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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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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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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]

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Insights into the North America Artificial Intelligence in Fashion Market to 2027 – Drivers, Restraints, Opportunities and Trends -…

DUBLIN--(BUSINESS WIRE)--The "North America 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 North America artificial intelligence in fashion market accounted for US$ 128.7 Mn in 2018 and is expected to grow at a CAGR of 37.9% over the forecast period 2019-2027, to account for US$ 2254.2 Mn in 2027.

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 North American countries are developing various policies and outlining best practices to implement artificial intelligence for promoting innovation in various industry sectors.

Further, the political agendas for North American countries are aligned with the development of Machine Learning (ML) and Artificial Intelligence (AI). Artificial intelligence technologies such as self-adapting machine learning, deep learning or Natural language processing are expected to transform the way businesses work. Governments of various North American countries are working on drafting robust and comprehensive set of regulations and policies for a holistic development of artificial intelligence in this region.

Reasons to Buy

Key Topics Covered:

1. Introduction

2. Key Takeaways

3. Research Methodology

3.1 Coverage

3.2 Secondary Research

3.3 Primary Research

4. Artificial Intelligence in Fashion Market Landscape

4.1 Market Overview

4.2 PEST Analysis - North America

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 Availability of a huge amount of data originating from different data sources

5.1.2 Increase in adoption of artificial intelligence in fashion industry to enhance operational efficiency and improve customer experiences

5.2 Key Market Restraints

5.2.1 Concerns related to data privacy and security

5.3 Key Market Opportunities

5.3.1 Huge investments in developing NLP enabled solutions are anticipated to flourish the market growth

5.4 Future Trend

5.4.1 Use of AI for predicting fashion trends

5.5 Impact Analysis of Drivers and Restraints

6. Artificial Intelligence in Fashion Market - North America Market Analysis

6.1 Overview

6.2 North America Artificial Intelligence in Fashion Market Forecast and Analysis

7. North America Artificial Intelligence in Fashion Market - By Offerings

7.1 Overview

7.2 North America Artificial Intelligence in Fashion Market Breakdown, by Offerings, 2018 & 2027

7.3 Solutions

7.4 Services

8. North America Artificial Intelligence in Fashion Market - By Deployment

8.1 Overview

8.2 North America Artificial Intelligence in Fashion Market Breakdown, by Deployment, 2018 & 2027

8.3 On-premise

8.4 Cloud

9. North America Artificial intelligence in fashion Market - By Application

9.1 Overview

9.2 North America 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. North America Artificial intelligence in fashion Market Analysis - By End User Industry

10.1 Overview

10.2 North America 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. North America Artificial Intelligence in Fashion Market - Country Analysis

11.1 Overview

11.1.1 North America Artificial Intelligence in Fashion Market Breakdown, by Key Countries

11.1.1.1 US Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

11.1.1.2 Canada Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

11.1.1.3 Mexico Artificial Intelligence in Fashion Market Revenue and Forecasts to 2027 (US$ Mn)

12. Artificial Intelligence in Fashion Market - Industry Landscape

12.1 Overview

12.2 Market Initiative

12.3 New Development

12.4 Top Five Company Ranking

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/mgbpsb

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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

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Bitcoin Sees 9% Gain as Turmoil Hits the Forex Markets – CoinDesk – CoinDesk

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Bitcoin is on the rise Thursday morning (UTC), showing resilience in the face of a global dash for dollars seen in the foreign exchange markets.

At time of writing, the cryptocurrency is trading near $5,670, representing a 9.4 percent gain on a 24-hour basis. Bitcoin found bids near $5,260 during the Asian trading hours and has been climbing since, according to CoinDesk's Bitcoin Price Index.

While bitcoin is flashing green against the U.S. dollar, most fiat currencies are currently trading in the red. For example, the British pound-to-dollar exchange rate is hovering near 1.1555, the lowest level since 1980. The currency pair has dropped by nearly 8 percent this week.

The Australian dollar fell to a 20-year low of 55 U.S. cents early on Thursday and is currently reporting a 0.6 percent drop on the day.

The greenback has gained in the past six trading days against all major currencies, as noted by macro analyst Holger Zschaepitz.

The surge indicates many investors are selling everything, even safe havens like Japan's yen and Swiss francs, to move their money into dollars over fears of a coronavirus-led recession in the global economy. If cash is king, then dollar cash is currently being world president," according to ING's head of global markets.

Bitcoin, however, isn't bowing down to the new cash overlord, and could see bigger gains if the U.S, equity markets put in a good performance in line with rising European stocks. At press time, the Euro Stoxx 50 the eurozone's benchmark index has added 1.3 percent to its value.

A risk reset on Wall Street cannot be ruled out, as central banks from Australia to Canada have launched easing programs to inject massive amounts of liquidity into the system.

Bitcoin's technical charts, too, are suggesting scope for a stronger recovery rally.

Daily chart

Bitcoin defended the psychological support of $5,000 on Wednesday and ended up producing a small hammer candle, validating seller exhaustion signaled on Monday.

A hammer candle occurs when sellers fail to keep prices at the lowest point of the day and is widely considered an early sign of a trend reversal.

The MACD histogram is printing higher lows below the zero line, indicating a drop in bearish momentum.

Hourly chart

Bitcoin produced a green marubozu candle in the 60 minutes to 10:00 UTC, which comprises a big body and small or no wicks. The bullish indicator shows buyers were in control from the session's open to its close.

The odds appear stacked in favor of a rise to the top of the ascending triangle at $5,926. A high-volume break above that level could cause more bargain hunters to join the market, producing a stronger rise to the next resistance at $6,425 (December low).

Conversely, a triangle breakdown would open the doors for a re-test of the March 16 low of $4,446.

Disclosure:The author holds no cryptocurrency at the time of writing.

The leader in blockchain news, CoinDesk is a media outlet that strives for the highest journalistic standards and abides by a strict set of editorial policies. CoinDesk is an independent operating subsidiary of Digital Currency Group, which invests in cryptocurrencies and blockchain startups.

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Heres What Caused Bitcoins Extreme Price Plunge – Forbes

Bitcoin and cryptocurrency markets were shocked last week when the bitcoin price briefly plunged to under $4,000 per bitcoin, sparking fears of a crypto wipeout.

The sudden fall was led by Seychelles-based bitcoin and cryptocurrency exchange BitMEX, with the bitcoin price dropping to a low of $3,600 on the exchange before it was closed for "maintenance."

The bitcoin price recovery to back over $5,000 per bitcoin was led by U.S.-based exchange Coinbase with BitMEX lagging far behind the other major exchanges and now crypto analysts have warned so-called leveraged trades, where investors can open positions much larger than their own capital and are popular on BitMEX, can lead to "extreme corrections."

The bitcoin price crashed last week, losing about half its value and causing panic among the bitcoin ... [+] and cryptocurrency community.

"On March 12th, bitcoin fell below $4,000. At one point, due to a backlog of liquidations, the price of bitcoin on BitMEX was over $300 below the price on other exchanges," said Geoff Watts, senior data scientist at U.S.-based Digital Assets Data, which has analysed last week's bitcoin sell-off.

"We're seeing a lot of leveraged trades in the crypto markets and that leverage can lead to extreme corrections during periods of high volatility."

On BitMEX users can borrow against their deposits up to a ratio of 100:1, providing traders the opportunity to amplify their gains, as well as potential losses.

During last week's crash, BitMEX users saw $750 million in bitcoin liquidated in a matter of minutes.

BitMEX has claimed its outage was caused by a planned DDoS attack against the exchange.

"At 02:16 UTC a botnet began a DDoS attack against the BitMEX platform," BitMEX chief executive Arthur Hayes wrote in a blog post this week.

"We discovered shortly afterward that this botnet had been responsible for a similar, yet unsuccessful, attack a month ago on 15 February."

Deribit, a smaller bitcoin and crypto exchange in the Netherlands, also experienced outages during bitcoin's flash crash last week, one during the sell-off and another after the recovery began.

BitMEX and Deribit, acting as two of the largest liquidity providers, experienced technical issues which may have likely contributed to the extreme volatility, Digital Assets Data researchers found.

Last year, the chief executive of the world's largest bitcoin and crypto exchange by volume, Binance, Changpeng Zhao warned the bitcoin price was potentially being inflated by the increased use of leveraged trades.

The bitcoin price continued to fall on Bitmex after it stabilized on other exchanges.

Bitcoin and cryptocurrency markets have fallen sharply over the last few weeks, plummeting along with traditional markets in the face of coronavirus chaos.

Bitcoin and and other major cryptocurrencies saw some $100 billion worth of value erased in just a week, with some senior figures in the crypto community warning confidence in digital assets has "evaporated"potentially leaving bitcoin and crypto vulnerable.

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Bitcoin loses half of its value in two-day plunge – CNBC

Omar Marques | LightRocket | Getty Images

Bitcoin lost its allure as a safe-haven asset this week.

The world's first and most widely held cryptocurrency dropped 50% over the past two days. Bitcoin sometimes referred to as "digital gold" fell more than 30% Friday to its weakest level since March 2019, according to data from CoinDesk.

The cryptocurrency briefly dropped below $4,000 Friday after starting the week above $9,000. It later recovered to roughly $5,400 as of the close of U.S. markets.Bitcoin Futures, meanwhile, were on pace for its worst week since debuting in December 2017.

The digital currency had been trading near the $10,000 level in mid-February. The slide began later in the month alongside global markets reeling from the quickly spreading coronavirus.

"Bitcoin's recent price action is primarily a result of the coronavirus outbreak affecting global markets and driving investors towards the safety of cash," said Joe DiPasquale, CEO of crypto investment firm BitBull Capital. "With this sharp decline, Bitcoin's potential as a safe-haven asset is being questioned, but we believe it is too early to seek any correlations between Bitcoin and other asset classes."

The bitcoin nosedive came amidst volatile trading on Wall Street this week. On Thursday, stocks saw their worstsince the "Black Monday" market crash in 1987. Stocks rose sharply Friday afternoon on the possibility of fiscal stimulus from governments around the world.

Other cryptocurrencies also dropped this week. The world second largest digital currency, ethereum, fell 46% this week while XRP lost nearly 40% of its value.

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Here is the real reason behind Bitcoin price drop – Cryptopolitan

Bitcoin price drop witnessed recently is one of the worst beatings in months while the cryptocurrency sphere as a whole sits tight. Well not just the cryptocurrency sphere, but the world is sitting tight as the Bitcoin price chart movement has correlated with the stock market in a weird movement.

Bitcoins recent massive selloff has made the analyst wrap their heads around the question that is this third sell-off a sign that Bitcoin has fewer chances to survive.

Among others, the crypto research firm Coin Metrics tried to decode the whole episode and have come up with this conclusion that those holders that held the currency for a short period are the reason that contributed to this.

CoinMetricsin its latestState of the Network reportstated that the long-term holders remained chose to stay on sidelines in this whole episode. The coins that were held for twelve months or less than that were responsible for driving out the thirty-eight price route.

This tends to explain the reason for Bitcoins one-year revived supply not having any major spike in the month of March.

The short-term holders were reportedly selling their coins at a loss.

The report also highlights the fact that the market value of Bitcoin to realized value (MVRV) slipped under 1.0. On 12th March, MVRV experienced its biggest drop since the year 2013. The Coin Metrics report explains:

an MVRV above one can signal that speculators have a higher average market valuation than holders. An MVRV below one, on the other hand, can signal that holders have (or had) a higher market valuation than current speculators.

Speculators now do not value Bitcoin more than the holders, and this could be the potential indicator that the leading cryptocurrency is moving closer to bottoming out.

Coronavirus is affecting everyone and everything and not just causing Bitcoin price drop. But this is an indirect effect of the market that is killing the coin at the moment. Truth be told, investors are also putting away their money in stable coin and other assets on account of extreme volatility.

Coronavirus effect on Bitcoin price drop has been observed on the market already in the correlation study with the stock market, where the two markets almost shadowed each other. The world is on lockdown and that means less retail trading, less of buying and less of business everywhere and not just in the cryptocurrency sphere.

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Bitcoin Lost Roughly 50% Of Its Value In A Day – Forbes

Bitcoin prices plummeted today, driven lower by various factors. (Photo by Chesnot/Getty Images)

Bitcoin prices plummeted today, shedding approximately half of their value as global markets were afflicted by widespread panic and liquidity problems.

The digital currency fell to as little as $3,867.09, CoinDesk figures show.

At this point, the cryptocurrency had plunged 49.6% from its price of more than $7,600 at the start of the day, and was trading at its lowest in almost a year, additional CoinDesk data reveals.

[Ed note: Investing in cryptocoins or tokens is highly speculative and the market is largely unregulated. Anyone considering it should be prepared to lose their entire investment.]

When explaining bitcoins extreme price decline, analysts emphasized concerns surrounding the coronavirus and a global liquidity crunch.

Further, several market observers pointed to Asian trading as fuelling the digital currencys sharp losses over the last several hours.

Most of crypto is driven by the Asian market, said Marouane Garcon, managing director of crypto-to-crypto derivatives platformAmulet. The threat of coronavirus is greater over there.

Joe DiPasquale, CEO of cryptocurrency hedge fund managerBitBull Capital, also weighed in, stating that as Asia woke up to the market crash, it helped drive prices down further.

John Iadeluca, founder & CEO of multi-strategy fundBanz Capital, emphasized the importance of trading activity in South Korea.

When investors in the East Asian nation woke up several hours ago and began their day, they realized the destruction that the global financial markets caused while it was their night time, he stated.

The psychological stance as well as global virus pandemic are playing perfectly into one another to see cash as the only possible current safe haven, said Iadeluca.

While this latest drop may look dire for bitcoin, there may also be a silver lining, according to Michael Conn, founder and managing partner of financial services firm Quail Creek Ventures,

He stated that while the liquidity crunch continues, the current situation is an overreaction and will lead to buying opportunities in the near future, he said.

Disclosure: I own some bitcoin, bitcoin cash, litecoin, ether and EOS.

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