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 &
Niharika Sharma is a Senior Software Engineer for Nasdaq’s Machine Intelligence Lab. She designs systems that gather, process and apply machine learning/natural language processing technologies on natural language data, generating valuable insights to support business decisions. Over the past years, she worked on Natural Language Generation (NLG) and Surveillance Automation for Nasdaq Advisory Services. We sat down with Niharika to learn more about how she got her start in computer science and how she approaches challenges in her career.
Can you describe your day-to-day as a senior software engineer at Nasdaq?
My day-to-day work involves collaborating with Data Scientists to solve problems, ideating business possibilities with product teams and working with Data/Software Engineers to transform ideas into solutions.
How did you become involved in the technology industry, and how has technology influenced your role?
My first exposure to Computer Science was a Logo programming class that I took as a
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 Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.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
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.”
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.
Bringing a new robot to market is exciting: new capability, new hardware, new services. The problem is when you get to software, where everything feels harder and takes longer than you think it should. Like Tesla’s full self-driving, which has all the hardware and intelligence it needs — with the possible exception of LIDAR — but is perpetually just … about … to … arrive … and even so, was recently savaged by Consumer Reports as buggy and ineffective.
Hardware is necessary, but software provides the animating intelligence that allows it to do useful, efficient, and safe work.
That’s why Nader Elm, CEO of autonomous drone company Exyn Technologies, compared robots today to the iPhone before the App Store: the hardware’s there, but the software layer is immature.
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
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
There are sizable, meaningful gaps in the knowledge collection and publication of podcast listening and engagement statistics. Coupled with still-developing advertising technology because of the distributed nature of the medium, this causes uncertainty in user consumption and ad exposure and impact. There is also a lot of misinformation and misconception about the challenges marketers face in these channels.
All of this compounds to delay ad revenue growth for creators, publishers and networks by inhibiting new and scaling advertising investment, resulting in lost opportunity among all parties invested in the channel. There’s a viable opportunity for a collective of industry professionals to collaborate on a solution for unified, free reporting, or a new business venture that collects and publishes more comprehensive data that ultimately promotes growth for podcast advertising.
Podcasts have always had challenges when it comes to the analytics behind distribution, consumption and conversion. For an industry projected to
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).