Global Quantum Computing Market 2020 : Worldwide Overview by Industry Size and Share, Future Trends, Growth Factors and Leading Players | D-Wave…

This report focuses on the Global Quantum Computing Market trends, future forecasts, growth opportunities, key end-user industries, and market players. The objectives of the study are to present the key developments of the market across the globe.

The latest research report on Quantum Computing market encompasses a detailed compilation of this industry, and a creditable overview of its segmentation. In short, the study incorporates a generic overview of the Quantum Computing market based on its current status and market size, in terms of volume and returns. The study also comprises a summary of important data considering the geographical terrain of the industry as well as the industry players that seem to have achieved a powerful status across the Quantum Computing market.

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Quantum Computing Market Segmentation

By Revenue SourceHardware, Software, Services

By ApplicationSimulation, Optimization, Sampling

By IndustryDefense, Healthcare & Pharmaceuticals, Chemicals, Banking & Finance, Energy & Power

The report has been curated after observing and studying various factors that determine regional growth such as economic, environmental, social, technological, and political status of the particular region. Analysts have studied the data of revenue, production, and manufacturers of each region. This section analyses region-wise revenue and volume for the forecast period of 2015 to 2026. These analyses will help the reader to understand the potential worth of investment in a particular region.

Global Quantum Computing Market: Competitive LandscapeThis section of the report identifies various key manufacturers of the market. It helps the reader understand the strategies and collaborations that players are focusing on combat competition in the market. The comprehensive report provides a significant microscopic look at the market. The reader can identify the footprints of the manufacturers by knowing about the global revenue of manufacturers, the global price of manufacturers, and production by manufacturers during the forecast period of 2015 to 2020.

The major players in the market D-Wave Systems Inc., Qxbranch, LLC, International Business Machines Corporation (IBM), Cambridge Quantum Computing Ltd, 1qb Information Technologies Inc., QC Ware Corp., Magiq Technologies Inc., Station Q Microsoft Corporation, Rigetti Computing, Research at Google Google Inc.

Global Quantum Computing MarketThis research report providesCOVID-19 Outbreakstudy accumulated to offer Latest insights about acute features of the Quantum Computing Market. The report contains different market predictions related to marketsize, revenue, production, CAGR, Consumption, gross margin, price, and other substantial factors. While emphasizing the key driving and restraining forces for this market, the report also offers a complete study of the future trends and developments of the market. It also examines the role of the leading market players involved in the industry including their corporate overview, financial summary andSWOT analysis.It presents the360-degreeoverview of the competitive landscape of the industries. Quantum Computing Market is showing steadygrowthandCAGRis expected to improve during the forecast period.

The main sources are industry experts from the global Quantum Computing industry, including management organizations, processing organizations, and analytical services providers that address the value chain of industry organizations. We interviewed all major sources to collect and certify qualitative and quantitative information and to determine future prospects. The qualities of this study in the industry experts industry, such as CEO, vice president, marketing director, technology and innovation director, founder and key executives of key core companies and institutions in major biomass waste containers around the world in the extensive primary research conducted for this study We interviewed to acquire and verify both sides and quantitative aspects.

Global Quantum Computing Market: Regional AnalysisThe report offers in-depth assessment of the growth and other aspects of the Quantum Computing market in important regions, including the U.S., Canada, Germany, France, U.K., Italy, Russia, China, Japan, South Korea, Taiwan, Southeast Asia, Mexico, and Brazil, etc. Key regions covered in the report are North America, Europe, Asia-Pacific and Latin America.

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Complete Analysis of the Quantum Computing Market:

Comprehensive assessable analysis of the industry is provided for the period of 2020-2025 to help investors to capitalize on the essential market opportunities.

The key findings and recommendations highlight vital progressive industry trends in the global Quantum Computing market, thereby allowing players to improve effective long term policies

A complete analysis of the factors that drive market evolution is provided in the report.

To analyze opportunities in the market for stakeholders by categorizing the high-growth segments of the market

The numerous opportunities in the Quantum Computing market are also given.

Report Answers Following Questions:

What are the factors driving the growth of the market?

What factors are inhibiting market growth?

What are the future opportunities in the market?

Which are the most dynamic companies and what are their recent developments within the Quantum Computing Market?

What key developments can be expected in the coming years?

What are the key trends observed in the market?

TABLE OF CONTENT

1 Report Overview

2 Global Growth Trends

3 Market Share by Key Players

4 Breakdown Data by Type and Application

5 United States

6 Europe

7 China

8 Japan

9 Southeast Asia

10 India

11 Central & South America

12 International Players Profiles

13 Market Forecast 2020-2025

14 Analysts Viewpoints/Conclusions

15 Appendix

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Global Quantum Computing Market 2020 : Worldwide Overview by Industry Size and Share, Future Trends, Growth Factors and Leading Players | D-Wave...

IBM plans to build a 1121 qubit system. What does this technology mean? – The Hindu

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Last week, IBM said it will build Quantum Condor, a 1121 qubit quantum computer, by the end of 2023. The company claims the system can control behaviour of atoms to run applications, and generate world-changing materials to transform industries. IBM says its full-stack quantum computer can be deployed via cloud, and that it can be programmed from any part of the world.

The technology company is developing a super-fridge, internally codenamed Goldeneye, to house the computer. The 10-foot-tall and 6-foot-wide refrigerator is being designed for a million-qubit system.

What are Qubits and quantum computers?

Quantum computers process data exponentially faster than personal computers do. They deploy non-intuitive methods, coupled with lots of computing, to solve intractable problems. These machines operate using qubits, similar to bits in personal computers.

The similarity ends there. The way quantum machines solve a problem is very different from how a traditional machine does.

A classical computer tries solving a problem intuitively. If they are given a command, they attempt every possible move, one after another, turning back at dead ends, until they find a solution.

Quantum computers deploy superposition to solve problems. This allows them to exist in multiple states, and test all possible ways at once. And qubits, the fundamental units of data in quantum computing, enables these machines to compute this way.

In regular computers, bits have either 0 or 1 value, and they come in four possible combinations - - 00, 01, 10, 11. Only one combination can exist at a single point of time, which limits processing speed.

But, in quantum machines, two qubits can represent same values, and all four can exist at the same time. This helps these systems to run faster.

This means that n qubits can represent 2n states. So, 2 qubits represent 4 states, 3 qubits 8 states, 4 qubits 16 states, and so on. And now imagine the many states IBMs 1121 qubit system can represent.

An ordinary 64-bit computer would take hundred years to cycle through these combinations. And thats exactly why quantum computers are being built: to solve intractable problems and break-down theories that are practically impossible for classical computers.

To make such large and difficult calculations happen, the qubits need to be linked together in quantum entanglement. This enables qubits at any end of the universe to connect and be manipulated in such a way that not one can be described without referencing the others.

Why are qubits difficult?

One of the key challenges for processing in qubits is the possibility of losing data during transition. Additionally, assembling qubits, writing and reading information from them is a difficult task.

The fundamental units demand special attention, including a perfect isolation and a thermostat set of one hundredth of a degree above absolute zero. Despite strict monitoring, due to their highly sensitive nature, they can lose superposition even from a slightest variation. This makes programming very tricky.

Since quantum computers are programmed using a sequence of logic gates of various kinds, programmes need to run quickly before qubits lose coherence. The combination of superposition and entanglement makes this process a whole lot harder.

Other companies building quantum computers

There has been a lot of interest in quantum computing in recent times. In 2016, IBM put the first quantum computer in the cloud. Google launched Sycamore quantum computer last year, and said it was close to achieving quantum supremacy.

This month, IBM released its 65-qubit IBM Quantum Hummingbird processor to IBM Q Network members, and the company is planning to surpass the 100-qubit milestone with its 127-qubit IBM Quantum Eagle processor next year. It is also planning to roll out a 433-qubit IBM Quantum Osprey system in 2022.

D-Wave systems, a Canada-based quantum computing company, launched its cloud service in India and Australia this year. It gives researchers and developers in these two countries real-time access to its quantum computers.

Honeywell recently outlined its quantum system, and other technology companies like Microsoft and Intel are also chasing commercialisation.

The ongoing experiments and analysis speak volumes on how tech companies are viewing quantum computers as the next big breakthrough in computing.

Quantum computers will likely deliver tremendous speed, and will help in solving problems related to optimisation in defence, finance, and other industries.

IBM views the 1000-qubit mark as the point from where the commercialisation of quantum computers can take off.

Excerpt from:
IBM plans to build a 1121 qubit system. What does this technology mean? - The Hindu

Quantum Information Processing Market Forecast 2020-2026| Post Impact of Worldwide COVID-19 Spread Analysis- 1QB Information Technologies, Airbus,…

The latest market research study launched by reportsandmarkets on Global Quantum Information Processing Market Report 2019 provides you the details analysis on current market condition, business plans, investment analysis, size, share, industry growth drivers, COVID-19 impact analysis, global as well as regional outlook.

This report will help youtake informed decisions, understand opportunities, plan effective business strategies, plan new projects, analyse drivers and restraints and give you a vision on the industry forecast.Further, Quantum Information Processing market report also covers the marketing strategies followed bytop Quantum Information Processing players, distributors analysis, Quantum Information Processing marketing channels, potential buyers and Quantum Information Processing development history.

Get Exclusive Sample Report on Quantum Information Processing Marketis available athttps://www.reportsandmarkets.com/sample-request/global-quantum-information-processing-market-report-2019?utm_source=thedailychronicle&utm_medium=38

Along with Quantum Information Processing Market research analysis, buyer also gets valuable information about global Quantum Information Processing Production and its market share,Revenue, Price and Gross Margin, Supply, Consumption, Export, Import volumeand values for followingRegions: North America, Europe, China, Japan, Middle East & Africa, India, South America, Others

In the Quantum Information Processing Market research report, following points market opportunities, market risk and market overview are enclosed along with in-depth study of each point. Production of the Quantum Information Processing is analyzed with respect to various regions, types and applications.The sales, revenue, and price analysis by types and applicationsof Quantum Information Processing market key players is also covered.

Quantum Information Processing Market Covers following MajorKey Players:1QB Information Technologies, Airbus, Anyon Systems, Cambridge Quantum Computing, D-Wave Systems, Google, Microsoft, IBM, Intel, QC Ware, Quantum, Rigetti Computing, Strangeworks, Zapata Computing.

COVID-19 can affect the global economy in 3 main ways: by directly affecting production and demand, by creating supply chain and market disturbance, and by its financial impact on firms and financial markets.

The objectives of the report are:

To analyze and forecast the market size of Quantum Information Processing Industry in the global market.

To study the global key players, SWOT analysis, value and global market share for leading players.

To determine, explain and forecast the market by type, end use, and region.

To analyze the market potential and advantage, opportunity and challenge, restraints and risks of global key regions.

To find out significant trends and factors driving or restraining the market growth.

To analyze the opportunities in the market for stakeholders by identifying the high growth segments.

To critically analyze each submarket in terms of individual growth trend and their contribution to the market.

To understand competitive developments such as agreements, expansions, new product launches, and possessions in the market.

To strategically outline the key players and comprehensively analyze their growth strategies.

Major Points from Table of Contents

1 Market Overview

2 Global and Regional Market by Company

3 Global and Regional Market by Type

4 Global and Regional Market by Application

5 Regional Trade

6 Key Manufacturers

7 Industry Upstream

Continue.

List of Tables and Figures..

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Quantum Information Processing Market Forecast 2020-2026| Post Impact of Worldwide COVID-19 Spread Analysis- 1QB Information Technologies, Airbus,...

How This Bangalore Based Startup Is Driving Innovation With Quantum Technology-Based Products – Analytics India Magazine

India has been a frontrunner when it comes to implementing new-age technology such as AI, machine learning and quantum technologies. In fact, Union Budget 2020 saw an allocation of INR 8,000 crore towards the development of technologies such as Quantum Cryptography and Quantum Communication.

Further building on quantum technology, and with a vision to drive disruptive innovations across multiple sectors with AI and quantum technology, Bengaluru-based Archeron Group is providing cutting-edge solutions for multiple industries.

Analytics India Magazine got in touch with the founder of Archeron Group to understand the tech behind it. Founded in 2015 by Aviruk Chakraborty, Archeron Group was established with a vision to drive disruptive innovations across multiple sectors that can help transform the world for the better.

The company is co-headquartered in Abu Dhabi and San Francisco. It has a state-of-the-art Global Development and Delivery Centre (GDDC) in Bengaluru. It holds strategic importance for the Group and is responsible for creating the entire solution portfolio of the groups which it has been able to take to the Middle East (UAE and KSA), North America, and the European Union.

Archeon extensively integrates AI and quantum computing in its flagship products. Chakraborty explained them as below:

1| Automated and Remote Sensing Agri Platform: This is an Agri analysis platform that uses remote sensing-based technologies to map the yield, the productivity of that field not only for the past 20 years but also to predict the next years yield and productivity.

Chakraborty stated, We have developed a vast array of solutions which include agricultural insurance automation, agriculture loan automation, crop classification and identification, soil analysis, fertilizer and pesticide requirement analysis, disease detection and real-time monitoring of the field, to name a few.

2| Bank/ NBFC automation using AI: The company is building a Quantum AI Bank using quantum technologies and artificial intelligence, which will be completely autonomous in its decision making. According to Chakraborty, the larger parameters of the bank such as risk ratios and macroscopic directives will be set by the board on a quarterly basis which gets translated into algorithmic performance parameters and hence executed for the next quarter.

3| Predictive Diagnostic Platforms: Archeron group has created an integrated national radiology platform using Deep Convolutional Neural Networks, where the radiological plates of all the patients all over the country is analysed and a diagnostic support system is created in which the doctors are given a second opinion on the radiological plates.

4| Quantum Cryptography: The company has designed Quantum Cryptography with a view to strengthen the payments infrastructure in India and make them 100% secure and unhackable. They used three-factor authentication as well as Quantum One Time pad to offer an end to end secure platform.

On being asked how the products are different from others in the market, Chakraborty pointed out that-

The company is using deep neural networks and cutting edge mathematical models such as Q-learning and GAN. They also use Remote Sensing, IoT, and Crispr-Cas9.

Chakraborty said, We are language agnostic developers as we have to not only develop solutions but also integrate them with the existing framework for existing clients. Archeron Group is using C++/ Python for the ML algorithms and prefers to build the ML models from scratch rather than using a pre-trained model.

They also work on the domains of blockchain, satellite imagery analysis, IoT, brain-computer interface, genetic programming for creating synthetic life along with standard machine learning and quantum computing, cryptography and communication frameworks.

Chakraborty said that in the next five years, the focus would be on increasing the adoption of healthcare, banking and agricultural solutions in UAE, India and the USA. The company is also focusing on global implementation of already developed technology and iteratively refining it to make the tech stack better.

A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box. Contact: ambika.choudhury@analyticsindiamag.com

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How This Bangalore Based Startup Is Driving Innovation With Quantum Technology-Based Products - Analytics India Magazine

Nokia says it’s focused on tech and customers, but political fights complicate things – CNBC

SINGAPORE Against a backdrop of rising tensions between the United States and China, Finnish telecom equipment maker Nokiasays it's focusing on areas it can control such as its technology and customers, a senior executive said Wednesday.

"From Nokia's perspective, we have to focus on the areas that we can control. What we can control are our own technology, our own go-to market and making sure that the service providers that we are supporting have continuous services and supply of the equipment and technology into their customer base," Jae Won, head of Asia Pacific and Japan at Nokia, said on CNBC's "Squawk Box Asia."

"The various geopolitical issues does provide some complications but as far as we are concerned, we focus on the technology that we can develop and we focus on the customers and the business opportunities that 5G and Industry 4.0 will provide for the future," he added.

The U.S. and China are fighting to dominate in new technologies, including artificial intelligence, quantum computing and 5G, whichrefers to the next generation of high-speed mobile internet that provides faster data speeds and more bandwidth. In fact, China has stepped up efforts to reduce foreign-reliance on high-end chips by investing heavily into its domestic semiconductor market.

Some experts have said in recent years that the U.S.-China rivalry could lead to the emergence of two internets.

Often referred to as a "splinternet," it is the possibility that the internet might be fragmented and governed by separate regulations such as those in the U.S. and in China and run by different services. If such a split were to occur, it would force technology companies to rethink their operational strategies in various markets, depending on which side each market is aligned with.

Nokia is one of the largest telecom equipment suppliers in the world, behind market leader Huawei. As countries rush to develop and roll out their 5G infrastructure, Nokia, alongside Sweden's Ericsson and South Korea's tech titan Samsung, is set to be one of the immediate beneficiaries in a U.S.-led campaign against China's Huawei.

The Chinese tech company is at the heart of the U.S.-China tech rivalry.

Not only is Huawei banned from participating in the 5G infrastructure in the U.S., its access to certain high-end technologies made in the U.S.or tech made using U.S. equipment, has been restricted. Washington has also urged allies to cut Huawei off from their own 5G infrastructure. Huawei is banned in Japan, Australiahas barred it from selling 5G equipment and most recently, the U.K.announced it will ban the company from its 5G networks.

Won said Nokia is supplying 5G equipment to telcos in Asia-Pacific, including South Korea, Japan, Australia, New Zealand and most recently in Singapore. "The momentum in this region for 5G is very strong and we expect this momentum will continue into 2021 and beyond," he said.

But the Finnish firm this month suffered a setback: Reuters reported that Nokia lost out to Samsung on a $6.64 billion contract to supply 5G equipment to Verizon in the U.S.

Continued here:
Nokia says it's focused on tech and customers, but political fights complicate things - CNBC

Immetas Therapeutics Announces Series A Financing to Advance Research on Inflammation Pathways in Aging and Develop Therapeutics for Cancer and…

EAST HANOVER, N.J.--(BUSINESS WIRE)--Immetas Therapeutics today announced it has raised a Series A financing of $11 million to advance research on inflammation pathways in aging and the development of novel, immune modulating treatments for cancer and inflammatory disease. Morningside Ventures was the sole investor in the financing round.

Morningsides investment is a significant endorsement of our approach to targeting inflammation pathways in aging and our clinical evidence-based discovery strategy, said J. Gene Wang, MD, PhD, co-founder and CEO. Emerging research that molecular pathways driving both aging and age-related diseases converge around chronic, low grade inflammation is creating a new set of opportunities to treat cancer and other serious diseases. Immetas is well positioned to capitalize on these new advances.

Dr. Wang added, Our approach prioritizes clinical evidence and a deep interrogation of disease mechanisms to guide drug discovery. This strategy is designed to reduce development risk resulting from the translational gap between laboratory findings and patients and ensure the development of superior and well-differentiated drugs.

Dr. Wang co-founded Immetas after a 20-year career at large pharmaceutical companies, including Merck, Abbott, GSK and Novartis, where he played integral roles in the successful development of major drugs, including Humira (adalimumab), Varubi (rolapitant), Zolinza (vorinostat) and Gardasil (human papilloma virus vaccine), and led multiple programs from discovery to clinical proof-of-concept. Dr. Wang received his M.D. from Peking University Medical Center and Ph.D. in Immunobiology from Yale University, followed by medical residency training at Yale New Haven Hospital.

Immetas other co-founder, Dr. David Sinclair, is an internationally recognized scientist known for his research on genes and small molecules that delay aging, including Sirtuin genes, resveratrol and NAD precursors. He was among TIME magazines 50 Most Influential People in Healthcare in 2018. Dr. Sinclair is Professor of Genetics at Harvard Medical School and co-Director of the Paul F. Glenn Center for Biology of Aging Research at Harvard and he serves as a science advisor to the Company.

We have a shared vision that inflammation is the fundamental and ultimate process driving aging and age-related cancers and inflammatory diseases, said Dr. Sinclair. Our approach is distinct from others that have targeted conventional age-related pathways and to date have proved challenging.

The Company is building a pipeline of biologic and small molecule drugs internally and through collaborations. Immetas lead program is aimed at designing a series of bi-specific antibodies to regulate inflammation in the tumor microenvironment and overcome resistance to conventional immune checkpoint therapies.

In connection with the financing, Dr. Lu Huang, MD, MBA, Managing Director at Morningside Ventures, joined the Immetas board of directors. Since joining Morningside in 2003, Dr. Huang has led nearly three dozen healthcare / life science investments in China and the United States.

About Immetas TherapeuticsImmetas discovers and develops novel therapeutics that modulate the innate immune system to treat age-related cancers and inflammatory diseases. The companys approach is based on emerging evidence that chronic low-grade inflammation is a fundamental process governing aging and age-related diseases and anchored in clinical evidence to mitigate development risk. Immetas was founded by J. Gene Wang, MD, PhD, a veteran in discovery and translational drug development in immunology/ inflammation and oncology, and David Sinclair, PhD, Professor of Genetics at Harvard Medical School and a leader in the molecular mechanisms of aging. The lead program in the companys growing pipeline is focused on engineering bispecific antibodies to modulate inflammation in the tumor microenvironment and overcome resistance to the conventional immune checkpoint therapies. Learn more at http://www.immetas.com

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Immetas Therapeutics Announces Series A Financing to Advance Research on Inflammation Pathways in Aging and Develop Therapeutics for Cancer and...

DS4 to rock Morden live at The Sound Lounge – Your Local Guardian

After their shows were cancelled due to the pandemic, DS4 will return to music by headlining at The Sound Lounge this weekend.

Rock band DS4 is set to headline at the grassroots music venue in Morden on Saturday September 25.

The audience at the Sound Lounge can expect a "rock and roll show with a vintage touch" between 7.00 pm to 9.30 pm.

Singer, songwriter and guitarist David Sinclair,says the performance is the groups lifeline.

DS4 came together when David Sinclair hooked up with guitarist Geoff Peel, a sage of the London blues circuit.

Together with Jos Mendoza, ex bass player Jack Sinclair and a cast of special guests, they recorded the album 4 in 2015. Gigging around London venues, including The Borderline, 100 Club and Half Moon Putney, and festivals such as Cornbury and North Wales Blues and Soul Fest, the DS4 has built up a dedicated following.

And won glowing testimonials for a show full of good time rock and roll energy, stirring personal blues ballads and wry narrative wit.The band featuring drummer Rory, has sold-out gigs at the Crawdaddy Club in Richmond, Dustys Blues Club in High Wycombe, Portobello Live and Gunnersbury Triangle Club.Their latest album Sweet Georgina has received ecstatic reviews, with their track 'The Rolling People' featuring on the CD covermount of Classic Rock magazine last year.

With years of entertaining and months of having to stay inside, the band are "over the moon" to be back in front of an audience.

A spokesperson for DS4 said: "The Sound Lounge can look forward to a glorious show featuring original songs from our first five albums.

"Mixed with classic covers of songs by Lou Reed, Chuck Berry and the Red Hot Chili Peppers.

"Live performances are the oxygen that keeps a band like us alive.

Its been a long time to manage without it, and this show is a lifeline for us.

"Zoom and Spotify are all very well.

"But we just cant wait to make contact with hearts and minds in the outside world again.

For ticket information visit http://www.thesoundlounge.org.uk/whats-on

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DS4 to rock Morden live at The Sound Lounge - Your Local Guardian

Everything About Pipelines In Machine Learning and How Are They Used? – Analytics India Magazine

In machine learning, while building a predictive model for classification and regression tasks there are a lot of steps that are performed from exploratory data analysis to different visualization and transformation. There are a lot of transformation steps that are performed to pre-process the data and get it ready for modelling like missing value treatment, encoding the categorical data, or scaling/normalizing the data. We do all these steps and build a machine learning model but while making predictions on the testing data we often repeat the same steps that were performed while preparing the data.

So there are a lot of steps that are followed and while working on a big project in teams we can often get confused about this transformation. To resolve this we introduce pipelines that hold every step that is performed from starting to fit the data on the model.

Through this article, we will explore pipelines in machine learning and will also see how to implement these for a better understanding of all the transformations steps.

What we will learn from this article?

Pipelines are nothing but an object that holds all the processes that will take place from data transformations to model building. Suppose while building a model we have done encoding for categorical data followed by scaling/ normalizing the data and then finally fitting the training data into the model. If we will design a pipeline for this task then this object will hold all these transforming steps and we just need to call the pipeline object and rest every step that is defined will be done.

This is very useful when a team is working on the same project. Defining the pipeline will give the team members a clear understanding of different transformations taking place in the project. There is a class named Pipeline present in sklearn that allows us to do the same. All the steps in a pipeline are executed sequentially. On all the intermediate steps in the pipeline, there has to be a first fit function called and then transform whereas for the last step there will be only fit function that is usually fitting the data on the model for training.

As soon as we fit the data on the pipeline, the pipeline object is first transformed and then fitted on each of the steps. While making predictions using the pipeline, all the steps are again repeated except for the last function of prediction.

Implementation of the pipeline is very easy and involves 4 different steps mainly that are listed below:-

Let us now practically understand the pipeline and implement it on a data set. We will first import the required libraries and the data set. We will then split the data set into training and testing sets followed by defining the pipeline and then calling the fit score function. Refer to the below code for the same.

We have defined the pipeline with the object name as pipe and this can be changed according to the programmer. We have defined sc objects for StandardScaler and rfcl for Random Forest Classifier.

pipe.fit(X_train,y_train)

print(pipe.score(X_test, y_test)

If we do not want to define the objects for each step like sc and rfcl for StandardScaler and Random Forest Classifier since there can be sometimes many different transformations that would be done. For this, we can make use of make_pipeling that can be imported from the pipeline class present in sklearn. Refer to the below example for the same.

from sklearn.pipeline import make_pipeline

pipe = make_pipeline(StandardScaler(),(RandomForestClassifier()))

We have just defined the functions in this case and not the objects for these functions. Now lets see the steps present in this pipeline.

print(pipe.steps)

pipe.fit(X_train,y_train)

print(pipe.score(X_test, y_test))

Conclusion

Through this article, we discussed pipeline construction in machine learning. How these can be helpful while different people working on the same project to avoid confusion and get a clear understanding of each step that is performed one after another. We then discussed steps for building a pipeline that had two steps i.e scaling and the model and implemented the same on the Pima Indians Diabetes data set. At last, we explored one other way of defining a pipeline that is building a pipeline using make a pipeline.

I am currently enrolled in a Post Graduate Program In Artificial Intelligence and Machine learning. Data Science Enthusiast who likes to draw insights from the data. Always amazed with the intelligence of AI. It's really fascinating teaching a machine to see and understand images. Also, the interest gets doubled when the machine can tell you what it just saw. This is where I say I am highly interested in Computer Vision and Natural Language Processing. I love exploring different use cases that can be build with the power of AI. I am the person who first develops something and then explains it to the whole community with my writings.

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Everything About Pipelines In Machine Learning and How Are They Used? - Analytics India Magazine

Machine Learning Answers: Facebook Stock Is Down 20% In A Month, What Are The Chances Itll Rebound? – Forbes

BRAZIL - 2020/07/10: In this photo illustration a Facebook logo seen displayed on a smartphone. ... [+] (Photo Illustration by Rafael Henrique/SOPA Images/LightRocket via Getty Images)

Facebook stock (NASDAQ: FB) reached an all-time high of almost $305 less than a month ago before a larger sell-off in the technology industry drove the stock price down nearly 20% to its current level of around $250. But will the companys stock continue its downward trajectory over the coming weeks, or is a recovery in the stock imminent?

According to the Trefis Machine Learning Engine, which identifies trends in the companys stock price data since its IPO in May 2012, returns for Facebook stock average a little over 3% in the next one-month (21 trading days) period after experiencing a 20% drop over the previous month (21 trading days). Notably, though, the stock is very likely to underperform the S&P500 over the next month (21 trading days), with an expected excess return of -3% compared to the S&P500.

But how would these numbers change if you are interested in holding Facebook stock for a shorter or a longer time period? You can test the answer and many other combinations on the Trefis Machine Learning Engine to test Facebook stock chances of a rise after a fall. You can test the chance of recovery over different time intervals of a quarter, month, or even just 1 day!

MACHINE LEARNING ENGINE try it yourself:

IFFB stock moved by -5% over 5 trading days,THENover the next 21 trading days, FB stock moves anaverageof 3.2 percent, which implies anexcess returnof 1.7 percent compared to the S&P500.

Trefis

More importantly, there is 62% probability of apositive returnover the next 21 trading days and 53.8% probability of apositive excess returnafter a -5% change over 5 trading days.

Some Fun Scenarios, FAQs & Making Sense of Facebook Stock Movements:

Question 1: Is the average return for Facebook stock higher after a drop?Answer:

Consider two situations,

Case 1: Facebook stock drops by -5% or more in a week

Case 2: Facebook stock rises by 5% or more in a week

Is the average return for Facebook stock higher over the subsequent month after Case 1 or Case 2?

FB stockfares better after Case 2, with an average return of 2.4% over the next month (21 trading days) under Case 1 (where the stock has just suffered a 5% loss over the previous week), versus, an average return of 5.3% for Case 2.

In comparison, the S&P 500 has an average return of 3.1% over the next 21 trading days under Case 1, and an average return of just 0.5% for Case 2 as detailed in our dashboard that details theaverage return for the S&P 500 after a fall or rise.

Try the Trefis machine learning engine above to see for yourself how Facebook stock is likely to behave after any specific gain or loss over a period.

Question 2: Does patience pay?

Answer:

If you buy and hold Facebook stock, the expectation is over time the near term fluctuations will cancel out, and the long-term positive trend will favor you at least if the company is otherwise strong.

Overall, according to data and Trefis machine learning engines calculations, patience absolutely pays for most stocks!

For FB stock, the returns over the next N days after a -5% change over the last 5 trading days is detailed in the table below, along with the returns for the S&P500:

Trefis

Question 3: What about the average return after a rise if you wait for a while?

Answer:

The average return after a rise is understandably lower than a fall as detailed in the previous question. Interestingly, though, if a stock has gained over the last few days, you would do better to avoid short-term bets for most stocks although FB stock appears to be an exception to this general observation.

FBs returns over the next N days after a 5% change over the last 5 trading days is detailed in the table below, along with returns for the S&P 500.

Trefis

Its pretty powerful to test the trend for yourself for Facebook stock by changing the inputs in the charts above.

What if youre looking for a more balanced portfolio? Heres a high quality portfolio to beat the market with over 100% return since 2016, versus 55% for the S&P 500. Comprised of companies with strong revenue growth, healthy profits, lots of cash, and low risk, it has outperformed the broader market year after year consistently

See allTrefis Price EstimatesandDownloadTrefis Datahere

Whats behind Trefis? See How Its Powering New Collaboration and What-Ifs ForCFOs and Finance Teams |Product, R&D, and Marketing Teams

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Machine Learning Answers: Facebook Stock Is Down 20% In A Month, What Are The Chances Itll Rebound? - Forbes

New machine learning, automation capabilities added to PagerDuty’s digital operations management platform – SiliconANGLE News

During a time when it seems as though the entire planet has gone digital, the role of PagerDuty Inc. has come into sharper focus as a key player in keeping the critical work of IT organizations up and running.

Mindful of enterprise and consumer need at such an important time, the company has chosen this weeksvirtual Summit event to unveil a significant number of new product releases.

We have the biggest set of releases and investments in innovation that were unleashing in the history of the company, said Jonathan Rende (pictured), senior vice president of product and marketing at PagerDuty. PagerDuty has a unique place in that whole ecosystem in whats considered crucial and critical now. These services have never been more important and more essential to everything we do.

Rende spoke with Lisa Martin, host of theCUBE, SiliconANGLE Medias livestreaming studio, during thePagerDuty Summit 2020. They discussed the companys focus on automation to help customers manage incidents, the introduction of new tools for organizational collaboration and a trend toward full-service ownership. (* Disclosure below.)

The latest releases are focused on PagerDutys expertise in machine learning and automation to leverage customer data for faster and more accurate incident response.

In our new releases, we raised the game on what were doing to take advantage of our data that we capture and this increase in information thats coming in, Rende said. A big part of our releases has also been about applying machine learning to add context and speed up fixing, resolving and finding the root cause of issues. Were applying machine learning to better group and intelligently organize information into singular incidents that really matter.

PagerDuty is also leveraging its partner and customer network to introduce new tools for collaboration as part of its platform.

One of the things weve done in the new platform is were introducing industry-first video war rooms with our partners and customers, Zoom as well as Microsoft Teams, and updating our Slack integrations as well, Rende explained. Weve also added the ability to manage an issue through Zoom and Microsoft Teams as a part of PagerDuty.

These latest announcements are a part of what Rende describes as a move in larger companies toward broader direct involvement of both developers and IT staff in operational responsibility.

There is a material seismic shift towards full-service ownership, Rende said. Were seeing larger organizations have major initiatives around this notion of the front-line teams being empowered to work directly on these issues. Full-service ownership means you build it, you ship it, you own it, and thats for both development and IT organizations.

Watch the complete video interview below, and be sure to check out more of SiliconANGLEs and theCUBEs coverage of PagerDuty Summit 2020. (* Disclosure: TheCUBE is a paid media partner for PagerDuty Summit 2020. Neither PagerDuty Inc., the sponsor for theCUBEs event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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New machine learning, automation capabilities added to PagerDuty's digital operations management platform - SiliconANGLE News

YouTube Will Now Harness Machine Learning To Auto-Apply Video Age Restrictions – Tubefilter

Beginning today, YouTube will roll out three updates with respect to Age-Restricted content part of an ongoing reliance on machine learning technology for content moderation that dates back to 2017, and in response to a new legal directive in the European Union (EU), the company said.

Age-restricted content is only available to logged-in YouTube users over 18, and includes videos that dont violate platform policies but are inappropriate for underage viewers. Videos can get age-restricted, for instance, when they include vulgar language, violence or disturbing imagery, nudity, or the portrayal of harmful or dangerous activities. (YouTube has just instituted minor changes as to where it draws these lines, the company said,which you can check out in full right here, and which will be rolled out in coming months).

Previously, age restrictions could be implemented by creators themselves or by manual reviewers on YouTubes Trust & Safety team as part of the broader video review process. While both of these avenues will still exist, YouTube will also begin using machine learning to auto-apply age restrictions a change that is bound to result in far more restrictions across the board.

A YouTube spokesperson described the move as the latest development in a multi-year responsibility effort harnessing machine learning and a testament to YouTubes ongoing commitment to child safety. In 2018, the platform began using machine learning to detect violent extremism and content that endangered child safety, and in 2019 expanded the technology to detect hate speech and harassment.

Even with more videos being age-restricted, YouTube anticipates the impact on creator revenues will be minimal or nonexistent given that videos that could fall into the age-restricted category tend to also violate YouTubes ad-friendly guidelines and thus typically carry no or limited ads. YouTube also notes that creators will still be able to appeal decisions if they feel their videos have been incorrectly restricted.

In addition to the integration of machine learning, YouTube is also putting a stop to a previous workaround for age-restricted videos which could be viewed by anyone when embedded on third-party websites. Going forward, embedded age-restricted videos will redirect users to YouTube, where they must sign in to watch, the company said.

And finally, YouTube is instituting new age verification procedures in the EU as mandated by new regulation dubbed the Audiovisual Media Services Directive (AVMSD), which can require viewers to provide additional proof of age when attempting to watch mature content.

Now, if YouTubes systems cannot verify whether a creator is actually above 18 in the EU, they can be asked to provide a valid ID or credit card number for which the minimum account-holding age is typically 18 by means of proof (pictured above). A prompt for additional proof of age could be triggered by different signals if, for instance, an account predominantly favors kid-friendly content and then attempts to watch a mature videos.

Given the countless forms of identification that exist across the EU, YouTube says that it is still working on a full rundown of acceptable formats.A spokesperson said that all ID and credit card numbers would be deleted after a users age is confirmed.

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YouTube Will Now Harness Machine Learning To Auto-Apply Video Age Restrictions - Tubefilter

Microsoft releases the InnerEye Deep Learning Toolkit to improve patient care – Neowin

Microsoft's Project InnerEye has been involved in building and deploying machine learning models for years now. The team has been working with doctors, clinicians, oncologists, assisting them in tasks like radiotherapy, surgical planning, and quantitative radiology. This has reduced the burden on the people involved in the domain.

The firm says that the goal of Project InnerEye is to "democratize AI for medical image analysis" by allowing researchers and medical practitioners to build their own medical imaging models. With this in mind, the team released the InnerEye Deep Learning Toolkit as open-source software today. Built on top of PyTorch and integrated heavily with Microsoft Azure, the toolkit is meant to ease the process of training and deploying models.

Specifically, the InnerEye Deep Learning Toolkit will allow users to build their own image classification, segmentation, or sequential models. They will have the option to construct their own neural networks or import them from elsewhere. One of the motivations behind this project was to provide an abstraction layer for users so that they can deploy machine learning models without worrying too much about the details. As expected, the usual advantages of Azure Machine Learning Services will be bundled with the toolkit as well:

The Project InnerEye team at Microsoft Research hopes that this toolkit will integrate machine learning technologies to treatment pathways, leading to long-term practical solutions. If you are interested in checking out the toolkit or want to contribute to it, you may check out the repository on GitHub. The full set of features offered under the toolkit can be found here.

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Microsoft releases the InnerEye Deep Learning Toolkit to improve patient care - Neowin

How Parkland Leverages Machine Learning, Geospatial Analytics to Reduce COVID-19 Exposure in Dallas – HIT Consultant

What You Should Know:

How Parkland Center for Clinical Innovation developed a machine learning-driven predictive model called the COVID-19 Proximity Index for Parkland Hospital in Dallas.

This program helps frontline workers to quickly identify which identify patients at the highest risk of exposure to COVID-19 by using geospatial analytics.

In addition, the program helps triage patients whileimproving the health and safety of hospital workers as well as the friends andfamilies of those exposed to COVID-19.

Since the earliestdays of the COVID-19pandemic, one of the biggest challenges for health systems has been to gainan understanding of the community spread of this virus and to determine howlikely is it that a person walking through the doors of a facility is at ahigher risk of being COVID-19 positive.

Without adequate access to testing data, health systems early-on were often forced to rely on individuals to answer questions such as whether they had traveled to certain high-risk regions. Even that unreliable method of assessing risk started becoming meaningless as local community spread took hold.

Parkland Health & Hospital System (the safety-net health system for Dallas County, TX) and PCCI (a Dallas, TX-based non-profit with expertise in the practical applications of advanced data science and social determinants of health) had a better idea. Community spread of an infectious disease is made possible through physical proximity and density of active carriers and non-infected individuals. Thus, to understand the risk of an individual contracting the disease (exposure risk), it was necessary to assess their proximity to confirmed COVID-19 cases based on their address and population density of those locations. If an exposure risk index could be created, then Parkland could use it to minimize exposure for their patients and health workers and provide targeted educational outreach in highly vulnerable zip codes.

PCCIs data science and the clinical team worked diligently in collaboration with the Parkland Informatics team to develop an innovative machine learning-driven predictive model called Proximity Index. Proximity Index predicts for an individuals COVID-19 exposure risk, based on their proximity to test positive casesandthe population density. This model was put into action at Parkland through PCCIs cloud-based advanced analytics and machine learning platform called Isthmus. PCCIs machine learning engineering team generated geospatial analysis for the model and, with support from the Parkland IT team, integrated it with their Electronic Health Record system.

Since April 22,Parklands population health team has utilized the Proximity Index for four keysystem-wide initiatives to triage more than 100,000 patient encounters and toassess needs, proactively:

1. Patients most at risk, with appointments in 1-2 days, were screened ahead of their visit to prevent spread within the hospital

2. Patients identified as vulnerable were offered additional medical (i.e. virtual visit, medication refill assistance) and social support

3. Communities, by zip-code, most at-risk were sent targeted messaging and focused outreach on COVID-19 prevention, staying safe, monitoring for symptoms, and resources for where to get tested and medical help.

4. High exposure risk patients who had an appointment at one of Parklands community clinics in the next couple of days were offered a telehealth appointment instead of a physical appointment if that was appropriate based on the type of appointment

In the future, PCCI is planning on offering Proximity Index to other organizations in the community schools, employers, etc., as well as to individuals to provide them with a data-driven tool to help in decision making around reopening the economy and society in a safe, thoughtful manner.

Many teams across the Parkland family collaborated on this project, including the IT team led by Brett Moran, MD, Senior Vice President, Associate Chief Medical Officer, and Chief Medical Information Officer at Parkland Health and Hospital System.

About the ManjulaJulka and Albert Karam

Manjula Julka, MD, FAAFP, MBA,is the Vice President of Clinical Innovation at PCCI. She brings more than 15years of experience in healthcare delivery transformation, leading a strong andconsistent track record of enabling meaningful outcomes.

Albert Karamis a data scientist at PCCI with experience building predictive models in healthcare. While working at PCCI, Albert has researched, identified, managed, modeled, and deployed predictive models for Parkland Hospital and the Parkland Community Health Plan. He is diverse in understanding modeling workflows and the implementation of real-time models.

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How Parkland Leverages Machine Learning, Geospatial Analytics to Reduce COVID-19 Exposure in Dallas - HIT Consultant

causaLens launches the first causal AI platform – Business Wire

LONDON--(BUSINESS WIRE)--causaLens, a deep-tech company predicting and optimising the global economy, has released the Worlds first causal Artificial Intelligence (causal AI) enterprise platform. Businesses no longer have to rely on curve-fitting machine learning platforms unable to handle the complexity of today's world. They are invited to join the real AI revolution with a platform that understands cause and effect.

The causaLens platform defines a new category of machine intelligence. Its next generation AI engine harnesses an understanding of cause and effect relationships to directly optimise business KPIs.

Businesses investing in the current form of machine learning (ML), including AutoML, have just been paying to automate a process that fits curves to data without an understanding of the real world. They are effectively driving forward by looking in the rear-view mirror, explains causaLens CEO Darko Matovski. Our platform takes a radically different approach. Causal AI teaches machines to understand cause and effect, a necessary step to developing true AI. This allows our platform to autonomously operate at a new level of abstraction that explains to businesses what actions they need to take to achieve their objectives.

causaLens has a track record of breaking new ground, having pioneered automated machine learning (AutoML) for time series data. The causal AI platform retains the advantages of comprehensive automation, allowing thousands of data sets to be cleaned, sorted and monitored at the same time. However, it combines it with causal models and insights that are truly explainable - traditionally the sole province of domain experts. Unique human knowledge is harnessed through intuitive interfaces for human-machine partnerships.

Since its inception in 2017, causaLens has worked with a range of corporates across multiple industries. Customers include some of the worlds largest Asset Managers, Hedge Funds, Tier-1 Investment Banks, Transportation and Logistics companies, and Energy and Commodity traders.

Masami Johnstone, Head of Information Services at CLS, whose products help clients navigate the changing Foreign Exchange marketplace, said: "The causaLens platform has enabled us to discover additional value in our data. Their causal AI technology autonomously finds valuable signals in huge datasets and has helped us to understand relationships between our data and other datasets.

Todays world is changing faster than ever before. Current state of the art ML barely scratches the surface of what machines can do. Causal AI is the next huge step forward.

Demonstrations of the product can be requested via causaLens.com.

causaLens

causaLens is pioneering Causal AI, a new category of intelligent machines that understand cause and effect - a major step towards true AI. Its enterprise platform is used to transform leading businesses in Finance, IoT, Energy, Telecommunications and others.

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causaLens launches the first causal AI platform - Business Wire

Machine Learning in Education Market Incredible Possibilities, Growth Analysis and Forecast To 2025 – The Daily Chronicle

Latest Research Report: Machine Learning in Education industry

Machine Learning in Education Market report is to provide accurate and strategic analysis of the Profile Projectors industry. The report closely examines each segment and its sub-segment futures before looking at the 360-degree view of the market mentioned above. Market forecasts will provide deep insight into industry parameters by accessing growth, consumption, upcoming market trends and various price fluctuations.

This has brought along several changes in This report also covers the impact of COVID-19 on the global market.

Machine Learning in Education Market competition by top manufacturers as follow: , IBM, Microsoft, Google, Amazon, Cognizan, Pearson, Bridge-U, DreamBox Learning, Fishtree, Jellynote, Quantum Adaptive Learning

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Global Machine Learning in Education Market research reports growth rates and market value based on market dynamics, growth factors. Complete knowledge is based on the latest innovations in the industry, opportunities and trends. In addition to SWOT analysis by key suppliers, the report contains a comprehensive market analysis and major players landscape.The Type Coverage in the Market are: Cloud-BasedOn-Premise

Market Segment by Applications, covers:Intelligent Tutoring SystemsVirtual FacilitatorsContent Delivery SystemsInteractive WebsitesOthers

Market segment by Regions/Countries, this report coversNorth AmericaEuropeChinaRest of Asia PacificCentral & South AmericaMiddle East & Africa

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Machine Learning in Education Market Incredible Possibilities, Growth Analysis and Forecast To 2025 - The Daily Chronicle

What is Model Governance and How it Works for Enterprises? – Analytics Insight

Model governance indicates the overall framework of how an organization control its model development and deployment workflow, including rules, protocols, and controls for machine learning models during production for example, access control, testing, validation, and the tracing of model results.

Although machine learning projects impact organisations, they dont always arrive at their full potential due to inefficiencies and mismanagement in the process. Machine governance is a priority for organisations to get the highest possible return on its machine learning investment.

Model governance indicates the overall framework of how an organization control its model development and deployment workflow, including rules, protocols, and controls for machine learning models during production for example, access control, testing, validation, and the tracing of model results. Tracking the model outcomes permits biases to be detected and rectified. It is important for models, which are programmed to learn as they may accidentally become biased that could bring out inaccurate or unethical results.

It is crucial for risk involved models to manage financial portfolios. As these models can impact on an individual or organizations finances directly, it is essential to verify and correct any biases or incorrect learning within the model.

As machine learning is a relatively new discipline, there are still a lot of inefficiencies that require to be advocated in ML processes. Machine learning projects can be missing essential value without model governance in place.

Clearing risk of model governance is vital to ensure that models involved with finances stay out of dangerous hazards. These models are programmed to continue learning along the run. However, these can understand biases if these are served with data. Datasets are capable of creating a bias which affects the decisions the model makes from that point on.

Model governance enables models to be audited and examined for speed, accuracy, and drift during production. It neglects any issues of model bias or inaccuracy, permitting models with risks involved to function smoothly.

Here are a few cases listed below to analyse the importance of model governance:

As mentioned before, the most glaring instance of why model governance is crucial in finance, but other industries require model governance as well. Banking industry uses machine learning models for many different processes that can be operated manually like credit scoring, interest rate risk modelling, and derivatives pricing.

Credit scoring models aid finance/ bank industry to make decisions in the loan approval process by delivering predictive analysis information concerning the potential for default or delinquency. It helps the bank to determine the risk costing they should use for the loan.

Interest rate risk models surveil earnings exposure to a range of potential market conditions and rate change to measure risk. The purpose of the model is to provide an overview of the potential dangers of the account it is monitoring.

These models estimate the value of assets by delivering a methodology for determining the cost of new products as well as complex products without market observations readily available. It is helpful for both the banks and investors to determine whether a business is worth investing in or not.

Serverless micro-service architecture for machine learning, algorithmia makes it the fastest route from development to deployment. It allows organizations to govern their machine learning operations securely with a healthy machine learning lifecycle. It manages MLOps with access controls to secure and audit machine learning models in production. Model governance algorithmias one of the benefits which ensures model accuracy by governing models and testing for speed, accuracy and drift.

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What is Model Governance and How it Works for Enterprises? - Analytics Insight

Machine Learning in Medical Imaging Market Incredible Possibilities, Growth Analysis and Forecast To 2025 – The Daily Chronicle

Overview Of Machine Learning in Medical Imaging Industry 2020-2025:

This has brought along several changes in This report also covers the impact of COVID-19 on the global market.

The Machine Learning in Medical Imaging Market analysis summary by Reports Insights is a thorough study of the current trends leading to this vertical trend in various regions. Research summarizes important details related to market share, market size, applications, statistics and sales. In addition, this study emphasizes thorough competition analysis on market prospects, especially growth strategies that market experts claim.

Machine Learning in Medical Imaging Market competition by top manufacturers as follow: , Zebra, Arterys, Aidoc, MaxQ AI, Google, Tencent, Alibaba,

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The global Machine Learning in Medical Imaging market has been segmented on the basis of technology, product type, application, distribution channel, end-user, and industry vertical, along with the geography, delivering valuable insights.

The Type Coverage in the Market are: Supervised LearningUnsupervised LearningReinforced Leaning

Market Segment by Applications, covers:BreastLungNeurologyCardiovascularLiverOthers

Market segment by Regions/Countries, this report coversNorth AmericaEuropeChinaRest of Asia PacificCentral & South AmericaMiddle East & Africa

Major factors covered in the report:

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IBM Partners With HBCUs to Diversify Quantum Computing Workforce – Diverse: Issues in Higher Education

September 21, 2020 | :

In partnership with historically Black colleges and universities (HBCUs), IBM recently launched a quantum computing research initiative to raise awareness of the field and diversify the workforce.

The IBM-HBCU Quantum Center, a multi-year investment, will fund undergraduate and graduate research, provide access to IBM quantum computers through the Cloud and offer student support.

Quantum computing is considered a fairly young field and quantum computers were not readily available in research labs until 2016. IBM was the first company to put a quantum computer on the Cloud, which allows it to be accessible from anywhere, according to Dr. Abraham Asfaw, global lead of Quantum Education and Open Science at IBM Quantum.

What that implies is that now anyone around the world can participate, he said. This is why we have this broad education effort, to really try and make quantum computing open and accessible to everyone. The scale of the industry is very small but we are stepping into the right direction in terms of trying to get more people into the field.

The 13 HBCUs that will be part of the initiative include Albany State University, Clark Atlanta University, Coppin State University, Hampton University, Howard University, Morehouse College, Morgan State University, North Carolina Agricultural and Technical State University, Southern University, Texas Southern University, University of the Virgin Islands, Virginia Union University and Xavier University of Louisiana.

Each of the schools was chosen based on how much the school focused on science, technology, engineering and mathematics (STEM).

Its very important at this point to be building community and to be educating everyone so that we have opportunities in the quantum computing field for everyone, said Asfaw. While at the same time, we are bringing in diverse perspectives to see where quantum computing applications could emerge.

Dr. Abraham Asfaw

The center encourages individuals from all STEM disciplines to pursue quantum computing. According to Asfaw, the field of quantum computing is considered highly interdisciplinary.

Teaching quantum computing, at any place, requires bringing together several departments, he said. So putting together a quantum curriculum is an exercise in making sure your students are trained in STEM all the way from the beginning to the end with different pieces from the different sciences instead of just one department altogether.

Diversifying the quantum computing workforce can also be looked at in two ways. One is getting different groups of people into the field and the other is bringing different perspectives into the field from the direction of the other sciences that could benefit from quantum computing, according to Asfaw.

We are in this discovery phase now, so really having help from all fields is a really powerful thing, he added.

IBM also plans to donate $100 million to provide more HBCUs with resources and technology as part of the Skills Academy Academic Initiative in Global University Programs. This includes providing HBCUs with university guest lectures, curriculum content, digital badges, software and faculty training by the end of 2020, according to IBM.

Our entire quantum education effort is centered around making all of our resources open and accessible to everyone, said Asfaw. [Our investment] is really an attempt to integrate HBCUs, which also are places of origin for so many successful scientists today, to give them opportunities to join the quantum computing revolution.

According to IBM, the skills academy is a comprehensive, integrated program designed to create a foundation of diverse and high demand skill sets that directly correlate to what students will need in the workplace.

The academy will address topics such as artificial intelligence, cybersecurity, blockchain, design thinking and quantum computing.

Those HBCUs involved in the academy include Clark Atlanta University, Fayetteville State University, Grambling State University, Hampton University, Howard University, Johnson C. Smith University, Norfolk State University, North Carolina A&T State University, North Carolina Central University, Southern University System, Stillman College, Virginia State and West Virginia State University.

While we are teaching quantum computing, while we are building quantum computing at universities, while we are training developers to take on quantum computer, it is important at this point to be inclusive and accessible as possible, said Afsaw. That really allows the field to progress.

This summer, IBM also hosted the 2020 Qiskit Global Summer School, which was designed for people to further explore the quantum computing field. The program involved three hours of lectures as well as hands-on learning opportunities. Many HBCU students were part of the program.

This shows you thats one piece of the bigger picture of trying to get the whole world involved in quantum education, said Asfaw. Thats the first place where HBCUs were involved and we hope to continue to build on even more initiatives going forward.

Sarah Wood can be reached at swood@diverseeducation.com.

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IBM Partners With HBCUs to Diversify Quantum Computing Workforce - Diverse: Issues in Higher Education

Artificial intelligence, robotics, quantum computing, sustainability & global volatility: DHL Logistics Trend Radar unveils trends that will shape…

In the fifth edition of the Logistics Trend Radar, DHL once more has revealed 29 key trends that will impact the logistics industry over the next years. The Report is the result of an extensive analysis of macro and micro trends, as well as the insights from a large partner network including research institutes, tech players, startups, and customers.

For us as logistics experts, it is important to forecast the challenges ahead and envision possible solutions so that we may best advise our customers. The mega trends that will continue to engage us are not unfamiliar: new technologies, growing e-commerce and sustainability, says Katja Busch, Chief Commercial Officer, DHL. But some areas will evolve faster than others, so there is the need to understand the underlying trends and their impact on logistics not least because of the impact of COVID-19 on global commerce and the entire workforce. As a world leader in logistics, we have the insights and the expertise to evaluate the situation.

Well over 20,000 logistics professionals and technology experts shared their perspectives on the future of the industry when visiting the DHL Innovations Centers over the last two years. The findings are consolidated and reflected on the Logistics Trend Radar which acts as a dynamic and strategic foresight tool that tracks the evolution of trends spotted in past editions, identifying present and future trends with every update.

The next big challenge will be future proofing the logistics workforce through training and upskilling in increasingly technologically sophisticated operations. This will take center stage on the strategic agendas of supply chain organizations in the years to come, said Matthias Heutger, Senior Vice President, Global Head of Innovation & Commercial Development at DHL. The Logistics Trend Radar serves as seismograph for future trends. Based on data from the last seven years, we can make longer-term forecasts and thus support our partners and customers to create roadmaps for their business as well as helping to structure and catalyze further industry-leading research and innovations. In this edition, we already see the impact of COVID-19 is accelerating trends that were already well underway big data analytics, robotics and automation, and IoT, all of which are underpinned by steady progress in artificial intelligence.

Acceleration of transformation processes

The fifth-edition Logistics Trend Radar indicates that we are experiencing an overall stabilization of trends from the past four years. However, with the logistics industry weathering the current global pandemic, transformation processes have been accelerated. COVID-19 has driven changes regarding recent logistics innovation, automation, and digital work more rapidly and has accelerated industry digitalization by years. Conversely, many trends initially perceived as disruptive game-changers for the logistics industry have yet to deliver on their disruptive potential. Self-driving vehicles and drones continue to be held back by legislative and technical challenges as well as limited social acceptance. Logistics Marketplaces are stabilizing on a few leading platforms, and established forwarders are entering the game with their own digital offerings, backed with robust global logistics networks. From cloud computing to collaborative robotics, big data analytics, artificial intelligence, and the Internet of Things, logistics professionals have to make sense of a vast market of novel technology. Modernizing all touchpoints of supply chains, from an elegant digital or customer journey, through fulfillment transport and final mile delivery is the new imperative for long-term success. Those who adopt and scale new technology and upskill workforces fastest, will have a competitive advantage on the market.

E-commerce growth continues to advance innovation and sustainability agendas

E-commerce is still growing rapidly and yet represents only a fraction of global consumer retail spending. Business-to-business e-commerce is expected to follow suit and dwarfs the consumer market size by a factor of three. The coronavirus pandemic has served not only to accelerate both e-commerce growth and supply chain innovation agendas. Key moves to scale and adopt new technology like intelligent physical automation, IoT-powered visibility tools, and predictive capabilities from AI will ultimately determine the ability to fulfill heightened customer demands and secure industry leadership positions in the future.

With governments, cities and solution providers commit to cut down on CO2 emissions and waste, sustainability now is an imperative for the logistics industry. Indicated by the increasing demand on sustainable solutions to reduce waste, leverage new propulsion techniques and optimize facilities, it is also on top of the supply chain agendas. Today, 90+ national bans on single-use plastics exists and bulky packaging causing 40 percent parcel void space, making a rethinking of the packaging inevitable. Sustainable Logistics optimization of processes, materials, new propulsion techniques, and smart facilities provide huge potential for logistics to become more environmentally friendly. Smart Containerization in transportation will also be important in developing environmentally friendly formats for delivery in congested cities.

DHL regularly publishes the Logistics Trend Radar as a key instrument for the global logistics community. Both within DHL and across industry, it has become an acclaimed benchmark for strategy and innovation, as well as a key tool to shape the direction of specific trends, most recently packaging, 5G, robotics and digital twins.

The fifth edition DHL Logistics Trend Radar, including information on deep dives and projects, is available for free download atwww.dhl.com/trendradar

SOURCE: DHL

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Artificial intelligence, robotics, quantum computing, sustainability & global volatility: DHL Logistics Trend Radar unveils trends that will shape...

OSTP, NSF, DoE, and IBM make major push to strengthen research in AI and quantum – BlackEngineer.com

Almost a month after the White House Office of Science and Technology Policy, the National Science Foundation, and the Department of Energy announced over $1 billion for the establishment of 12 new artificial intelligence (AI) and quantum information science (QIS) research institutes nationwide, IBM announced its first IBM Quantum education and research initiative for Historically Black Colleges and Universities (HBCU).

Led by Howard University and 12 additional HBCUs, the statement said the IBM-HBCU Quantum Center will offer access to its quantum computers, as well as collaboration on academic, education, and community outreach programs.

In addition, as part of the companys continued efforts around diversity and inclusion, IBM will make a $100M investment in technology, assets, resources, and skills development through partnerships with additional HBCUs through the IBM Skills Academy Academic Initiative.

We believe that in order to expand opportunity for diverse populations, we need a diverse talent pipeline of the next generation of tech leaders from HBCUs. Diversity and inclusion is what fuels innovation and students from HBCUs will be positioned to play a significant part of what will drive innovations for the future like quantum computing, cloud, and artificial intelligence, said Carla Grant Pickens, Chief Global Diversity & Inclusion Officer, IBM.

The $1 billion announced by the White House Office of Science and Technology Policy, the National Science Foundation (NSF), and the U.S. Department of Energy will go to National Science Foundation-led AI Research Institutes hosted by universities across the country, including at the University of Oklahoma, Norman, University of Texas, Austin, University of Colorado, Boulder, the University of Illinois at Urbana-Champaign, University of California, Davis, and the Massachusetts Institute of Technology.

The 13 HBCUs intending to participate in the Quantum Center were prioritized based on their research and education focus in physics, engineering, mathematics, computer science, and other STEM fields. They include;

Albany State University Clark Atlanta University Coppin State University Hampton University Howard University Morehouse College Morgan State University North Carolina Agricultural, and Technical State University Southern University Texas Southern University University of the Virgin Islands Virginia Union University Xavier University of Louisiana.

Howard University has prioritized our efforts to support our students pathway to STEM fields for many years with exciting results as we witness more and more graduates becoming researchers, scientists, and engineers with renowned national companies. Our faculty and students look forward to collaborating with our peer institutions through the IBM-HBCU Quantum Center. Were excited to share best practices and work together to prepare students to participate in a quantum-ready workforce, said President Wayne A. I. Frederick.

The HBCUs who are part of the Skills Academy Academic Initiative include Clark Atlanta University, Fayetteville State University, Grambling State University, Hampton University, Howard University, Johnson C. Smith University, Norfolk State University, North Carolina A&T State University, North Carolina Central University, Southern University System, Stillman College, Virginia State, and West Virginia State University.

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OSTP, NSF, DoE, and IBM make major push to strengthen research in AI and quantum - BlackEngineer.com