia DGX SuperPOD to "accelerate the AI and data science development lifecycle."

The platform gives companies access to Nvidia DGX SuperPODTM supercomputers through optimized AI workflow software, and the tool is hosted remotely by Equinix. According to a statement from the company, the Base Command Platform is the first Nvidia-powered hybrid cloud offering available through the Nvidia AI LaunchPad partner program.

The tool is tailored for organizations that have large-scale, multiuser and multi-team AI workflows looking to push AI projects into production.

Nvidia announced that Adobe was already using the tool to help researchers and data scientists work "simultaneously on shared accelerated computing resources to speed up the development of new AI-powered software features and applications."

Abhay Parasnis, CTO and chief product officer at Adobe, said the platform requires little effort to onboard AI developers.

"Our team is exploring the potential of Base Command Platform to simplify the machine learning development workflow," Parasnis said.

The tool is supported by a number of Nvidia partner organizations like NetApp and Equinix, and Weights and Biases, which offers MLOps software for the Base Command Platform.

In addition to a cloud-based user interface, the tool comes with a command-line API, integrated monitoring and reporting dashboards to accelerate the AI development lifecycle, incorporating a "broad range of AI and data science tools" like the Nvidia NGCTM catalog of AI and analytics software.

Equinix vice president Steve Steinhilber added that businesses often struggle to provide the simple yet powerful digital infrastructure that researchers and scientists can share efficiently when it comes to AI.

The Base Command Platform is "the fastest and most cost-effective way to tap into the leading performance of an Nvidia DGX SuperPOD to accelerate AI development, seamlessly access distributed data lakes wherever they may be located via Equinix Fabric, and quickly deploy developed and tested algorithms to inference engines all over the world," Steinhilber explained.

Kim Stevenson, senior vice president and general manager of the foundational data services group at NetApp, noted that the tool was a cloud-hosted solution for end-to-end AI development with fully managed AI infrastructure.

"Enterprises want to simplify AI experimentation and streamline workflow management across teams of users and jobs," Stevenson said.

zdnet.com

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From: Frank Sully8/2/2021 3:40:15 PM
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Nvidia is tracking more than 8,500 AI startups with $60B in funding

Dean Takahashi @deantak

August 2, 2021 8:00 AM



Nvidia is tracking more than 8,500 AI startups through its Inception AI startup program. Those companies have raised more than $60 billion in funding and come from 90 countries, Nvidia said.

Based on estimates from market researcher Pitchbook, the Nvidia numbers represent roughly two-thirds of all AI startups. Overall, Nvidia believes there are about 12,000 AI startups in the world.

“It’s a good picture of the landscape,” said Serge Lemonde, global head of Nvidia Inception, in an interview with VentureBeat.

Across the startups, the definition of an AI company is changing, as many companies across all industries are adopting AI. There are new uses of AI emerging as companies adopt deep learning neural networks. The Inception companies now include more than high-performance computing, graphics, and other common startups.

“The fastest growing segments or verticals in the healthcare itself are around pharma and AI biology,” Lemonde said. “We launched the program in 2016. And every year, it’s been growing faster. In 2020, we had a plus 26% growth in the number of members joining Inception, and just this first half of this year is already plus 17%. So AI adoption is impacting every industry.”

The Inception program provides assistance and software for AI startups, and it’s Nvidia’s way of introducing AI companies to its hardware products such as its AI chips. The data from the ecosystem gives the companies a lot of insights into the AI economy.

Regional strengths



Above: Nvidia’s Inception program tracks AI startups.
Image Credit: Nvidia

The U.S. leads the world with nearly 27% of the Inception AI startups. Those U.S. companies have raised more than $27 billion. And of the U.S. startups, 42% are based in California. That means more than one in 10 AI startups are based in California, with 29% in the San Francisco Bay Area. This underscores the draw of Silicon Valley for startup founders and VC funding, Lemonde said.

Following the U.S. is China, in terms of both funding and company stage, with 12% of Nvidia Inception members based there. India comes in third at 7%, with the United Kingdom right behind at 6%.

Taken together, AI startups based in the U.S., China, India and the U.K. account for just over half of all startups in Nvidia Inception. Following in order after these are Germany, Russia, France, Sweden, Netherlands, Korea and Japan.

Industry focus

In terms of industries, healthcare, information technology services (IT), intelligent video analytics (IVA), media and entertainment (M&E) and robotics are the top five in Nvidia Inception. AI startups in healthcare account for 16% of Inception members, followed by those in IT services at 15%.

AI startups in IVA make up 8%, with M&E and robotics AI startups tied at 7%.

Recent growth



Above: Nvidia’s Inception AI startups are from the green countries.
Image Credit: Nvidia

More than 3,000 AI startups have joined Nvidia Inception since 2020. Similar to data across Inception as a whole, AI startups from the U.S. account for the largest segment (27 percent), followed by China (12 percent), and India and the U.K. (tied at 6 percent).

“Some countries are accelerating their ecosystem of AI startups by investing money and encouraging the local players to create more companies,” Lemonde said. “We saw India growing these last couple of months, and so India is definitely now the third country with 7% of the AI startups in the world.”

Additionally, startups that have joined since 2020 are concentrated in the same top five industries, though in slightly different order. IT services leads the way at 17%, followed by healthcare at 16%, M&E at 9%, IVA at 8% and robotics at 5%.

Within the top two industries — healthcare and IT services — there’s more detail among AI startups who have joined since 2020. The dominant segment within IT services is computer vision at 27%, with predictive analytics in second place at 9%. The top two segments in healthcare are medical analytics at 38% and medical imaging at 36%, though the fastest growth is among AI startups in the pharma and AI biology industries at 15%.

Virtual and augmented reality startup companies are far outpacing any other segment within M&E, mostly due to the pandemic. These startups are coming to Nvidia Inception with a shared vision of building an ecosystem for the metaverse, the universe of virtual worlds that are all interconnected, like in novels such as Snow Crash and Ready Player One.

Healthcare AI startups skyrocketed during the pandemic as well, with growth in medical imaging and more.

“Now it’s about biology, pharma, DNA, and more,” Lemonde said. “I think there is a lot of growth there as well. We saw during COVID new verticals grow fast like virtual reality and augmented reality. We saw the usage of AI go up but this metaverse shared vision in many countries grow up.”

Growing regional hubs



Above: Regional Advantage by Annalee Saxenian studied the rise of Silicon Valley over Boston.
Image Credit: Annalee Saxenian

Since Inception’s launch in 2016, it has grown more than tenfold. This growth has accelerated year over year, with membership increasing to 26% in 2020, and already reaching 17% in the first half of 2021.

To grow a big AI hub in a region, Lemonde believes it’s most important to have good universities and educational infrastructure in a region.

“If you look at the top countries, the governments push technology, invest in science and AI, invest computing infrastructures in their countries, and push for investments,” he said.

Nvidia Inception is a program built to accommodate and nurture every startup that is accelerating computing, at every stage in their journey. All program benefits are free of charge. And unlike other accelerators or incubators, startups never have to give up equity to join. After the startups graduate from Inception, Nvidia hands them off to its developer relations and sales departments.

“In our program, what we are looking at is to help them all,” Lemonde said. “The lesson here is really having this window on the landscape and helping the startups all around the world is helping us understand at the new trends. We can help more startups by developing our software and platforms for the upcoming trends.”

venturebeat.com

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From: Frank Sully8/3/2021 7:46:39 AM
1 Recommendation   of 2381
 

I have become moderator of the Baidu (BIDU) board. Read the Introduction header as I have updated it. Also, I posted a representative sample of the important news in the last six months, starting with message 1814. If you have any concerns, suggestions or questions please PM me.

Subject 55838

Cheers,
Frank Sully


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From: Glenn Petersen8/4/2021 6:25:28 AM
2 Recommendations   of 2381
 
Why Nvidia’s $40 billion bid for Arm could be in jeopardy

PUBLISHED WED, AUG 4 20215:29 AM EDT
UPDATED 47 MIN AGO
Sam Shead @SAM_L_SHEAD
CNBC.com

KEY POINTS

-- The deal, one of the biggest semiconductor takeovers ever seen, was announced last September to much fanfare, although competition regulators around the world soon announced plans to investigate the acquisition.

-- Probes were launched in the U.S., the U.K., China and Europe after companies like Qualcomm, Microsoft, Google and Huawei complained that the deal was bad for the semiconductor industry.

-- The U.K. is reportedly considering blocking the deal on national security grounds, while China and Europe’s probes are reportedly subject to delays.

LONDON — Nvidia’s $40 billion bid to buy U.K.-based chip designer Arm from Japan’s SoftBank has started to look increasingly uncertain in recent weeks.

The deal, one of the biggest semiconductor takeovers ever seen, was announced last September to much fanfare, although competition regulators around the world soon announced plans to investigate the acquisition. Probes were launched in the U.S., the U.K., China and Europe after companies like Qualcomm, Microsoft, Google and Huawei complained that the deal was bad for the semiconductor industry.

The U.K. investigation, being led by the Competition and Markets Authority, is also taking national security concerns into account. The CMA submitted its initial report to U.K. Culture Secretary Oliver Dowden on July 20.

The assessment contains worrying implications for national security and the U.K. is currently inclined to reject the takeover, according to a report from Bloomberg on Tuesday, citing an unnamed source familiar with the matter. A separate unnamed source said the U.K. was likely to conduct a deeper review into the merger as a result of national security concerns, Bloomberg reported. CNBC was unable to independently verify the report.

It’s unclear how U.K. national security will be impacted if Arm goes from being Japanese-owned to U.S.- owned but governments have come to view semiconductor technology as a vital asset amid the global chip shortage.

An Nvidia spokesperson told CNBC: “We continue to work through the regulatory process with the U.K. government. We look forward to their questions and expect to resolve any issues they may have.” Arm and the U.K. government did not immediately respond to CNBC’s request for comment.

The deal, which was initially expected to close by March 2022, also risks being held up elsewhere. In June, Chinese antitrust lawyers reportedly told The Financial Times that China’s investigation could take the deal beyond the 18-month window given by Nvidia in Sept. 2020.

Meanwhile, European regulators are thought to be reluctant to consider the case until after the summer holidays, according to a Reuters report published in June that cites people familiar with the matter, who say this could make it difficult for Nvidia to close the deal by March next year.

The purchase agreement gives the two companies the option to extend the deadline to September 2022. But, at that point, either company can walk away if the deal does not receive government approval.

What is Arm?

Cambridge-based Arm sells its chip blueprints and licenses to chip manufacturers around the world; it is viewed as a “neutral player” and is sometimes referred to as the “Switzerland of the chip industry.”

Some of these manufacturers, which compete with Nvidia, are concerned that the Santa Clara-headquartered chip giant could make it harder for them to access Arm’s technology.

Nvidia has repeatedly insisted that it won’t change Arm’s business model and that it will invest heavily in the company to help it meet increasing demand.

Nvidia’s share price does not seem to have been affected following the Bloomberg report. It closed at $198.15 on Tuesday, up almost 1% for the day.

Elsewhere, another semiconductor acquisition is also being scrutinized. U.K. Prime Minister Boris Johnson has ordered the national security adviser, Stephen Longrove, to review the takeover of Newport Wafer Fab, the U.K.’s largest semiconductor wafer manufacturing facility. The company is being acquired by Chinese-owned Nexperia for £63 million ($88 million).

Nvidia’s $40 billion bid for Arm could be in jeopardy (cnbc.com)

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From: Frank Sully8/4/2021 11:12:43 PM
1 Recommendation   of 2381
 
Nvidia stock gains after Rosenblatt price target boost

Aug. 04, 2021 2:32 PM ET
NVIDIA Corporation (NVDA)
by Brandy Betz, SA News Editor

Justin Sullivan/Getty Images News
  • Calling the company a best-in-class artificial intelligence play, Rosenblatt Securities reiterates a Buy rating on Nvidia (NASDAQ: NVDA)and raises the price target from $200 to $250.
  • Analyst Hans Mosesmann makes the move the day after a fireside chat with company management.
  • The analyst notes that Nvidia ( NVDA) also has "growth vectors into next generation networking/DPU adoption and early-days of autonomous driving."
  • Mosesmann thinks the $40B acquisition of SoftBank's Arm chip unit is unlikely to happen, which the Street is slowly realizing, but says the stock "will work nonetheless."
  • NVIDIA ( NVDA) shares are up 2.2% to $202.41.

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From: Frank Sully8/5/2021 1:46:22 AM
   of 2381
 
AutoX Robotaxis Now Using NVIDIA DRIVE, NVIDIA Acquiring DeepMap, & DiDi Booming On NVIDIA DRIVE’s Back

By
Zachary Shahan

Five years ago, in a blogging competition about “what will be the most important technological development over the next 10 years that will have the greatest impact in reducing climate change risks,” I concluded that the answer was robotaxis. If true robotaxis, broadly available and deployed in cities around the world, come to fruition, the potential reduction in emissions is immense. This is assuming they are electrically powered, but that seems most sensible for several reasons — especially by the middle of the decade.

Naturally, many of us think that Tesla is quite far ahead in the development of broadly applicable, cost-competitive robotaxi hardware and firmware. However, it certainly isn’t the only name in town, and there are also many who think that Tesla’s approach cannot lead to true robotaxis. One other tech company you have to keep on the table of possibilities is NVIDIA. Aside from being a tech giant in other realms, one of the advantages NVIDIA has is that it supplies hardware — and increasingly software services — for a bunch of automakers. Also, as the industry has evolved, NVIDIA has looked more seriously at providing integrated, robust technology partnerships with these automakers — not just as a supplier, but as a team working with automakers’ driver-assist or self-driving teams.

With all of that in mind, NVIDIA has rolled out a series of 6 news stories in the past two months related to autonomous driving. In this article, I’m going through 3 of those that relate to the tech giant’s NVIDIA DRIVE solutions. Let’s catch up and check those out.

AutoX Robotaxis in Service NowProbably the biggest story of the batch is that AutoX, a self-driving vehicle startup out of China, has launched its 5th generation robotaxi platform and the platform uses NVIDIA DRIVE. The system’s “automotive-grade GPUs to reach up to 2,200 trillion operations per second (TOPS) of AI compute performance.”

We did cover the rollout of AutoX robotaxis in January, when they launched to the public in Shenzhen, the 5th largest city in China (population over 12 million). It’s a solid testament to NVIDIA that a company with robotaxis on the road just upgraded to the new NVIDIA DRIVE platform. “Safety is key,” said Jianxiong Xiao, founder and CEO of AutoX. “We need higher processing performance for safe and scalable robotaxi operations. With NVIDIA DRIVE, we now have power for more redundancy in a form factor that is automotive grade and more compact.”

Even more impressive that this service is in place in the high-traffic, highly complex streets of Shenzhen. NVIDIA notes, “Safely navigating such chaotic streets requires sensors that can detect obstacles and other road users with the highest levels of accuracy. The Gen5 system relies on 28 automotive-grade camera sensors generating more than 200 million pixels per frame 360-degrees around the car. (For comparison, a single high-definition video frame contains about 2 million pixels.)” Mind blowing. “In addition to cameras, the robotaxi system includes six high-resolution lidar sensors that produce 15 million points per second and surround 4D radar.”

Now, Tesla fans will quickly point out that Tesla recently ditched radar because it basically just got in the way, and that Tesla is working to solve broad, general AI challenges. Nonetheless, let’s not miss the fact that NVIDIA DRIVE is being used in robotaxis that are in service right this moment in one of the largest and most traffic-heavy cities on Earth.

“At the center of the Gen5 system are two NVIDIA Ampere architecture GPUs that deliver 900 TOPS each for a truly level 4 autonomous, production platform. With this unprecedented level of AI compute at the core, Gen5 has enough performance to power ultra complex self-driving DNNs while maintaining the compute headroom for more advanced upgrades.

“This capability makes it possible for the vehicles to react to high-traffic situations — like dozens of motorcycles and scooters cutting in or riding the opposite way at the same time — in real time, and continually improving, learning how to manage new scenarios as they arise.”

See — other systems can learn, too.

AutoX is just getting started, with plans to roll out robotaxis in cities around the world and with large automotive partners like Honda and Stellantis. And NVIDIA is just getting started, as well.

NVIDIA Acquires DeepMap

To further improve its mapping solutions for aforementioned autonomous driving systems, NVIDIA announced in June that it was acquiring DeepMap. Clearly, there’s an implication of deep learning in that name — it’s all AI all the time these days. The summary highlights from that announcement: “DeepMap expected to extend NVIDIA mapping products, scale worldwide map operations and expand NVIDIA’s full-self driving expertise.
NVIDIA is an amazing, world-changing company that shares our vision to accelerate safe autonomy,” said James Wu, co-founder and CEO of DeepMap. “Joining forces with NVIDIA will allow our technology to scale more quickly and benefit more people sooner. We look forward to continuing our journey as part of the NVIDIA team.” DeepMap cofounders James Wu and Mark Wheeler previously worked at Google, Apple, and Baidu, so going back into a tech giant must feel a little bit like going home after getting DeepMap off the ground and acquired by NVIDIA.

What’s so special about DeepMap? Well, we don’t have an insight into the coding (and seeing it wouldn’t help me much anyway), but the key appears to be the crowdsourced data collection from a broad fleet of vehicles, which “lets DeepMap build a high-definition map that’s continuously updated as the car drives.” Naturally, the coding must be good, too. Getting integrated into NVIDIA DRIVE, it will certainly collect a lot more data and benefit from fast-growing deployment.

The acquisition hasn’t closed yet — going through all of the paperwork and lawyers necessary, it’s expected to close this quarter.

DiDi Goes Public, Also Benefiting From NVIDIA DRIVE
DiDi robotaxis, courtesy of DiDi & NVIDIA.

Gigantic Chinese ride-hailing company DiDi just went public about a month ago, raising a ginormous $4.4 billion. Not too shabby, but note that DiDi has nearly 500 million active users across 71 countries and 10,000 cities. NVIDIA took the moment to note that DiDi “is developing its upcoming robotaxi platform on NVIDIA DRIVE AGX Pegasus.”

The question is, who isn’t using NVIDIA DRIVE?

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From: Frank Sully8/5/2021 11:04:57 AM
   of 2381
 
Interview With Murali Gopalakrishna, GM, Robotics @ NVIDIA

05/08/2021

NVIDIA created the Isaac robotics platform, including the Isaac Sim application on the NVIDIA Omniverse platform for simulation and training of robots in virtual environments

For this week’s practitioners series, Analytics India Magazine (AIM) got in touch with Murali Gopalakrishna, Head of Product Management, Autonomous Machines and General Manager for Robotics. He also leads the business development team focusing on robots, drones, industrial IoT and enterprise collaboration products at NVIDIA. In this interview, we discuss in detail the robotics solutions developed by NVIDIA and their significance.

AIM: Can you tell us about how NVIDIA is building robotics solutions to be used at scale?Murali: Robotics algorithms can be mainly classified into (1) sensing/perception, (2) mobility (motion/path planning), and (3) robot control. All these fields are seeing significant innovation in the recent past with AI/Deep Learning playing an important role. With NVIDIA GPU-accelerated AI at the edge computing platforms, manufacturers will be able develop complex algorithms and deploy robotics at scale.

Robots have to sense, plan and act. To develop robots that are autonomous and efficient, developers have to accelerate algorithms for the complete stack. Algorithms such as object detection, pose estimation and depth estimation are used to perceive the environment, create a map of the environment and localise the robot in the environment. Algorithms such as free space segmentation are used for planning the efficient path for the robot, while control algorithms determine the commands for the robot to go on the planned path. Advances in AI and GPU-accelerated computing are making all these algorithms more accurate and faster, creating robots that are more capable and safe.

Ease of use and deployment have made the NVIDIA Jetson platform a logical choice for over half a million developers, researchers, and manufacturers building and deploying robots worldwide. We provide a full suite of tools and SDKs for developers and companies scaling robotics and automation applications:
  • Open source packages for ROS/ROS2 (Human Pose Estimation, Accelerated AprilTags), Docker containers, Cuda library support and more.
  • For training: NVIDIA Transfer Learning Toolkit (TLT) helps reduce costs associated with large scale data collection, labeling, and eliminates the burden of training AI/ML models from the ground up. This enables developers to build and scale production quality pre-trained models faster with no code. Auto Mixed Precision allows developers to train with half precision while maintaining the network accuracy achieved with single precision, enabling significantly faster training time.
  • For real-time inference: NVIDIA TensorRT is a high-performance SDK for deep learning, including a DL inference optimizer and runtime that delivers low latency and high throughput for inference applications. NVIDIA Triton Inference Server simplifies the deployment of AI models at scale in production. It is an open source inference serving software that lets teams deploy trained AI models from any framework on any GPU or CPU-based infrastructure (cloud, data center, or edge).
  • For perception: NVIDIA DeepStream SDK helps developers build and scale AI-powered Intelligent Video Analytics apps and services. DeepStream offers a multi-platform scalable framework with TLS security to deploy on the edge and connect to any cloud.
  • NVIDIA Fleet Command is a hybrid-cloud platform for managing and scaling AI at the edge. From one control plane, anyone with a browser and internet connection can deploy applications, update software over the air, and monitor location health.
  • NVIDIA Jarvis is an application framework for multimodal conversational AI services that delivers real-time performance on GPUs.

AIM: What is the scope of these solutions?

Muralli: Powerful GPU-based AI-at-the-edge computing, along with a full spectrum of sensors, are widely implemented in the field today. Fueled by AI and DL, sensor technologies that power perception for real-time decision making have revolutionised several areas of robotics, including navigation, visual recognition and object manipulation.

Today’s AI-enabled robots perform myriad tasks and functions, allowing them to work as “cobots” in close collaboration with humans in complex environments including warehouses, retail stores, hospitals and industrial environments as well as in our homes. AI and DL continue to play a significant role in the programming of robots, speeding development time for roboticists and helping advance these systems from single functionality to multi functionality.

And there’s no arguing the pandemic accelerated the need and urgency for robotics deployment, especially in healthcare, logistics, manufacturing and retail.
  • Healthcare: To minimise contact and support shortage of staff and resources, robots have found invaluable use in the delivery of medicine/supplies, patient monitoring, medical procedures, temperature detection, and UV disinfectant applications in public and private spaces.
  • Logistics: From pick-n-place to last mile delivery, robots have clearly become indispensable with the ever-increasing need for efficiencies across the supply chain and e-commerce.
  • Manufacturing: Using AI/DL to create the factory-of-the future, leveraging robots and cobots for no-touch manufacturing as well as enabling zero downtime to increase productivity and efficiency.
  • Retail: From cleaning, inventory and safety (temperature detection, mask detection, social distancing) to shelf-scanning and self-checkout, robots are transforming the shopping experience.We have a large customer base in a diverse set of industries like agriculture, manufacturing, healthcare and logistics (e.g., John Deere in agriculture and Komatsu in construction). Most of the last mile delivery robots are using NVIDIA technology (Postmates, JD-X , Cianio, etc.)

AIM: Tell us about NVIDIA Isaac Sim.Murali: NVIDIA created the Isaac robotics platform, including the Isaac Sim application on the NVIDIA Omniverse platform for simulation and training of robots in virtual environments before deploying them in the real world. NVIDIA Omniverse is the underlying foundation for all our simulators, including the Isaac platform. We’ve added many features in our latest Isaac Sim open beta release including ROS/ROS2 compatibility and multi camera support, as well as enhanced synthetic data generation and domain randomization capabilities which are important for generating datasets to train perception models for AI based robots.

Simulation technology like Isaac Sim on Omniverse can be used for every aspect: from design and development of the mechanical robot, then training the robot in navigation and behavior, to deploying in a “digital twin” in which the robot is simulated and tested in an accurate and photorealistic environment before deployed in the real world.

AIM: What are the current challenges and what does the future hold for robotics?

Muralli: One of the most interesting areas of development is cobots, which can be deployed in areas where robots have not been used thus far. Traditionally, the use of robots on factory floors posed safety risks and were deemed too dangerous to work alongside humans, and therefore these machines were typically placed in isolated environments or caged. Enter cobots. Though designed to work in close proximity with humans, cobots faced several challenges like limited capabilities and inability to think, putting a damper on their widespread adoption.

But now, thanks to advancements in AI, which brings intelligence to cobots, we’re seeing these systems make real-time decisions that ensure safety in the factory-of-the future, while maintaining and optimizing productivity. This includes training a cobot to perceive the environment around it and adapt accordingly — allowing it to reduce its speed, adjust its force/strength, detect changing working conditions, or even shut down safely before it interferes with a human in its proximity. By leveraging the power of AI, coupled with changes in cobot design (softer materials, new types of joints, removal of sharp edges, etc.), we’re seeing the emergence of applications and use cases that were not previously feasible (e.g., robots in commercial kitchens, etc.)

Robots are being taught what to do, and how to improve upon complex tasks, as quickly as within a few hours or overnight (versus what used to take weeks or even months)! AI techniques such as one-shot learning, transfer learning, imitation learning, reinforcement learning, etc. are no longer confined to research papers; many of these methods are in practical use today for real-world robotics deployments.

AIM: How do you see the Robotics landscape evolving in India?

Muralli: Manufacturing is increasingly reliant on robotic production. For example, the automotive industry. Our collaboration with BMW for example, begins with creating a digital twin of a future factory in Omniverse and laying out the entire robotic managed production lines digitally, before committing to physical construction. Other industries benefiting from robotics are the industrial & nuclear power sectors. For example, warehouse and inventory management, materials transportation, quality inspection and predictive maintenance in the former & internal reactor inspection and emergency response in the latter.

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From: Frank Sully8/6/2021 9:07:18 AM
   of 2381
 
Blue Whale Growth adds Nvidia to top-10 06 August 2021

Lead manager Stephen Yiu shares more insight into why he recently added semiconductor firm Nvidia to the £1bn LF Blue Whale Growth fund.

By Abraham Darwyne,
Senior reporter, Trustnet

trustnet.com

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From: Frank Sully8/6/2021 5:51:06 PM
   of 2381
 
Nvidia has absolutely trounced AMD in this vital chip area

By Sead Fadilpašic about 7 hours ago

AI is the future and Nvidia knows it

Despite the competition coming in droves, Nvidia has managed to maintain its leading position in the global market for Artificial Intelligence (AI) chips used in cloudand data centers.

Nvidia has also managed to maintain a large gap between itself and the rest, according to a new report from technology research firm Omdia, which claims that it took 80.6% of the market share of global revenue in 2020.

Last year, the company generated $3.2 billion in revenue, up from $1.8 billion the year before. The bulk of its earnings came from GPU-derived chips, for which Omdia says are the leading type of AI processors used in cloud and data center equipment.

Whether or not Nvidia keeps its dominant position in the future remains to be seen, as Omdia expects the market for AI processors to quickly grow and attract many new suppliers. Global market revenue for cloud and data center AI processors rose 79% last year, hitting $4 billion.

By the time we reach 2026, the company expects revenue to increase ninefold, to $37.6 billion.

For Jonathan Cassell, principal analyst, advanced computing, at Omdia, one advantage Nvidia has over the competition is its familiarity among the clients.

“NVIDIA’s Compute Unified Device Architecture (CUDA) Toolkit is used nearly universally by the AI software development community, giving the company’s GPU-derived chips a huge advantage in the market," he noted.

"However, Omdia predicts that other chip suppliers will gain significant market share in the coming years as market acceptance increases for alternative GPU-based chips and other types of AI processors.”

Growing competition

Omdia sees Xilinx, Google, Intel and AMD as the biggest contenders for at least a larger piece of the AI pie. Xilinx offers field-programmable gate array FPGA products, Google’s Tensor Processing Unit (TPU) AI ASIC is employed extensively in its own hyperscale cloud operations, while Intel’s racehorse comes in the form of its Habana AI proprietary-core AI ASSPs and its FPGA products for AI cloud and data center servers.

AMD, currently ranked fifth, offers GPU-derived AI ASSPs for cloud and data center servers.

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From: Frank Sully8/7/2021 1:28:32 PM
   of 2381
 
NVIDIA, Qualcomm Will Use TSMC’s 5nm And 3nm Nodes For 2022 – Pokde.Net

QUALCOMM
By Financial News On Aug 7, 2021



TSMC is reportedly looking at another great year in 2022, with both NVIDIA and Qualcomm returning to TSMC’s advanced processes. We recently saw both NVIDIA and Qualcomm make the jump to Samsung, with NVIDIA’s Ampere GPUs and also Qualcomm’s flagship Snapdragon 888 manufactured by Samsung.

Business is apparently going so well at TSMC that not only have they fully booked their existing 7nm and 5nm nodes, but the production capacity for the upcoming 3nm node is also completely exhausted. TSMC recently increased the target capacity for their 5nm and 3nm nodes, with the queue for their 7nm process also full.

According to the report, TSMC is looking quite optimistic, with large orders from Apple, Qualcomm, NVIDIA and even Intel. They are also expanding their capacity with fabs being set up in several countries outside of Taiwan. Their main competition would remain Samsung, but apparently the Korean foundry is not so favorable.

Reports have suggested that Samsung’s advanced process nodes are rather lackluster in terms of yield and stability. But as we’ve always emphasized, if the price is right, you’d definitely see chipmakers using Samsung’s foundries. Samsung has traditionally offered a cost advantage, but the recent price hike on their part could mitigate this somewhat.

fishinvestment.com

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