Artificial Intelligence is Becoming the Future of Investment Platforms – EnterpriseTalk

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

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

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

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

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

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

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

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...

The influence of artificial intelligence on the current trends of material science – Economic Times

The recent years have experienced a burgeoning growth in the development of statistical and machine learning within the domains of materials science and polymer chemistry. Interestingly, or rather unnoticeably, the concept of artificial intelligence was prevalent in the material science community for the past couple of decades. For instance, more than 15 years ago, a symposium proceeding conducted by the Materials Research Society had a session titled Combinatorial and Artificial Intelligence Methods in Materials Science. The trend has evolved recently with contemporary topics like high throughput screening, particle simulation accelerator, and using computational data sets to develop ground states.

The first question I asked myself is, why is this field proliferating now? Furthermore, if the area had been into practice 15 years ago, what happened to the techniques since then? Well, this somewhat resembles the rise and fall of the artificial intelligence, which generally has the crest and the trough, commonly termed as the resurgence and AI winters respectively.

The first spark was seen in 1956, when the context of artificial intelligence was created. Back then, the scientist didnt know how to deal with the computational science. Moreover, there was no proper bridge that could link the experimental data with the theoretical data obtained from computational programming. The domain became more reinforced during the 1980s with the advent of powerful algorithms like backpropagation (for neural networks) and kernel methods (for classification). Now, with the integration of deep learning along with the growth in graphics processing units, the computational techniques have opened up a lot of avenues in the field of material sciences.

But, is the current technique enough to bridge the distance between the materials and the scientific community?

I guess, yes. The primary element which determines the robustness of an artificial intelligence processing and operation is the availability of large volumes of arranged data, which the literature terms as libraries. These libraries enable us to use the machine learning fundamentals, but at the same time provide the scope to interpret them physically.

If harmonized and processed precisely, artificial intelligence not only allows us to accelerate our scientific developments but also the way particular research can be conducted. That is why you will find various recent articles that focus on ways to develop quicker routes to perform the same contemporary experiments. In this context, the Materials Genome Initiative, which was launched in 2011, had the sole intention to accelerate the material discovery process and to scale them up. The primary steps they used to establish the above goals were to apply the high throughput algorithm, both the theoretical and experimental modeling, to develop accessible libraries and repositories. Since then, the datasets have become a traditional solution to deal with complex problems in material sciences. The course of evolution eventually developed various datasets that contain thousands of experimental and theoretical data points including the Automatic Flow for Materials Discovery (AFLOWLIB), Joint Automated Repository for Various Integrated Simulations (JARVIS), density functional theory (DFT)), Polymer Genome, Citrination, and Materials Innovation Network.

The question remains- how exactly do these advanced techniques help us to develop a new perspective in material sciences? Well, let me give you an elementary example. Let say; I have developed a robust library with machine learning which hosts data for alloy designing. Once I know what kind of alloy to fabricate, I can set the parameters in the library to find the most optimized set of materials and operation tools which can fetch me the desired results in the least required time. Can we do the same using experimental and pure theoretical techniques? No, since most of the time shall be consumed while conducting trails from the vast set of the data. Moreover, these libraries can be extended to accelerate the synthesis optimization process, along with integrating train models to classify the crystal structures and defects. The most recent application involves the development of various de novo molecules for reinforced molecular designs for identifying materials with specific properties desired for various sensible operations.

As a concluding note, the availability of such databases and amalgamating them with theoretical and machine learning methods offer the potential to alter how materials science is approached substantially.

DISCLAIMER : Views expressed above are the author's own.

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The influence of artificial intelligence on the current trends of material science - Economic Times

BWIRE: Artificial Intelligence could be helpful in dealing with… – Citizen TV

By Victor Bwire

Its during such times like the current corona virus outbreak that we ask our ourselves hard questions including how can innovations especially Artificial Intelligence (AI), help us deal with improve our responses and handling of such challenges.

Elsewhere it has been shown what AI and machine automated applications can do several things previously done by human; from smart cities, to facial recognition not the basic on your phone, but cameras that can detect that you have been to four malls in the city within the past one week and recognize your face when you are exiting at the airport to fully automated sales points and ticketing centers.

AI-powered tracking and warning systems, intensive observation methodologies and testing can be of huge importance to the country in the war on the corona virus.

Availability of government-run big-data platforms such CCTVs and others stores information of all citizens and foreign nationals and integrates all these for use. With such information, its easier to track those whom s/he had met during that time, and bring them under observation and medical tests.

AIe ensures prompt execution of all these steps. Hospitals, ambulance services, mobile test labs all rely on IT sector and technology to deliver prompt and efficient services.

Outside its negative side, would the implementation of the huduma number in time have been useful to the country in dealing with the current corona virus, especially in tracking down possible suspects, those self isolating and travel history of people- through obviously the issue of individual privacy has been raised before.

There have been previous innovations where technological applications and mobile phones have been used to tracking and offering health services to malaria patients and reproductive health services to teens.

Tech giants including Google, Facebook, Huawei through their various applications have been working over night elsewhere to support the dealing of the corona virus outbreak. Hopefully, Kenya, which has the presence of such big techs will eventually benefit from such technological innovation.

Today while reading an article by Eunice Kilinzo online, technology and medical services, my mind went to the many other stories I have read relating to technological innovations including mobile phone applications that have helped in enhancing the delivery of health services to Kenyans. Kilonzo talks about Ada a mobile application that uses artificial intelligence (AI) to track symptoms to get to the probable cause of an ailment.

The app, developed by Ada Health, a Germany-based health tech company, combines a database for 160 different diseases with intelligent reasoning technology.

Its reported that South Korea is fighting the virus by using big-data analysis, AI-powered advance warning systems and intensive observation methodology-the government-run big-data platform stores information of all citizens and resident foreign nationals and integrates all government organisations, hospitals, financial services, mobile operators, and other services into it, which is then integrated and used.

Huawei who are behind the 5G technology and working with Safaricom, who you be assisting Kenya in coming with applications through mobile phones to in mapping out and alerting health providers about the epicenters. Mobile phone operators safaricom and airtel have already reduced and or removed charges on their money transfer services.

I know Huawei since January started on a work from home service, office cleaning, social distance disinfecting office vehicles, employee shuttle buses and checking every employees and guests temperature, ensuring that all staff who had travelled from any country with any cases have been undergoing 14 days self-isolation and requiring all employees submit daily survey to confirm their health and those of their family in case they need support from us.

The bigger assignment I would expect from them is to scale up management of the critical telecommunications infrastructure and IT systems for government as well as telecommunications companies, which is the back borne of the countrys public awareness, information sharing and money transfer services.

Huawei must ensure that all telecoms systems function and can handle the current cashless economy we are dealing with because of the outbreak including working with Safaricom for M-PESA as well as other critical hardware and software.

I belong to a group started by facebook called Coronavirustechhandbook.com, where guys are posting innovations and efforts by tech experts and companies to make a contribution to the handling of the outbreak. For example, http://www.trackmycircle.com site where you log your contacts and you will be notified by email when a peer (or 3rd degree) is found COVID-19 positive so that you can self-isolate. Another interesting innovation is on http://www.worldmeters.info

With such big technology giants like Google, Facebook, IBM and other having AL research hubs across the continent, we expect technology to play a big roe in dealing with the corona virus on the continent.

Could we see AI-powered drones used in the tracking and monitoring and identification of cases and related interventions in combating the outbreak? We want to see players like the media using technology like skype, google recorders and related audio applications to carry out interviews and bring is news without necessarily attending face-to- face interviews and reporting to newsrooms.

Can those in charge of dissemination of information consider doing multimedia messages that can be shared across the country including with community and locally based journalists to help in public education.

Bwire is the Head of Media Development and Strategy at the Media Council of Kenya

Video Of The Day: PSA: How to protect yourself and others from Coronavirus

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BWIRE: Artificial Intelligence could be helpful in dealing with... - Citizen TV

Asia Pacific Artificial Intelligence in Fashion Market to 2027 – Featuring Amazon.com, Catchoom and Facebook Among Others – ResearchAndMarkets.com -…

DUBLIN--(BUSINESS WIRE)--The "Asia Pacific Artificial Intelligence in Fashion Market to 2027 - Regional Analysis and Forecasts by Offerings; Deployment; Application; End-User Industry" report has been added to ResearchAndMarkets.com's offering.

The Asia Pacific artificial intelligence in fashion market accounted for US$ 55.1 Mn in 2018 and is expected to grow at a CAGR of 39.0% over the forecast period 2019-2027, to account for US$ 1015.8 Mn in 2027.

Real-time consumer behavior insights and increased operational efficiency are driving the adoption of artificial intelligence in fashion industry. Moreover, the availability of a large amount of data originating from different data sources is one of the key factors driving the growth of AI technology across the fashion industry.

Artificial Intelligence has already disrupted several industries, including the retail and fashion industry. The fashion industry so far has been one of the primary adopters of the technology. The fashion retailers these days are leveraging several revolutionary technologies, including machine learning, like augmented reality (AR) and artificial intelligence (AI), to make seamless shopping experiences across the channels, from online models to brick and mortar stores. Fashion retailers are progressively moving towards the AI integration within their supply chain, where more focus is being on customer-facing AI initiatives.

The artificial intelligence in fashion market is fragmented in nature due to the presence of several end-user industries, and the competitive dynamics in the market are anticipated to change during the coming years. In addition to this, various initiatives are undertaken by governmental bodies to accelerate the artificial intelligence in fashion market further.

The governments of various countries in this region are trying to attract FDIs in the technology sector with the increasing need for enhanced technology-related services. For instance, China's government relaxed the restrictions on new entries with an objective to encourage overseas and private capital to invest in its economy. This factor is anticipated to drive the demand for artificial intelligence in fashion market in this region.

Reasons to Buy

Key Topics Covered:

1. Introduction

2. Key Takeaways

3. Research Methodology

4. Artificial Intelligence in Fashion Market Landscape

4.1 Market Overview

4.2 PEST Analysis - Asia Pacific

4.3 Ecosystem Analysis

4.4 Expert Opinions

5. Artificial Intelligence in Fashion Market - Key Market Dynamics

5.1 Key Market Drivers

5.1.1 Accessibility of massive amount of data from different data sources

5.1.2 Real time consumer behaviour insights and increased operational efficiency are driving the adoption of AI in fashion industry

5.2 Key Market Restraints

5.2.1 Concerns associated with data privacy and security

5.3 Key Market Opportunities

5.3.1 Advent of Natural Language Programming (NLP) to fashion industry

5.4 Future Trend

5.4.1 Prediction of Fashion Trends With AI

5.5 Impact Analysis of Drivers and Restraints

6. Artificial Intelligence in Fashion Market - Asia Pacific Market Analysis

6.1 Overview

6.2 Asia Pacific Artificial Intelligence in Fashion Market Forecast and Analysis

6.3 Market Positioning - Five Key Players

7. Asia Pacific Artificial Intelligence in Fashion Market - By Offerings

7.1 Overview

7.2 Asia Pacific Artificial Intelligence in Fashion Market Breakdown, by Offerings, 2018 & 2027

7.3 Solutions

7.4 Services

8. Asia Pacific Artificial Intelligence in Fashion Market - By Deployment

8.1 Overview

8.2 Asia Pacific Artificial Intelligence in Fashion Market Breakdown, by Deployment, 2018 & 2027

8.3 On-premise

8.4 Cloud

9. Asia Pacific Artificial intelligence in fashion Market - By Application

9.1 Overview

9.2 Asia Pacific Artificial intelligence in fashion Market Breakdown, By Application, 2018 & 2027

9.3 Product Recommendation

9.4 Virtual Assistant

9.5 Product Search and Discovery

9.6 Creative Designing and Trend Forecasting

9.7 Customer Relationship Management (CRM)

9.8 Others

10. Asia Pacific Artificial intelligence in fashion Market Analysis - By End User Industry

10.1 Overview

10.2 Asia Pacific Artificial intelligence in fashion Market Breakdown, By End User Industry, 2018 & 2027

10.3 Apparel

10.4 Accessories

10.5 Cosmetics

10.6 Others

11. Asia Pacific Artificial Intelligence in Fashion Market - Country Analysis

11.1 Overview

11.1.1 APAC Artificial Intelligence in Fashion Market Breakdown, By Key Country

11.1.1.2 China Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

11.1.1.3 India Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

11.1.1.4 Japan Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

11.1.1.5 South Korea Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

11.1.1.6 Rest of APAC Artificial Intelligence in Fashion Market Revenue and Forecast to 2027 (US$ Mn)

12. Artificial Intelligence in Fashion Market - Industry Landscape

12.1 Overview

12.2 Market Initiative

12.3 New Development

13. Company Profiles

13.1 Adobe Inc.

13.2 Alphabet Inc. (Google)

13.3 Amazon.com, Inc.

13.4 Catchoom

13.5 Facebook Inc.

13.6 Huawei Technologies Co., Ltd.

13.7 IBM Corporation

13.8 Microsoft Corporation

13.9 Oracle Corporation

13.10 SAP SE

For more information about this report visit https://www.researchandmarkets.com/r/cw9ef5

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Asia Pacific Artificial Intelligence in Fashion Market to 2027 - Featuring Amazon.com, Catchoom and Facebook Among Others - ResearchAndMarkets.com -...

The next step in digital transformation: is Artificial Intelligence production-ready for green sand foundries? – Foundry-Planet.com

Kasper and Frans, thank you for joining us today. To kick off, can you tell us briefly why using Artificial Intelligence (AI) in a green sand foundry is a good idea?Kasper: DISA has been helping foundries collect, visualise and analyse their data with our Monitizer suite for a few years now. Adding AI capabilities to do more with this data is a logical next step and its a big one. Monitizer | PRESCRIBE which is what our AI product is called harnesses the power of AI to optimise the whole foundry process, significantly reducing scrap while increasing capacity and production predictability.

Frans: Theres a lot of hype around AI so at DataProphet, we like to quote real results to show whats possible. Over the last two years, the average AI-driven defect reduction across all of our manufacturing customers is 40%. With some, its 80% or 100%. Few foundries take full advantage of Industry 4.0 techniques so the potential for them is enormous.

Our Expert Execution System (EES), enabled by AI, has helped a foundry in South Africa cut defect rates in grey iron engine block castings by 50% in the first month. For the first time ever, they achieved zero internal defects on all shipped castings over three months and now save over $100k every month.

How does AI help deliver these kinds of results?Kasper: The key word here is automation. Many green sand foundries already collect and analyse process data but its usually limited to single sub-processes like moulding or pouring. The data for each process stays separate and basic manual analysis is done using spreadsheets or simple statistics.With an entire foundry line, optimisation can involve hundreds or even thousands of variables across all the different process stages. Making sense of that complexity manually is just impossible. AI automates this analysis, using the cloud to access vast computing capacity. Thats the only way to handle the complex, large sets of data that will give us new insight that will in turn make a genuine difference to a foundrys performance.

So what does an AI solution like Monitizer | PRESCRIBE actually do?Frans: It starts by analysing historic production and quality data to learn from past mistakes and corrections, to find what works and what doesnt. It considers how the parameters within and across all the different processes are related, how each one influences the other and what the ultimate combined effect on quality is.From that analysis, Monitizer | PRESCRIBE finds the optimal process parameters and tolerances for a particular casting and process. Knowing the best recipe, it can prescribe hence the name the best actions to take to improve quality.

Kasper: A good example is where, even though all your process parameters are within tolerance, you still might see bad quality castings. Often, this is because one metric is slightly high, another is slightly low and so on. Its a specific combination of values that produces the defect, not a single extreme one. Because the AI has learnt how parameters like grain size, moisture content, pouring speed or inoculation rate influence each other, it can pick the right settings for minimum defects.

So thats like a much more effective version of todays offline analysis. How does AI help you apply those learnings during real production?Kasper: Monitizer | PRESCRIBE applies what it has learnt to live data keeping an eye on what your foundry is doing right now, in real time. That gives you dynamic process control, reacting instantly as conditions change, like ambient air temperature or sand moisture content, and telling operators on the line the optimal settings or actions to take in time to prevent defects occurring. It keeps on learning too, constantly optimising the production process towards zero scrap and improving other metrics like productivity and resource use.Frans: Data-driven, real-time optimisation is sophisticated second-order control. By constantly monitoring machine and process data, then telling you which adjustments to make and again monitoring their effect, our AI tool gradually gets every part of your process running in harmony. You achieve a stable operating regime with the best quality and minimum quality variance. A good analogy is with an autonomous car which can automatically keep you in the middle of a motorway lane.By constantly computing the optimum process parameters, our AI keeps your process in the middle of the lane.

Its clear that automation and data analytics have enormous potential but many foundries have yet to adopt the basics here. So is it really possible for any green sand foundry to make use of AI?Kasper: We see digital as a four-step journey where you start with data collection and visualisation, then move at your own speed towards analytics, AI and automatic process control. Of course, we can help customers do all of that very quickly if they want to.Our NoriGate is the only hardware involved for data collection and everything else is a cloud service which we can deploy in any foundry or with existing data collection infrastructure. That makes it very quick and resource-efficient to deploy. You wont need any new IT hardware, data scientists or any extra staff.

We can digitise every step in the green sand process, take data from paper records or pull it from Excel, and give you a single trustworthy, time-stamped database ready for investigation. At each step, you can achieve significant benefits.The point is that, no matter if you are just starting out or are digitally advanced, there are things we can do that help you take the next step very rapidly indeed.

So you dont have to be a rocket scientist to make use of AI?Frans: AIs inner workings can be complicated to understand but together we have developed it into a packaged service that works for foundries. Its not hard to implement it and its not capital-intensive. As Kasper says, everything you need to collect, store and report on the data is already available from DISA and well proven.Some foundries think they are too old school for digital, but AI projects can be realised when theres no strong data environment or even if they havent really previously captured data at all. Our partnership with DISA enables very rapid digital progress in any type of foundry.

Does your partnership between an industrial AI company and a foundry equipment expert make your solution different to the other AI products we see emerging?Kasper: A lot of vendors say they have an AI system, but a pure IT company may never have seen a foundry from the inside before. We bring a combination of deep foundry experience and DataProphets award-winning expertise in manufacturing data science with more than 35 engineers, statisticians and computer scientists dedicated to developing AI solutions. This collaboration makes our service uniquely practical and effective. Its already tried and tested in a green sand foundry environment and were finding that fact is very attractive for customers. For example, we are currently installing the full Monitizer suite including MonitizerPRESCRIBE at a large European foundry group.

From DataProphets point of view, how does DISAs experience in green sand foundries help an AI project succeed?Frans: When you implement an AI solution in manufacturing, its vital to capture domain knowledge completely and correctly. As the leading OEM supplier, DISA know green sand intimately and are very much the experts in the foundry environment. They know what to do and which questions to ask right at the start. That means value from a running system arrives in weeks, not months or years.

DISAs customers also trust them to keep their promises and they understand that MonitizerPRESCRIBE will be delivered and managed through them. If DISA puts its name to it, customers know it will be an effective, high quality product and that will be supported in five years time and in ten or twenty years too.

Is this AI solution just for DISA customers?Kasper: The entire Monitizer suite, including NoriGate and MonitizerPRESCRIBE, is machine-agnostic, so its not limited to DISA machines or even to the green sand process. Monitizer is a Norican-wide solution, so every foundry can benefit from it, whether its pouring iron or die-casting aluminium.

Frans, with your experience, how do you think foundries compare to other manufacturers in their application of digital tools?Frans: Some other manufacturing environments are now quite sophisticated in their use of software and data, which is not often the case for foundries. With IoT infrastructure and Expert Execution Systems like MonitizerPRESCRIBE, there is a real opportunity for foundries to leapfrog the older IoT systems and access the very latest technology without having to make an enormous investment.

Are there any common misconceptions about AI you hear from your foundry customers?Frans: They can be worried that their data might be used in another customers AI which never happens. MonitizerPRESCRIBE can ingest and interpret all a customers foundry data and that certainly doesnt include data from other customers.

Monitizer | PRESCRIBE is designed with full tenant sandboxing: every clients datastore, database, and model is uniquely encrypted, and every component is isolated from every other component in the system. It is not possible to mix data or models between clients and the data is safeguarded with every possible measure.

Kasper: Some people think AI needs another in-house IT system thats big, complex and very expensive. But Monitizer | PRESCRIBE is an online service, it simply gives you a tool to optimise quality and productivity. Also, when we talk to foundry staff, some fear an AI system will come in and take over their job. But this isnt about taking jobs. The information AI gives will help them make better decisions and improve their own performance. It will make them look good.

Are there any other AI-related advantages for foundry owners and their workforces?Kasper: Theres a generational change going on in our industry. Engineers with 30 or 40 years experience are retiring and our customers are worried that their knowledge of how to keep their own unique processes running correctly will be lost. But their knowledge is encoded within historical process data. Monitizer | PRESCRIBE can access that and put it to work. With more automation, the foundry also becomes a cleaner, more attractive place to work. You can spend most of the time in an office-like control room, which will be more appealing to todays potential recruits.

Frans: By learning from human intelligence, expressed in millions of decisions made over the years, the AI becomes the central knowledgebase for the foundry. Then it can support less experienced engineers and operators in their decision making. A lot of value for manufacturing customers lies in selecting and extracting those good decisions so theyre never lost.

If AI helps foundries move from offline analysis to continuous guidance, what comes next?Frans: The end goal is a foundry that runs its own processes automatically similar to what the autonomous vehicle industry is aiming to achieve with cars. Staff will gradually move from continuously analysing processes and adjusting machines to focus on tasks theyre better suited for like innovation, creation and ideation. The plant of the future will re-configure itself for the optimal delivery of new customer orders, adjusting its configuration, production schedule, energy consumption and staff roles to give maximum efficiency.

Kasper: The system will adjust settings automatically, for example, when sand properties change, and you need more additives, or if the humidity changes and the sand dries out faster so you need to add more moisture. All these variations are corrected manually today and, even with Monitizer PRESCRIBEs real-time advice, usually still will be, but the system will handle it all automatically in future.

How close is this fully autonomous future?Frans: Were not there yet, but it will definitely happen for some foundries in the next few years. Most foundries are starting to collect data and analyse it, so they are being assisted by data today. Our system goes from there to guiding them with specific real-time recommendations. The self-driving foundry is the next stop on the journey.

Kasper: Were already helping customers fully automate parts of their DISA line, like moulding and pouring, or sand mixing and moulding, though complete automation of the whole line is a little way ahead at the moment. But I think it will arrive a lot sooner than completely autonomous cars.

Many thanks to both Kasper and Frans for a fascinating explanation of how they are working together to bring AI to foundries.

DISAs AI solution Monitizer | PRESCRIBE is currently live with selected pilot customers and will be available in the coming months. More information can be found here. [https://www.disagroup.com/en-gb/foundry-products/digital-solutions/monitizer/monitizer-prescribe]

See the rest here:
The next step in digital transformation: is Artificial Intelligence production-ready for green sand foundries? - Foundry-Planet.com

This Artificial Intelligence Stock Raised Its Dividend on "Black Thursday" – Nasdaq

As many now know, last Thursday was an historic day in the stock market. On March 13, 2020, the S&P 500 plunged 9.5% in a single day, the worst daily drop since "Black Monday" in 1987. The plunge came the day after President Trump delivered an underwhelming speech that included a European travel ban. However, stocks rallied on Friday after news of more government stimulus, emergency measures to boost testing, and the purchasing of oil for the country's strategic reserve. Negotiations for a comprehensive support package for the economy are also ongoing.

However, one tech company was tuning out the noise. Semiconductor equipment maker Applied Materials (NASDAQ: AMAT) decided to announce an increase in its dividend on the exact same day the market went into freefall. Is that a sign of confidence, or foolishness?

Image source: Getty Images.

Applied Materials announced that it would raise its quarterly dividend by a penny, from $0.21 to $0.22, a 4.8% boost. Applied's dividend yield is now 1.86%, but that's with a very modest 27.5% payout ratio. The higher dividend will be paid out on June 11, to shareholders of record as of May 21. CEO Gary Dickerson said: "We are increasing the dividend based on our strong cash flow performance and ongoing commitment to return capital to shareholders. ... We believe the AI-Big Data era will create exciting long-term growth opportunities for Applied Materials."

Semiconductors and semiconductor equipment companies have historically been known to be cyclical parts of the tech industry. However, it appears Applied Materials believes the overarching trends for faster and smarter semiconductors should help the company power through a near-term economic disruption. As chip-makers make smaller and more advanced chips, Applied's machines are a necessary expenditure.

But can the long-term trends buffer the company in a times of a potential global recession?

It should be known that the semiconductor industry was already in a downturn last year in 2019, and was beginning to come out of it in early 2020. For Applied, last quarter's results exceeded the high end of its previous guidance, with revenue up 11% and earnings per share up 21%.On Feb. 12, management also guided for solid sequential growth in Q2 even while lowering its prior numbers by $300 million because of coronavirus as of that date.

On a Feb. 12 conference call with analysts, Dickerson reiterated that optimism:

We believe we can deliver strong double-digit growth in our semiconductor business this year as our unique solutions accelerate our customers' success in the AI-Big Data era... our current assessment is that the overall impact for fiscal 2020 will be minimal. However, with travel and logistics restrictions, we do expect changes in the timing of revenues during the year. We are actively managing the situation in collaboration with our customers and suppliers.

While many businesses across the world have seen severe interruptions, it's unclear if the chip industry will be affected as much as others, despite its reputation for cyclicality. While consumer-related electronics may take a temporary hit to demand, a more stay-at-home economy means the need for faster connections, which could actually increase demand for servers and base stations.

Memory chip research website DrameXchange released a report on March 13, outlining its current projections for the DRAM and NAND flash industries as of March 1, along with an updated "bear case" scenario should the coronavirus crisis escalate into a global recession, which was updated on March 12.

Category

Current 2020 Projections

Bear Case 2020 Projections

Notebook computer shipments

(2.6%)

(9%)

Server shipments

5.1%

3.1%

Smartphone shipments

(3.5%)

(7.5%)

DRAM price growth

30%

20%

NAND flash price growth

15%

(5%)

Data source: DrameXchange.

Notice that the enterprise-facing server industry looks poised to withstand a potential severe downturn much better than consumer-facing notebook or smartphone industry. In addition, DRAM prices are poised to increase in 2020 even in a recession, as prices had already crashed last year and the industry cut back on capacity. NAND flash had an earlier downturn than DRAM, and was already beginning to come out of it, so it has more potential with a decline in pricing.

In addition, the largest global foundry Taiwan Semiconductor (NYSE: TSM), just said on March 11 that its capacity for leading-edge 5nm chip production was already "fully booked," and that volume production would begin in April. That indicates continued strong demand for leading-edge logic chips.

So while there may be some more softness in certain parts of the chip industry, there are still relatively strong segments as well. Therefore, Applied may not face revenue declines in 2020, but rather a mere absence of previously forecast growth. Yet even if that happens, growth will likely be deferred to 2021, not totally lost, as eventually the demand for chips will increase.

After its decline, Applied Materials stock trades at just 17 times trailing earnings, and just 14.7 times projected 2020 earnings, though 2020 projections may come down. Still, that's a reasonable price to pay for Applied, especially in a zero-interest rate environment. The company has just as much cash as debt, and its recent dividend raise on the market's darkest day in recent history shows long-term confidence. Risk-tolerant investors with a long enough time horizon thus may want to give Applied -- and the entire chip sector -- a look after the dust settles.

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Billy Duberstein owns shares of Applied Materials and Taiwan Semiconductor Manufacturing. His clients may own shares of the companies mentioned. The Motley Fool owns shares of and recommends Taiwan Semiconductor Manufacturing. The Motley Fool recommends Applied Materials. The Motley Fool has a disclosure policy.

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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This Artificial Intelligence Stock Raised Its Dividend on "Black Thursday" - Nasdaq