C3 AI launching a technology licensing office after getting broad AI software Patent

Patent License Agreement

Enterprise AI software platform vendor C3 AI will begin licensing its model-driven architecture and other technologies later this year after receiving a broad, new “omnibus” U.S. patent for its flagship product, the C3 AI Suite.

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Fuji joins Investors backing AI Robotic startups

Robot Investing

The integration of AI with robotics applications continues to attract tech investors who presumably sense that machine learning tools can help transform static industrial robots into agile assemblers of different products in varying volumes. The latest example is a collaboration between […]

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Deloitte’s upcoming Center for AI Computing aims to help customers grow AI use

Cable Board

With AI use continuing to grow in adoption throughout enterprise IT, Deloitte is creating a new Deloitte Center for AI Computing to advise its customers, explain the technology and help them use it in their ongoing business and growth plans. Designed to provide a cloud-accessible accelerated platform that Deloitte clients can […]

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Bosch partners with Fetch.ai to ‘transform’ digital ecosystems using DLTs

Hand sander

Bosch has partnered with Cambridge-based AI blockchain startup Fetch.ai with the aim of transforming existing digital ecosystems using distributed ledger technologies (DLTs). The global engineering giant will test key features of Fetch.ai’s testnet until the end of this month and will deploy a node on the network. The strategic engineering project between Fetch.ai and Bosch…

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Peak expands access to AI ‘Decision Intelligence’

AI Decision Intelligence

AI deployments are steadily migrating to retail applications as commercial brands seek to get a better handle on supply chains along with marketing and sales.
Increased demand for embedded AI software used to guide those decisions is on the rise, benefitting early movers offering automation tools designed to inform decisions on logistics and marketing. Among them is U.K.-based Peak AI, which this week announced a $21 million Series B venture round, bringing its total investor funding to $43 million.

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Scaling for Robot Intelligence

Robot intelligence

RIOS CorporationJust now·3 min readBy Matt ShafferTechnologically, the last 30 years or so have been shaped by advancements in computation, and the ability to build machines that can make decisions independent of human operators is a direct result of this progress. With the growing global demand for machines that can perform labor, intelligent automation will bring about the real changes needed to deliver at scale. Though historically, robotic systems with embedded intelligence are inherently more difficult to build with reliability because they operate in the real world — a world with less regularity and more unpredictable consequences than the carefully-designed frameworks of the digital world. Given the challenges, it is not surprising to consider that factory automation is still largely driven by human workers who perform tasks that are often repetitive, but difficult to automate.Machine learning is most effective at scale, where the experiences of many systems can be aggregated.Automation is non-trivial, but it is not due to the fact that research cannot solve a lot of these problems — but that it only became a possibility more recently. There are certainly quite a few reasons for this, some of which have to do with the hardware and computational advancements, and others that revolve around data. But there is another interesting theory going around that is articulated by Sara Hooker in “The Hardware Lottery”. She postulated that research directions in the field of machine learning are often explored due to software and hardware available at the time, rather than being motivated by the most promising ideas. This theory is aligned with our premise at RIOS that advancing the capabilities of robots is heavily dependent on both specialized hardware and software that must coevolve.Robots in the real world have traditionally been programmed in isolation on a single task, rather than leveraging collective knowledge as in simulated environments..Today, we are reaching an inflection point, and there is a monumental opportunity to develop custom hardware and software systems that enable robots to take on increasingly open-ended tasks without the need of reprogramming for each new instruction. We can do this by taking the lessons of the internet to apply data at scale to robotics. By strategically designing systems with the intent of learning from them, and building the infrastructure to support information sharing, we can adapt more quickly to new tasks and master the ones we are already familiar with. The real promise of applying machine learning to robotics is not teaching a single robot to learn for itself, but to aggregate experience from a vast network of robots so that they can improve at scale.A core tenet of what we do at RIOS is to build machines with this idea in mind. Like hardware, skills and behaviors should be transferable across platforms when possible, and each deployed system should be able to share what it has learned with other systems. At a high level, you can think of this as storing knowledge rather than just data to reduce the need for retraining. The result is a class of robots that can do a variety of tasks and address new challenges with less development time. By building distributed robots that continuously learn from both their environment and the collective experience of others, we can help push intelligent robotics forward at scale much in the same way that the information economy benefited from the web.The next generation of technological progress is starting to favor organizations that can rapidly assemble the best technologies of the web-era and use them to take fields like robotics in new directions. In many parts of the world, where labor shortages exist or workers are subjected to poor conditions, this couldn’t come at a better time. Moreover, what used to be a long lead-time in deploying new systems or developing solutions is disappearing as robots can reuse not just hardware, but prior knowledge when taking on new tasks. As more robots fill empty roles in factories, we’ll start wonder how we lived without them, and eventually forget they are doing the most thankless of work for us without any complaints.Matt Shaffer is the RIOS Director of Artificial Intelligence and is the architect behind the brain of our robots. This article is a shortened version of Matt’s article Scaling Artificial Intelligence for Robotics in 2021.

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Sunlight’s AI Hypervisor gains Nvidia GPU support to boost Edge deployments

Edge Computing

Sunlight, the U.K.-based specialist in virtualizing data-intensive applications, announced Nvidia GPU support for its “lightweight” hypervisor designed to boost the performance of edge AI deployments. GPU support for its NexVisor platform would…

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Fetch.ai partners with FESTO on decentralised manufacturing marketplace

Festo.ai

AI blockchain startup Fetch.ai is partnering with industry veteran FESTO to launch a decentralised marketplace for manufacturing. Fetch.ai is based in Cambridge, UK and has built an impressive team of talent with experience from DeepMind, Siemens, Sony, and a number of esteemed academic institutions. The company is working on decentralised autonomous “agents” which perform real-world…

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New Open AI Energy initiative launches to expand AI Use in Energy Industry

Energy wave over rooftop

Shell, C3.ai, Microsoft and Baker Hughes are collaborating on an Open AI Energy Initiative (OAI) that aims to grow AI use across the energy and process manufacturing industries. The OAI is envisioned by the partners as an open ecosystem of AI technologies that will provide a framework for energy operators, service providers, equipment providers and the software vendors who serve them to create new AI and physics-based models, monitoring, diagnostics and more to help solve critical industry needs, according to the group.

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How enterprise AI use will grow in 2021: Predictions from our AI experts

Woman in collage

Among IT executives, leaders, experts and supporters in the field of AI, the opinions and insights vary widely. In this AI 2021 predictions roundup for EnterpriseAI readers, we have gathered the thoughts and comments from a sampling of our experts who shared their thoughts with us on the AI marketplace and coming innovations over the next year. Their conversations are intriguing.

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AI Virtualization and Orchestration Startup Run: AI Captures $30M in Series B Funding

AI Stack of the Future

Upstart AI vendor Run:AI dove into AI by creating Kubernetes-based software to help customers get more production out of their existing AI infrastructure investments. Run:AI’s software is an orchestration and virtualization layer that pools together compute resources so they can be instantly allocated on demand as needed.

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U.S. leads world in AI, but here comes China

USA China Race

The United States is maintaining its early lead over China and the European Union in development and application of artificial intelligence technology, according to a new report from the Information Technology and Innovation Foundation (ITIF). But while the Americans’ lead is still substantial today, China is poised to close the gap, while the EU is largely failing to keep up, the report found.

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Nvidia unveils Certified Server Program offering Pre-Built Servers for AI applications

Nvidia Certified Support

Nvidia today launched a certified systems program in which participating vendors can offer Nvidia-certified servers with up to eight A100 GPUs. Separate support contracts directly from Nvidia for the certified systems are also available. Besides the obvious marketing motives, Nvidia says the pre-tested systems and contract support should boost confidence and ease deployment for those taking the AI plunge. Nvidia-certified systems would be able to run Nvidia’s NGC catalog of AI workflows and tools.

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Honing In on AI, U.S. Launches National Artificial Intelligence Initiative Office

To drive American leadership in the field of AI into the future, the National Artificial Intelligence Initiative Office has been launched by the White House Office of Science and Technology Policy (OSTP). The new agency was established under the American Artificial Intelligence Initiative Act of 2020, which was enacted and codified into law to expand many existing AI policies and initiatives throughout the federal government.

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$400M in C2 funding raised by automotive AI Chip vendor Horizon Robotics

Horizon Robotics, a Chinese automotive AI chip startup that is developing specialized, complex chips for use in autonomous vehicles, has raised another $400 million in its latest C2 round of funding. That’s on top of $150 million in C2 funding that it secured earlier in December, according to the company, bringing its total C2 funding so far to $550 million. Horizon Robotics had announced late in 2020 that it was pursuing a total of $700 million in C2 round funding.

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AI to transform Healthcare of the future in the shadow of COVID-19

AI technologies are already in place in many retail stores and other industries, but a July 2020 study by chipmaker Intel Corp. found that 84% of the respondents — made up of 234 senior decision-makers inside U.S. healthcare organizations — have already deployed or expect to deploy AI within their healthcare operations. That’s up from a previous Intel study in 2018, when a little more than one-third of the respondents said they were using or planning to implement AI.

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China’s adaptive robot maker Flexiv raises over $100 million

As businesses around the world look to automate production lines and supply chains, companies making the robots are attracting great investor interest. The latest to get funded is Flexiv, which closed a Series B round north of $100 million from investors including China’s on-demand services giant Meituan.

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Robotic Vision startup Akasha Imaging aims to improve Industrial Manufacturing with AI

With $10.75 million in Series A funding arriving earlier this month, AI robotic vision startup Akasha Imaging is now bulking up its team to ready itself to deliver its nascent AI vision technology to industrial manufacturers by mid-2021.

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AI: Fact versus Fiction

AI adoption inside U.S. companies  is soaring, with a recent PwC study concluding that 86 percent of 1,032 IT respondents believe that AI will be a mainstream technology for their companies in 2021. The problem, though, is that under closer inspection, some AI is nothing more than marketing hype once you look under the hood. For CIOs looking to integrate AI, what they need is a way to evaluate if an AI product or service is actually harnessing the power of AI– or if it’s a mirage.

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