How can AI help in investment decisions? And if there are challenges, how does your platform help to resolve those challenges?

As to why investors in general need AI, there are enormous amounts of data out there, and there is an ongoing battle over that available data. The industry as a whole now produces all kinds of data-based financial reporting and statements, and investors and industry players alike can buy really well-structured data as a result. AI has the ability to study massive amounts of this data and identify patterns.

Let us assume we identified a stock pattern today, and we want to figure out what to do next: buy or sell. AI can find somewhat similar patterns that existed in history and then analyze what happened right after. Knowing what happened after the pattern in the past may suggest what may happen in the future from today.

How Bots Are Altering the Future of Enterprise

We can identify patterns for stocks, Forex, ETFs, mutual funds, and even currencies. With that said, some patterns will not work for certain stocks; that is why people need a complete picture, including discovery, testing, and a presentation of results.

What if there is a challenge and if they are having a problem identifying the patterns? How does then AI support this kind of investor?

Challenges can also be patterns. Let us assume there is a significant drop in the market today; AI can go back through historical data and find similar significant drops in the market to come to pattern-based conclusions, such as which particular stocks continue to go down and which stocks tend to quickly bounce back. And in that regard, AI helps to solve the challenges in conjunction with human involvement, where humans can take these signals and use them for making better trading decisions.

That perspective raises the question: can AI effectively trade or manage a portfolio without any human involvement? So far, there is only one recorded example, a hedge fund claiming no human involvement. In all other cases, at this moment, humans have some kind of involvement. Today, the best minds in the finance industry are working on solutions that can help interpret challenges or anomalies in the market, including significant drops or significant jumps. Beyond AI, many companies use robots to work on these solutions, too. They look at the expense ratio and come up with the best-case scenario we are talking about the fully automated robots which can solve the challenges that arise.

Are there any security challenges in data processing of this type?

Data security challenges are the same whether AI is involved or not you have to be secure either way. With that said, you do need to protect against the black swans when something unexpected happens, and the AI can react and perform a problematic money maneuver.

Voice-based AI Assistant Certainly the Future of Workplace

Think about the verification challenges when people put driverless cars on autopilot, and the driverless car sees something unexpected. There is a chance it will crash, like Tesla demonstrated recently when the human fully relied on autopilot. When it comes to AI and investing a lot of money could be on the line.

So you see AI as a future of investment platforms? How is your platform leveraging AI differently?

Ans: Yes, absolutely. It is an enormous amount of power, and no human being can compete with the speed and volume of this power when applied to trade.

Here is the main difference with AI in our approach: to make it convenient for our users, we test a lot of strategies in advance, and that means that a typical investor gets access to a secure cloud. In our secure, local cloud, we run a lot of pre-calculations over different strategies. We run tens of thousands of different strategies simultaneously. We dont know what is going to happen with these tens of thousands of strategies, but we know that if the user on our site wants to use one of them, then it is going to be pre-calculated. That way, the person has more immediate access to our data and analysis. And that is our main feature that a person can use our AI on request.

Rebirth of Industries in the Era of Intelligent Automation

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Artificial Intelligence is Becoming the Future of Investment Platforms - EnterpriseTalk

scar lvarez-Machancoses,1,2 Enrique J DeAndrs Galiana,1 Ana Cernea,1 J Fernndez de la Via,1 Juan Luis Fernndez-Martnez2

1Group of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, Oviedo 33007, Spain; 2DeepBiosInsights, NETGEV (Maof Tech), Dimona 8610902, Israel

Correspondence: Juan Luis Fernndez-MartnezGroup of Inverse Problems, Optimization and Machine Learning, Department of Mathematics, University of Oviedo, C. Federico Garca Lorca, 18, Oviedo 33007, SpainEmail jlfm@uniovi.es

Abstract: The complexity of orphan diseases, which are those that do not have an effective treatment, together with the high dimensionality of the genetic data used for their analysis and the high degree of uncertainty in the understanding of the mechanisms and genetic pathways which are involved in their development, motivate the use of advanced techniques of artificial intelligence and in-depth knowledge of molecular biology, which is crucial in order to find plausible solutions in drug design, including drug repositioning. Particularly, we show that the use of robust deep sampling methodologies of the altered genetics serves to obtain meaningful results and dramatically decreases the cost of research and development in drug design, influencing very positively the use of precision medicine and the outcomes in patients. The target-centric approach and the use of strong prior hypotheses that are not matched against reality (disease genetic data) are undoubtedly the cause of the high number of drug design failures and attrition rates. Sampling and prediction under uncertain conditions cannot be avoided in the development of precision medicine.

Keywords: artificial intelligence, big data, genomics, precision medicine, drug design

This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution - Non Commercial (unported, v3.0) License.By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms.

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On the Role of Artificial Intelligence in Genomics to Enhance Precisio | PGPM - Dove Medical Press

The battery sector is turning to artificial intelligence for clues on how to improve recharging rates without increasing the degradation of lithium-ion batteries.

Last month, a team from Stanford University, the Massachusetts Institute of Technology and the Toyota Research Institute published findings from battery testing aimed at cutting electric-vehicle charging times down to 10 minutes. The research, published in Nature, revealed how artificial intelligence could speed up the testing process required for novel charging techniques.

The researchers wrote a program that predicted how batteries would respond to different charging approaches and was able to cut the testing process from almost two years to 16 days, Stanford reported. The technique was used to evaluate 224 possible high-cycle-life charging processes in just over two weeks, the researchers said.

The research effort has been in progress for at least three years. In 2017, the Toyota Research Institute committed $35 million to artificial intelligence battery research, initially focusing on new materials.

Last year, the research partners claimed artificial intelligence could help predict the useful life of lithium-ion batteries to within 9 percent of the actual life cycle of the products.

The standard way to test new battery designs is to charge and discharge the cells until they die,co-lead author Peter Attia, now of Tesla but then a Stanford doctoral candidate in materials science and engineering, said in a press release at the time.

Since batteries have a long lifetime, this process can take many months and even years. Its an expensive bottleneck in battery research.

Independentof these efforts, a Canadian firm called GBatteries is using artificial intelligence in a bid to cut lithium-ion battery charging times down to five minutes. The company has succeeded in recharging an electric scooter battery in less than 10 minutes.

The main challenge with extremely fast charging is that it heats up and degrades the battery, GBatteries co-founder and Chief Commercial Officer Tim Sherstyuk told GTM.

The rates that can be achieved with todays fast-charging technology, which are slow by gas-station filling standards, are already problematic for batteries, he said.

Most fast-charging initiatives focus on novel chemistries that wont degrade easily, Sherstyuk said. GBatteries, meanwhile, uses artificial intelligence to monitor the state of the battery as it is charging.

Once the impedance of the battery reaches a critical level, the GBatteries algorithm pauses charging long enough to avoid irreversible damage. This allows charging to proceed in a series of high-intensity pulses at a rate much faster than is possible with traditional methods.

The GBatteries technology works for small batteries and has been demonstrated on power tools, cutting charging times from between 30 to 60 minutes down to 11. But scaling it up to cope with an electric vehicle battery pack is going to take a while, said Sherstyuk.

Even if artificial intelligence can help crack the means to charge electric vehicles as quickly as you now fill your tank with gas, it will take a while for the auto industry to incorporate the technology into the mainstream. The time horizon is years, not months.

Nevertheless, there is plenty of industry interest in tackling the problem.

Charging time is usually the fourth concern that people raise when considering to go electric or not, after upfront cost, range of the vehicle and where [to] charge, said Aaron Fishbone, director of communications at GreenWay, which operates a fast-charging network across Eastern Europe.

So, while not a top-tier issue, its still one raised by many people.

GBatteries pulse charging will require a lot more testing before it might be considered appropriate for the 50+ kilowattpower ratings required for electric vehicles, Fishbone said. In the meantime, high-power recharging is already reducing the time it takes to charge a battery.

Although there are not yet many cars that can take them, a 150-kilowatt charger can add 100 kilometers (62 miles) of range to an electric vehicle within a little over seven minutes, Fishbone said.

Nonetheless, anything which can speed up charging time without degrading battery life is a welcome development and can lead to other innovations which push the whole industry."

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Battery Researchers Look to Artificial Intelligence to Slash Recharging Times - Greentech Media News

TUSTIN, Calif.--(BUSINESS WIRE)-- Canon Medical Systems USA, Inc. has received 510(k) clearance on its Advanced intelligent Clear-IQ Engine (AiCE) for the Vantage Galan 3T MR system, further expanding access to its new Deep Learning Reconstruction (DLR) technology. This technology, which is also available across a majority of Canon Medicals CT product portfolio, uses a deep learning algorithm to differentiate true MR signal from noise so that it can suppress noise while enhancing signal, forging a new frontier for MR image reconstruction.

AiCE was trained using vast amounts of high-quality image data, and features a deep learning neural network that can reduce noise and boost signal to quickly deliver sharp, clear and distinct images, further opening doors for advancements in MR imaging. Capabilities include:

AiCE utilizes a next generation approach to MR image reconstruction, further proving Canon Medicals leadership and commitment to innovation in diagnostic imaging, said Jonathan Furuyama, managing director, MR Business Unit, Canon Medical Systems USA, Inc. With the expansion of this unique DLR method across modalities and into MR, were elevating diagnostic imaging capabilities for our customers by bringing the power of AI to routine imaging to provide more possibilities in improving patient care than ever before.

About Canon Medical Systems USA, Inc.

Canon Medical Systems USA, Inc., headquartered in Tustin, Calif., markets, sells, distributes and services radiology and cardiovascular systems, including CT, MR, ultrasound, X-ray and interventional X-ray equipment. For more information, visit Canon Medical Systems website at https://us.medical.canon.

About Canon Medical Systems Corporation

Canon Medical offers a full range of diagnostic medical imaging solutions including CT, X-Ray, Ultrasound, Vascular and MR, as well as a full suite of Healthcare IT solutions, across the globe. In line with our continued Made for Life philosophy, patients are at the heart of everything we do. Our mission is to provide medical professionals with solutions that support their efforts in contributing to the health and wellbeing of patients worldwide. Our goal is to deliver optimum health opportunities for patients through uncompromised performance, comfort and safety features.

At Canon Medical, we work hand in hand with our partners - our medical, academic and research community. We build relationships based on transparency, trust and respect. Together as one, we strive to create industry-leading solutions that deliver an enriched quality of life. For more information, visit the Canon Medical website: https://global.medical.canon.

* AiCE MR is applicable to neuro and knee imaging

View source version on businesswire.com: https://www.businesswire.com/news/home/20200318005103/en/

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Canon Medical's 3T MR System Receives FDA Clearance for Artificial Intelligence-Based Image Reconstruction Technology - BioSpace

SAN DIEGO, March 18, 2020 (GLOBE NEWSWIRE) -- smartboost, an AI Digital Agency, announced today its new company name and business model. The founders of CNG Marketing and SIO Digital have merged their companies together to create smartboost, the first ever AI marketing agency.

In 2014, smartboost founders Giovanni Letellier and Clement Connor created CNG Digital Marketing, a digital marketing agency that was focused on building small businesses. CNG utilized advanced technology to reach and exceed clients goals. The company quickly grew from two founders to over 15 employees in its first year.

In 2016, CNG created its sister company, SiO Digital, that focused more on medium to large businesses, SiO Digital, was also an AI-powered and data-driven marketing agency. Giovanni transferred his responsibilities as CEO of CNG to Clement and took the role of Chief Strategist, so he could dedicate more time to SIOs growth and future projects.

After a successful six years in partnership with CNG and SIO and while servicing over 100 clients and growing, Giovanni and Clement wanted to merge the two companies to become smartboost.

"Our new name goes much deeper than just a new website and brand colors. It represents the merging of one of the first AI-powered Marketing Agencies with a best-in-class creative digital agency. The future starts now, said Giovanni Letellier, Founder and CEO of smartboost.

smartboost is proud to be the first AI-powered marketing agency alongside an innovative digital creative agency. smartboost is at the forefront of digital marketing and has a proven track record of building businesses through data-driven digital analytics. When business owners are working with smartboost marketers, designers, developers, and engineers, theyre all doing the same job: driving results through data.

smartboost will continue to grow as a collective and is dedicated to staying ahead of marketing trends through advanced AI-technology. This is an exciting time for many types of businesses looking to take advantage of the digital age. By working with smartboost, business owners will be working with the most innovative and technologically advanced agency in the United States.

About smartboost

smartboost is comprised of a highly-skilled team of creative marketers, scientists, and mathematicians all experts in our fields. With a proven track record of data-driven results by the notion of excellence, we see people and Artificial Intelligence working in symbiosis to help businesses survive and grow. smartboost is focused on impact and transparency and our technology is in a constant state of transformation.

PR Contact Kathleen Gonzales kathleen@elevated-pr.com

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San Diego-Based Company takes Digital Marketing to the next Level by Launching the First Artificial Intelligence Marketing Agency in the United States...