Category Archives: Artificial Intelligence

What can AI do for you? A lot, as the recent Intel AI Summit 2021 has shown. Artificial intelligence opens up a broad range of possibilities, from tiny devices to the massive cloud. Suppose you dont know where to start or how to develop and scale up your ideas and innovation further. In that case, the two-day summits contents are now available on-demand to inspire you with the latest from the Intel AI technology stack, as well as successful customer use cases from the Asia Pacific and Japan Territory (APJ-T).

AI is no longer the purview of those in the know. In the executive keynote, Dr. Nash Palaniswamy gave an overview of Intels AI strategy: Taking AI from being specialized, proprietary and for the few to being ubiquitous, open and for all. The Vice President, SMG, General Manager, AI, HPC, and Datacenter Accelerators Solutions and Sales at Intel also shared the companys cutting-edge offerings in the field from hardware and software applications to AI deployment in the cloud and edge ecosystems. His address was just the beginning of a virtual event featuring over 20 industry expert speakers leading more than 25 innovative sessions, which you can view at your leisure until May 2022.

An overview of how users implement AI on the Intel Architecture (IA) platform was presented by Hong Wei Yi, Asia AI Sales Director, DCG Sales Group at Intel. She curated some examples of how AI and the platform led to easier deployment, better performance, and lower total cost of ownership (TCO) in various applications such as drug discovery, neuro-linguistic programming (NLP), and more.

Get a front-row seat to the fireside chats and hear how organisations make wonderful things with Intel AI, such as Tokopedia, the largest e-commerce start-up in Indonesia, and Max Kelson, an AI consultancy based in Australia.

The fireside chat with Tokopedia brought together three speakers representing the collaboration between the start-up, Intel and Google Cloud Platform (GCP), that helped the company scale up and turn AI into ROI (return on investment): Tahir Hashmi, technical fellow, and VP of growth engineering at Tokopedia; Erwin Huizenga, APAC Solution Lead Machine Learning at GCP; and Ayu Ginanti, APJ-T Cloud Lead at Intel. They discussed how they achieved success, from overcoming funding shortfalls, skills, and data challenges to measuring the success of AI projects as they went from lab demo to live, daily.

Tokopedia was launched in 2009 and became a unicorn six years later. It has been using Google Cloud since 2018. In May, it merged with ride-hailing and payments unicorn GoJek to create the GoTo Group. GoTo is currently Indonesias most prominent digital services platform and contributes about 2% of the countrys GDP, with more than 100 million active users. Last July, GoTo announced its collaboration with Google Cloud for its next growth phase.

We look forward to our continued partnership with Intel and Google Cloud as a key technology partner to support GoTos continued expansion across cloud infrastructure, data with cloud artificial intelligence (AI) and machine learning (ML), as well as productivity and collaboration needs with Google Workspace. We hope that this partnership can also empower us to provide the convenience of accessing high availability and scalable services from anywhere at any time for business, especially MSMEs, and consumers, said Herman Widjaja, Chief Technology Officer, Tokopedia, in the announcement.

Google Cloud is supported by Intel architecture, which provides the most demanding enterprise workloads and applications security, compute, and memory requirements.

Meanwhile, the meldCX breakout presentation shed light on how GCP built its Edge AI solutions on Intel technology. Joy Chua, EVP of strategy and development at meldCX, shared how the independent software vendor (ISV) overcame its business challenges and accelerated its IoT (Internet of Things) journey with AI building blocks. She illustrated the process from product ideation to deployment for its customers like Australia Post and Westpac.

Australia Post upgraded its package shipping with meldCX Concept SALi (Smart Automated Lodgement API) self-service kiosks last year. The kiosks use machine learning and computer vision technology to scan and detect each package, thus automating the parcel delivery operations. This led to a 67% reduction in queues and 95% accuracy in recognizing handwritten labels at Australias number one provider of postal services. Concept SALi leverages Intel AI technology such as Intel Core Processors, Intel Movidius, and the Intel OpenVINO Toolkit.

The Intel AI Summit on-demand sessions also featured Demo Showcases with a line-up of specialists from Intel and its partners, namely Databricks, Dell Technologies, Fortanix, Hewlett Packard Enterprise, LAB3, Lenovo Global Technology, and L&T Technology Services. All sessions from Day 2 of the summit are also available such as the keynote address by Pradeep K Dubey, senior fellow and director of parallel computing lab at Intel, and his special guests. They talked about how Intel technology, software, and innovations help clear the path forward in AI by making it more scalable, productive, performant, and intelligent.

The Intel AI Summit 2021 on-demand can be accessed here. Begin something extraordinary with AI today.

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Expand your horizon with the Intel AI Summit 2021 on-demand - Tech Wire Asia

Using finely tuned hardware, a specialized network, and large data storage, supercomputers have long been used for computationally intense projects that require large amounts of data processing. With the rise of artificial intelligence and machine learning, there is an increasing demand for these powerful computers and, as a result, processing power is rapidly increasing. As such, the growth of AI is inextricably linked to the growth in processing power of these high-performing devices.

Supercomputers arent new. The term appeared in the late 1920s and the CDC 6600 (released in 1964) is generally considered to be the first true supercomputer. Early supercomputers used only a few extremely powerful processors but, in the late 1990s, computer experts realized that stringing together thousands of off-the-shelf processors would yield the greatest processing power. Current state-of-the-art supercomputers have over 60,000 massively parallel processors to approach petaflop performance levels.

Mindful of the threats to security that are posed by supercomputers, a consortium of countries, including the United States, Germany, and South Korea, developed the Wassenaar Arrangement, which restricts the sale of, among other things, supercomputers that can be used for military purposes. Nonetheless, supercomputers can be found in most countries pursuing AI research.

As such, much of the development of AI is predicated on two pillars: technologies and human capital availability. Our prior reports for Brookings, How different countries view artificial intelligence and Analyzing artificial intelligence plans in 34 countries, detailed how countries are approaching national AI plans, and how to interpret those plans. In a follow-up piece, Winners and losers in the fulfillment of national artificial intelligence aspirations, we discussed how different countries were fulfilling their aspirations along technology-oriented and people-oriented dimensions. In our most recent post, The people dilemma: How human capital is driving or constraining the achievement of national AI strategies, we discussed the people dimension and so, in this piece, we will examine how each country is prepared to meet their AI objectives in the second pillarthe technology dimension.

In order to analyze each countrys technology preparedness, we assembled a country-level dataset containing: the number and size of supercomputers in each country, the amount of public and private spending on AI initiatives in each country, the number of AI startups in each country, and the number of AI patents and conference papers each countrys scholars produced. This resulted in ten distinct data elements.[1]

As with our previous analyses, we conducted a factor analysis to determine if any of the data elements were closely related. Closely related items can be mathematically combined into a composite factor, which aids in interpretation. In this factor analysis, two clear factors emerged. The first factor contained country ranks by theoretical peak computer performance, number of processing cores, number of supercomputers, and maximal LINPACK performance achieved; country ranks for the number of conference papers and journal papers; and the country rank for the number of patents. The second factor contained private and public investments in AI. One field, AI startups, was not closely associated with either factor and was dropped from further analysis.

It is clear that all of the fields in the first factor are either directly related to technology or its use in research. As a result, we name this factor Technology and Research. The second factor is solely focused on investments, and so we name this field Investments.

Figure 1 shows where a select group of countries sit along these sub-dimensions.

We interpret and name the quadrants as follows. The countries that are in the upper right-hand corner we dub Leaders; these have both a robust technology and research platform (factor one) and substantial public/private investments (factor two). Countries in the lower right quadrant we dub Technology Skilled. These countries have a strong current technology and research platform but are lacking strong public and private investments. Countries in the upper left quadrant we dub Funding Positioned, and are countries that have a strong funding stream but are behind in terms of technology and research. Finally, we dub the lower left quadrant Unprepared, which reflects countries that are both lacking in technology and research and are also lacking from a funding perspective.

The race for technology dominance is clearly a two-horse race between the U.S. (94th percentile for technology and research and 96th percentage for investment) and China (94th percentile for technology and research and 91st percentage for investments). While the U.S. holds a very slight lead overall, both countries are in the top three positions for every single one of our data elements. This is not surprising, as the size of the U.S. and Chinese economies (largest and second-largest respectively at $20 trillion and $15 trillion respectively) dwarf Japan, which is the third-largest economy ($4.9 trillion). As a result, we see no technology-centric hindrances for either country to continue to excel.

The United Kingdom (75th percentile in technology and research and 88th percentile in investments), France (75th percentile in technology and research and 81st percentile in investments), Japan (87th percentile in technology and research and 75th percentile in investments), and Germany (83rd percentile in technology and research and 68th percentile in investments) form a strong chase pack to the two leaders. Of the four countries, we view the United Kingdom as being in the strongest position to challenge the U.S. and China and this is based on their stronger investments in technology. We feel that these investments will allow them to close the gap more quickly than the other countries are capable of. However, we cannot ignore the fact that Japans economy is the largest of the four and this suggests that, if they decide to do so, they can quickly accelerate their efforts.

India (57th percentile in technology and research and 78th percentile in investments), Canada (68th percentile in technology and research and 60th percentile in investments), South Korea (71st percentile in technology and research and 60th percentile in investments), and Italy (71st percentile in technology and research and 60th percentile in investments) complete the Leaders quadrant. As with the United Kingdom, India is also well-positioned from a funding standpoint and should quickly separate itself from the other four countries.

Almost without exception, there is a strong relationship between the countrys economic size and its position in our quadrants. The U.S. is certainly leveraging its status as the worlds largest economy and is making dramatically larger technology investments than almost any other country; China is a close second. While we were concerned with the U.S. position from a people perspective, there are no similar concerns from a technology standpoint. America remains a world leader in digital innovation and supercomputers are no exception to that fact.

The uncomfortable reality for the U.S. is that its economic strength is very helpful to make the necessary technology infrastructure investments which are necessary but not sufficient to be successful in the pursuit of AI but this economic strength has little or no bearing on the other necessary element the ability to attract the necessary people to develop and implement its AI strategy. By contrast, China also has the economic strength for the necessary technology infrastructure investments but also has a sizeable population to provide the people power which is also necessary. In other words, China has both conditions necessary for AI success while the U.S. only has one of them. As such, China is currently in far better shape than the U.S. to achieve its AI goals and, without changes on the people front, the U.S. will fall increasingly far behind.

In our next post, we will exclusively focus on what the U.S. needs to do to improve its position and in our subsequent posts, we will examine different teaming strategies that leverage each countrys respective strengths.

[1]: These were: Rpeak (country rank by theoretical peak computer performance), Cores (country rank by number of processing cores), Count (country rank by number of supercomputers), Rmax (country rank by maximal LINPACK floating point calculation performance achieved), AI Startups (country rank for number of AI-based startups), Private Investment (country rate for private investments in AI), Public Investments (country rank for public investments in AI), AI Conference Papers (country rank for number of AI conference papers), AI Journal Papers (country rank for number of AI papers) and AI Patents (country rank for number of AI patents).

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How countries are leveraging computing power to achieve their national artificial intelligence strategies - Brookings Institution

When University of Vermont researcher Josh Bongard and his colleagues in Massachusetts began working on a project to build robots using artificial intelligence and frog stem cells, one of their first challenges was communicating in a common vocabulary: Were they building biological robots, engineered organisms or something entirely new?

Normally, scientists know whether they're working with a living creature or a machine. But since their collaboration began in the summer of 2018, Bongard and his fellow researchers including computer scientists, roboticists, biologists, and a philosopher and biomedical ethicist have grown accustomed to the ambiguities inherent in their work.

They've created what they call "reconfigurable organisms" in scientific literature and "xenobots" in mainstream press. The latter is a cheeky portmanteau of "robot"; Xenopus laevis, the frog species they use; and the prefix "xeno," meaning "alien" or "strange." Be they machines or organisms, xenobots move independently, are self-powered and self-replicate before they run out of power or "die."

"Xenobots do a good job of cutting away at those distinctions," Bongard said in an interview. "For me, part of the fun of science is that new discoveries challenge our preconceived notions."

Bongard, 47, is a professor of computer science and director of the Morphology, Evolution & Cognition Laboratory at UVM. A celebrity of sorts in his field, Bongard is collaborating on the xenobots project with researchers at Tufts and Harvard universities. They include Michael Levin, a biology professor and director of Tuft's Allen Discovery Center.

Their goal has been to explore how AI can be used to design and build robots out of cells rather than conventional materials such as metal, plastics and computer components. These novel creations could be the prototypes for an entirely new class of autonomous organic machines. One day, xenobots could be used in cutting-edge medical therapies, as environmental cleanup tools and as tools to decode the mysteries of how cells function.

Xenobots also raise an important philosophical question: What is life?

In January 2020, Bongard and Levin's team published its first paper on how its AI designed a xenobot that could walk. In March 2021, it published another about making a xenobot that could swim.

The team's latest paper, "Kinematic self-replication in reconfigurable organisms," published last month in the Proceedings of the National Academy of Sciences of the United States of America, attracted considerable attention, both within the scientific community and from the mainstream press with good reason. The paper describes using a supercomputer to design self-replicating robots out of living cells, a feat that immediately brings to mind scenarios like those in The Terminator or Jurassic Park. In fact, in the latter movie, it was scientists' use of frog DNA that let the prehistoric genie out of the bottle.

Notwithstanding Hollywood's scary-monster scenarios, xenobots are tiny, simple, innocuous organisms, Bongard said, as unlikely to escape from the laboratory and take over the world as a skin biopsy. Swallowing one or several would cause you no harm whatsoever.

What exactly are xenobots, and how are they made? As Bongard explained, researchers take about 5,000 genetically unmodified frog stem cells, then rearrange them into a new pattern that would never arise in nature.

One of the first surprises for the researchers, Bongard said, was the discovery that, once they rearranged frog cells, not only would the new cell grouping not die or revert to its natural shape, it would also retain its new form.

"We all learned in high school biology that frog DNA encodes frogs and human DNA encodes humans. It seems that's not the case," Bongard said. "Whatever genes are doing, it's more complicated than that."

In these experiments, the frog DNA remained unchanged. What defined the xenobot was not its DNA but how its cells were configured. "In xenobots," Bongard explained, "the shape ... dictates what a xenobot is going to do. It dictates whether it's going to replicate, how it replicates [and] what it's going to look like."

To find a shape that would create more xenobots, the team used a form of AI called an evolutionary algorithm, which was programmed into DeepGreen, UVM's Colchester-based supercomputer. It created a virtual environment, akin to a video game, using virtual cells it assembled at random. It then put that virtual xenobot into a virtual petri dish and watched what happened.

If the virtual xenobot couldn't replicate itself, the AI discarded that design and moved on to a new one. If it created a virtual xenobot that could replicate itself somewhat, the AI continually revised and tested the design to improve it.

After testing numerous designs at lightning speed, the AI finally created a virtual xenobot that was adept at self-replication. This evolutionary process, which in nature would take millennia, took DeepGreen about a month, Bongard said.

Next, Doug Blackiston, a senior scientist at Tuft's Allen Discovery Center and Harvard's Wyss Institute for Biologically Inspired Engineering, meticulously assembled actual xenobots by hand under a microscope using frog cells. The process took about four hours per xenobot.

Blackiston then put them into a petri dish and watched what they did. At slightly less than a millimeter in diameter, each xenobot was visible to the naked eye and looked like a poppy seed that moved around the petri dish using its cilia, hairlike structures on its exterior.

As the xenobot moved, it accumulated loose cells in its environment, like a broom collecting dirt. A video posted on the Proceedings of the National Academy of Sciences website described the process thus: "A swarm of frog-cell parents push frog cells into piles that mature into self-moving 'children.'"

Because frog cells contain a small amount of yolk that powers them, each xenobot lasted about 10 days, after which the yolk was depleted and the xenobot stopped functioning.

But as Bongard pointed out, the second-generation xenobots are neither clones of the original xenobot nor its descendants in the traditional biological sense. While each cell contains a set of chromosomes and DNA, when a xenobot produces offspring, it doesn't impart its genetic material onto the next generation. (This is one reason the team uses the term "replicate" rather than "reproduce.") In fact, the DNA between a "parent" and "child" xenobot may be the same or different.

The project draws ideas from various scientific disciplines. Blackiston spent much of his career researching how life-forms morph from one form to another, such as how tadpoles become frogs and how certain animals regenerate limbs and organs. A butterfly researcher, he spent years studying how memory is carried over from caterpillar to butterfly.

Blackiston, who had no prior experience in robotics or computer science before joining the team, said that the overwhelming response to the work has been "positive and huge." Why?

"It's exciting. It's scary. It's a little bit creepy but also cool," he said, "so everyone sees something in this project that they like."

Perhaps not everyone. Blackiston said his work has "enraged" some fellow developmental biologists, in part because of the terminology the team uses, such as describing xenobots as "biological robots," or "biobots."

"I've never heard a roboticist bat an eye when you call it that," he said. "They'll say, 'Who cares what the material is? I don't care if it's made out of wood or metal or cells. If it's something you design, it's programmable and it moves, it's a robot.'"

Even the word "organism" is problematic, he said, because biologists themselves can't agree on a definition. Some argue that an organism must have certain signatures of life, such as growth, metabolism and the ability to reproduce. But that definition immediately raises red flags, he said. A mule, which is a cross between a horse and a donkey, is sterile but still considered an organism.

Xenobot research also raises metaphysical questions, such as whether the team has created a new life-form and, if so, what ethical norms it should follow.

Jeantine Lunshof is a philosopher and ethicist at Harvard's Wyss Institute and a member of the research team. Though Lunshof is neither a biologist nor a roboticist, "I would use the term 'new life-forms,'" she said.

Lunshof poses ethical reality checks for the researchers, asking philosophical questions that other team members might not necessarily pose, such as: What are the larger implications of this research? What are the potential risks and harms compared to the potential benefits?

As a bioethicist, Lunshof said, she was acutely sensitive to the fact that this research became public in the midst of a pandemic, especially given speculation that the SARS-CoV-2 virus had escaped from a government laboratory. Early on, she inquired about biosafety and potential hazards should this material leave the lab. The team reassured her that it works with cells gathered at no harm to the frogs, which regularly shed these kinds of cells into the environment.

Nevertheless, some of Lunshof's colleagues believe that her involvement in the project puts her personal and professional reputation at risk. Social scientists have been particularly critical, she said, accusing her of "being in the wrong camp" and giving the research her ethical seal of approval.

"There is a very common misunderstanding that ethics is a justification process, that I give research legitimacy by not condemning it, which is completely wrong," she said. "As scientists and ethicists, we need to earn the trust of the public ... and we need to be good stewards of that trust."

To that end, if Lunshof were to see scientific decisions or practices that she found ethically unacceptable, she could bring them to the attention of the universities' institutional review boards, which must approve and oversee all research involving living creatures.

Lunshof also considers what society might forgo by not pursuing this kind of research.

Possible applications for xenobots are diverse, Bongard said. They could include cleaning up radioactive and other toxic contamination and removing microplastics from the oceans. His colleagues at Tufts and Harvard cited the long-term potential for developing cancer treatments, regenerative therapies for regrowing damaged organs and diseased tissues, and even antiaging and life-extending technologies. There's also interest in using xenobot technology to grow meat in laboratories rather than on factory farms, which could offer both environmental and ethical benefits.

Blackiston is intrigued by the potential to design xenobots for environmental use. Currently, if conventional robots break down or lose function, they pollute their environment with batteries, heavy metals and other debris.

By contrast, xenobots could detect pollutants in waterways and study the root systems in hydroponic growing operations. They might coat the exterior of a decaying bridge and reinforce its structure, then biodegrade once the task is complete. Unlike drones or conventional robots, xenobots could perform their work without direct human or computer intervention.

For now, scientists are using xenobots solely for basic research. Bongard likened them to microscopes that eventually may help scientists better understand how cells communicate and what causes them to malfunction, as in cancer cells.

"As scientists," he said, "we don't have a good handle on the language of cells, what they say to one another and the conditions under which they change their tune."

Regarding the philosophical question of whether xenobots have crossed the threshold into a new life-form, "I'm not a biologist, so I'm not going to throw my hat into that ring," Bongard said. "Whatever xenobots are, they're putting more pressure onto our assumptions of what life-forms are."

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A UVM Researcher Uses Artificial Intelligence and Frog Cells to Create Self-Replicating 'Xenobots' - Seven Days

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.

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The Science of Machine Learning - Pace News

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.

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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 competence percentage based on actual games that happened, and that competence 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. Weve got soccer and cricket, we've got all sorts of data on the platform. 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 -...

13.01.2022From Tobias Gundermann

The initial question in the title of this article might seem a bit odd at a first glance as it is probably rarely the case that the terminologies "game theory" and "metal casting" are used together in one sentence. So, how can both be brought together so that one serves as a baseline to optimize the other?

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The answer to that question lies in data science, machine learning and the increasingly arising field of explainable artificial intelligence (XAI). If you are curious how it works, then take a few minutes and read on!

Let's first clear the dust a bit and have a short look at what "game theory" actually is.

"The branch of mathematics concerned with the analysis of strategies for dealing with competitive situations where the outcome of a participant's choice of action depends critically on the actions of other participants." [1]

Well, the definition from Oxford Dictionaries doesn't seem to help much for our understanding, so let's visualize it with the following example:

Consider you have a football game with 11 players in each team. After thrilling 90 minutes of high-class football, both teams split up 2:1. The principles of game theory could now be used to find out how much each of the players contributed to the end-result. (Basically, how valuable were the individual players for their team.)

There are various approaches to calculate the contributions of the individual players. The specific approach we will have a look at in the following lines of this article are Shapley Values (invented by Lloyd Shapley in 1951). Shapley values are used to calculate the average marginal contribution of each individual player - basically the average contribution of each player across all possible orders in which they can be brought into the match. [2]

Further, let's take the use-case of quality prediction in the casting of aluminum wheels with the low-pressure die casting process. In this, molten and degassed aluminum is stored in the holding furnace of a low-pressure casting machine. The casting process takes place in 3 steps:

a) The pressurization in which pressure is applied to the holding furnace which causes the molten aluminum to rise through the riser tube into the mold

b) Filling up the mold during which the pressure is increased to fill the mold in a controlled and uniform way

c) The solidification in which a high pressure is applied to prevent shrinkage in the casted wheel.

The problem faced by our customer in this case were microporosities, blow holes and shrinkage which lead to an increased cost & remelting, excessive emissions and reduced OEE.

To enable operators, shift supervisors, process engineers and foundry managers to proactively take corrective actions in order to avoid scrap, a machine learning (ML) based model can be developed to predict the quality of the casting during the LPDC process. This model then takes real-time data collected within the production process (e.g. temperatures, air cooling rates, pressures etc.) to continuously monitor the casting process in near real-time.

The predictive quality model helps to detect quality deviations as early as possible and enable the engineers to make adjustments and to eliminate the root-causes of the quality deviations. But what if the root-causes and measures to be taken are unknown?

That's exactly where both of the terminologies "game theory" and "process optimization" come together and the connection of these is explainable artificial intelligence (XAI).

Explainable Artificial Intelligence (XAI) describes a field of research for the development, advancement and improvement of methods to make predictions or classifications of ML-based models interpretable and/or functionally comprehensible.

Given a ML-based model which predicts the product quality based on the collected process parameters, technologies such as SHAP (SHapley Additive exPlanations; based on the above-mentioned Shapley-Values) can be used to determine the most influential process parameters (players) with regard to their effect (contribution) on the product quality (result of the match). This is achieved in the form of so-called feature importance scores which assign a value to each of the input parameters of the model depending on their effect of the output of the model. [4] A visualization of SHAP-values can be seen in the following extract from the TVARIT Industrial AI Software (TiA) for the quality prediction in the aluminum casting process (please note that the concrete parameter names and values have been changed due to data privacy reasons):

Extract from TiA (TVARIT Industrial AI Software) for quality prediction in the aluminum casting process; Feature Importance.

(Source: TVARIT GmbH)

Extract from TiA (TVARIT Industrial AI Software) for quality prediction in the aluminum casting process; SHAP Values.

(Source: TVARIT GmbH)

With the help of SHAP-Values in the form of feature-importance scores, manufacturing engineers receive information on the most influential parameters for achieving the target product quality based on the collected data from their production processes.

Now given that quality deviations of castings are known in real-time and game theory helps to understand the root-causes of casting errors, the remaining question is still: How do the casting set-points need to be adjusted dynamically to avoid casting errors?

In this case, we have to go one step further than conventional game theory but modern AI technology also provides a solution here: So-called prescriptive dynamic recipes. These give dynamic recommendations for optimal casting set-points.

The methodologies used here are advanced clustering methods that determine how the various influencing factors (data such as set-points, pressure and temperature curves in the casting machine and ambient conditions in the foundry etc.) play together to create a good (or rejected) casting.

If you have difficulties in following the last sentence, dont worry let's break it down step-by-step:

Clustering: "Cluster analysis is the name given to a set of techniques which ask whether data can be grouped into categories on the basis of their similarities or differences [4].

This time, the definition of Andrew M. McIntosh gives us pretty concrete hints on how this might work put into manufacturing practice: The clustering is applied to group castings by their similarity. The metrics used to measure the similarity here are the influencing factors (data such as set-points, sensor values etc.) for that particular casting (or that particular batch).

Prescriptive analytics then identify which of these groups (called clusters) have the best quality results which then can be used to identify the optimal values for the influencing factors. This can be seen below in the Principal Component Analysis (basically a 2-dimensional representation of the influencing factors for the sake of visualization). Here, the green group has been identified as the optimal group (cluster) of influencing factors as the castings (the red crosses) that lie in that area have the best quality results. The gradient of the crosses indicates the timing (the darker crosses are the most recent castings).

Principle component analysis.

(Source: Tvarit GmbH)

Okay now that we got pretty technical and understand that prescriptive analytics define the optimal values of influence factors: How can this be used to reduce scrap?

Put in practice, the knowledge of the optimal values of influence factors can be used to define how the set-points need to be adjusted so that the casting process lies within these optimal cluster. These recommendations are then called prescriptive dynamic recipes (shown in the screenshots below).

Prescriptive dynamic recipes.

(Source: Tvarit GmbH)

To get back to the initial question of this article: Artificial intelligence and game theory help to optimize casting processes in the following way:

[1] Curtis, S. (2013). The Law of Shipbuilding Contracts (4th ed.). Informa Law from Routledge. Definition of game theory. (2018). (Oxford University Press) Retrieved May 2018, from Oxford Dictionaries: https://en.oxforddictionaries.com/definition/us/game_theory [2] Shapley, Lloyd S., und Alvin E. Roth, Hrsg. The Shapley Value: Essays in Honor of Lloyd S. Shapley. Cambridge [Cambridgeshire] ; New York: Cambridge University Press, 1988. [3] Lundberg, Scott M., Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, und Su-In Lee. Explainable AI for Trees: From Local Explanations to Global Understanding. arXiv:1905.04610 [cs, stat], 11. Mai 2019. http://arxiv.org/abs/1905.04610.%5B4%5D Andrew M. McIntosh, ... Stephen M. Lawrie, in Companion to Psychiatric Studies (Eighth Edition), 2010

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How Artificial Intelligence and Game Theory Can Help to Reduce Scrap in Metal Casting - SPOTLIGHTMETAL

ROCKVILLE, Md., Jan. 10, 2022 /PRNewswire/ -- America is getting older faster. According to the U.S. Census Bureau, the number ofpeople aged 65 or older in the United States will grow to 95 million by the year 2060 and will account fornearlyone-quarterofthepopulation.Artificialintelligence(AI)andtechnologysolutionshaveasignificantpotential to transform quality of life and improve health care outcomes for older Americans, includingthosewithAlzheimer'sDisease andRelatedDementias(AD/ADRD).

To meet this challenge, the AI/Tech + Aging (a2) Collective is announcing the a2 Pilot Awards, a nationalcompetitionthatwillearmark $40millionover thenext5years forpromisingpilotprojects that leverageAI and other transformative technology to support healthy aging and persons living with AD/ADRD. Thea2 Collectiverepresents the National Institute on Aging's (NIA) Artificial Intelligence and TechnologyCollaboratories for Aging Research (AITC) program, which is dedicated to helping Americans live longer,betterthroughtheapplicationofAI and emergingtechnologies.

"AI and transformative technology that supports America's aging population is projected to be a multi-trillion dollar market opportunity," says Stephen Liu, a2 Collective's Managing Director Head ofMarketing & Business Development. "Tech giants and AI startups cannot afford to overlook how thisdemographicwill interactwiththeemerging #AgeTech economy."

The a2 Pilot Awards is funded by the NIA through three AITCs at Johns Hopkins University, theUniversity of Massachusetts-Amherst, and the University of Pennsylvania, with coordination supportprovided by Rose Li & Associates, Inc. Pilot applicants can request up to $200,000 in non-dilutive directcosts to be expended within a 12-month period, with multi-year commitments and time extensionsdetermined bytheawarding AITC.

Each AITC will offer pilot awardees access to a wealth of resources, translational services, and state-of theartfacilities,includingsoftwareandhardwareplatforms,datasets,andpopulationstudysites.

Awardeeswillalsobeeligibletoapplyfor$10,000inAWScredits.

Applications for the inaugural a2 Pilot Awards will be accepted from January 10 through February 18,2022.Pleasevisitoura2PilotAwardswebsiteforeligibility requirements.

CONTACTS:

Stephen C. Liu, Managing Director Head of Marketing & Business Development [emailprotected] 1.310.210.7066

Robert Verhein, Managing Director Head of Operations [emailprotected] 1.240.552.9224

SOURCE a2 Collective

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$40M Available for Artificial Intelligence and Transformative Technology Innovators to Improve Care and Health Outcomes for Older Americans -...

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

Galleon and Birdoo are the latest innovative solutions to help maximize animal health and flock performance

WAYZATA, Minn., Jan. 11, 2022 /PRNewswire/ -- Poultry producers are always looking for ways to improve the efficiency and health of their flocks. Advancements in technology, like artificial intelligence, can help accelerate these improvements, identify previously unknown problems and anticipate outcomes. Cargill is assembling a portfolio of AI-driven innovations through proprietary development and strategic partnerships to go beyond nutrition and help customers optimize their operations with actionable insights.

Galleon Microbiome Analysis a comprehensive broiler microbiome health assessment toolThere is an interdependency between the condition of the gut microbiome and the flock's health. Therefore, understanding the gut microbiome allows producers to optimize animal health and performance. Cargill's patent-pending Galleon tool enables broiler producers to decide how changes such as in raw materials, diet, additives, vaccine programs, and farm management practices influence the microbiome of their flock.

Using a simple swab from a live bird, Cargill scientists analyze a customer's flock health using Galleon's robust database of poultry microbiome, developed over a decade using a global data set and nearly 100 trial studies. The analysis is further augmented using statistical analysis, machine learning and artificial intelligence capabilities to provide producers with a comprehensive report and recommended interventions to address issues. In addition, results are unbiased towards a specific product.

Galleon can help producers:

Improve the flock's performance for economic benefit by means including but not limited to switching feed and additives or improving management practices.

Support a healthy flock.

Help provide information when a flock is not performing well.

Pinpoint reasons why different farms with the same inputs, like feed management and antibiotic regimens, have different performance results.

Story continues

"An animal's gut microbiome influences its health in so many ways," said Cargill's principal microbiome researcher, Dr. Briana Kozlowicz. "We've accumulated an industry-leading volume of microbiome data that we can now tap into to provide actionable insights to our customers to improve the performance of their flocks."

Birdoo for real-time, hands-free measurement of broiler weight performance through advanced imaging and predictive analysisFeed is the highest cost input for poultry producers and the primary contributor to their birds' health. At the same time, obtaining accurate animal weight is a time-consuming and labor-intensive flock management effort.

To help producers better track broiler performance, Cargill has teamed up with digital technology enablement firm, Knex, to develop 'Birdoo," a first-of-its-kind technology that leverages proprietary computer visioning and artificial intelligence that combines hands-free, real-time flock insights with predictive modeling data. This helps producers make informed decisions quicker while supporting their bottom lines through better animal health and well-being, increased uniformity and improved performance of their flocks.

Birdoo will help Cargill producers:

Replace manual weighing with precision through 3D imaging: Get greater than 95% accuracy on broiler weight estimation with no labor required to clean or calibrate devices, thus improving human and animal welfare.

Track broiler performance and weight variability in real-time: A cloud-based platform allows farmers, technical assistants, nutritionists and management to track animal performance and anticipate issues for better resolutions and outcomes.

Reduce processing variability and save on costs through better harvest planning: Weight prediction data helps planners harvest flocks more efficiently and sustainably by improving the feed conversion ratio and saving on feed (on average 10-30g per bird), thus reducing variability and the number of downgrades at the processing packing plant.

"We talk with our customers every day, listen to what they need, and are committed to delivering innovative solutions, like Galleon and Birdoo, to help their businesses thrive," says Adriano Marcon, President of Cargill's animal nutrition business. "We're combining our deep animal nutrition expertise with leading-edge technologies to deliver actionable insights that address their unique animal health and production challenges."

Cargill innovation demos at the International Production & Processing Expo (IPPE)To see in-person demonstrations of Galleon and Birdoo at the upcoming IPPE, visit the Cargill's booth #B8159 and attend the "Tech Talks" featuring Galleon on January 25 and Birdoo on January 26.

Additional broiler performance solutions for poultry producers to consider

BinSentry for safer, hands-free measuring of feed bin inventory; and

COMPASS by Intelia for real-time environmental and broiler performance tracking.

To learn more about any of Cargill's solutions for poultry production, reach out to your local Cargill representative.

About CargillCargill's 155,000 employees across 70 countries work relentlessly to achieve our purpose of nourishing the world in a safe, responsible and sustainable way. Every day, we connect farmers with markets, customers with ingredients, and people and animals with the food they need to thrive.

We combine 155 years of experience with new technologies and insights to serve as a trusted partner for food, agriculture, financial and industrial customers in more than 125 countries. Side-by-side, we are building a stronger, sustainable future for agriculture. For more information, visit Cargill.com and our News Center.

Cargill is the exclusive market provider for Birdoo in the Americas.

Cargill, Inc. (PRNewsFoto/Cargill) (PRNewsfoto/Cargill, Inc.)

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Cargill expands portfolio of artificial intelligence-powered innovations to give poultry producers actionable insights - Yahoo Finance

Crypto industry peaks deserve more than two hands to handle; more traders are looking into AI as an advantage. Quick analysis and making the right calls at the right time can be a game-changer. Will AI be the new frontier in the crypto markets?

Admittedly, precise calls in the crypto space or any other industry are scarce, with complex graphs and other determinants to contemplate. Decent patterns at the right pace, coupled with a thirsty investor, can always get the job done. Therefore, in the latter part of 2021 and well into 2022, AI is vital, probably the missing piece on how to short bitcoin on trading platforms such as PrimeXBT.

Opening positions early and investigating them can create value, albeit these smart moves have been there for a while. Crypto bots have had their time in the crypto space for some time, bringing valuable information and highlighting unpopular moves that give impressive outcomes. Achieving smart and efficient abilities to gain an advantage goes beyond basic financial knowledge, and brokers contemplating this require advanced programming knowledge.

Tons of bots on the web exist for this sole reason, no wonder many warnings to pick the ones that complete the job efficiently. Decentralized AI platforms create two-way communications with traders, and give almost accurate predictions.

However, it is imperative to understand how AI bots work before putting them to task, as some smart technologies make use of neural networks. Such networks can determine the dynamics in the market, predict market fluctuations and make accurate descriptions of the next days trading patterns. Some AI bots have made accurate predictions better than experienced traders have, and improvements on these tools will be valuable in PrimeXBT and other trading platforms.

For humans, it is a tiring endeavor to peer through tons of data to make charts and other predictive elements. Nevertheless, with AI, it is easy to go through magazines, forums, blogs, and articles to understand the direction of different digital assets. AI goes through this in record time and comes up with better charts to determine future trends; some take a fraction of the time used by humans. Noteworthy, these tools can come in handy for novice traders and reduce the workload for experienced ones.

Crypto markets are awash with many issues to contemplate and can squeeze the juice out of the traders. Investors contend with a lack of liquidity, sudden fluctuations, and disappointing pricing on digital assets. In addition to that, high fees often dampen the mood, which to some extent discourages some traders. Some machine learning tools are helping solve these problems. Side matching protocols are a thing and have helped connect bankers and brokers with lower fees to consider.

Money Token already uses AI to give customers loans using crypto as collateral. Further, Amanda, the Money Token AI tool can handle complex issuesissuing loans and customer support. The support ends when the loan is repaid, meaning the AI tool is fully autonomous with occasional tweaks from Money Tokens developers.

While impressive, AI tools are a major security concern. Some tools created by anonymous developers are out to steal API keys for platforms such as PrimeXBT and different exchanges. Therefore, traders must understand the AI tools they entrust their information with to prevent fraud. Most bots cannot work without API keys, though this is a sign to be cautious, users must further investigate a bot. Steps such as activating two-factor authentication provided by exchanges are simple ways to safeguard crypto assets.

Noteworthy, the security issues should not frustrate the understanding of how important trading bots are in the crypto industry. They are not human, so not prone to emotions. AI tools analyze an instant and lastly make minor mistakes when programmed in the right way. AI tools are valuable in the highly volatile crypto industry, making the right call at the right time can be that slight advantage for a novice trader.

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Trusting Artificial Intelligence (AI) with Crypto Tradesis it Time to Ditch the Hard Work? - The Tech Education