All of us have heard of the term artificial intelligence. Often abbreviated simple as AI, this technology has actually existed since before the 1970s. The only major difference is that it was nearly impossible to implement on a large-scale basis until relatively recently. AI is used as a predictive tool within online search engines.

It determines what pop-up advertisements you see when visiting specific websites. It can even be used to enhance the security and comfort of your home. So, it only makes sense that artificial intelligence has made its presence known within the world of virtual sports betting. Lets take a look at why this is great news for players and the entire industry.

One notable advantage of artificial intelligence involves the ability to create more accurate odds in relation to a specific sporting event. This is accomplished through the use of advanced algorithms and all technicalities aside, players are simple provided with more information during any given session. This enables them to make relevant decisions at the most appropriate times; increasing their chances of walking away a winner. Platforms with a Greater Degree of PersonalisationArtificial intelligence can also be used to create a more personalized betting experience. Here are some examples to consider:

It is therefore clear to understand why the majority of sports betting enthusiasts are keen to become involved with a provider that is able to offer a more organic experience.

We need to keep in mind that the presence of artificial intelligence can be seen across the entire online gaming industry. Whether referring to entertaining platforms such as Mega Moolah which offer truly massive jackpots or slots with player-specific bonuses, the future is here today.However, there is one major difference in terms of sports betting. AI has the ability to collate massive amounts of data at any given time. Those who are provided with more information are more likely to make informed wagering decisions. In the past, this would have to be performed manually. Artificial intelligence programs can now scour the Internet for the latest sports-related news and updates in a matter of seconds.

We should finally end by addressing an important question. Is artificial intelligence set to dominate the world of online sports betting into the foreseeable future? Some industry analysts firmly believe this observation while others claim that the human element will remain at the center. Either way, it will be interesting to see what is in store.Above all, even the most advanced AI platforms can only go so far. Successful wagers will still rely heavily upon time, patience and experience. Those who are able to leverage the best of both worlds should therefore perform quite well.

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How Artificial Intelligence is Improving the Online Sports Betting Experience - MWWire

Part one of this discussion appeared a few days ago. Before we continue with part two, here's a look into the past.Twenty years ago, I conducted an interview with Frederic Friedel for Europe checs entitled "The Nightmare of the Kings". It was on the occasion of the match "Brains in Barhain" that Kramnik and Deep Fritz played, in October 2002, in the Persian Gulf. Here are two excerpts from this interview in which we were already talking about Artificial Intelligence and ethics.

Europe-Chess:First of all, a question of ethics. In your opinion, in a world increasingly controlled by computers, is the future of the Earth in better hands with programs, rather than with humans?

Frdric Friedel:"I don't know, but what I do know for sure is that we humans have done a terrible job. We have exterminated hundreds of thousands of species in just one millennium. We have persecuted, tortured, terrorised, and also spread misery. Today, we tolerate that almost half of humanity lives below the poverty line, that millions of people suffer from malnutrition. On the other hand, we have a small privileged group of people, each of whom can afford the equivalent of 10,000 lunches a minute in a luxury restaurant. Could an "IT administration" do a better job? Honestly, I don't know, but I have the irrational feeling that computers could maybe improve on it.

EE:In what way do chess programs show intelligence?

Frederic Friedel:"In addition to its extraordinary capacity of calculation of two million positions per second, the performance of Fritz is already 'intelligent'. Fritz is undoubtedly a highly successful application of this branch of computer science,even if its mode of reasoning is different from that of a human being. Humans use their experience, their intuition. They use long term planning, starting with the understanding a position. Fritz, on the other hand, adds, subtracts, compares! Before reaching a fundamental analysis of the position, it performs hundreds of millions of micro-actions. Chess is oneparticular universe. In other fields, such as music, the applications resulting from Artificial Intelligence would be totally different. A program could give you the illusion of listening to Bach, but a virtuoso musician would immediately make out the difference. Whereas Fritz is able to play Kasparov-like games! ... I studied philosophy, and worked on this subject: what is intelligence? Fritz is intelligent, in a sense that this concept will have in twenty years' time."

Now on to part two of the telephone discussion I conducted with Frederic in December 2020.The article appeared in the February 2021 issue of Europe checs, whichcan be bought here.Jean-Michel was advised and guided byHenri Assoignon, from the administrative desk of Europe Echecs.

Self-awareness

It's just a machine. It has no consciousness or feelings as we understand them. We have specific connections in our brain that make us react according to the circumstances, the situations we are experiencing. We interpret them as pleasure, pain and all other kinds of emotions. We would have to invent a new word to express what computers "feel". They may be stronger than us in many areas, but they are not aware of it. In the human sense, self-awareness is precisely what distinguishes human beings, as well as some animals, from all other species. In my opinion, computers will achieve what experts call "singularity" in the relatively near future. I think that within 20 or 30 years they will be as intelligent as we are. They will be able to build new computers themselves, which they are already doing, by the way. Today's processors, with hundreds of millions of transistors, are mainly designed by computer algorithms. My son is a very competent programmer. Today he no longer writes programs. He tells the computer what he wants to program, and the computer does it for him. Instead of just writing a program, he writes programs that write programs for him.

When they're as smart as we are, they won't just build the cars, like the ones they already help to design. They will do everything faster and better than humans. What we don't know is what will happen when they are 10, 50 or even 100 times smarter than us. One thing is for sure. We can't stop them. We can't stop Artificial Intelligence by pressing an "off" button. If the European Union and the United States, for example, were to decide to stop AI completely, other countries, such as South Korea, Japan, Iran, India or Israel, may continue on this path. Computers create vast amounts of wealth and energy. They help design nuclear reactors, super-efficient electric or hydrogen cars, they can optimize production or even run the whole economy. We won't be able to stop that. They can help us, in general, to improve our lives. We may end up just telling them what we want and letting them decide how to do it. They may often improve on our wishes. In the future, they may be able to say to us: It's not a better car that you need, it's a new mode of transport. This will be the case in many fields of application, such as medicine, health, economy...

If we retain an optimistic vision, computers will be at our side. In the best-case scenario: they will listen to us and help us improve our lives. But there is a pessimistic vision. I use it to provoke people and make them think about these issues of the future. Let's say that computers become 100,000 times smarter than we are. They will be the ones to tell us what to do. They will decide, and we won't be able to do anything about it. We won't be able to destroy them. That's one possible scenario. But I like to continue to believe that they will make the world a better place for humans, that they will help us to preserve the environment, to improve our quality of life. I even hope that the computers will feel some sort of gratitude. They may think, Originally, it was these strange monkeys that created us. We have to take care of them." Knowing where AI is going is something that concerns all of us.

The famous game played by the computer Hal against an astronaut in Stanley Kubrick's film (released in 1968) is nothing more than a game between a computer and an amateur. Fritz could have played in the same way and he could have said to you, as early as 1992 or 1993: "Sorry, Frank, but you lost." Fritz is a program that can only do one thing: play chess. It can't take control of the spaceship, like in the film. HAL is indeed a form of Artificial Intelligence, as we conceive it from here "some time in the 21st century". Hal is self-aware. It has nothing to do with AlphaZero or Fat Fritz, which are just neural networks.

One of the key areas of chess programs is the exploration of new ideas. A program like Fat Fritz will show you moves that have never been played before. As I told you, if theory considers that you should not take the pawn, it may tell you: "just take it!" If you ask it why, it won't be able to answer you. To understand, you will have to play against it and find out for yourself why it is good. This is beneficial for chess because it invites players to be braver, to take more risks by testing new ideas on the chessboard. When I look at Magnus Carlsen's games, I can see that he works with AI programs. He is not the only one, of course.

The evolution of chess databases allows you to constantly upgrade your knowledge. ChessBase 16 does this automatically for you. You think you have found a new move in a certain variation. The program will sift through millions of games in a second or two to tell you that it is not new. It has already been played in seven or eight games. Here they are, and here's how the games continued! Or how they should have continues, because it has already considered this unplayed move. You can analyse with the program to understand perfectly what it says.

You can also ask the program to maintain your own repertoire of openings. You tell it what kind of variations you like to play. It replies: "Ok, give me time to think about it!" You pour yourself a coffee and come back to see the result. The program shows you a complete repertoire, as well as the most recent additions to each line. ChessBase 16 can tell you: "An amateur played this move. It is excellent, but he made a mistake a few moves later and lost." The program tells you instantly how he should have played. The program even knows what is good for an amateur, a strong club player or a super GM. It advises you accordingly.

When we created ChessBase in 1987, I had no idea what was going to happen, and I don't think anyone was either. Forty years ago I had made two documentaries on computer chess for German television. I was interested in what was then called "artificial intelligence", still in inverted commas. In one of them I said what computers will never be able to do. I was completely wrong. At the time, I thought they would never be able to drive a car, walk on two feet, recognise a human face, understand a speech. Today they can do all of that. Computers listen to us and talk to us. They understand our questions and are able to give us useful answers.

I don't know if the computers will be our friends. We have to find a way for them to remain at our service, to take care of humans, even if they become much smarter than us. Computers are not in competition with us. They don't need the resources of the earth, the trees, the water, or even the air. They just need energy, and there is a fantastic source of energy near us: the sun. It's a gigantic fusion reactor. A single asteroid is enough to maintain billions of AI entities. If they run out of energy, they just have to travel 1000 kilometres closer to the sun. And so, fortunately, computers are not going to fight us for terrestrial resources. They may see us as irresponsible people destroying our own planet. But they can also continue their own expansion in the universe.

If I give your name to Google, it knows who you are, your phone number, your address, the things you are interested in, the things you like to buy. If you give a name to ChessBase 16, the program will show you everything about that player: what he looks like, the evolution of his Elo rating, how he played at certain ages, his favourite systems, his favourite variations, his greatest tournament successes, etc. It allows you to prepare yourself against him, to adapt your game to his style of play. It can even imitate his style and play against you.

I am currently working on a project to make a weak chess engine. This is a personal project. If you have an Elo of 2500 or 2600, you can learn a lot by playing against Fritz. Below this level you may not understand anything about what he plays. I want a chess engine to be weaker. When my son played against the early versions of Fritz, he concluded that in chess you can never win material and you will always be crushed in less than 20 moves. Fritz was relentless. I want it to make human mistakes. The objective is to allow amateurs to enjoy playing, to learn to improve. Fritz 16 and 17 already have special "friend" levels that do this to some degree. This chess engine will play moves that allow the opponent to gain an advantage. It will then tell you if you have missed any opportunities. I want to improve this aspect, implement "Artificial Stupidity".

ChessBase has democratised the game and its practice to a large extent. Forty years ago, some players, Spassky, Karpov, Kasparov, had a considerable advantage in their preparation and training. They had their own teams of grandmasters who supported them. Their coaches were very expensive: "Ok, I'll show you how you could beat this opponent, but you pay me 800 or 1000 dollars, or you pay me a monthly salary." Today, if you want to train like the world champion, to have all the tools he uses, it costs you 200 to 300 Euros. We have democratised preparation. In tennis, the best players have special rackets and shoes. They have the best training conditions. In chess, everyone has the same tools. Garry Kasparov was the best player in the world and he had the best team of analysts. But he encouraged us to build ChessBase, mainly to share his advantages with everyone. For this I am eternally grateful to him.

"Chess playing computers are too strong for humans today. It was a mistake to think that if we developed very powerful chess machinesthe game would become boring, that there would be a lot of draws, (strategic) manoeuvres, or that a game would last 1800, 1900 moves, during which nobody could break through. AlphaZero is totally the opposite. For me, it was complementary, because it plays more like Kasparov than like Karpov! It discovered, in fact, that it could sacrifice material to launch an aggressive operation. It is not creative, it just sees patterns, the chances. But that makes chess more aggressive, more attractive. Magnus Carlsen said that he has studied the games of AlphaZero, and that he has discovered certain elements of the game, certain connections. He may have thought of a specific move, but never dared to consider it. Now we all know it works.Garry Kasparov

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Chess and Artificial Intelligence (2) - Chessbase News

DUBLIN, Feb. 15, 2021 /PRNewswire/ -- The "Global Automotive Artificial Intelligence Market By Component (Hardware, Software, Service), By Technology, By Process, By Application, By Vehicle Type, By Demand Category, By Company, By Region, Forecast & Opportunities, 2026" report has been added to ResearchAndMarkets.com's offering.

The Global Automotive Artificial Intelligence Market is expected to grow at a steady rate during the forecast period. The Global Automotive Artificial Intelligence Market is driven by the growing adoption of advanced automotive solutions such as advanced driver assistance system (ADAS), adaptive cruise control (ACC), blind sport alert, among others by different OEMs. Additionally, government regulations to improve the safety in vehicles while assuring environmental sustainability is further expected to propel the market. Furthermore, ongoing technological advancements and new product launches by the major OEMs operating in the market is expected to create lucrative opportunities for the market growth through 2026. However, lack of proper automotive IT infrastructure especially in the emerging countries can hamper the market growth during the forecast period. Besides, high procurement and operational costs further restricts the market growth over the next few years.

The Global Automotive Artificial Intelligence Market is segmented based on component, technology, process, application, vehicle type, demand category, company and region. Based on technology, the market can be categorized into deep learning, machine learning, context awareness, computer vision, natural language processing and others. The deep learning segment is expected to register the highest CAGR in the market during the forecast period on account of the increasing popularity and adoption of self-driving cars.

Additionally, the major players operating in the market are also investing a lot in the development of self-driving cars. While the computer vision segment is also expected to grow significantly on account of its use in autonomous vehicles for signal recognition, image recognition, driver monitoring, among others. Based on process, the market can be fragmented into signal recognition, image recognition and data mining. The data mining segment is expected to dominate the market owing to the large volumes of data being generated and processed in autonomous and semi-autonomous vehicles. Based on application, the market can be grouped into human-machine interface, semi-autonomous driving and autonomous driving. The human-machine interface segment is expected to dominate the market owing to the growing need for providing enhanced customer experience.

Regionally, the automotive artificial intelligence market has been segmented into various regions including Asia-Pacific, North America, South America, Europe, and Middle East & Africa. Among these regions, Asia Pacific is expected to register the highest growth in the overall automotive artificial intelligence market owing to the growing demand for premium vehicle and increased adoption of AI and AI based services and solutions especially among the autonomous and semi-autonomous vehicles in the region.

The major players operating in the automotive artificial intelligence market are NVIDIA Corporation, Alphabet Inc., Intel Corporation, IBM Corporation, Microsoft Corporation, Harman International Industries Inc., Xilinx Inc., Qualcomm Inc., Tesla Inc., Volvo Car Corporation and others. Major companies are developing advanced technologies and launching new services in order to stay competitive in the market. Other competitive strategies include mergers & acquisitions and new service developments.

Objective of the Study:

The publisher performed both primary as well as exhaustive secondary research for this study. Initially, the publisher sourced a list of service providers across the globe. Subsequently, the publisher conducted primary research surveys with the identified companies. While interviewing, the respondents were also enquired about their competitors. Through this technique, the publisher could include the service providers which could not be identified due to the limitations of secondary research. The publisher analyzed the service providers, distribution channels and presence of all major players across the globe.

The publisher calculated the market size of the Global Automotive Artificial Intelligence Market by using a bottom-up approach, wherein data for various end-user segments was recorded and forecast for the future years. The publisher sourced these values from the industry experts and company representatives and externally validated through analyzing historical data of these product types and applications for getting an appropriate, overall market size. Various secondary sources such as company websites, news articles, press releases, company annual reports, investor presentations and financial reports were also studied.

Key Target Audience:

The study is useful in providing answers to several critical questions that are important for the industry stakeholders such as service providers, suppliers and partners, end-users, etc., besides allowing them in strategizing investments and capitalizing on the market opportunities.

Key Topics Covered:

1. Product Overview

2. Research Methodology

3. Impact of COVID-19 on Global Automotive Artificial Intelligence

4. Executive Summary

5. Voice of Customer

6. Global Automotive Artificial Intelligence Market Outlook6.1. Market Size & Forecast6.1.1. By Value6.2. Market Share & Forecast6.2.1. By Component (Hardware, Software, Service)6.2.2. By Technology (Deep Learning, Machine Learning, Context Awareness, Computer Vision, Natural Language Processing, Others)6.2.3. By Process (Signal Recognition, Image Recognition, Data Mining)6.2.4. By Application (Human-Machine Interface, Semi-autonomous Driving, Autonomous Driving)6.2.5. By Vehicle Type (Passenger Cars v/s Commercial Vehicles)6.2.6. By Demand Category (OEM v/s Aftermarket)6.2.7. By Company (2020)6.2.8. By Region6.3. Product Market Map

7. Asia-Pacific Automotive Artificial Intelligence Market Outlook7.1. Market Size & Forecast7.2. Market Share & Forecast7.3. Asia-Pacific: Country Analysis

8. Europe Automotive Artificial Intelligence Market Outlook8.1. Market Size & Forecast8.2. Market Share & Forecast8.3. Europe: Country Analysis

9. North America Automotive Artificial Intelligence Market Outlook9.1. Market Size & Forecast9.2. Market Share & Forecast9.3. North America: Country Analysis

10. South America Automotive Artificial Intelligence Market Outlook10.1. Market Size & Forecast10.2. Market Share & Forecast10.3. South America: Country Analysis

11. Middle East and Africa Automotive Artificial Intelligence Market Outlook11.1. Market Size & Forecast11.2. Market Share & Forecast11.3. MEA: Country Analysis

12. Market Dynamics12.1. Drivers12.2. Challenges

13. Market Trends & Developments

14. Competitive Landscape14.1. NVIDIA Corporation14.2. Alphabet Inc.14.3. Intel Corporation14.4. IBM Corporation14.5. Microsoft Corporation14.6. Harman International Industries Inc.14.7. Xilinx Inc.14.8. Qualcomm Inc.14.9. Tesla Inc.14.10. Volvo Car Corporation

15. Strategic Recommendations

16. About the Publisher & Disclaimer

For more information about this report visit https://www.researchandmarkets.com/r/2d457n

Media Contact:

Research and Markets Laura Wood, Senior Manager [emailprotected]

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Global Automotive Artificial Intelligence Market (2020 to 2026) - by Component, Technology, Process, Application, Vehicle Type, Demand Category,...

LONDON, Feb. 16, 2021 /PRNewswire/ -- Opsmatix, an innovative provider of AI-powered omnichannel operations automationsolutions,today announcesthat Calastone, the world'slargest global funds network, has implemented the Opsmatix SaaS platformto process increasing business and email volumes into Calastone'sOperations Team.

Currently, Calastone supports some 2,500 clients in 44 countries and territories and processes over 200 billion of investment value every month. Opsmatix was selected following a rigorous proof of concept that demonstrated the system'sunrivalled automation capabilities in terms of categorising and understanding the intent of incoming client queries. The new system will enable the firm to scale itsclient handling capability as the firm growswhilst continuing to improve the clientexperience.This new approach reduces manual interaction on time-consuming tasks allowingthem to focus on more productive activities.

"We pride ourselves on providing aworld-class support serviceto our clients and look to how we can leverage the best technologies to drive continuous improvement,"says Mike Davies, Calastone'sGlobal Head of Operations.Opsmatix allows us to streamline the workflow management within the team enabling greater operational leverage and ultimately enhancing the overall client experience.Crucially we gain a much-improved system to manage workflow, together with an elegant case management user interface which enables us to categorise, escalate and manage any production issues in a more rigorous manner."

Justin Forrest, CEO at Opsmatix concluded. "We are delighted to be working and partnering with a customer of the calibre of Calastone. This relationship demonstrates Opsmatix'scapabilities and validates the many benefits the solution will deliver to the financial services sector and cross-industry. AI has come of age and is now a business imperative for all corporate operational functions using omnichannel communications involving unstructured data.Our goal is to be at the forefront of technology innovation and corporate advancement, and we are confident that Opsmatix has a pivotal partto play."

About Calastone

Calastone is the largest global funds network, connecting the world's leading financial organisations.

Our mission is to help the funds industry transform by creating innovative new ways to automate and digitalise the global investment funds marketplace, reducing frictional costs and lowering operational risk to the benefit of all. Through this, we generate the opportunity for the industry to deliver greater value back to the end investor.

Over 2,500 clients in 44 countries and territories benefit from Calastone's services, processing 200 billion of investment value each month.

Calastone is headquartered in London and has offices in Luxembourg, Hong Kong, Taipei, Singapore, New York, Milan and Sydney.

About Opsmatix

Opsmatix applies Artificial Intelligence (AI) to automate business communications and processes. It improves efficiency & quality, reduces repetitive tasks and accelerates operations based on multi-lingual long-chain omnichannel communications involving unstructured data and processes which require significant human intervention. Applications range from front-line customer service staff, contact centres and customer onboarding to manually intensive communications in the back office, including logistics and fulfilment. The OpsmatixSaaS platformsignificantly reduces the requirement for the wholesale offshoring of operational processing and call centres. The company was founded in 2018 and is basedin London.

Contact us via our website athttps://www.opsmatix.com/

SOURCE Opsmatix

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Calastone implements Opsmatix Artificial Intelligence solution Improving client handling and efficiency within client operations - PRNewswire

The COVID-19 pandemic affected the entire world in some way or the other.  |  Photo Credit: iStock Images

In a rapidly advancing globalisation that has turned the entire Earth into one huge village, speedy connectivity and communication also ensured a rapid advance of the COVID-19 pandemic that began with a strain of the novel coronavirus that first emerged in Wuhan, China in late 2019. Now, as per a science paper published in Nature Communications, "The spread of influenza can be modelled and forecast using a machine-learning-based analysis of anonymized mobile phone data. The mobility map, presented in Nature Communications this week, is shown to accurately forecast the spread of influenza in New York City and Australia."

The year 2020 dawned with the world bracing to handle a possible crisis and by the end of the year, global deaths reached nearly 2 million.

To cut the long story short, mankind has now been through so much in terms of mental agony, pain, loss, death, long-lasting illnesses and economic downslide - all on account of this pandemic - despite rapid advances in science - that it has begun to dread the prediction by environmentalists and scientists that we have just entered a pandemic era and more such pandemics are likely to come.

Predicting the onset of a Pandemic:According to a report in the BBC, a team of scientists has used artificial intelligence (AI) to work out where the next novel coronavirus could emerge.

The researchers are reportedly putting to use a combination of learnings from fundamental biology and tools pertaining to machine learning.

This is not mere conjecture and the scientists are taking ahead of what they have gained from similar experiments in the past. Their computer algorithm predicted many more potential hosts of new virus strains that have previously been detected.The findings have been published in the journal Nature Communications.

According to this report in Nature Communications, the spread of viral diseases through a population is dependent on interactions between infected people and uninfected people. The Building-models that predict how the diseases will spread across a city or country currently make use of data that are sparse and imprecise, such as commuter surveys or internet search data.

Dr Marcus Blagrove, a virologist from the University of Liverpool, UK, who was involved in the study, emphasises the need to know where the next coronavirus might come from.

"One way they're generated is through recombination between two existing coronaviruses - so two viruses infect the same cell and they recombine into a 'daughter' virus that would be an entirely new strain."

Scientists say that to get the prediction algorithm right, the first step was to look for species that were able to harbour several viruses at once. Lead researcher Dr Maya Wardeh, who is also from the University of Liverpool, successfully deployed existing biological knowledge to teach the algorithm to search for patterns that made this more likely to happen.

We were able to predict which species had the chance for many coronaviruses to infect them... Either because they are very closely related (to a species known to carry a coronavirus) or because they share the same geographical space.

This step concluded that many more mammals were potential hosts for new coronaviruses than previous surveillance work - screening animals for viruses - had shown.

How could the findings be useful?One thing that seems to be widely accepted is the claim by scientists that COVID-19 is not the last pandemic we are seeing and that scientists believe another pandemic will happen during our lifetime.The scientists say their findings could help to target the surveillance for new diseases - possibly helping prevent the next pandemic before it starts. But the researchers warn against demonising the animal species. They point out that "spill-over" of viruses into human populations tends to be linked to human activities like wildlife trade, factory farming and keeping animals cooped up in unhygienic conditions.

"But it's virtually impossible to survey all animals all the time, so our approach enables prioritisation. It says these are the species to watch," the University of Liverpool researcher added.

The scientists say the "ideal" use of this technique would be to help find viruses as they're recombining.

"If we can find them before they get into humans," said Dr Blagrove. "Then we could work on developing drugs and vaccines and on stopping them from getting into humans in the first place."

As they say, forewarned is forearmed.

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Liverpool scientists deploy Artificial Intelligence to develop model that predicts the next pandemic - Times Now

PHOTO:Adobe

Last September, Gartner published its Hype Cycle for AI in which it identified two emerging trends (and five new AI solutions) that would have an impact on the workplace. One of those trends was what Gartner described as the democratization of AI. While there are many ways that this can be interpreted, in simple terms what it meansfor workersis the general distribution and use of AI across the digital workplace to achieve business goals.

In the enterprise, the target deployment of AI is now likely to include customers, business partners, business executives, salespeople, assembly line workers, application developers and IT operations professionals. As AI reaches a larger set of employees and partners, it requires new enterprise roles to deliver it to a wider audience.

While this was an emerging trend last summer, with COVID-19 and the adoption of many new technologies to enable remote working, the widespread use of AI, while still only anecdotal, now appears to be an established fact in the workplace.

Bill Galusha of senior director of marketing at Calsbad, Calif.-based digital intelligence company ABBYY points out, however, that this is not a new phenomena. In the past couple of years, weve seen AI enabling technology like OCR and machine learning become more accessible to non-technical employees and partners through no code/low code platforms, he said.

He points out that thetechnologies designed to help workers understand and extract insights from content have been in high demand as more digital workers increase the number of tasks a knowledge workers have to perform.

In practical terms these new AI platforms enable users to design cognitive skills that are can be easily trained to take unstructured data from type of document like invoices, utility bills, IDs, and contracts, or access trained cognitive skills available through online digital marketplaces. This new approach to making it easy to train machine learning content models and deliver them as skills in a marketplace are certainly going to fuel the online growth and reusability of AI as businesses look to automate all types of content-centric processes across the enterprise, he said.

Related Article:The Risks and Rewards of the Citizen Developer Approach

However, if AI is being used widely across the enterprise, it does not necessarily follow that it is providing business value to every organization, according to Chris Bergh, CEO of Cambridge, Mass.-based DataKitchen, a DataOps consultancy and platform provider.

AI is being deployed everywhere we look, but there is a problem that no one talks about. Machine learning tools are evolving to make it faster and less costly to develop AI systems. But deploying and maintaining these systems over time is getting exponentially more complex and expensive, he told us.

Data science teams are incurring enormous technical debt by deploying systems without the processes and tools to maintain, monitor and update them. Further, poor quality data sources create unplanned work and cause errors that invalidate results.

This is the heart of the problem and one that is likely to impact the bottom line of any business that uses AI. The AI code or model is a small fraction of what it takes to deploy and maintain a model successfully. This means that the delivery of a system that supports an AI model in an application context, is an order of magnitude more complex than the model itself. You can't manage the lifecycle complexity of AI systems with an army of programmers. The world changes too fast. Data constantly flows and models drift into ineffectiveness. The solution requires workflow automation, he said.

There is another problem for businesses too. Given the explosion in the amount of data that is available to them, at first glance you would think that developing AI was getting easier and, consequently, easier to deploy democratized across the enterprise. Not so, according to Chris Nicholson, CEO of San Francisco-based Pathmind, which develops a SaaS platform that enables businesses to apply reinforcement learning to real-world scenarios without data science expertise.

The real problem, he argues is that you cannot decouple algorithms from data, and the data is not being democratized, or made available, across the organization. In many cases, as with GDPR, the data is getting harder to access and because the data is not being democratized, most startups and companies will not be able to train AI models to perform well, because each team is limited to the data it can access.

In a few cases, a general-purpose machine-learning model, can be trained and made available behind an API. In this case, developers can build products on top of it, and that very particular type of AI is slowly percolating into products and impacting customers lives. But, in most cases, businesses have custom needs that can only be met by training on custom data, and custom data is expensive to collect, store, label and stream, he said. At best, AI is a feature. In the best companies, data scientists embed with developers to understand the ecosystem of the data and the code, and then they embed their algorithms in that flow.

Like the discussion around citizen data scientists (and democratizing data science), business leaders need to know what they want this new democratized AI to do. They will not be able to design and build AI models from scratch; that will always require an understanding of what the underlying methods and parameters do, which requires theoretical knowledge.

Given some gray box AI systems, one can envision such systems learning to solve well-defined classes of problems when they are trained or embedded by non-AI experts, Michael Berthold, Switzerland-based KNIME CEO and co-founder, said. Examples he cites are object recognition in images, speech recognition, or probably also quality control via noise and image tracking. Note that already here choosing the right data is critical so the resulting AI is not already biased by data selection.

I think this area will see growth, and if we consider this democratization of AI, then yes, it will grow, he added. But we will also see many instances where the semi-automated system fails to do what it is supposed to do because the task did not quite fit what it was designed to do, or the user fed it misleading information data.

It is possible to envision a shallower training enabling people to use and train such preconfigured AI systems without understanding all the algorithmic details. Kind of like following boarding instructions to fly on a plane vs. learning how to fly the plane itself.

If organizations take this path to develop AI, there are two ways enterprises can push AI to a broader audience. Simplify the tools and make them more intuitive, David Tareen, director of AI and analytics at Raleigh, N.C-based SAS told us.

Simplified Tools - A tool like conversational AI helps because it makes interacting with AI so much simpler. You do not have to build complex models but you can gain insights from your data by talking with your analytics.

Intuitive Tools - These tools should make AI easier to consume by everyone. This means taking your data and algorithms to the cloud to become cloud native. Becoming cloud native improves accessibility and reduces the cost of AI and analytics for all.

In organizations do this, they will see benefits everywhere. He cites the example of an insurance company that uses AI throughout the organization will reduce the cost of servicing claims, reduce the time to service claims, and improve customer satisfaction compared to the rest of the industry. He adds that some enterprise leaders are also surprised to learn that enabling AI across the enterprise itself involves more than the process itself. Often culture tweaks or an entire cultural change must accompany the process.

Leaders can practice transparency and good communication in their AI initiatives to address concerns, adjust the pace of change, and result in a successful completion of embedding AI and analytics for everyone, everywhere.

Original post:

Why Artificial Intelligence May Not Offer The Business Value You Think - CMSWire

People put the intelligence in artificial intelligence

Is the digital workforce ready to take over? Well, not quite. Artificial intelligence may be capable of assuming many tasks, but it will be some time, if ever, that it could replace jobs on a widespread basis. It simply has too many limitations.

Instead, we need to acquaint a generation of workers with technologies to take on the more mundane, repetitive portions of their jobs, and in turn elevate their decision-making roles within enterprises. Thats the word from Steve Shwartz,AI author, researcher and investor, who points out that the notion of AI taking jobs is a myth. However, AI will have a profound impact on employment.

Shwartz, author of the just-published book Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity, points out that many people are concerned that intelligent robots will be able to read manuals, take courses, and eliminate all our jobs. Fortunately, this is science fiction.

Todays AI systems are only capable of learning functions that relate a set of inputs to a set of outputs, he says. This simple paradigm has enabled fantastic technological accomplishments such as facial recognition, language translation, and cars that can see and avoid pedestrians. However, these learned functions have no more intelligence than a function that translates Fahrenheit temperatures to Celsius temperatures.

It would take a huge breakthrough to create intelligent robots, and todays AI researchers have only vague ideas about how to create such a breakthrough, Shwartz says. Such a breakthrough is about as likely as time travel.

The bottom line is that any job that requires commonsense reasoning is safe; probably for our lifetimes. Maybe forever, he continues. People-oriented skills in finance, marketing, sales, and HR are probably safe. The types of jobs that will be impacted and not necessarily negative impacted are ones that involve repetitive decision-making that can be learned by AI systems.

Rather than replace jobs, AI is replacing tasks especially repetitive, data-oriented analyses are candidates for automation by AI systems. If it is possible to create a large training set of examples in which each example is labeled with the correct answer, that analysis can likely be learned by an AI system, says Shwartz.

Another task category that AI will enhance is repetitive customer service interactions, he continues. AI-based chatbots are assuming more customer-service work, and customer service jobs that involve a human following a script to interact with customers are at the most risk. Human interactions that require real, unscripted conversations are not at risk.

For non-technical careers, the greatest impact is the availability of massive amounts of data, Shwartz says. The field of marketing has already been transformed by data. Marketers analyze data from Google to determine which keywords to buy. They analyze huge amounts of customer data to determine which campaigns should be targeted to which customers. And they analyze massive databases of web traffic to determine what changes to make to their websites. Todays marketers need to be data analysts. Most companies are relying more and more on data to drive the business. Many formerly non-technical jobs now require extensive data analysis. Workers who do not adapt will be left behind.

While AI will be replacing many repetitive tasks and amplifying intelligence through data, the most exciting opportunities will be seen with the creation of new types of businesses. Shwartz was a founder of one of the first AI companies, Cognitive Systems, in 1986. As an angel investor, Shwartz now sees large numbers of startups whose business models are only possible because of AI technology: Computer-vision technology enables computers and robots to identify objects, faces, and activities. Startups are developing in-store products that identify customers and provide highly personalized offers direct to their smartphones. Companies are developing surveillance products for law enforcement and the military. Startups are creating AI-based medical applications to read MRIs and diagnose diseases. Other vendors are using other types of AI technology to detect fraud and stop cyber-attacks, analyze legal documents, predict the weather, improve search results, and even design golf clubs.

Along with achieving greater sophistication and better mimicking human reasoning, AI also brings additional challenges, Shwartz relates. Computer-vision systems have been shown to be biased against minorities. It is not only unethical for companies to roll out biased systems, but also bad for business. In Europe, due to GDPR regulations, it is illegal and similar regulations are almost certain to follow in the US. These biases are often created inadvertently using biased data. Ensuring systems are non-discriminatory can be harder than developing the technology in the first place.

More:

Needed: People To Put The Intelligence In Artificial Intelligence - Forbes

It has been a landmark year for Artificial Intelligence. What was once the reserve of science fiction is now becoming an intrinsic part of our everyday lives. From voice-controlled digital assistants in our homes to customer service chat bots, AI is now entrenched in the mass market. Most significantly, it has also been a year in which AI in healthcare has put down roots for a more radical transformation.

AI and machine learning have been quietly revolutionising the health sector for years by delivering everything from robotic surgery and 3D image analysis to intelligence biosensors that allow diagnoses and treatments to be managed remotely. But while the COVID-19 pandemic has been devastating, it has also catalysed technological developments in and awareness of healthcare AI. In the first quarter of 2020 alone, almost $1bn was invested in AI-focused healthcare start-ups and a recent projection shows the global industry growing at a rate of 44% until 2026.

The potential uses of Artificial Intelligence in the healthcare sector are vast, and the technology is rapidly gaining momentum with investors as a result. With its applications ranging from disease prevention and diagnostics to acute care and long-term disease management, the industry is reaching a tipping point in 2020 and AI is finally becoming mainstream.

Yet it still seems we have only scratched the surface; and like any revolution witnessed in real time, the possibilities are seemingly limitless. For healthcare providers and associated organisations, it remains a real challenge to turn vision into reality. To move from testing to regular use, and to change the patient experience more fundamentally, organisations wanting to engage with AI must approach the issue strategically.

The technology behind Artificial Intelligence is evolving at breakneck speed, but the real test of an organisation is how it can harness and implement that technology for its own ends. The pressure of the pandemic has no doubt accelerated innovations, but before we look at how they can be put into practice, it is useful to consider what AI actually is and what it looks like in a healthcare setting.

At its core AI is machine learning, which is comprised of three cognitive nodes: computer vision, natural language processing and data inference. Computer vision is the eyes of AI, as it is capable of recognising visual patterns, objects, scenes and activities in digital imagery far quicker humans. Natural language processing refers to the technology that recognises and understands spoken language. Structured data inference is the technology that uses data, most often numerical, to solve problems. We have seen exciting developments for healthcare in all three in 2020.

Take natural language processing, which has come under the spotlight during the pandemic as healthcare providers have been forced to move operations online. The telehealth industry has grown exponentially because it has enabled providers to automate and streamline basic services in order to free up resources to deal with the crisis. In France, for instance, telemedicine appointments increased from 10,000 to a staggering 500,000 per week during the initial peak of the pandemic.

Recent developments in AI show that telehealth can be more than a platform for consultation. One startup, Vocalis Health, is exploring the use of voice data as a biomarker for disease progression. Using AI, the technology can detect signs of pulmonary hypertension in specific segments of speech, which can be recorded into a smartphone. Similar efforts are being focused on voice-based COVID-19 screening apps and also on using data to track neurological conditions like Parkinsons disease. The potential for this is significant and it promises to elevate telehealth to whole new level.

Huge strides in healthcare AI have been made by larger operations too, such as Alphabets AI subsidiary DeepMind. In November, DeepMinds AlphaFold project revealed it had in large part resolved a half-century-old challenge for scientists by understanding how a protein folds into a unique three-dimensional shape. This paves the way for a much greater understanding of diseases and the creation of designer medicines. On a wider scale, it even can help break down plastic pollution. Once more, the implications are enormous and not only for research scientists but for the role of Artificial Intelligence in the healthcare sector as a whole.

AIs ability to solve incredibly complex problems using huge sets of data far surpasses our own; and for the decades ahead, the sky really is the limit for the businesses pioneering change so how can a healthcare provider think about effectively building-in such developments into strategy?

Artificial Intelligence is a vast field with many potential applications. There is no single, fool proof blueprint for its implementation, so healthcare organisations looking to harness its potential must make choices that fit their financial and technical capabilities.

The first key question that providers should ask themselves before embarking on their AI journey is: do we have the capacity to build out these capabilities in-house? Having the internal resources, proprietary data and capital to develop AI solutions in-house comes with obvious benefits in terms of control, but businesses will need to decide for themselves whether its realistic given their goals and timeline.

Next, should we consider partnerships or acquisitions? Even with the best resources and in-house capabilities, partnerships can rapidly increase the development and deployment of AI systems and tools. Investments in AI start-ups or acquisitions of smaller companies can also give an organisation fast access to development phases and provide greater expertise and capabilities.

Finally, businesses will need to think about which key enablers will accelerate their AI strategy. This means thinking about everything from building or acquiring new technologies, to leadership alignment and team allocation.

We know that AI can transform many aspects of healthcare; and as we have seen this year, it is evolving rapidly on a global scale. However, healthcare providers engaging with AI face specific challenges, especially when implementing it.

Data is AIs raison dtre: without a continuous supply of data, AI technology simply could not have achieved what it has to date. However, it can also be a nuisance for organisations which are grappling with the challenge of dirty data, which is not yet standardised and remains disparate. Privacy protocols and security requirements present additional barriers to progress, but as they concern protections for patient rights, these are hills that must be climbed. Consent for the use of patients data and the need to address perceived bias in algorithms are additional ethical issues of which all organisations must be wary.

Necessity is the mother of invention, which explains in part why so much ground has been made this year. However, the healthcare business model could do more to incentivise innovation. While there is a broad range of industry players in this sector, larger technology companies are known to lure talent away from start-ups, who also face difficulties scaling up their products without partnerships.

These challenges are certainly real, but they are by no means insurmountable. While the success of engaging with AI relies on careful preparation, it is an innovation that is not just worth pursuing, but one that will be integral to healthcares story in the years to come. As such, organisations need to prioritise AI initiatives and plan for implementation. On a basic level, this means ensuring leadership is on board and the right talent is being supported.

Many organisations throughout the healthcare chain are already deep into their digital transformation journey. While some of these will have well-developed AI strategies in play, others will not. It is worth bearing in mind that the road to AI-enabled healthcare is long, which makes having a strategy to turn vision into reality key to a successful journey.

Overall, approaches may vary and will be dependent on specialism and sub-sector. But what sets healthcare ahead of other industries is the universal recognition of the power of AI and machine learning, and the sheer scale from start-ups to multinational companies involved.

The medical landscape of tomorrow is likely to look very different, but it is down to healthcare organisations across the board to steer their own path in a future defined by Artificial Intelligence.

This article is from issue 16 ofHealth Europa.Clickhere to get your free subscription today

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Continued here:

From vision to reality: the rise of Artificial Intelligence in the healthcare sector - Health Europa

Frederic Friedel was a science journalist when he co-founded ChessBase in 1987 in Hamburg. It's still the headquarters of the German firm, which has become the world leader in chess software. His partner, the programmer Matthias Wllenweber, created the architecture of the first professional chess database in history: ChessBase 1.0. The iconic Fritz was born in 1991, developed under DOS by Frans Morsch and brought to light under Windows by Mathias Feist.

The "guru" of ChessBase is now 75 years old. He believes that Artificial Intelligence can be the key to the future, so that humans can live better on earth. He is as optimistic and enthusiastic as ever. He expresses his hopes, but also his fears and doubts. How will we coexist with computers of a new type when they have become as intelligent as we are, and even more so?

The following article was based on a telephone discussion conducted in December 2020 by Europe-checs editor Jean-Michel Pechine.

The article appeared in the February 2021 issue of Europe checs, which can be bought here.

Jean-Michel was advised and guided byHenri Assoignon, from the administrative desk of Europe Echecs.

This "general public" game program started modestly, but its computing power developed exponentially. In 2002, Deep Fritz drew a classic match against Kramnik (4-4), as did X3D Fritz against Kasparov in 2003 (2-2). In 2006, Kramnik lost 4-2 to Deep Fritz, and the taste for man-machine matches was over. The German firm continued to improve its flagship programme. Version 15 was developed by Vasik Rajlich, the creator of Rybka. Last November, it launched version 16 of ChessBase. That ushered in a new era by integrating specific revolutionary applications. Artificial Intelligence is in vogue. Frederic Friedel's new child prodigy, Fat Fritz, was launched a year earlier. It is a neural network program. Unlike its predecessors, it was not taught to play chess by human masters. It plays millions of games against itself and draws its own conclusions from them, becoming stronger and stronger. In one year, the prototype has gone from an absolute beginner's level to an Elo rating flirting with 3600 points!

This is the magic of technology and Fredric Friedel is delighted. He views his programs like his own children. How could he have imagined, 34 years ago, that his company would revolutionize the world of chess like no other player or theorist had done before? His meeting with Garry Kasparov in 1985 was decisive. The world champion became involved in the process of creating ChessBase. Kasparov's brute force helped to finish the job. It was the time of the computer pioneers, from Atari to Windows. Like Steve Jobs, co-founder of Apple, Frederic Friedel's desire was to democratise access to high technology. This was also Kasparov's wish, he stresses in his interview. ChessBase offered everyone the opportunity to acquire state-of-the-art tools to prepare themselves, at an affordable cost. Chess became globalised.

This "general public" game program started modestly, but its computing power developed exponentially. In 2002, Deep Fritz drew a classic match against Kramnik (4-4), as did X3D Fritz against Kasparov in 2003 (2-2). In 2006, Kramnik lost 4-2 to Deep Fritz, and the taste for man-machine matches was over. The German firm continued to improve its flagship programme. Version 15 was developed by Vasik Rajlich, the creator of Rybka. Last November, it launched version 16 of ChessBase. That ushered in a new era by integrating specific revolutionary applications. Artificial Intelligence is in vogue. Frederic Friedel's new child prodigy, Fat Fritz, was launched a year earlier. It is a neural network program. Unlike its predecessors, it was not taught to play chess by human masters. It plays millions of games against itself and draws its own conclusions from them, becoming stronger and stronger. In one year, the prototype has gone from an absolute beginner's level to an Elo rating flirting with 3600 points!

With this program we carried out an experiment in Artificial Intelligence" explains Frederic Friedel. We used the same strategy as Google DeepMind with AlphaZero, which was developed by my old friend Demis Hassabis. We created our own program, which we called Fat Fritz. How did we do it? In December 2017, a DeepMind Artificial Intelligence project manager, Thore Grpel, came to see us in Hamburg. He revealed all his secrets to us, and we used the same basic techniques. After that, for a year, I had this very powerful computer right here under my desk. It was playing against itself, all the time, nearly 90,000 games a day in total tens of millions of games. A similar computer in Brazil was retrieving the games and learning from them. This project was led by my friend and colleague Albert Silver.

The only thing we did at the beginning was to teach it the basic rules. How the queen, a rook, a knight move, what is allowed or not allowed (like castling conditions), and the purpose of the game. After its first hundreds of games it played like an absolute idiot. After a few thousand, it started to play at the level of a beginner, and after a few million, Fat Fritz became really strong. It learned what it takes to win. It knew how to evaluate a position. It knew the value of the pieces, the value of a bishop, a knight. It understood that a queen is generally worth eight or nine pawns, depending on the situation. It knew which strategy to adopt. It went on to become the strongest entity that had ever played chess, stronger than Fritz or Komodo.

So Fat Fritz learned all on its own. Chess programmers are among the first human beings to directly experience the power of this new programming technique. The applications are infinite and will develop in all spheres of life. They will touch all fields, science, technology, writing and even the legal world. We can show billions of legal decisions to AI and, again, it learns from each of them. In the end, it may render more competent and fairer verdicts than human judges.

There has been nothing comparable to this revolution since the dawn of humanity. It is as if an alien lifeform had landed on our planet, coming from a distant galaxy. Suddenly we have a machine that may not think like a human being, but it acts in a similar. It may not be able to tell you how it arrives at its decisions. Take the example of chess: if you ask the AI program why one move is better than another, it will tell you: "Because statistically it is 1% better than the next best move." It cannot explain its "reasoning" in human terms. However, this mysterious way of thinking has already made it considerably stronger than the best player in the world.

Fat Fritz's current classification is around 3500 to 3600 Elo. Nobody can beat it, but chess players can use it to try ideas and see how it reacts. You test a novelty or a specific move in a known position and see how it responds. You think, "Oh, that's interesting, it takes the pawn or, on the contrary, why didn't it take it?" I'll explain it to you differently. Fat Fritz can leave a piece hanging. A GM who is analysing this position may say that the program is playing a really rotten move, and will try to demonstrate why. Five moves later, the GM will say: okay, maybe it wasn't a losing move, but whatever it was, it wasn't good. And five moves later, he'll see that it's a winner, that it was a brilliant move!

In the openings, Fat Fritz likes to play 1.e4 and 1.d4, which remain the best moves, according to it. The program will not play 1.h4, for example. Now, we have no idea about its strategy in the openings. It has played millions of games and prefers certain starting patterns. Then we started to show it the games of the best players in history, contained in MegaDatabase. With them, it learned the different styles of play of the humans: aggressive, tactical, positional, strategic, etc. It changed its style in a way that we find very interesting. But it continues playing against itself, to discover things that no human had discovered before. It learns to evaluate positions differently. It also has to discover elementary things, for example that three queens win against zero queens, a situation that never happens in games between humans.

Part two of the interview to follow soon...

The rest is here:

Chess and Artificial Intelligence (1) - Chessbase News

This is an automated machine translation of an article published by Business Insider in a different language. Machine translations can generate errors or inaccuracies; we will continue the work to improve these translations. You can find the original version here.

The asset management industry is moving at the same pace as the planet as a whole.

Increased digitization and the use of digital tools is taking hold. Artificial intelligence is making its way into the financial industry and one of the debates is whether it can end up doing away with the figure of the manager and whether, in addition, it is the key factor on which indexed management - an investment strategy based on replicating indexes - is focused.

Business Insider Spain has exclusively interviewed Unai Ansejo, CEO of Indexa Capital, a fintech focused on indexed management and with a growing volume of clients, to discuss this series of questions about the future of the investment scheme, as well as delving into the expansion of its range of products with the launch of occupational pension plans.

Focusing on the advantages of artificial intelligence when it comes to managing the assets in which to invest Ansejo expounds that from his professional experience he realizes that long-term savings is not about using an algorithm that beats others, but rather about greatly reducing costs, diversifying and being invested in different areas.

"I'm incredulous of these things," he relates about nonparametrics. "I have analyzed many quantitative investment funds for more than 20 years and they always seemed very good, but then there came a time when something happened or there was any problem," he adds.

Therefore, as he explains, in the end, artificial intelligence is a very broad concept, but they would still be algorithms in which you create a series of entry points to then find an exit.

"What happens is that the process by which inputs become outputs is a black box: you don't know," he says.

At Indexa Capital, they don't use artificial intelligence to build investment models but instead focus on criteria they think are reasonable for how portfolios should be constructed over the long term: diversify a lot, reduce costs, incorporate the effect of direct taxes into portfolio construction. "In my view, AI as such is not the best way to obtain long-term performance," he notes.

Artificial intelligence with a Spanish stamp to revolutionize the financial sector: Ultramarine, the investment technology that stops trading if it detects uncertainty in the market.

Ansejo assures, however, that in the fintech they use technology a lot: "Our goal is that half of our team are technical profiles such as engineers, analysts or developers and we use technology for what needs to be done: automating processes where a person does not contribute any value".

For example, something that automates, as he relates, is that, once the client's portfolio is configured, based on their risk profile, they apply an algorithm that is public to guide how the allocation of their investors should be. "When you already have a model portfolio the daily management of your portfolio, or the request for a withdrawal to find the best fund in which there is a lower tax impact can be automated," he explains.

The Indexa Capital CEO asserts that you can't automate portfolio construction."You can't ask a computer or a machine what to invest in because there are many parameters to take into account," he says.

In this way, Ansejo reveals that to build their portfolios they carry out a quarterly review in which they try to see, among other things, if there is a new asset class in which they can invest cheaply and efficiently.

On the other hand, Indexa Capital has expanded its range of indexed products by incorporating occupational pension plans. "We do it with indexing because we think it's the best way to maximize your options to monetize a portfolio over the long term," he says. "What we have is 32,000 clients for whom this proposition works," he adds.

Along these lines, Ansejo says that they have had pension plans for 4 years and with a very clear vocation: that they should be indexed because they are cheaper. However, they saw that, apart from individual plans, in employment plans (where it is the company that creates a payment plan and contributes for the worker) the solutions available were once again very analogical. "Everything with a lot of paper and regulatory information," he describes.

On the other hand, they were usually active management, oriented towards SMEs and high costs. " So we decided to launch it to make it easier for an SME to have a plan quickly and online, and we did so by incorporating another feature, which is the life cycle," he says.

Ansejo confirms that they incorporated a large dose of innovation: that it could be done digitally, low costs and life cycle. "So, the response we are having is very good, although the amount we have is small, it is normal because in the end, when you create an employment plan you are contributing little by little to your employees," he says.

See the rest here:

Artificial intelligence helps automation, but can't tell you where to put your money, Indexa CEO says - Business Insider

With every passing year, humans become more dependent on technology. That has several advantages but also some dangers, which Rajiv Malhotra reveals in his book, 'Artificial Intelligence and the Future of Power'.An internationally acclaimed author who has studied computer science and done extensive research on India's history, Rajiv Malhotra has interesting insights on what artificial intelligence is doing to our nation and how it will affect us in the future. He looks into how artificial intelligence will alter every aspect of our lives, from an international, to national to a personal level.Here is an excerpt of the book to give you an idea on it:Excerpts from 'Artificial Intelligence and the Future of Power' by Rajiv MalhotraThe AI-based concentration of power has taken on a terrifying new aspect. When we think of global power, countries like the US, China, and Russia readily come to mind. But today, private companies are accumulating immense power based on their ability to leverage AI and big data as tools to influence, manipulate and even control the minds of people.Some of these private companies may soon become more powerful than many nation-states, but the shift will not be obvious. They will not fly a flag or manage a currency (although some are attempting to launch their own cryptocurrency), and they will not wield military power, at least not directly. However, their unprecedented knowledge of people and things around the world, coupled with their ability to disrupt and alter the physical world and manipulate peoples choices, will lead to a new nexus of power. Such companies will decide who will, and who will not, be given access to this new form of power, and on what terms.Not one Indian company is a player in this league. Most unfortunate is that a large number of talented Indians work for American and Chinese companies in an individual capacity, including in top executive positions, but not as owners. Indians who do own companies tend to sell their stake when the right offer comes along. Whenever innovative entrepreneurs anywhere in the world develop a promising breakthrough, digital giants or venture firms that serve as their proxies are waiting to buy them out. As a result, hundreds of instant millionaires are being created at the individual level, including many living in India.I view this trend as the return of Britains East India Company, which started out in 1600 as a modest private company for the purpose of making profit from lucrative trade with India. Over its 250-year history, the East India Company became the worlds largest private business, amassing more wealth, income and military power than even its own British government. Despite being a private company, it became a colonial powercollecting taxes, operating courts, and running the military and other functions of state across many kingdoms within India. At the time, the East India Company had more ships, soldiers, money and territory under its control than any European government, though now it is remembered as a rogue machine. Since then, the lines between government and private companies have often blurred.

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Excerpt: 'Artificial Intelligence and the Future of Power' by Rajiv Malhotra - Times of India

(Left to right) Berkeley Andrus, Nathaniel Robinson, Jay Cui, Ben Cullen, Nathaniel Carlson, Professor Nancy Fulda, Hazar Handal and Nathan Tibbetts are all members of the DRAGN Labs research team. The research team specializes in language communication within artificial intelligence. (Allison McArthur)

BYU research group DRAGN Labs is making big progress in its efforts to better artificial intelligence.

DRAGN is an acronym for Deep Representations and Architectures for Generative systems and Natural language understanding.The teams projects focus on artificial intelligence applications, research and language processing.

BYU professor Nancy Fulda started DRAGN Labs in August 2019 with only a handful of students. Since then the team has seen progress during its research of conversational artificial intelligence.

Computer science graduate student Berkeley Andrus and undergraduate applied and computational mathematics student Nate Robinson have worked under Fulda since the beginning of DRAGN Labs.

Were trying to make computers better at understanding what people say and write, then also be able to speak and write back to us, Andrus said.

DRAGN Lab students meet either in their teams or as an entire group once a week. Andrus said he sees many different backgrounds in the lab, with students majoring in math, computer science and even genetics, and values being able to know who has what specialty and collaborate with them.

A notable project during his time at DRAGN labs was working on natural language understanding. He focused on how video games figured out user speech (what people were saying as they played) and how the computer could respond.

The biggest project from Robinsons team has been creating a new algorithm to control biased language generated by artificial intelligence programs. These programs can generate huge amounts of text and have human-level fluency.

A lot of the time, these programs text is biased or just talks about whatever it wants to, so we created a new algorithm to control what it can or cannot say, he said.

Robinson is currently working on a machine translation project that explores different methods and combinations of un-studied languages.

Sometimes when you finish a project, the end product is really cool to sit back and look at. I think to myself, I made this and nobody understands it better than I do. Some projects take over a year, so its really satisfying to see the final reward, Robinson said.

Andrus said he wishes he could tell students who might be interested in the data or computer science field that BYU is a great environment for trying lots of things.

When starting his major, he said it was difficult to see how research success manifested differently than it might for other career fields. It takes a lot of time, but its really fulfilling.

Excerpt from:

BYU lab works to improve artificial intelligence communication - The Daily Universe - Universe.byu.edu

Numerous chronic conditions manifest with unpredictable symptoms, which can sometimes make it difficult for clinicians to take necessary action in a timely manner when tending to patients. Researchers at U.Va. Health working in the field of predictive analytics have created a software that uses artificial intelligence to estimate a patients relative risk by combining physiological data from thousands of previous patients, with a current patient's physiological state. The software is crucial in allowing clinicians to assess a patients risk for deterioration sooner than they normally would, allowing them to take often critical proactive actions towards maintaining the patients health.

Life-threatening conditions such as lung failure, sepsis or acute respiratory distress syndrome can all manifest in a patient without displaying warning signs to clinicians until the patient is in a critically debilitating condition. This can leave providers with limited time to make imperative decisions for patients and may thus threaten chances of survival.

Dr. Randall Moorman, cardiologist and innovator in the field of predictive analytics monitoring, realized this healthcare dilemma early on in his career.

Sometimes we can look back at the data that we had about those patients, and we can see that we should have seen it coming, Moorman said.

In attempts to better monitor patient stability through early detection, many hospitals around the world have resorted to using a standardized point system, which consists of recording certain physiological parameters and outputting a standardized score that can then be used to predict the patients stability. For instance, in England the National Early Warning Score measures pulse rate, blood pressure, respiratory rate, oxygen levels, temperature and consciousness level in patients, allocating an individual score for each factor and totaling the scores. When the total reaches a threshold number designated by healthcare facilities, it alerts clinicians to take action.

However, Moorman found that such point systems were sometimes ineffective in monitoring the patient since they uniformly depended on the patient reaching a particular threshold score before clinicians were alerted. While threshold score monitoring may be helpful in some situations, these systems are not designed to indicate risk specific to each physiological factor, failing to utilize statistical tools like regression models, which use multiple variables to predict an outcome.

One of the benefits of many machine learning approaches [is] you get a continuous gradation of risk from all the possible numbers that might come in, no thresholds [are] allowed, Moorman said.

Additionally, tools like NEWS can be restraining since they do not focus on symptoms specific to a certain patient population, like cardiac patients, but instead rely on a one size fits all model.

Our own point of view has been that this is not a one-size-fits-all problem at all, that the predictors of deterioration in one part of the hospital are going to be very different from elsewhere in the hospital, Moorman said.

Generalizing symptoms can lead to clinicians who depend on a standardized score when trying to predict any patients disease progression, further leaving more room for ambiguity in executing care plans since the numbers are not always clearly indicative of a particular condition.

Approximately 20 years ago, Moorman decided to apply certain predictive concepts to proactively diagnose neonatal sepsis, which is a bacterial infection that occurs in the bloodstream of premature infants and can be deadly if not diagnosed early on. Sepsis has been particularly difficult for healthcare providers to diagnose since premature infants are unable to aptly communicate discomfort and are too fragile to have many diagnostic tests conducted on them.

Moorman analyzed data from several infants infected with sepsis and recognized distinct patterns in the heartbeat of infants that occurred before sepsis began. He then quantified the heart rate data for the heartbeat abnormality and created a software which would detect this abnormality and alert clinicians. The HeRO software, coupled with observations and skillset of clinicians, allowed for them to proactively integrate the softwares findings into their care, culminating in a 20 percent decline in premature infant mortality as shown by a randomized trial.

Consequently, Moorman expanded his work to create predictive models for adults, attempting to address a multitude of diseases using evidence from data coming from approximately 200,000 patients who have been admitted to U.Va. Health previously.

We present to the clinicians, not just the risk of sepsis, but we have developed predictive tools for early detection of other kinds of clinical deterioration like lung failure or bleeding or the need to be transferred to an ICU, Moorman said.

One of his primary goals is to use the benefits of Big Data analysis in predicting outcomes for future patients.

[We are working] toward the idea of taking all of the data that comes out from a patient and analyzing it in such a way that we can tell the clinicians that someone's risk for something bad is going up, Moorman said.

Contrary to standardizing softwares like NEWS, the Continuous Monitoring of Event Trajectories software relies on constant monitoring of the patient and previous data, working to apply algorithms which output the patients status and risk of experiencing a serious event in the next 12 hours, updating every 15 minutes. CoMET updates models by calculating the cumulative contribution of physiological information from patients including data from their electronic medical records, EKG signals, vital signs and laboratory results.

The added machine learning approach allows for patients to be assessed relative to the outcomes from thousands of other patients and is more specific to the individual patient by displaying models specific to the patients unit.

At this point we have generated truly, hundreds of predictive models, depending on where you are in the hospital, what kind of things might go wrong and what information is available, Moorman said.

The Prediction Assistant screen uses regression to display patient risk by showing comets for each patient being monitored in the unit, with more stable patients represented as small and close to the bottom of the graph, while patients at higher risk are represented by larger and brighter comets. Each of the comets are graphed as a measure of a combination of factors most relevant to the hospital unit.

University cardiologist Jamieson Bourque, in collaboration with Jessica Keim-Malpass, associate professor of nursing and pediatrics, have recently begun a two-year randomized controlled study of the CoMET software in patients in the medical-surgical floor for cardiology and cardiovascular surgery patients at the U.Va. Hospital. They intend to analyze the long term outcomes of patients and prove the softwares utility to help patients through providing clinicians with valuable predictive models from physiological data.

What CoMET does is allows you to see the small incremental changes in heart rate, respiratory rate, vital signs [and] labs that can sort of fly under the radar, but when all those values are added together, that may signify a more significant change, Bourque said.

The team is also in the process of developing a predictive model specifically for COVID-19. However, it is waiting to gain more data to better understand the unpredictable nature of the disease so is currently using pre-existing models for the respiratory distress that accompanies COVID-19. The researchers feel that a predictive model could potentially be largely beneficial to dealing with COVID-19 patients since it could help anticipate some of the unpredictable symptoms which have shown to cause mortality.

At unexpected times, a fair number of patients do deteriorate drastically, and then there are very big decisions to be made in this time of constrained resources or this time of full hospitals, Moorman said.

Main challenges researchers face with integrating CoMET involve educating clinicians on reading the patterns as well as helping them integrate the softwares usage into their daily workflow. With CoMET, clinicians are suggested to utilize the proactive warning signs and learn to construct a care plan sooner than they normally would.

Keim-Malpass, who is also trained as a nurse, is able to incorporate her first-hand perspective to CoMETs design by attempting to ensure that nurses and other clinicians in the hospital can adapt their responsibilities to the proactive nature of the software. She spoke of a time when nurses recognized a spike in the patients CoMET score trajectory that allowed them to prevent sepsis when the patient was still stable.

They went ahead and preemptively took blood cultures, and a few hours later they came back positive that they had blood infection, that they were heading towards sepsis, so that patient got antibiotics sooner [than] they would have, Keim-Malpass said.

In the future, the team plans to use more data to enhance the COVID-19 model and to implement CoMET to other hospitals around the nation.

Conflict of interest disclosure: Randall Moorman is Chief Medical Officer and owns equity in AMP3D, which licenses technology from UVALVG and markets the CoMET monitor.

Original post:

Artificial intelligence used to monitor patients with chronic diseases and COVID-19 - University of Virginia The Cavalier Daily

On the Joe Rogan podcast this week, Tesla CEO Elon Musks inner Ralph Nader was on full display, with Musk promoting federal regulation of artificial intelligence, criticizing the auto industrys campaign against seat belts and safety regulation, and praising modern airbags as crazy good.

In the middle of a three and a half hour conversation, Rogan triggered the discussion on regulation when he said he was worried about artificial intelligence.

We should have oversight of some kind, Musk said. A regulatory agency like the FAA (Federal Aviation Administration) or the FDA (Food and Drug Administration). We need an acronym to oversee this stuff.

Rogan expressed doubts about a government agency getting the job done.

The probability of industry capture is higher if its an industry body than if it is the government, Musk said. Its not zero if it is the government. There are plenty of instances of regulatory capture of a government agency. But the probability is lower than if it is an industry group. At the end of the day somebody has to go and tell Facebook, or Google or Tesla, this is okay or it is not okay. Or at least report back to the public this is what we found. Otherwise the inmates are running the asylum. And these are not necessarily friendly inmates.

Im not a fan of lets have the government do lots of things, Musk said. You want to have the government do the least amount of stuff. The right role of government is for it to be the referee on the field. When the government starts being a player on the field, thats problematic. Or when you start having more referees than players, which is the case in California, then thats not good. You cant have no referees. Everyone agrees that a referee might be annoying at times, but it is better to have a referee than not.

Rogan said Im just worried that its going to be too late, by the time these things become sentient, by the time they develop the ability to analyze what the threat of human beings are and whether or not human beings are essential

Im not saying that having regulatory agencies is some panacea or reduces the risk to zero, Musk said. There is still some significant risk even with a regulatory agency. Nonetheless, the good outweighs the bad and we should have one.

It took a while before there was an FAA, Musk said. There were a lot of plane companies cutting corners. It took a while before there was an FDA. What tends to happen is some company gets desperate, they are on the verge of bankruptcy and they are like we will just cut this corner, it will be fine. And then, somebody dies.

Look at seat belts. Now we take seat belts for granted. But the car companies fought seat belts like there was no tomorrow.

Really, they fought them? Rogan asked.

For decades, Musk said. The data was absolutely clear that you needed seat belts. The difference in fatalities with seat belts versus not seat belts is gigantic and obvious. Its not subtle. But still, the car companies fought seat belts for ten to twenty years. A lot of people died.

Now, these days with advanced airbags, I think we might have come full circle and no longer need seat belts if you have advanced airbags.

What if the car flips? Rogan asked.

You are just covered its airbags everywhere, Musk said. Modern airbags are so good it will blow your mind how good they are. At Tesla, we even update the software to improve how the airbags deploy. We will calculate are you an adult, how much do you weigh, are you sitting in this part of the seat or that part of the seat? You may be a baby. Are you a toddler?

Based on the weight? Rogan asked.

Not just the weight, but the pressure distribution on the seat. Are you sitting on the edge of your seat? Are you a fifth percent female or 95 percent male? The airbag firing will be different depending on where you are sitting on the seat, what size you are, and what your orientation is. And well update it over the years. It gets better over time.

A child could be sitting in the front seat? Rogan asked.

Unbelted child sitting in a bad position probably still fine, Musk said. The seat belt is like if you wear the seat belt thats nice. The airbag is doing the work. Airbag technology is crazy good. You want the airbag to inflate and then deflate, otherwise you are going to be asphyxiated.

We go way beyond the regulatory requirements. We got the lowest probability of injury of any cars they ever tested.

We get five stars in every category and subcategories. And if there was a sixth star, we would get a sixth star.

But then Musk admitted the star safety rating is kind of bullshit.

If a smart car hits a freight train, it doesnt matter how good your safety system is, you are screwed. If you are in a little car and it gets hit by a big car, the big car will win. A low star rating in a big car hitting a high star rating in a small car the small car is screwed. Small cars are not safe.

What about your small car? Rogan asked.

Our Model 3 is not small, Musk said.

What about the Roadster? Rogan asked.

The Roadster is not super safe, Musk said. The original Roadster is not super safe. Its safe for a car like that, but safety maximization is not the goal in a sports car.

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Elon Musk Talks Auto Safety and Regulation of Artificial Intelligence with Joe Rogan - Corporate Crime Reporter

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The report Artificial Intelligence in Medicine Market 2020-2026 focuses on outlining and narrating the key influencing elements for the growth of the market. It also proposes a thorough study of the market stature (revenue), market share, key market segments, distinct geographic regions, dominant market players, and prime industry trends. The aim of this report is to portray the forthcoming market trends and revenue prognosis for the global Artificial Intelligence in Medicine market for the next five years.

According to the research report, Global artificial intelligence in medicine market is expected to grow at a CAGR of 49% and is anticipated to reach around USD 15,000.00 Million by 2026. Artificial intelligence in medicine has the probable to expressively convert the character of the doctor and revolutionize the preparation of medicine.

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Artificial Intelligence in Medicine Market by Top Manufacturers (2020-2026)

Bay Labs Inc

Welltok

CloudMedx Inc.

Siemens Healthcare GmbH

Nvidia Corporation

Enclitic

Next IT Corp.

General Electric

General Vision

Google Inc

IBM Corporation

iCarbonX

Koninklijke Philips

maxQ

Microsoft

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Pre & Post Impact of COVID-19 Pandemic on Artificial Intelligence in Medicine Market

The COVID-19 outbreak has affected economies and industries in various countries due to lockdowns, travel bans, and business shutdowns. The decrease in the overall market growth due to COVID-19 is also affecting the Post growth of the Artificial Intelligence in Medicine market owing to the shutting down of factories, obstacles in the supply chain, and a downturn in the world economy.

This report presents a comprehensive overview, market shares, and growth opportunities of Artificial Intelligence in Medicine market by product type, application, key manufacturers, and key regions and countries. In addition, this report discusses the key drivers influencing market growth, opportunities, the challenges, and the risks faced by key manufacturers and the market as a whole. It also analyzes key emerging trends and their impact on present and future development.

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The points that are discussed within the report are the major market players that are involved in the market such as market players, raw material suppliers, equipment suppliers, end users, traders, distributors and etc.

The complete profile of the companies is mentioned. And the capacity, production, price, revenue, cost, gross, gross margin, sales volume, sales revenue, consumption, growth rate, import, export, supply, future strategies, and the technological developments that they are making are also included within the report. This report analyzed 12 years of data history and forecast.

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Artificial Intelligence in Medicine Market is expected to rise at a remarkable CAGR during the Forecast Period 2020-2026 KSU | The Sentinel Newspaper...

Artificial intelligence (AI) is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. In simpler terms, it is making computers think like humans. The term is used to describe machines that mimic cognitive functions such as learning and problem solving.

While the term was coined in 1956, AI has since advanced by leaps and bounds thanks to advanced algorithms, increased data volumes, and improvements in computing power and technology. In the 1950s, early AI research delved into topics such as problem solving and symbolic methods. Ten years later, the US Department of Defense expressed interest and began to train computers to mimic basic human reasoning. By 2003, intelligent personal assistants were produced long before Siri or Alexa were introduced.

Popular examples of artificial intelligence include AI autopilots on commercial flights, spam filters, mobile check deposits, and voice-to-text features on mobile devices.

To understand how AI works, understanding the sub domains of AI and how these domains can be applied to various industry fields is critical.

AI is being used in every industry, and the demand for AI capabilities only continues to grow.

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AI Definition & Meaning | What is Artificial Intelligence?

Artificial Intelligence has immense potential catering to various aspects of the world be it economic, environment related, social or anything for that matter. AI has made taking decisions based on data easier than ever. Machines with deep learning capabilities have changed our lives for better. With this being said, one of the hottest topics that has garnered attention from across the globe is Can Artificial Intelligence aid in achieving Sustainable Development Goals? Yes, it can! Infact, there are sectors that have already been using this advanced technology of AI in meeting their goals. Some areas where this has proven successful are

The importance of education can just not be put into words. Not only does it open door to a plethora of career options to choose from, but also grooms you as a person. Gone are the days when getting educated required the presence of someone to guide you through. But today, education is far more accessible thanks to Artificial Intelligence. Getting educated without human teachers is probably one of the best innovations AI has come up with in the education sector. It cannot have got any better for the visually challenged students for the sole reason that they too can fulfil their desire of being educated with the help of voice assistants.

AI is also capable of monitoring the students performance from time to time. Recommending content based on the students past experience is yet another area that AI focuses on. All in all, the future is set to see more number of students getting trained by AI powered machine tutors rather than human tutors.

No matter which country you live in, this sector has a unique importance. It is just not possible to imagine life without this sector. Artificial intelligence can help in detecting diseases in plants and also target weeds. Farmers are now using AI forecasting models to predict upcoming weather patterns, thus enabling them to make better decisions.

Needless to say, this is that one sector that people can never get tired of praising. And when the world is shook by a pandemic like the 2020 virus, then the efforts put in by this sector needs no special mention. Since the data pertaining to the healthcare sector is insanely huge, Artificial Intelligence has the ability to collect and process this data for faster treatment. Coming up with technologies to check whether the person is cancerous or not, to estimate the probability of a person to develop cancer, to name a few are taking shape because of AI. India is marching towards an AI driven economy with every passing day. It has partnered with Microsoft to eradicate preventable blindness using an AI-enabled portable eye-scanning device that helps detect retinal diseases. In addition to all of this, AI is being used to deal with the cyber-security attacks in this sector as well.

The havoc created by disasters needs no special mention. AI promises to be a saviour here as well. It plays a pivotal role in minimizing the damage caused due to disasters. Artificial intelligence helps improve dam and barrage water release to minimize the risks.

The above are just few of the many areas where AI has worked wonders. AI has huge potential to serve a lot of sectors. If we come together and put Artificial Intelligence into its best use, then a better society awaits all of us in the years to come.

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Artificial Intelligence and Sustainable Development Goals - Analytics Insight

Copenhagen: Using patient data, artificial intelligence can make a 90 per cent accurate assessment of whether a person will die from COVID-19 or not, according to new research at the University of Copenhagen.

Body mass index (BMI), gender, and high blood pressure are among the most heavily weighted factors. The research can be used to predict the number of patients in hospitals, who will need a respirator and determine who ought to be first in line for a vaccination. The results of the study were published in the journal Scientific Reports -- Nature.

Artificial intelligence is able to predict who is most likely to die from the coronavirus. In doing so, it can also help decide who should be at the front of the line for the precious vaccines now being administered across Denmark.

The result is from a newly published study by researchers at the University of Copenhagen's Department of Computer Science. Since the COVID pandemic's first wave, researchers have been working to develop computer models that can predict, based on disease history and health data, how badly people will be affected by COVID-19.

Based on patient data from the Capital Region of Denmark and Region Zealand, the results of the study demonstrate that artificial intelligence can, with up to 90 percent certainty, determine whether an uninfected person who is not yet infected will die of COVID-19 or not if they are unfortunate enough to become infected. Once admitted to the hospital with COVID-19, the computer can predict with 80 percent accuracy whether the person will need a respirator.

"We began working on the models to assist hospitals, as, during the first wave, they feared that they did not have enough respirators for intensive care patients. Our new findings could also be used to carefully identify who needs a vaccine," explains Professor Mads Nielsen of the University of Copenhagen's Department of Computer Science.

Older men with high blood pressure are highest at risk The researchers fed a computer program with health data from 3,944 Danish COVID-19 patients. This trained the computer to recognise patterns and correlations in both patients' prior illnesses and in their bouts against COVID-19.

"Our results demonstrate, unsurprisingly, that age and BMI are the most decisive parameters for how severely a person will be affected by COVID-19. But the likelihood of dying or ending up on a respirator is also heightened if you are male, have high blood pressure or neurological disease," explains Mads Nielsen.

The diseases and health factors that, according to the study, have the most influence on whether a patient ends up on a respirator after being infected with COVID-19 are in order of priority: BMI, age, high blood pressure, being male, neurological diseases, COPD, asthma, diabetes and heart disease.

"For those affected by one or more of these parameters, we have found that it may make sense to move them up in the vaccine queue, to avoid any risk of them becoming infected and eventually ending up on a respirator," says Nielsen.

Predicting respiratory needs is a must. Researchers are currently working with the Capital Region of Denmark to take advantage of this fresh batch of results in practice. They hope that artificial intelligence will soon be able to help the country's hospitals by continuously predicting the need for respirators.

"We are working towards a goal that we should be able to predict the need for respirators five days ahead by giving the computer access to health data on all COVID positives in the region," says Mads Nielsen, adding: "The computer will never be able to replace a doctor's assessment, but it can help doctors and hospitals see many COVID-19 infected patients at once and set ongoing priorities."

However, technical work is still pending to make health data from the region available for the computer and thereafter to calculate the risk to the infected patients. The research was carried out in collaboration with Rigshospitalet and Bispebjerg and Frederiksberg Hospital.

Link:

Artificial Intelligence can predict whether you will die from COVID-19 - Free Press Journal

Artificial intelligence (AI) is rapidly being explored or adopted by many industries for a wide array of applications. Today it is creating a string of opportunities in space industry use-cases too. As artificial intelligence emerges as a popular theme in space exploration,it is also being deployed for many critical tasks too.

For instance scientists have leveraged artificial intelligence, for charting unmarked galaxies, supernovas, stars, blackholes, and studying cosmic events that would otherwise go unnoticed.One of the recent illustration of this application was when CHIRP (Continuous High-Resolution Image Reconstruction using Patch Priors) Algorithm helped in creating first-ever image of a black hole. CHIRP is a Bayesian algorithm used to perform de-convolution on images created in radio astronomy. It used the image data from the Event Horizon Telescopes to carry further image processing. Even images from the Hubble Space Telescope are used to simulate galaxy formation and further classification using deep learning algorithms.

Artificial intelligence also proves resourceful in classifying heavenly bodies, especially exoplanets. A couple of years ago, a research team developed an artificial neural networks algorithm, to classify planets, based on whether they resemble present-day Earth, early Earth, Mars, Venus or Saturns largest moon, Titan. These five bodies are most potentially habitable objects in our solar system and are therefore associated with acertain probability of life.

In regards to life in outer space, Researchers atNASAs Frontier Development Lab(FDL) employed generative adversarial networks, or GANs, to create 3.5 million possible permutations of alien life based on signals from Kepler and the European Space Agencys Gaia telescope.

Besides, NASA has teamed up with Google to train its artificial intelligence algorithms to sift through the data from the Kepler mission to look for signals from an exoplanet crossing in front of its parent star. With the help of Googles trained model, NASA managed to discover two obscure planets Kepler-90i and Kepler-80g. In 2019, astronomers from the University of Texas at Austin, teamed with Google, to useAI for uncovering two more hidden planets in the Keplerspace telescope archive (Keplers extended mission, called K2).They used an AI algorithm that sifts through Keplers data to ferret out signals that were missed by traditional planet-hunting methods. This helped them discover the planets K2-293b and K2-294b.

Under the Artificial Intelligence Data Analysis (AIDA) project, which is funded under the European Horizons 2020 framework, an intelligent system is being developed that can read and process data from space. The key object of this project is to enable the discovery of new celestial objects, using data from NASA.

AI applications can also found in the field of satellite imagery. Data based on satellite imagery offers insights on several global-scale economic, social and industrial processes, which was previously not possible. Some examples include Earth Observer 1 (EO-1) satellite, SKICAT, ENVISAT. These satellites leverage artificial intelligence to provide actionable insights for agencies, governments and businesses, and help them in making accurate decisions.

While humans are capable ofinterpreting, understanding, and analyzing images collected by satellites, it does cost us time and resources while waiting for a satellite to move back around to the same position to further refine image analysis. Artificial intelligence helps eliminate the necessity for large amounts of communication to and from Earth to analyze photos and helps determine whether a new photo needs to be taken. Moreover, it saves processing power, reduces battery usage, and fast-tracks the image gathering process.

In case of space mining, artificial intelligence will augment mining machinery with intelligence that will empower them to extract minerals and identify any hazards or solve minor issues at hand without the need for immediate support from humans on Earth. Meanwhile, NASA is also developing a companion for astronauts aboard the ISS,called Robonaut, which will work alongside the astronauts or take on tasks that are too risky for them. According to NASAs blog, Robonaut 2 is slowly approaching human dexterity implying tasks like changing out an air filter can be performed without modifications to the existing design.

Artificial intelligence has also helped us develop space humanoids like Kirobo from Japan Aerospace Exploration Agency, Dextre from Canadian Space Agency, and AILA from German Research Center for Artificial Intelligence to help astronauts in space missions. NASAs free-flying robotic system,Astrobee, uses AI to help astronauts reduce their time on routine duties, leaving them to focus more on the things that only humans can do. We also have CIMON or (Crew Interactive Mobile Companion), an AI powered robot that floats through the zero-gravity environment of the space station to research a database of information about the ISS. In addition to the mechanical tasks assigned, CIMON assesses the moods of its human crewmates at the ISS and interacts accordingly with them.

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Beyond the Unknown: Applications of Artificial intelligence In Space - Analytics Insight

Bingo and AI The changing relation between entertainment and artificial intelligence

Artificial Intelligence has been on the tech agenda for more than two decades now, although its impact on our day-to-day lives is perhaps yet to be fully realized. Perceptions of AIs innovation are broad and have altered over time.

For many, AI is an exciting new tool that will improve the quality of our lives, at home and work, by being able to deliver functions that will save us time and enhance our experiences in the world of leisure and entertainment. But for others, AI is perceived as a threat to our livelihoods. Old-fashioned perceptions push the narrative that technology must be managed and developed slowly or not developed at all to protect our ways of life.

This has contributed to AI receiving only sporadic funding and being dismissed by some as a pipe dream. However, we will discuss the positive impact that AI is having on the world of digital entertainment, and in particular in the world of bingo and online casino gaming.

Although many may not realize AI is already readily utilized as a part of several mainstream digital entertainment experiences. For example, Netflix viewers will receive recommendations of what to watch next based on the technology. AI is used to interpret data and produce an algorithm that displays film and TV suggestions that will likely appeal to the user based on their previous habits. In this instance, AI is helping to deliver a much more bespoke, personalized experience to paying subscribers.

In gaming, AI can be used to set a difficultly level based on the players abilities and can make configuration recommendations to enhance the players experience. Where human guidance is not possible, AI helps to keep new players on track.

Source: Pexels

Other gaming sectors, such as online casino gaming, have contrasting relationships with AI. Some platforms utilize the technology in a similar way to traditional console titles to automatically change personalization. It can also be usedto speed up manualprocesses, such as repeating a previous bet or warning the player against twisting in a game of blackjack when they have a good hand.Others instead use more tried and tested innovations such as random number generators to ensure games are fair.

Some businesses in the casino sector have adopted the technology to try and enhance the experience for players. Sue Dawson from Best New Bingo Sites explains how In real money online gaming, AI can be used for targeting marketing and advertising so that players receive promotional offers that are tailored to their preferences and behavior. For instance, you might receive an offer of free spins for the slot game you play most oftenat the time of day youre most likely to play. The games themselves are strictly regulated, though, and must use verified RNG to ensure that all players have the same chance of winning.

Source: Pexels

As is the case in any area of technology, its fascinating to speculate what the future may hold for AI. Already developers are experimenting with its use in spheres like art and even literature. Art AI Gallery offers images for sale that have been generated by artificial intelligence, while developers have experimented with using AI to write plays, make music, and script films.

Its clear from the evidence that AI is already a major part of many lives, and as the technology behind it advances, its likely that will see further leaps forward taken in the years and decades to come.

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Bingo and AI: The changing relation between entertainment and artificial intelligence - Analytics Insight