DUBLIN--(BUSINESS WIRE)--The "Global Mobile Artificial Intelligence (AI) Market (2021-2026) by Technology Node, Product, and Application and Geography, Competitive Analysis and the Impact of Covid-19 with Ansoff Analysis" report has been added to ResearchAndMarkets.com's offering.

The Global Mobile Artificial Intelligence Market is estimated to be USD 7.2 Bn in 2021 and is expected to reach USD 22.08 Bn by 2026, growing at a CAGR of 25.12%.

Artificial Intelligence and Machine Learning are considered a part of the day-to-day operations of huge enterprises in various disciplines.

The worldwide mobile artificial intelligence (AI) market is expected to rise significantly during the projected period. Factors such as growing demands for artificial intelligence (AI) in smartphones, rising need for AI-capable processors, and rising investment in AI technologies fuel the growth of the mobile artificial intelligence (AI) market. Artificial intelligence technology is widely employed in various ground-breaking applications in manufacturing, automotive, and video games.

AI technology is now used in the smartphone business, and it extends well beyond applications such as digital assistants. Several smartphone makers are striving to create AI capabilities that meet the expectations of their customers. For the worldwide mobile artificial intelligence (AI) industry, more investment in inexpensive AI processors and R&D dedicated to camera applications is an opportunity.

The high cost of artificial processors, on the other hand, is a key stumbling block to the worldwide mobile artificial intelligence (AI) market's development.

The market is segmented further based on Technology Node, Product, and Application and Geography.

Countries Studied

Competitive Quadrant

The report includes a Competitive Quadrant, a proprietary tool to analyze and evaluate the position of companies based on their Industry Position score and Market Performance score. The tool uses various factors for categorizing the players into four categories. Some of these factors considered for analysis are financial performance over the last 3 years, growth strategies, innovation score, new product launches, investments, growth in market share, etc.

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Market Dynamics

Drivers

Restraints

Opportunities

Challenges

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/cqjyuu

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Global Mobile Artificial Intelligence Market (2021 to 2026) - by Technology Node, Product, and Application and Geography - ResearchAndMarkets.com -...

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Prosus (OTCPK:PROSF) reports a significant amount of cash in hand to acquire new targets. The company is using artificial intelligence capabilities to offer more personalized recommendations to users. Besides, PROSF's food delivery is increasing its offering by delivering groceries. With double-digit revenue growth and growing free cash flow, PROSF's DCF model results in a valuation of $155 per share. In my view, the current market price is a joke, and I will be buying shares.

Prosus is a holding that is investing in high-growth markets like online food delivery, fintech, and classifieds. Management finds target markets that are growing at a double-digit rate. Most of the business segments operated by Prosus report sales growth between 32% and 145%:

Source: IR

I believe that most analysts will be expecting significant revenue growth in the coming years. Note that the company has reported 53% sales growth y/y, and management is targeting an internal rate of return close to 22%.

I cannot really say whether Prosus will deliver the same results in the coming years. However, I believe that it is quite likely that sales growth continues at a double-digit rate. This is also the belief of many investment analysts.

Source: IR

I am also quite optimistic about the future transaction executed by Prosus. Note that the company makes on average close to $2.6 billion via acquisitions and investments. Given the increase in the net asset value of the portfolio, I believe that management is quite skilled in the M&A market. It knows how to identify growth as well as to sell companies at a decent multiple. In the future, I would expect this know-how to enhance the portfolio growth:

Source: IR

As of September 30, 2021, Prosus reported $6 billion in cash and $7 billion in short-term investments. I believe that the company has sufficient liquidity and financial shape to acquire new targets or make new investments.

Management also reported $3.8 billion in goodwill, which I appreciate for two reasons. First, it means that management has expertise in the acquisition of targets. We could expect more acquisitions in the future. Second, if management has successfully calculated the operating synergies, we could be expecting revenue and FCF growth.

Source: Quarterly report

I am not concerned about the current amount of leverage. The company reports financial debt of close to $10.5 billion, but the net debt is negative:

Source: Quarterly Report

I believe that Prosus will make tons of money once the company's food delivery business segment increases its offering. If the company is successful in bringing groceries to clients, the target market will most likely significantly increase:

Online grocery presents a large growth opportunity, where structural category dynamics are attractive (high frequency and average order value) but online penetration is low compared to other ecommerce categories. We have seen a significant switch over the past year, with the market's transition to online accelerated by the pandemic.

We will continue to grow our core food-delivery markets and build adjacencies - local food-service brands, grocery and convenience delivery, and more. Source: Annual Report

Regarding the company's classifieds business segment, I am quite optimistic about the new artificial intelligence training offered by Prosus. Management noted in the annual report that the company's machine learning capabilities enhanced personalized recommendations to users. In my opinion, if investments in AI technology continue, and it is also used in other companies owned by Prosus, revenue growth could be substantial:

During the year, our ML models had a substantial impact on our search, lead qualification, and trust and safety initiatives. This has enabled us to advance further in becoming a smart, convenient and trusted way for people to make big and small life choices. Personalised recommendations using item2vec technology enable our products to make 'smart' alternative suggestions to our users. Accurate job recommendations, as well as online price valuation in the motors category, are prime examples of offering transparency and peace of mind to our customers. Finally, automated content moderation keeps our platforms safe and trusted. Source: Annual Report

I made several assumptions about the future revenue breakdown. In 2022, I believe that the most relevant business segments will be online classifieds with 31% of total revenue, food delivery with 29% of the total amount of sales, payments, and fintech. I will also assume 2022 sales of $7.5 billion:

My Figures

I studied the growth of each target market to design my first DCF model. I assumed that the classifieds business segment will grow at a CAGR of 29% because the e-commerce market is expected to grow at that rate:

Global E-Commerce Market 2021-2025 The analyst has been monitoring the e-commerce market and it is poised to grow by $ 10. 87 trillion during 2021-2025, progressing at a CAGR of almost 29% during the forecast period. Source: The Global E-Commerce Market

I also assumed that the food delivery would grow at a CAGR of 10%, and the fintech's growth would stay close to 27.5%:

The global online food delivery services market size is expected to grow from $115.07 billion in 2020 to $126.91 billion in 2021 at a compound annual growth rate (CAGR) of 10.3%. Source: Online Food Delivery Services Market Growth To Accelerate

The "Global FinTech Market, By Technology, By Service, By Application, By Region, Competition Forecast & Opportunities, 2026" report has been added to ResearchAndMarkets.com's offering. The Global FinTech Market was valued at USD7301.78 billion in 2020 and is projected to grow at a CAGR of 26.87% during the forecast period. Source: Global FinTech Market Report 2021: Market was Valued at (globenewswire.com)

My results include 2032 sales of close to $58.85 billion, and FCF margin of close to 23%. I believe that my figures are close to the expectations of other analysts out there:

My Figures

Using the previous sales figures, I assumed an EBITDA margin of 5%-10%, which I believe is conservative in the tech sectors in which Prosus invests. I also added depreciation and amortization of $350-$600 million, and capital expenditures worth $200-$320 million. Finally, the resulting FCF/Sales would stand at 0.5%-7.5%, and 2032 FCF would stay close to $4.25 billion:

My Figures

In this case, including some of the associates in which Prosus invested is very relevant. The company owns 28% of Tencent (OTCPK:TCEHY), and 25.7% of VK (OTC:MLRYY). In total, I believe that these two investments are worth $167.814 billion:

Ycharts

Source: Vk

I took a look at some of the competitors and their EV/EBITDA multiples. Peers trade at 10x-41x FCF. However, I believe that Prosus could trade at much more than its peers because it holds investments in companies that are very young. Their business history will most likely be longer than that of competitors, so using a large EV/FCF makes sense:

Putting everything together, I used a WACC of 5%, which implied a 2032 terminal value of $205 billion, and a net present value of $9.75 billion. If we sum the stakes in Tencent and VK and divide by 15 billion shares, the target price stands at $25:

Source: My Results

In the best-case scenario, I will be assuming that Prosus will buy and invest in impressively profitable companies. Notice that Prosus usually targets an internal rate of return of 20% for early-stage partnerships. So, in this case, I would be expecting many of such successful deals:

For early-stage partnerships in new or high-growth markets, the risk profile is clearly high. In these cases, we look for venture-style IRRs well over 20%, understanding that operational success will determine the outcome. Source: Annual Report

I would also expect mergers among the company's owned by Prosus. Think about it. Prosus has many companies operating in the same business segment. In my view, if management decides to merge some teams, shareholders may benefit from certain operating synergies. As a result, we could see an increase in the FCF margins:

Source: Prosus

I would also include an increase in the company's net income thanks to transactions, sales, and other disposals. The company sells and buys companies on a daily basis. I am not thinking out of the box by assuming that more transaction could be very beneficial for Prosus. In this regard, let me mention one example of a transaction executed by Prosus. It is the merger between OLX and OfferUp, which resulted in a net gain of $114.8 million.

In July 2020, OLX merged its US letgo business with OfferUp, two of America's most popular apps to buy and sell in the US. OLX contributed its US letgo business. The total consideration was US$360.0m, including cash of US$100.0m. On disposal of the US letgo business, the group recognised a gain of US$114.8m in 'Net gains on acquisitions and disposals'. Source: Annual Report

Under this particular and a bit unlikely case scenario, I assumed that the classifieds business segment would grow at 35% y/y. I also believe that with sufficient M&A transactions, food delivery sales could reach 15% sales growth, and the fintech sales growth could reach 27.5%. In sum, 2032 sales would be equal to $75-$85 billion:

My Figures

I assumed an EBITDA margin of 10%, which is lower than the EBITDA margin reported by most competitors. With this figure, I obtained 2032 EBITDA of $7.5-$8.5 billion.

Source: Ycharts

If we also assume an EBIT margin of 8.5%, conservative D&A, capex, and changes in working capital, 2032 FCF would stand at $5.75 million:

My Figures

With the previous financial figures, I would expect significant optimism about Prosus. The cost of equity would most likely decline, so the WACC would be lower than that in the previous case scenario. My results include an implied market capitalization of $470-$475 billion, and an implied share price of $30:

My Results

Prosus invests mostly in online platforms, which may suffer considerable damage from new data regulations. As a result, the company may have to use more resources to protect the data of employees and clients. In this case scenario, I would expect a significant decline in the company's FCF margins:

Most of our businesses are subject to extensive laws and regulations: legal or regulatory developments, including changes in tax laws, may have an adverse impact on our businesses. A number of new laws and regulations around consumer protection and privacy have been passed globally. Source: Annual Report

In the past, management found investment opportunities at beneficial EV/FCF multiples. The future may not be that sweet. If the company does not identify attractive opportunities for whatever reason, the IRR would most likely decline. As a result, traders may sell shares, which may lead to an increase in the cost of equity and the WACC. In sum, we may see a significant decline in the company's valuation:

We may not find investment opportunities that fit our strategy and deliver an expected return more than our cost of capital. Portfolio risk may prove to be higher than we assumed to accept, which could negatively impact IRR and lead to a decline in the valuation of Prosus and/or Naspers. Source: Annual Report

Another clear risk comes from miscalculations of the valuation. If management pays too much for its acquired companies, accountants may have to impair the goodwill accumulated. In this detrimental case scenario, we may see a reduction in the book value per share and the expected FCF.

Prosus has a considerable amount of cash to invest in new targets. That's not all. In my view, if management continues to invest heavily in machine learning and AI technology, more users will visit the company's classifieds domains. I would also expect significant revenue growth from the offerings proposed by the food delivery business segment. Finally, if management also decides to merge some of its targets, FCF margins would also increase. Under the best conditions, I obtained a target price of $155. Traders are buying shares at less than $30, so I will be a buyer too.

Visit link:
Prosus: New Artificial Intelligence Capabilities And Cheap - Seeking Alpha

This story is part of Fixs Whats Next Issue,which looks ahead to the ideas and innovations that will shape the climate conversation in 2022, and asks what it means to have hope now. Check out the full issue here.

Imagine youre standing at the edge of a soybean field in Iowa. In the distance, a combine harvester guided by GPS rolls across a field that has been leveled with the aid of a laser, as the farmer at the wheel monitors weather data on her phone. These tools, part of an approach to agronomy called precision agriculture, promise to increase yields and reduce costs by maximizing efficiency. That could help ensure the world grows enough food to feed an expanding population, even as climate change makes that task ever harder.

But theres one small problem. Precision agriculture is not that precise, says Soumik Sarkar, an associate professor of mechanical engineering at Iowa State University. Although things like GPS currently provide the most efficient route for planting and harvesting, and farmers use lasers to help level the land, even the most tech-savvy farmers lack the ability to, say, target specific crops with pesticides rather than spraying an entire field.

Optimizing and reducing the usage of water, chemicals, and other resources, while actually growing the amount of crops to feed a growing population with the land we have, is a challenge that you need to pretty much throw the kitchen sink at to solve, Sarkar says.

The Artificial Intelligence Institute for Resilient Agriculture at Iowa State University is one of four groups building that proverbial sink with what it calls ultra-precision agriculture. The institute hopes to, over the next three to five years, use data science, machine learning and other artificial intelligence technologies to provide corn and soybean growers with personalized recommendations that will boost crop production. The tools also could help industry scientists develop seeds that deliver higher yields while resisting drought and other stressors.

This will become increasingly important as drought and desertification shrink the worlds acreage per capita of arable land, and the worlds population balloons to 9.7 billion by 2050. As researchers look to artificial intelligence, drones, and robotics to help agronomists prepare for this future, industry analysts expect the global market for precision agriculture, valued at $4.84 billion in 2018, to reach $10.16 billion within two years.

The ideas are out there, says Sarkar, but theres a lot of opportunities and gaps still existing that one could fill with these new technologies.

The institute is a cooperative effort between industry stakeholders like John Deere and institutions like the Iowa Soybean Association, which represents 10,000 farmers, and the University of Arizona. The idea sprang from a call put out by the National Science Foundation in August 2020 seeking proposals for the creation of artificial intelligence research centers.

The outfit at Iowa State, founded last September, is one of 11 established in eight disciplines, including agriculture, following a $220 million investment from the National Science Foundation and the Department of Agriculture, which are betting heavily on AI to transform agronomy and food pathways. Iowa State will receive $20 million over the next five years to develop its technology.

The institute plans to create whats called a predictive digital twin framework a tool used to virtually model anything from a single plant to an entire farm (creating a digital copy, or twin, of the real-life environment). Such a tool can quickly and easily test myriad scenarios for everything from the optimal amount of nitrogen to apply to a field to which hybrid plants to cultivate. Engineers in industries like aviation rely on such models to determine when, say, an airplane might require repairs. Bringing the technology to agriculture has the potential to help agronomists make day-to-day decisions and future plans with greater certainty and efficiency.

Beyond quickly testing hypotheses or seeing how decisions big and small might play out, the digital twin framework could allow farmers to vastly expand their expertise by combining two traditionally siloed types of data: Even as the models continuously assimilate data on things like weather, soil composition, and plant genetics, they will draw on the collective knowledge of farmers, botanists, and others.

We have been doing agriculture for millennia, so theres a lot of domain knowledge that exists with the agronomists, with breeders, with practitioners, and so on, says Baskar Ganapathysubramanian, principal investigator at the Artificial Intelligence Institute for Resilient Agriculture and a professor of mechanical engineering at Iowa State University.

Of course, an AI is only as smart as the information used to train it. Reducing inaccuracies and biases such as focusing only on data from Iowa or industrial farms is baked into the institutes process for developing the digital twin. To that end, the institute will rely on backstops, such as placing sensors in fields to provide feedback on any discrepancies, and consult experts in fields ranging from soil science to botany.

No matter how much we try, therell always be some sort of bias or inaccuracy, says Sarkar. Our whole effort is to reduce it as much as we can.

A digital twin could be remarkably powerful in a laboratory, but even more so in multiple laboratories and, one day, on farms. Nirav Merchant, the director of the Data Science Institute at the University of Arizona and co-principal investigator of the National Science Foundation-funded CyVerse, is working with the institute in Iowa on the development of a platform called Cyberinfrastructure-AI Institute for Resilient Agriculture. Nicknamed Sierra, it aims to allow everyone involved in developing the digital twin to quickly and easily share petabytes of data annually.

Down the road, Sierra will act as the gateway to the data and tools farmers and scientists can use to assemble their own models. Though its too soon to know just how the digital twin might be accessible, one idea is to make it an open source platform or license it to collaborating institutions. Ganapathysubramanian says the institute is creating these tools with lay users in mind. The focus is not just on the technical aspects, he says, but also on the deployment, adoption, and democratization aspects of these AI technologies.

Because farmers in precision agriculture already have access to global positioning systems, theyre currently inundated with tons of information that can be difficult to navigate. Farmers make these decisions every year, every day, and sometimes make mistakes, says Peter Kyveryga, a senior research scientist with the Iowa Soybean Association. Some kind of system approach would be very useful here, and thats where I see the value in this project.

Further in the future, digital twins could lead to quicker development of more fruitful crops. Propagating multiple generations of plants to create strains that, for example, provide increased yield or drought resistance can take six to 10 years. By using a digital twin, you can accelerate this process by moving some of the physical testing into virtual testing, says Ganapathysubramanian.

Ganapathysubramanian says farmers and researchers outside the institute could be using digital twins within three years. His team plans to begin testing the technology in the field with this springs growing season.

One common concern raised in any discussion of artificial intelligence is the loss of jobs to machines and robots. That may not be an issue in agriculture, where bringing advanced technologies to farm communities could offset a labor force that is shrinking as farmers retire and young people move away. As long as theres a compelling value proposition for [farmers], I think theyre very eager to jump on board, Ganapathysubramanian adds.

To promote workforce development and train interested folks in using AI tech, the institute is coordinating with organizations like the Native Nations Institute and Iowa Soybean Association on outreach efforts, including to women and Indigenous farmers.

Its an opportunity for us to listen and learn, says N. Levi Esquerra, a member of the Chemehuevi tribe, scientific advisory board member for the institute, and senior vice president for Native American Advancement & Tribal Engagement at the University of Arizona. How can we make sure that the Indigenous voice is understood, our way of life has been understood?

When it comes to agriculture and just our way of life, sometimes I think those things are lost on advances but theres a time and season for everything, and its just showing proper respect and moving forward with that, says Esquerra.

The institute is developing curricula on the use of advanced technologies to prepare students for a future of ag-tech.

Everyone involved concedes that the technology is in its infancy and there surely will be unexpected hurdles to its adoption when it begins rolling out. But theyre equally confident that it is the future of agriculture. Theres going to be explosive growth, Ganapathysubramanian predicts.

The team will deploy any AI-driven tech to be developed in greenhouses this winter in cornfields come spring. If all goes according to plan, you may one day soon hear the hum of a drone taking aerial images, glimpse a motion-capture camera documenting leaf movement, and perhaps even spot a robot measuring a plants water uptake.

Explore more from Fixs Whats Next Issue:

Originally posted here:
The future of farming? Think artificial intelligence, robots, and drones. - Grist

As a new round of registration is set to commence, the Ministry of Finance is planning to use artificial intelligence to ensure that the state welfare cards issued this year are given only to eligible low-income earners. The Ministry plans to implement new technology to cut down on fraud and to keep urgent financial assistance money from going to those who are not in need at the expense of those who are.

The new artificial intelligence system will connect the systems of multiple state agencies to do real-time verification of anyone who applies for state welfare. The Deputy Finance Minister says that linking and cross-referencing all these agencies will easily filter out applicants who do not qualify for benefits.

The state welfare is intended for only the lowest earners without significant savings to support them in lean times. An applicant cannot have more than 100,000 baht in savings and tiered benefits are based on earnings of less than 100,000 baht per year.

Those who earn between 30,000 baht and 100,000 baht annually thats a maximum of 8,333 baht per month are eligible to receive a 200 baht monthly supplement from the government. The payment each month is raised to 300 baht for those who earn under 30,000 baht per year 2,500 baht a month or less. 300 baht isnt much for many people, but for those lowest earners, its at least 12% of their monthly income.

The artificial intelligence has been put in place to try to weed out people we are not struggling with extreme poverty and to stop them from taking advantage of the system designed to help the poorest in Thai society. The Deputy Finance Minister predicted between 14 and 15 million people would apply and be eligible for the benefit this year.

Last year saw 14.6 million approved applications into the programme, and its reasonable to expect that many who may have been just above the 100,000 baht threshold last year might have suffered further from the Covid-19 pandemic and fallen below the qualifying level this year.

SOURCE: National News Bureau of Thailand

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Artificial intelligence intended to prevent abuse of state welfare - The Thaiger

Our Startups series looks at companies and founders who are innovating in the fields of athlete performance, fan engagement, team/league operations and other high-impact areas in sports. If youd like to be considered for this series, tell us about your mission.

* * * * *

Worlds shortest elevator pitch: We're the only platform that allows users to use artificial intelligence to create their very own custom predictive models, as well as come up with player production and fantasy and prop stats.

Company: Pine Sports

Location: Ridgewood, New Jersey

Year founded: 2020

Website/App: https://www.pine-sports.com/Funding round to date: Were self-funded.

Who are your investors? Were self-funded.

Are you looking for more investment? I would say were potentially looking for smart money, sort of a strategic partner. We've already had a lot of great conversations with VCs who already love what we're doing and want to be in the space. They see the product as a differentiator, with the artificial intelligence and the custom modeling. If it makes sense, that's definitely a path well consider.

Tell us about yourself, co-founder Mike Yam: My co-founder, Vijay Dewan, and I are high school friends. Both of us

Co-founder and NFL Network broadcaster Mike Yam

Who are your co-founders/partners? Vijay was commuting to work two hours a day and decided to go back to his roots in computer programming. COVID was one particular thing he was interested in and used programming tools that are in Python to try to understand whether this was going to be nothing or something important. As the tools said, it was something major. During COVID, over time, he learned to translate the programming skills he learned analyzing COVID data to other data. He called me and said: Is there AI in sports? Are people using AI? Is there AI modeling that any user can do? The answer was really no. We couldn't find anything. He built the programming. I know the sports. And we teamed up, and it's been great since.

How does your product/service work? From the product and service standpoint, there are three different verticals for the website. There's what I think is sort of the gold standard in terms of predictive modeling, which we call Predict. Its super easy to use. You pick a sport, select how many seasons you want to go back with our artificial intelligence, you pick a rolling average and then you get the opportunity to select from over 200 stats to determine what decides the outcome of a game. The artificial intelligence essentially does the rest. It gives you a confidence percentage based on actual games that happened, and that confidence percentage is for upcoming games. The other vertical would be Explore, which is so insanely easy to use. Basically has a drop-down menu after you type in a player's name. Within three clicks, our machine learning tool basically does the rest. You type in the categorywhether it's points, rebounds or assists or any other stats and how many games you want it to go backand it'll give you a prop line if you want to use that for prop bets or a fantasy projection really is the outcome. What's really a cool differentiator on that particular product is the normalized number, which takes into consideration an opponents defense. That's really cool. Weve got a robot that basically tells you it likes the over, the under, and then gives you that projection. Having tools like that is insanely helpful for fantasy players. The third vertical is the social side of it. A lot of our users right now are able to take a lot of the learning insights they're using from playing around with the data, and then write articles surrounding the things they're learning. It's really cool to see the community sort of exchange a lot of different ideas.

What problem is your company solving? The problem is that users right now don't have the ability to create custom, predictive modeling using artificial intelligence anywhere. The ones who do know how to do it have to have a background in coding. Pine is no-code AI, and anyone essentially within a few clicks can figure out how to use these predictive models or basically create the predictive models. Other sites are kind of using Excel spreadsheets, but there's no real math and there's certainly no real analysis on actual games that have happened. The way we like to look at it is we're letting the artificial intelligence do what the brain can't do. I worked with so many coaches and players in my career and they can reference specific plays like Sean McVay. What AI does, though, is take every single play from every single game and analyze it. There's literally not a human being that is going to be able to do what AI can. As a sports fan, you have the ability to tell the perfect memory of AI what's important, and that leads you to an outcome or potentially an outcome.

What does your product cost and who is your target customer? Free right now for users. We were invite-only, but the site is now open for anyone to use the tools for free. For our user base, we've been in beta and they made more than 13,500 custom model projections. Doing a lot of different iterations and getting feedback from a lot of users.

How are you marketing your product? The marketing comes already on the platform with our writers who are using the tools to write about their learned insights. Anyone who goes to pine-sports.com has the ability to read the articles our users are generating. There's Twitter amplification, as well. So, anyone who's posting articles, that immediately goes to our Twitter feed, not to mention our Discord feed where there's a few hundred users that are on there, just swapping different ideas, watching the games together, and having some fun. Our marketing is our users. Hopefully, our goal is that they love the platform and tell their friends. Thats our current marketing, and it's been going well.

You pick a sport, select how many seasons you want to go back with our artificial intelligence, you pick a rolling average and then you get the opportunity to select from over 200 stats to determine what decides the outcome of a game. The artificial intelligence essentially does the rest.

How do you scale, and what is your targeted level of growth? From a scalability standpoint, its once we open up and have the ability to kind of hammer home our user base and let them use the tools. The sky's the limit, to be honest with you, because these tools don't exist. You have other sites that are charging an ungodly amount of money for inferior tools. To me, the scalability is just going to come as soon as we open it up. For us, it's about community. We have a very strong community, both on the platform and on Discord. Our targeted level of growth is to scale that community to a point where it's large. The great thing about Pine is that it's language agnostic and data agnostic. What I mean by that is people from all different countries can write in whatever language they want. We're getting more data on the platform month after month... From our perspective, it's getting users from across the globe involved in the community. We don't have specific numbers. It's about scaling smart, which means making sure the community's sort of ethos is the same as it has been since the beginning. Which is that it's people who are helping each other, people who are trying to make better bets and people who are trying to grow together. We have a large target market. In terms of growth, if you think of other people in the space, or just generally the social space, I looked at Redditwhich grew sort of linearly for a long time because what they did was they made a product that was good and people found it. I don't think you need that extreme exponential growth; I think you need to focus on community first.

Who are your competitors, and what makes you different? I actually don't think there's competitors for what we're doing because no one out there is currently putting artificial intelligence in the hands of normal sports fans. The closest thing would be Action Labs. But once again, no AI, no machine learning, no math, no analysis of actual games.

Whats the unfair advantage that separates your company? The easiest way to describe this is imagine having a friend who has a perfect memory. That's artificial intelligence. You get to tell it what's important about sports, and then the AI does the rest of the work. The unfair advantage for us is having a friend who literally has a perfect memory and never forgets anything.

What milestone have you recently hit or will soon hit? One of the biggest milestones is opening up the site for everyone and growing the community and seeing people engage in the data, messing around with building models and having fun with it. That's the biggest next milestone, opening this thing up and going outside of beta. What is really important is giving people the ability to use AI for themselves. Right now, AI is being used against you every day of the week, whether it's Amazon predicting what you're going to buy next and shoving that in your face or whether it's Netflix telling you what show you're going to want to watch next or whether it's Facebook telling you what ads you're going to want to watch. Its literally the same AI model. They're being used to take your attention away. What we're trying to do is show you what AI is and give you the ability to use it to help yourself. Some recent notable trends: We had more than 8,000 prop projections in December, and Pine users are averaging more than five minutes on the site. For context, according to similarweb.com, thats more than Yahoo Sports, CBS Sports, NBA.com and NBCSports.

In what ways have you adapted to the COVID-19 pandemic? Pine Sports actually came because of COVID. Vijay was aggregating data in local jurisdictions in New Jersey, realizing there's a hole in the use of AI for sports fans and empowering those users. In a lot of ways, Pine doesn't exist without COVID.

Beyond the pandemic, what obstacles has your company had to overcome? Time is one of them. It feels like there's never enough time in the day for the two of us. Relationships, just sort of leaning on the people we've worked with in the past. A lot of times that's been an advantage for us, but getting it in front of the right people, the decision makers. The people we have talked to have really loved the product. Overall, we're in a really good place. The track line of where we're trending is certainly an upward trajectory. Were taking something that's really complicated and trying to simplify it. In the sports space, unfortunately, a lot of people take really simple data analytics and dress it up to make it sound complicated. When we first opened up to beta users, it was a really complicated product and was really hard to use. It was chiseling and simplifying until we got something that still held that core of being really good at what it does, but also easy to use. That was our largest challenge to date. Some people might call that stickiness, but it's getting people who are in the sports base to understand the value of what we're providing.

What are the values that are core to your brand? The best way to describe that is looking at Pine Sports as a way to empower sports fans by fulfilling two different goals. One would be to put the power of artificial intelligence in their hands. The other side of it is to create a community. Vijay and I talk about this all the time and just how cool it is to see people using the tools, writing about the tools and then writing about how much they love the tools and how it's helping them just become a smarter fan. Making every sports fan smarter is one of the main objectives of Pine.

What does success ultimately look like for your company? We want every sports fan to use our tools. We live in sort of a day and age where people are scared of the math, scared of the data. Vijay and I have been able to pare down this product so that it is, at its core, super easy to use but also still really powerful. Once again, to see people engage with Pine and to see their reactions when we run a demo for them and that light bulb goes off, is a really cool thing and in a lot of ways gratifying for the two of us.

What should investors or customers know about youthe person, your life experiencesthat shows they can believe in you? First and foremost, Vijay and I were high school friends who were really fortunate to have a lot of advantages because of the sacrifices of our parents and our families. I would classify us both as grinders who really work hard on our craft. For me, specifically, as a storyteller and someone who's been around sports my entire career, I want to see a lot of our users use the data and have fun with it, but I also want people to get a sense of control that just didn't exist before Pine. Vijay spent a good portion of his career as an attorney, but also as a federal prosecutor putting bad people away. At the end of the day, at our core, both of us want to do the right thing. Right now, from a sports perspective, the right thing is putting the power of artificial intelligence into the hands of every sports fan and making it easy to use.

How has the rise of legalized gambling affected Pine Sports? Artificial Intelligence is about making predictions about the future. Betting is about making predictions about the future. Fantasy is too, but the more you are giving people the opportunity to make predictions about the future, the more useful our tool is. Betting is clearly very important. Everything we do is making predictions about the future, which is why AI is so important, because it allows us to do that in a way that's really smart. Some people might see a line for Tom Brady that's 310 yards and they might have no idea how many yards he's passed for previously. They may not even really think about his opponents, but the AI will do all of that for you and give you a projection about the future and do really complicated math to do that. So, yes, betting is important. Betting is about making predictions about the future and putting your money down. AI, at its core, helps you make really smart, informed decisions about the future. Thats where we shine.

Do you have a favorite quote about leadership? Vijay and I have similar backgrounds and a similar thought process. Both of us feel it's important to listen first. Thats something both of us do when we're doing demos or even bouncing back and forth ideas. But the biggest thing we try to ask ourselves on a regular basis is: What's your impact? It goes back to the predictive nature and the power of artificial intelligence. The impact we're trying to have on sports fans and those communities of users is the ability to give you something that is very difficult to master, but making it really simple so that anyone can use it. We're trying to build a community of people who want to be smarter and give them those tools.

Question? Comment? Story idea? Let us know at [emailprotected]

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Pine Sports Is the Intersection Between Artificial Intelligence and Game Prediction, as in the Photographic Memory You Always Wished You Had -...

Artificial intelligence will be the common theme in the top 10 technology trends in the next few years, and these are expected to quicken breakthroughs across key economic sectors and society, the Alibaba Damo Academy says.

The global research arm of Chinese technology major Alibaba Group says innovation will be extended from the physical world to a mixed reality, as more innovation finds its way to industrial applications and digital technology drives a green and sustainable future.

"Digital technologies are growing faster than ever," Jeff Zhang, president of Alibaba Cloud Intelligence and head of Alibaba Damo, said in a report released on Monday.

"The advancements in digitisation, 'internetisation' and intelligence are redefining a digital world that is characterised by the prevalence of mixed reality.

"Digital technology plays an important role in powering a green and sustainable future, whether it is applied in industries such as green data centres and energy-efficient manufacturing, or in day-to-day activities like paperless office."

The report was compiled after analysing millions of public papers and patent filings over the past three years and conducting interviews with about 100 scientists.

Clouds, networks and devices will have a more clearly defined division of labour in the coming years. Photo: Alibaba Damo

The rapid development of new network technology will fuel the evolution of cloud computing towards a new system called cloud-network-device convergence.

The system will allow clouds, networks and devices to have a more clearly defined division of labour.

Clouds will function as brains and will be responsible for centralised computing and global data processing, while networks will serve as the interconnecting tracks that join various forms of networks on the cloud to build an ubiquitous, low-latency network.

The global cloud computing market is projected to grow to $947.3 billion by 2026, from $445.3bn in 2021, according to data platform Markets and Markets, with adoption set to increase in sectors where initiatives to work from home are prevalent.

AI is pegged to replace computers as the main production tool in scientific discovery. Photo: Alibaba Damo

AI would be a boon to scientists, with Alibaba Damo saying it will replace computers as the main production tool in scientific discovery, helping to improve efficiency in each phase of the research process from the formation of initial hypothesis to experimental procedures and the distillation of experimental findings.

This will shorten research cycles and improve the productivity of scientists.

Machine learning can process massive amounts of multidimensional and multimodal data and solve complex scientific problems, allowing scientific exploration to flourish in areas previously thought impossible, it said. As such, AI will also help to discover new scientific laws.

The global scientific research and development services sector is, unsurprisingly, a big market. The sector is forecast to grow to $822.49bn this year and $1.3 trillion by 2026, from $725.56bn in 2021, data provider ReportLinker says.

Cloud computing and AI will drive the rapid development of and demand for silicon photonics technology, Photo: Alibaba Damo

As defined by Intel, a silicon photonic chip is the combination of two of the most important inventions of the 20th century the silicon integrated circuit and the semiconductor laser. Unlike its electronic counterparts, it enables faster data transfers over longer distances.

The rise of cloud computing and AI will drive the rapid development of silicon photonics technology and demand. The widespread use of the chips is expected in the next three years.

Research company Markets and Markets predicts that the market will grow to $4.6bn by 2027, from $1.1bn in 2021.

The current challenges, according to Alibaba Damo, are mainly in the supply chain and manufacturing processes since the design, mass production and packaging of silicon photonic chips have not been standardised and scaled up, leading to low production capacity, low yields and high costs.

Applying AI in the renewable energy sector can also contribute to achieving carbon-neutrality. Photo: Alibaba Damo

Renewable energy is one of the sectors attracting the most attention as governments prioritise sustainability. But due to the unpredictable nature of renewable energy power generation, integrating renewable energy sources into the power grid presents challenges that affect the safety and reliability of the grid.

Alibaba Damo said the application of AI in the industry is critical and indispensable in capacity prediction, the scheduling of optimisation, performance evaluations, failure detection and risk management, all of which translates into improving the efficiency and automation of electric power systems and maximising resource use and stability. It would also be a key factor for achieving carbon neutrality.

A recent report by Allied Market Research said the global renewable energy market, which was worth $881.7bn in 2020, is expected to reach about $2tn by 2030.

Major economies have programmes in place to make renewables a significant part of their energy mix by that year: the US and China are on track to generate up to 50 per cent and 40 per cent, respectively, of their electricity from renewables.

The convergence of AI and precision medicine is expected to boost the integration of medical expertise. Photo: Alibaba Damo

As the Covid-19 pandemic has proved, any unexpected medical crisis will force the industry to hasten its research to achieve pinpoint accuracy.

With the medical field highly dependent on individual expertise involving a lot of trial and error, coupled with varying efficacies from patient to patient, the convergence of AI and precision medicine is expected to boost the integration of expertise and new auxiliary diagnostic technology.

It will serve as a "high-precision compass" for clinical medicine a compass that doctors can use to diagnose diseases and make medical decisions as quickly and accurately as possible.

The medical world is already reaping the advantages of AI. For example, using AI in the early screening of breast cancers can reduce the false negative rate by 5.7 per cent in the US and 1.2 per cent in the UK, Alibaba Damo said, citing country statistics.

The global precision medicine market is poised to grow to $118.32bn by 2025, from $72.58bn in 2021, driven by companies resuming their operations and adapting to the new normal while recovering from the effects of Covid-19, according to ReportLinker.

Privacy-preserving computation techniques and its successors will be critical to effective, safe and secure data sharing. Photo: Alibaba Damo

Privacy-preserving computation, as its name implies, is the use of techniques to process data in utility bills, for example without revealing a user's information. In an era where one of the largest challenges is ensuring the security of data while allowing it to flow freely between computing entities, this vertical is gaining traction as a viable solution to this challenge.

Alibaba Damo said that the next three years will see groundbreaking improvements in the performance and interpretability of privacy-preserving computation, and witness the emergence of data trust entities that provide data sharing services based on the technology.

Research company Gartner says that by 2025, half of large organisations will introduce privacy-enhancing computation for processing data in untrusted environments, while professional services company Accenture said its techniques will be critical to effective, safe and secure data sharing.

However, the application of the technology has been limited to a narrow scope of small-scale computation due to performance bottlenecks, lack of confidence in the technology and standardisation issues, Alibaba Damo said.

The next three years would see a new generation of XR glasses that have an indistinguishable look and feel. Photo: Alibaba Damo

The development of technologies such as cloud-edge computing, network communications and digital twins brings extended reality the combination of real and virtual worlds and human-machine interaction into "full bloom", Alibaba Damo said.

XR glasses aims to further develop immersive mixed reality Internet. It will reshape digital applications and revolutionise the way people interact with technology in any scenario from entertainment and social networking, to office and shopping, to education and healthcare.

The XR market was valued at $27bn in 2018 and is expected to hit $393bn by 2025 at a healthy compound annual growth rate of 69.4 per cent, according to data provider Market Research Future.

Alibaba predicts that a new generation of XR glasses that have an indistinguishable look and feel from ordinary glasses will enter the market in the next three years and will serve as a key entry point to the next generation of the Internet.

Perceptive soft robots are seen to replace traditional robots in the manufacturing industry in the next five years. Photo: Alibaba Damo

Perceptive soft robots are flexible, programmable and deformable, and are empowered by advanced technologies such as flexible electronics and pressure adaptive materials. This enables them to handle complex tasks in various environments.

AI further enhances their perception system, making them smarter and applicable to more industry functions such as for surgeries in the medical field.

Unlike conventional robots, perceptive soft robots are machines with physically flexible bodies and enhanced perceptibility towards pressure, vision and sound, allowing them to perform highly specialised and complex tasks and the ability to adapt to different physical environments.

The soft robotics market, still in its early stages, was valued at $1.05bn in 2020 and is expected to reach $6.37bn by 2026 at a CAGR of 35.17 per cent, according to Mordor Intelligence.

Alibaba Damo said the emergence of perceptive soft robotics will change the course of the manufacturing industry, from the mass-production of standardised products towards specialised, small-batch products.

In the next five years, it will replace conventional robots in the manufacturing industry and pave the way for wider adoption of service robots in daily life.

Satellite-terrestrial integrated computing can enable digital services to be more accessible and inclusive across Earth. Photo: Alibaba Damo

Current terrestrial networks and computing capabilities cannot catch up to the growing requirements for connectivity and digital services around the world, and is especially prominent in sparsely-inhabited areas such as deserts, seas and space.

Satellite-terrestrial integrated computing, Alibaba Damo says, creates a system that integrates satellites, satellite networks, terrestrial communications systems and cloud computing technologies, enabling digital services to be more accessible and inclusive across Earth.

The global satellite communication market was valued at $65.68bn in 2020 and is expected to hit $131.68bn by 2028, according to data provider Verified Market Research.

In the next three years, Alibaba Damo expects to see a large increase in the number of low-Earth orbit satellites, and the establishment of satellite networks with high-Earth orbit satellites.

In the next five years, satellites and terrestrial networks will work as computing nodes to constitute an integrated network system, providing ubiquitous connectivity.

The co-evolution of large and small-scale AI systems would create a new 'intelligent' one. Photo: Alibaba Damo

Future AI is shifting from the race on the scalability of foundation models to the co-evolution of large and small-scale models via clouds, edges and devices, which is more useful in practice.

In the co-evolution paradigm, foundation models deliver the general abilities to small-scale models that play the role of learning, inference and execution in downstream applications, Alibaba Damo said.

Small-scale models will also send the feedback of the environment to the foundation models for further co-evolution. This mechanism mutually enhances both large and small-scale models via positive cycles.

The would-be new "intelligent system" brings three merits: it makes it easier for small-scale models to learn the general knowledge and inductive abilities, which are then fine-tuned to their specific application scenarios; the system increases the variety of data for the foundation models; and it helps achieve the best combination between energy efficiency and training speed.

The global AI market was valued at $62.35bn in 2020 and is seen to expand at a robust CAGR of 40.2 per cent from 2021 to 2028, according to Grand View Research.

Continuous research and innovation directed by technology giants are driving the adoption of advanced technologies in industry verticals, such as automotive, health care, retail, finance and manufacturing, but AI has brought technology to the centre of organisations, it said.

Updated: January 11th 2022, 4:14 AM

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Artificial intelligence to influence top tech trends in major way in next five years - The National

When you work in the digital sphere, it is easy to become disconnected. A year ago, clinical professor and former Wall Street data analyst Frank Parisi, alongside other Pace faculty, conceptualized a space where individuals with an interest in data science and machine learning could connect. We wanted to make a central repository for all kinds of data, where we have the computational power to do interesting things, work together and collaborate across the University and, in the long-term, with outside partners for research, said Parisi.

Now, the space has been set up, the machines moved in, and Paces Computational Intelligence Lab is open for business.

Computational intelligence refers to the machine learning and data analysis abilities of a computerits what allows us to collect data, speak to Siri, and play the newest video game. Jon Lee, a clinical professor, was one of the architects of the lab and he believes it will be unique in what it offers.

There are other Pace hubs that exist for design, digital forensics, and cybersecurity, he says. This will be a proper space for computational intelligence specifically, from Artificial Intelligence, pattern recognition and machine learning.

This will be a proper space for computational intelligence specifically, from Artificial Intelligence, pattern recognition and machine learning.

Parisi notes that not only can the lab be a tutoring resource for those learning programming languages like Python and R, but it can also elevate the quality of our data scientist professionals.

My particular favorite aspect is the development of conceptual workshops, where we cover things like probability theory, how to build models, and statistical computing, he explains.

Having a physical lab with quality equipment also means that students and faculty engaged in deeper analysis will not have to rely on remote Google servers. Furthermore, as Lee notes, the lab will serve as a way to get Seidenberg students engaged, active, and doing amazing things on campus, especially those who are feeling disconnected after COVID-19.

All in all, the Computational Intelligence Lab will empower Pace faculty and students to do what they do bestconnect, innovate, and build great things for both today and tomorrow.

Want to see the Computational Intelligence Lab or learn how you can get involved? Reach out to co-directors Frank Parisi atfparisi@pace.edu or Yegin Genc at ygenc@pace.edu.

See the article here:
The Science of Machine Learning - Pace News

Samsung is working on artificial intelligence chips that use in-memory computing.

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Samsung Electronics has announced the development of an in-memory computing system that combines memory and system semiconductors.

For the first time, non-volatile memories, dubbed magnetoresistive random access memory, are being used to enable the new technology, according to the worlds largest memory chipmaker.

Samsung has announced the development of an in-memory computing technology that combines memory and system semiconductors.

For the first time in the world, non-volatile memories, dubbed magnetoresistive random access memory, are enabling the new technology, according to the worlds largest memory chipmaker.

Data is stored in memory chips and computed by separate processor chips in a traditional computer architecture.

In-memory computing, on the other hand, can perform both data storage and computing in a memory network at the same time.

As a result, Samsung claims that in-memory computing consumes significantly less power because data does not need to be moved, a feature that could be useful in next-generation artificial intelligence chips.

Many have attempted to achieve in-memory computing based on MRAM due to its benefits, which include fast operation speed, endurance, and large-scale production, but the technology has eluded them, according to Samsung.

According to the company, its MRAM in-memory computing chip classified handwritten digits with a 98 percent accuracy and detected faces from scenes with a 93 percent accuracy.

Nature will publish the findings in a future issue.

The articles first author is Samsung researcher Jung Seung-chul.

In-memory computing is similar to the brain in the sense that computing also occurs within the network of biological memories, or synapses, Jung said in a statement.

In-memory computing, according to experts, is one of the semiconductor industrys most futuristic concepts.

Professor Han Tae-hee of Sungkyunkwan University told Nokia News News Korea that Samsung appears to have made a technological breakthrough based on potential-rich MRAM.

However, well need a completely different software ecosystem, including different operating systems, to fully embrace in-memory computing.

As a result, whether the new Samsung technology will be commercially viable remains to be seen, he said.

At nokiamobilephonenews.co.uk, Nokia News provides an objective, continuously updated stream of breaking news from the United States and around the world.

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Samsung is working on artificial intelligence chips that use in-memory computing. - Nokia News

Satyaki Banerjee, CEO-Medical Imaging, Trivitron Healthcare highlights the role of technology in medical imaging

The new millennium brought in a transition from conventional Screen-film Radiography to Computed Radiography (CR) and gradually over the last two decades to Direct Digital Radiography (DR). The future however lies in Artificial Intelligence (AI) enabled Radiography systems.

Computed Radiography cassettes use photo-stimulated luminescence plates to capture the X-ray image, instead of traditional X-ray film. The exposed CR cassette goes into a digitiser that converts the latent image stored on the plate into a digital image that can be further processed, edited and viewed on a computer/screen. CR is a two-step process, the first being Image Acquisition and the second step being the image readout by the digitiser.

Digital Radiography systems use Active-matrix Flat Panel Detectors consisting of a scintillator detection layer deposited over an active matrix array of thin-film transistors and photodiodes. With DR, the image is converted into digital data in real-time and is available for review within seconds. Flat Panel Detectors using a scintillator like GOS (Gadolinium Oxysulfide) or CsI (Cesium Iodide) are called Indirect Conversion Systems. When the scintillator is exposed to X-ray, the beam is absorbed and converted to fluorescent light. A photodiode array further converts the fluorescent light to an electric charge and the corresponding TFT switch completes the readout process in real-time.

DR systems have significantly higher dose efficiency than CR systems. They are two to three times more efficient at converting dose to signal than CR. This increased dose utilisation means that a DR can produce the same image quality as CR at a lower dose or that DR can produce higher contrast resolution images than CR using the same dose.

In clinical practice, a DR system generally requires a 40 per cent lower radiation dose than CR or Screen-Film systems. The latest generation of wireless DR detectors with automatic beam detection offers the flexibility of retrofit in a traditional system designed for use with CR or screen film, but with the benefits of much higher throughput, portability and flexibility. In addition, panels are now equipped with a data processing engine and carry their own calibration files, which allow the images to be corrected on the panel. On-panel image corrections increase the image transmission speed and reliability.

The digital image whether it has been acquired by a CR or a DR system is generally transferred to a PACS system in the form of a DICOM file. During the process of report writing, a radiologist would generally rely on powerful image processing software that provides an array of tools to critically review the high-resolution image leading to diagnosis. The current trend is to further enhance these software tools by incorporating Artificial Intelligence algorithms that can predict diagnostic scores/probabilities and can assist the radiologist to arrive at highly accurate and conclusive diagnoses within a shorter time period.

AI algorithms involving deep learning are increasingly being deployed for image-recognition tasks. Deep learning is based on a neural network structure that emulates to a great extent the working of the human brain. Neural network structures are designed to learn differential and delineating features from clinical data automatically, enabling them to approximate very complex nonlinear relationships. Once these neural network structures are trained with statistically significant samples of clinical images with expert radiologists specifying a structured approach towards identifying image patterns for a conclusive diagnosis, the software is able to emulate the human thinking process and predict diagnostic scores leaving the task of the confirmatory diagnosis to the expert eyes of the radiologist.

The advancement in computing hardware, availability of a large repository of expertly tagged clinical imaging data in digital form to train the neural network structures and continuous refinement of AI algorithms have led to the development of applications that can perform extremely precise differential diagnoses. Many modern Digital Radiography Systems come equipped with software tools that has anatomy specific AI tools that can assist radiologists to deal with much higher caseloads and quickly perform very precise diagnosis.

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The role of digital technology and artificial intelligence in medical imaging - Express Healthcare

Visualized are two-dimensional pressure fluctuations within a larger three-dimensional magnetically confined fusion plasma simulation. With recent advances in machine-learning techniques, these types of partial observations provide new ways to test reduced turbulence models in both theory and experiment. Credit: Image courtesy of the Plasma Science and Fusion Center

MIT researchers are testing a simplified turbulence theorys ability to model complex plasma phenomena using a novel machine-learning technique.

To make fusion energy a viable resource for the worlds energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MITs Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces factors that impact fusion reactor designs.

To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasmas behavior. However, first principles simulations of this region are among the most challenging and time-consuming computations in fusion research. Progress could be accelerated if researchers could develop reduced computer models that run much faster, but with quantified levels of accuracy.

For decades, tokamak physicists have regularly used a reduced two-fluid theory rather than higher-fidelity models to simulate boundary plasmas in experiment, despite uncertainty about accuracy. In a pair of recent publications, Mathews begins directly testing the accuracy of this reduced plasma turbulence model in a new way: he combines physics with machine learning.

A successful theory is supposed to predict what youre going to observe, explains Mathews, for example, the temperature, the density, the electric potential, the flows. And its the relationships between these variables that fundamentally define a turbulence theory. What our work essentially examines is the dynamic relationship between two of these variables: the turbulent electric field and the electron pressure.

In the first paper, published in Physical Review E, Mathews employs a novel deep-learning technique that uses artificial neural networks to build representations of the equations governing the reduced fluid theory. With this framework, he demonstrates a way to compute the turbulent electric field from an electron pressure fluctuation in the plasma consistent with the reduced fluid theory. Models commonly used to relate the electric field to pressure break down when applied to turbulent plasmas, but this one is robust even to noisy pressure measurements.

In the second paper, published in Physics of Plasmas, Mathews further investigates this connection, contrasting it against higher-fidelity turbulence simulations. This first-of-its-kind comparison of turbulence across models has previously been difficult if not impossible to evaluate precisely. Mathews finds that in plasmas relevant to existing fusion devices, the reduced fluid models predicted turbulent fields are consistent with high-fidelity calculations. In this sense, the reduced turbulence theory works. But to fully validate it, one should check every connection between every variable, says Mathews.

Mathews advisor, Principal Research Scientist Jerry Hughes, notes that plasma turbulence is notoriously difficult to simulate, more so than the familiar turbulence seen in air and water. This work shows that, under the right set of conditions, physics-informed machine-learning techniques can paint a very full picture of the rapidly fluctuating edge plasma, beginning from a limited set of observations. Im excited to see how we can apply this to new experiments, in which we essentially never observe every quantity we want.

These physics-informed deep-learning methods pave new ways in testing old theories and expanding what can be observed from new experiments. David Hatch, a research scientist at the Institute for Fusion Studies at the University of Texas at Austin, believes these applications are the start of a promising new technique.

Abhis work is a major achievement with the potential for broad application, he says. For example, given limited diagnostic measurements of a specific plasma quantity, physics-informed machine learning could infer additional plasma quantities in a nearby domain, thereby augmenting the information provided by a given diagnostic. The technique also opens new strategies for model validation.

Mathews sees exciting research ahead.

Translating these techniques into fusion experiments for real edge plasmas is one goal we have in sight, and work is currently underway, he says. But this is just the beginning.

References:

Uncovering turbulent plasma dynamics via deep learning from partial observations by A. Mathews, M. Francisquez, J. W. Hughes, D. R. Hatch, B. Zhu and B. N. Rogers, 13 August 2021 , Physical Review E.DOI: 10.1103/PhysRevE.104.025205

Turbulent field fluctuations in gyrokinetic and fluid plasmas by A. Mathews, N. Mandell, M. Francisquez, J. W. Hughes and A. Hakim, 1 November 2021, Physics of Plasmas.DOI: 10.1063/5.0066064

Mathews was supported in this work by the Manson Benedict Fellowship, Natural Sciences and Engineering Research Council of Canada, and U.S. Department of Energy Office of Science under the Fusion Energy Sciences program.?

Excerpt from:
Using Artificial Intelligence To See the Plasma Edge of Fusion Experiments in New Ways - SciTechDaily