Artificial Intelligence Market by Technology, Hardware, End-user Industry, and Region – Global Forecast to 2026

DUBLIN–(BUSINESS WIRE)–Oct 14, 2020–

The “Artificial Intelligence (Chipsets) Market by Technology (Machine learning, Natural Language Processing, Context Aware Computing, Computer Vision), Hardware (Processor, Memory, Network), End-User Industry, and Region – Global Forecast to 2026” report has been added to ResearchAndMarkets.com’s offering.

The AI (chipsets) market is expected to be valued at USD 7.3 billion in 2020 and is likely to reach USD 57.8 billion by 2026, at a CAGR of 40.1% during the forecast period.

Major drivers for the market are increasingly large and complex datasets driving the need for AI, the adoption of AI for improving consumer services & reducing operational costs, the growing number of AI applications, the improving computing power, and growing adoption of deep learning and neural networks.

The major restraint for the market is the lack of a skilled workforce. Critical challenges facing the AI (chipsets) market include low return on investment, creating models &

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Creating fuel from thin air with artificial leaves

Device
Artificial leaves could one day provide fuel

The sun produces more than enough energy for human activities, but we still can’t capture enough of it, points out Erwin Reisner, energy and sustainability professor at Cambridge University.

He heads a team of researchers trying to capture more of that free energy.

While solar panels have made big advances in recent years, becoming cheaper and more efficient, they just provide electricity, not storable liquid fuels, which are still in great demand.

“If you look at the global energy portfolio and what’s needed, electricity only covers maybe 20-25%. So the question is when we have covered that 25%, what do we do next?” asks Prof Reisner.

His answer is to look to nature: “Plants are a huge inspiration, because they have learned over millions of years how to take up sunlight and store the energy in energy carriers.

“I really believe that artificial

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The Next Generation Of Artificial Intelligence

The field of artificial intelligence moves fast. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless.

If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

What will the next generation of artificial intelligence look like? Which novel AI approaches will unlock currently unimaginable possibilities in technology and business? This article highlights three emerging areas within AI that are poised to redefine the field—and society—in the

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SparkCognition Advances the Science of Artificial Intelligence with 85 Patents

AUSTIN, Texas, Oct. 12, 2020 /PRNewswire/ — SparkCognition, the world’s leading industrial artificial intelligence (AI) company, is pleased to announce significant progress in its efforts to develop state of the art AI algorithms and systems, through the award of a substantial number of new patents. Since January 1, 2020, SparkCognition has filed 29 new patents, expanding the company’s intellectual property portfolio to 27 awarded patents and 58 pending applications.

“Since SparkCognition’s inception, we have placed a major emphasis on advancing the science of AI through research – making advancement through innovation a core company value,” said Amir Husain, founder and CEO of SparkCognition, and a prolific inventor with over 30 patents. “At SparkCognition, we’ve built one of the leading Industrial AI research teams in the world. The discoveries made and the new paths blazed by our incredibly talented researchers and scientists will be essential to the future.”

SparkCognition’s patents

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10 Essential Leadership Qualities For The Age Of Artificial Intelligence

AI and automation will change the very nature of work. It’s really important that leaders don’t ignore this AI- and data-driven revolution – what I call the “intelligence revolution” – or allow other leaders in the organization to ignore it. Working out how to use AI, dealing with people-related challenges, avoiding the ethical pitfalls of AI, making sure you have the right technology in place, and so on – all are key considerations for the business leaders of today and tomorrow.

This technology revolution will change what it means to be a good leader. It makes sense, then, that business leaders in the intelligence revolution will need to adapt. The way we run businesses will change, and the successful leaders of the future will need a slightly different skillset from the traditional skills associated with leaders.

What sort

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Artificial Intelligence Technology Solutions Announces Regulation Crowdfunding for Robotic Assistance Devices Mobile Subsidiary

Artificial Intelligence Technology Solutions, Inc., (OTCPK:AITX), is pleased to announce that its majority owned subsidiary Robotic Assistance Devices Mobile, Inc. (RAD-M) has begun a short private placement offering in accordance with Regulation Crowdfunding (Reg. CF) adopted by the U.S. Securities and Exchange Commission (SEC) through TruCrowd. Full details can be found here: https://us.trucrowd.com/equity/offer-summary/RAD-M.

RAD-M announces the launch of its dedicated website https://investinradm.com/ that identifies that letters of intent for pre-orders worth more than $16 million in total potential revenue of ROAMEO units have been received from a variety of dealers and end users including Fortune 500 companies.

“I believe this is a great opportunity for a wide variety of investors and enthusiasts to join the #RADArmy as we continue to define the Autonomous Remote Services industry that we expect will join manned guarding and physical security as a multi-billion dollar industry,” said Steve Reinharz, Founder and President of RAD-M. “End

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Researchers use artificial intelligence language tools to decode molecular movements

UMD researchers use artificial intelligence language tools to decode molecular movements
Scientists from the University of Maryland applied a language processing system to the movements of a riboswitch molecule pictured here, to understand how and when the molecule takes different forms. Credit: Zachary Smith/UMD

By applying natural language processing tools to the movements of protein molecules, University of Maryland scientists created an abstract language that describes the multiple shapes a protein molecule can take and how and when it transitions from one shape to another.


A protein molecule’s function is often determined by its shape and structure, so understanding the dynamics that control shape and structure can open a door to understanding everything from how a protein works to the causes of disease and the best way to design targeted drug therapies. This is the first time a machine learning algorithm has been applied to biomolecular dynamics in this way, and the method’s success provides insights that can also help advance

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Silk fibers improve bioink for 3D-printed artificial tissues and organs — ScienceDaily

How do you test, in early-stage research, whether a potential pharmaceutical effectively targets a human tumor, organ, or some other part of the body? How do you grow a new hand or some other body part? Researchers are in the early stages of using 3D cell printing technology to make developments like these happen. A standard way — currently unavailable — to fix the cells in place after printing would help researchers avoid having to ‘reinvent the wheel’ in every new investigation.

In a study recently published in Materials Today Bio, researchers from Osaka University have used silk nanofibers obtained by mechanical disintegration to enhance the printing process without damaging the cells or cell assemblies. An attractive point of silk for this application is that silk is believed to be a safe material for humans. This development will help bring 3D cell printing research out of the laboratory and

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Applying artificial intelligence to science education

science
Credit: CC0 Public Domain

A new review published in the Journal of Research in Science Teaching highlights the potential of machine learning—a subset of artificial intelligence—in science education. Although the authors initiated their review before the COVID-19 outbreak, the pandemic highlights the need to examine cutting-edge digital technologies as we re-think the future of teaching and learning.

Based on a review of 47 studies, investigators developed a framework to conceptualize machine learning applications in science assessment. The article aims to examine how machine learning has revolutionized the capacity of science assessment in terms of tapping into complex constructs, improving assessment functionality, and facilitating scoring automaticity.

Based on their investigation, the researchers identified various ways in which machine learning has transformed traditional science assessment, as well as anticipated impacts that it will likely have in the future (such as providing personalized science learning and changing the process of educational decision-making).

“Machine

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Smartphone Tracking Data And Artificial Intelligence Turn People’s Movements Into Detailed Insights And Profits

Not all businesses experienced a setback due to COVID-19. Cosmose AI, a company that uses machine learning to predict who will go shopping as well as when and where, plus measures the effectiveness of online ads to online and in-person store visits, expanded during the pandemic. Valued at $100 million after a Series A investment round by Tiga Investments, OTB Ventures, and TDJ Pitango, many retailers turned to the insights provided by Cosmose AI’s artificial intelligence-powered service to figure out how to best operate during the pandemic and prepare for a new future.

Insights for Retailers from Cosmose AI’s AI-Powered Platform

Founded in 2014, Cosmose AI gathers anonymized mobile phone data including user IDs, location info, and more from more than 1 billion smartphones, more than 400,000 apps, 360,000 stores and then

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