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Alphabet and Amazon are among the companies best positioned to take advantage of future artificial intelligence disruption in the technology industry, our analysis shows.

The assessment comes from GlobalDatas Thematic Research ecosystem, which ranks companies on a scale of one to five based on their likelihood to tackle challenges like artificial intelligence and emerge as long-term winners of the technology sector.

According to our analysis, Alphabet, Amazon, Microsoft, Apple, Alibaba, Baidu, ByteDance, Nvidia, Z Holdings, Airbnb, Inspur Electronic, ASML, ABB, Tesla, Siemens, GE, Darktrace, Expedia, Mythic, Alibaba Pictures, Groq, Cerebras, TSMC, Horizon Robotics, UiPath, Automation Anywhere, SambaNova, iFlytek, AMD, Wayfair, Cambricon, Graphcore, Zapata Computing, Cambridge Quantum and Suning.com are the companies best positioned to benefit from investments in artificial intelligence, all of them recording scores of five out of five in GlobalDatas Advertising, Application software, Cloud services, Consumer electronics, Ecommerce, Enterprise security, Industrial automation, IT infrastructure, IT services, Music, Film, & TV, Publishing, Semiconductors, Social media and Telecom infrastructure Thematic Scorecards.

The table below shows how GlobalData analysts scored the biggest companies in the technology industry on their artificial intelligence performance, as well as the number of new artificial intelligence jobs, deals and patents from the companies since August 2021.

The final column in the table represents the overall score given to that company when it comes to their current artificial intelligence position relative to their peers. A score of five indicates that a company is a dominant player in this space, while companies that score less than three are vulnerable to being left behind. These can be read fairly straightforwardly.

The other data points in the table are more nuanced, showcasing recent artificial intelligence investment across a range of areas over the past year. These metrics, where available, give an indication of whether artificial intelligence is at the top of executives minds now, but high numbers in these fields are just as likely to represent desperate attempts to catch-up as they are genuine strength in artificial intelligence.

For example, a high number of deals could either indicate that a company is dominating the market, or that it is using mergers and acquisitions to fill in gaps in its offering.

This article is based on GlobalData research figures as of 19 August 2022. For more up-to-date figures, check the GlobalData website.

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Revealed: The technology companies leading the way in artificial intelligence - Verdict

Monday 22 August 2022 7:34 am

WITHOUT a doubt, there is vast potential for advancement and benefit to society arising out of the application of artificial intelligence.

Around half of businesses plan to use AI or advanced machine learning in some capacity in the next three years. Transport Secretary Grant Shapps has said self-driving cars could be on our roads as early as next year. This has, predictably, put the debate over artificial intelligence centre stage. What circumstances are cars taught to anticipate? What happens in the event of the unexpected, as often happens on our roads?

There are complex legal, societal and ethical questions to consider. This includes the classic trolley dilemma around who to save when there are choices and how does one stop a robot going rogue?

As AI becomes more common-place, regulators have been seeking to grapple with this and more wide-reaching issues. A major focus for the impending EU Regulation of AI is the need for transparency, fairness and non-bias.

There will also be requirements to report on, with powers for people to be compensated for biased, unethical or incorrect outcomes as well as unfair treatment of data.

When you layer on privacy regulators requirements around how personal data is used the compliance journey for suppliers, adopters and users of AI can be arduous, particularly as new laws emerge and because the laws are and will differ across the world.

Last month, the UK Government set out proposals on the future regulation of AI, calling for people to share their views on the suggested approach. The governments approach is arguably lighter touch than the EU Regulation, aiming to create proportionate and adaptable rules. Both Ofcom and the CMA would be empowered to interpret and implement the key principles.

An ethical approach to the use of AI is not just essential to ensure legal compliance. Potential fines of up to 30,000,000 in the EU, 6 per cent of global turnover and the threat of major reputational damage and erosion of significant value make this a business imperative.

But how can businesses ensure their AI isnt artificially intolerant? Most of it will come down to using the right data and processing it correctly. This is before we even enter the complicated question of whether it is right to use the data, which then leads to a whole set of ethical concerns around fairness.

For example, if the technology is using data from the past as to who has been successful for a role, it could simply lead to unearthing only white male candidates of a certain age, because historically those were the people given most opportunities.

There have already been a number of cases where bias has produced unfair results for example through mortgage applications in non-UK banks. And, of course, the choice as to what decisions an AI system makes for autonomous vehicles and weapons is yet another example of the need to ensure the right guardrails are in place.

At least where systems dont completely think for themselves, some businesses are already grappling with these issues. If they dont theres the potential for reputation damage or even fines.

Boardrooms must understand what the plethora of regulations are going to expect of them and their business and they must prepare a clear action plan including their approach to ethics in technology or corporate social responsibility. Only then can the true potential of AI be unleashed for good without the risk/threat of artificial intolerance or incorrectness..

Read more here:
Artificial intelligence is the new frontier of ethical tests - City A.M.

Today, AI learning requires a connection to a remote server to perform heavy computing calculations. These researchers say changing that could transform health care.

ORLANDO, Fla. Our computers, devices, smart watches, video monitoring systems, etc...- we rely on connectivity to the internet and dont think twice about it. Now, scientists are developing technology for artificial intelligence that will allow it to work even in remote areas.

Self-driving cars, drone helicopters and medical monitoring equipment; its all cutting-edge technology that requires connection to the cloud. Now, researchers at the University of Central Florida are developing devices that wont rely on an internet connection.

What we are trying to do is make small devices, which will mimic the neurons and synapses of the brain, researcher at the University of Central Florida, Tania Roy, PhD, explains.

Right now, artificial intelligence learning requires a connection to a remote server to perform heavy computing calculations. Scientists are making the AI circuits microscopically small.

Roy emphasizes, Each device that we have is the size of 1/100th of a human hair.

The AI can fit on a small microchip less than an inch wide eliminating the need for an internet connection, meaning life-saving devices could work in remote areas. For example, helping emergency responders find missing hikers.

We would send a drone which has a camera eye, and it can just go and locate those people and rescue them, Roy says.

The scientists say with no need for an internet connection, the AI would also work in space, where no AI technology has gone before.

The same UCF team is expanding on their work with artificial brain devices, and they are developing artificial intelligence that mimics the retina in the human eye, meaning someday, AI could instantly recognize the images in front of it. The researchers say this technology is about five years away from commercial use.

If this story has impacted your life or prompted you or someone you know to seek or change treatments, please let us know by contacting Shelby Kluver at shelby.kluver@wqad.com or Marjorie Bekaert Thomas at mthomas@ivanhoe.com.

Watch more 'Your Health' segments on News 8's YouTube channel

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Is the future of artificial intelligence internet-free? These researchers hope so - WQAD Moline

Artificial intelligence is used in cryptocurrency trading for several reasons. If youre looking to make money trading cryptocurrencies, applying AI software is essential. Without it, your trade accuracy on a platform like 1K-Daily will be low and your profits will be limited. Here are some uses of artificial intelligence in cryptocurrency trading: Forex and crypto markets are known for their high volatility. As a result, many traders use automated trading software to minimize risk and increase their profit margin.

However, this can also have negative side effects if the algorithm is set up in the wrong way or implemented incorrectly. Your machine trades with human psychology it reacts to news, values trends, and emotions. If you implement AI software into your trading strategy, you can run it past yourself to identify counter-intuitive actions or omitted details that would otherwise yield a negative outcome. Therefore, applying artificial intelligence technology in cryptocurrency trading has several advantages over traditional methods:

Artificial intelligence can help you interpret market data more accurately than humans ever could have done on their own. Humans are good at recognizing patterns and making approximate judgments based on limited data. AI on the other hand has the potential to make much better decisions because its good at recognizing different types of data. AI software can help you identify patterns and trends that other humans might miss, and therefore reduce the chance of making mistaken conclusions and making rash decisions.

Machine learning can teach itself to identify and reduce trading risks based on historical data. If you set up a trading bot using AI and it detects certain types of market risk that you didnt account for, you can change the settings to reduce the risk to an acceptable level.

Many automated trading systems are set up to trade only against other AI systems. Because they dont want to lose any profit when the market is trading against them, they keep track of the number of trades made against each system and add them to an AI-Loss bucket. If the bucket looks full, the system will shut down and mark the traded asset as not suitable for trading. This practice can lead to market inefficiency when an automated system isnt designed to be constantly online.

Instead of the full market, only a small percentage of trades are being conducted and the rest are being monitored by the trading system. If the system isnt monitoring the full market all the time, it might miss important trading events that have a major effect on the overall market.

Fee-basis is one of the main factors that determine the profitability of a trading strategy. If a lot of trades are being made at a low fee, it might be possible to reduce market risk and increase profits by running a high-fee trading strategy. However, if the trading strategy is set up only to make a small profit, it might be unable to detect fee-basis manipulation that could lead to loss. For example, suppose you set up an automated trading strategy to buy and sell cryptocurrencies in which you charge a 2% trading fee.

You notice that the market is relatively low and youre able to make money off that. If you set up a high-fever strategy and start making large losses, it might be possible for you to mistake a low-volume market for a low-fee one and end up paying a higher fee than necessary.

You might have heard that trading is like a game of cat and mouse. In this game, the player controls the mouse, which is moved across the trading floor. The trading strategy is to move the mouse as close as possible to the cat while holding the mouse button down. With time and experience, the player controls the mouse and eventually manages to catch the cat.

Traders who use AI software can try this experiment with their strategy. By training the algorithm to identify predictable market movements, you can take advantage of these movements to buy or sell more frequently. This increased trading frequency can improve your trading accuracy and result in better trading outcomes.

Trading is a game of risk management and trading software can help you do this better. AI technology has the potential to make your trading more accurate, which can result in higher profits.

AI software can detect patterns and trends faster than humans ever could have done on their own. It can also identify and reduce trading risks by teaching itself to identify market movements that other traders might miss. If your machine trades only against other machines, it might be able to catch movement that human traders arent aware of, but if its set up for profit-making purposes, it may be unable to detect fee-basis manipulation that could lead to loss.

AI software can identify patterns and trends faster than humans ever could have done on their own. By training the algorithm to recognize predictable market movements, you can take advantage of these movements to increase your profits while reducing risk.

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How Experts Apply Artificial Intelligence Technology In Crypto Trading - Android Headlines

Elbow Climate already has projects in place in Ecuador, Africa and the United States.

An Israeli company is combining artificial intelligence (AI) with satellite data in a new way, It does this to measure carbon uptake on land and at sea.

while doing, Elbow Climate contributes to preventing global warming and climate change by helping remove carbon from the atmosphere, The company has been based in Tel Aviv since 2019 and uses data from sensors on satellites to create accurate maps.

The carbon is stored there, benefiting landowners and governments by selling offset credits to polluting companies, The process is to submit actual carbon data that was put together by hand. For example, measuring the diameter of a tree trunk to see an increase in biomass.

Screenshot of an interactive map showing a portion of the African forest in red, showing a decrease in underground carbon, an increase in green, and no change in white. Photo: Courtesy Elbow Clima

with machine learning, The company teaches techniques to combine data from satellite sensors that scan vegetation up to 30 centimeters below ground level, The soil and roots are there, along with real-world information, allowing you to identify patterns that are used as the basis for carbon predictions in similar environments.

AI detects correlations that a human would not findAriella Charney, Elbows director of operations, said. From humans to the smallest plants, all life on the planet is based on carbon. For millions of years, nature has balanced the carbon that enters the atmosphere with the carbon that leaves and is stored., For example, breathing, like volcanoes, emits CO2.

Most companies that store carbon and sell credits are based in nature. And its forms range from the conservation of forests to the method of sustainable agricultural use. Using Elbows data, they have a good idea of how much carbon they are storing and how much they can offset., And it achieves this with a resolution of 50 square centimeters per pixel.

For its part, the software regularly monitors over time, once a year, to ensure that the same forests that store carbon and make money arent cut down or burned and add carbon dioxide. turns into an emitter., The company has already signed several agreements to provide its mapping services.

Originally posted here:
Using Satellites And Artificial Intelligence, An Israeli Climate Technology Maps Where Carbon Is "hiding" - Aurora - Nation World News