How machine learning can bridge the communication gap – ComputerWeekly.com

In October 2019, an Amazon employee in Melbourne, Australia bumped into another person while cycling on the road. As she was assuring that person that she would help, she realised that he was deaf and mute and had no clue on what she was saying.

The awkward situation could have been avoided if assistive technology was on hand to facilitate communication between the two parties. Following the incident, a team led by Santanu Dutt, head of technology for Southeast Asia at Amazon Web Services, got down to work.

Within ten days or so, Dutts team built a machine learning model that was trained on sign languages. Using images of a person gesturing in sign language that were captured from a camera, the model could recognise and translate gestures into text. The model also could convert spoken words into text for a deaf-mute person to see.

Dutt said the model can also be customised to translate speech into sign languages as the machine learning services and application programming interfaces (APIs) are available and open though he has not seen that demand yet. But once you write a small bit of code, training the machine learning model is easy, he said.

There is still more work to be done. As the training was performed with signs gestured against a white background, the efficacy of the model in its current form would be limited in actual use.

Our team had limited time to showcase this and we wanted to bump up something to showcase for experimental purposes, Dutt said, adding that organisations can use tools such as Amazon SageMaker to edit and train the model with more images and videos to recognise a larger variety of environments.

As the training process is intensive, Dutt said organisations with limited resources can use Amazon SageMaker Ground Truth to build training datasets for such machine learning models quickly. Besides automatic labelling, Ground Truth also provides access to human labellers through the Amazon Mechanical Turk crowdsourcing service.

This will also help to improve the models accuracy rate. The more data you have, the more accurate the model gets, Dutt said, adding that developers can set confidence levels and reject results that fall below a certain level of accuracy.

Dutt said AWSs public sector team has engaged non-profit organisations in Australia to conduct a proof-of-concept that makes use of the machine learning model, as well as those in other countries through credits that offset the cost of using AWS services to train and deploy the model.

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From streaming hive data to acoustics, SAS uses machine learning, analytics to boost bee populations – WRAL Tech Wire

CARY SAS wants to help save the worlds No.1 food crop pollinator the honey bee. And its doing so right in the Triangles backyard.

To coincide with World Bee Day, the Cary-base software analytics firm today confirmed it is working on three separate projectswhere technology is monitoring, tracking and improving pollinator populations around the globe.

They include observing real-time conditions of beehives using an acoustic streaming system; working with Appalachian State University on the World Bee Count to visualize world bee population data; and decoding bee communication to maximize their food access.

By applying advanced analytics and artificial intelligence to beehive health, we have a better shot as a society to secure this critically important part of our ecosystem and, ultimately, our food supply, said Oliver Schabenberger, COO and CTO of SAS, in a statement.

Researchers from the SAS IoT Division are developing a bioacoustic monitoring system to non-invasively track real-time conditions of beehives using digital signal processing tools and machine learning algorithms available in SASEvent Stream Processingand SAS Viya software.

By connecting sensors to SAS four Bee Downtown hives at its headquarters in Cary, NC, the team startedstreaming hive datadirectly to the cloud to continuously measure data points in and around the hive, including weight, temperature, humidity, flight activity and acoustics. In-stream machine learning models were used to listen to the hive sounds, which can indicate health, stress levels, swarming activities and the status of the queen bee.

To ensure only the hum of the hive was being used to determine bees health and happiness, researchers used robust principal component analysis (RPCA), a machine learning technique, to separate extraneous or irrelevant noises from the inventory of sounds collected by hive microphones.

The researchers found that with RPCA capabilities, they could detect worker bees piping at the same frequency range at which a virgin queen pipes after a swarm, likely to assess whether a queen was present. The researchers then designed an automated pipeline to detect either queen piping following a swarm or worker piping that occurs when the colony is queenless.

SAS said the acoustic analysis can alert beekeepers to queen disappearances immediately, which is vitally important to significantly reducing colony loss rates. Its estimated the annual loss rates of US beehives exceed 40 percent and between 25-40 percent of these losses are due to queen failure.

With this system, SAS said beekeepers will have a deeper understanding of their hives without having to conduct time-consuming and disruptive manual inspections.

As a beekeeper myself, I know the magnitude of bees impact on our ecosystem, and Im inspired to find innovative ways to raise healthier bees to benefit us all, said Anya McGuirk, Distinguished Research Statistician Developer in the IoT division at SAS.

The researchers said they plan to implement the acoustic streaming system very soon and are continuing to look for ways to broaden the usage of technology to help honey bees and ultimately humankind.

SAS is also launching a data visualization that maps out bees counted around the globe for theWorld Bee Count, an initiative co-founded by theCenter for Analytics Research and Education(CARE) at Appalachian State University.

The goal: to engage citizens across the world to take pictures of bees as a first step toward understanding the reasons for their alarming decline, SAS says.

The World Bee Count allows us to crowdsource bee data to both visualize our planets bee population and create one of the largest, most informative data sets about bees to date, said Joseph Cazier, Professor and Executive Director at Appalachian State Universitys CARE, in a statement.

In early May, the World Bee Count app was launched for users both beekeepers and the general public, aka citizen data scientists to add data points to the Global Pollinator Map. Within the app, beekeepers can enter the number of hives they have, and any user can submit pictures of pollinators from their camera roll or through the in-app camera. Through SAS Visual Analytics, SAS has created avisualization mapto display the images users submit via the app which, it says, could potentially provide insights about the conditions that lead to the healthiest bee populations.

In future stages of this project, SAS said, the robust data set created from the app could help groups like universities and research institutes better strategize ways to save these vital creatures.

Representing the Nordic region, a team from Amesto NextBridge won the 2020 SAS EMEA Hackathon, which challenged participants to improve sustainability using SAS Viya. Their winning project used machine learning to maximize bees access to food, which would in turn benefit mankinds food supply.

In partnership withBeefutures, the team developed a system capable of automatically detecting, decoding and mapping bee waggle dances using Beefutures observation hives and SAS Viya.

Observing all of these dances manually is virtually impossible, but by using video footage from inside the hives and training machine learning algorithms to decode the dance, we will be able to better understand where bees are finding food, said Kjetil Kalager, lead of the Amesto NextBridge and Beefutures team. We implemented this information, along with hive coordinates, sun angle, time of day and agriculture around the hives into an interactive map in SAS Viya and then beekeepers can easily decode this hive information and relocate to better suited environments if necessary.

SAS said this systematic real-time monitoring of waggle dances allows bees to act as sensors for their ecosystems. It may also uncover other information bees communicate through dance that could help us save and protect their population.

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From streaming hive data to acoustics, SAS uses machine learning, analytics to boost bee populations - WRAL Tech Wire

How does Machine Learning Revolutionizing the Mobile Applications? – Customer Think

Machine learning is a subset of AI and a study of algorithms that enables software to think and behave like humans without any separate programming. Didnt get the technical definition? Lets make you understand with a real-life example of machine learning.

You are shopping online, and the website is showing you some recommendations of the other products based on the product you are currently viewing or have added to the cart.ORYou have searched luxury watches on the search engine for once and then switched to some other website or started watching a video. Suddenly, you see the advertisements of different luxury watches on the website.

Now, you must be thinking that how come the luxury watches ads appeared out of nowhere, or why did the website recommending products similar to the one you have added to the shopping cart or currently viewing?

Machine Learning is the answer to these and other similar questions. The technology uses various computer algorithms to read and analyze user behavior patterns that further help in making suggestions or recommendations. This potential of behaving like a human being and taking intelligent decisions is increasing the use of machine learning in todays mobile applications. Other benefits of this technology are chatbots, image recognition and tagging, analyzing user behavior, advanced search options, optical character recognition, increased security and privacy, etc.

Curious to know more? Read the below-given information to know how machine learning is making difference to mobile applications in various fields:

ML-based eCommerce mobile applications help with the two most important aspects of the business, i.e., customer support and self-service. With a mobile application powered by machine learning and natural learning processes, businesses can look into customers behavior and suggest to them different products without making a human sit on the backend and observe every customers activity (which is next to impossible).

Moreover, such applications also assist in communicating and interacting with customers to listen and resolve their queries with pre-programmed answers. Yes, automating customer support using chatbots is one of the greatest applications of machine learning. Personalizing product search and promotions, detecting and preventing frauds, checking analytics, and forecasting trends are some other benefits of ML provide to the eCommerce apps.

ML-enabled applications are also assisting the healthcare domain by automating medical diagnosis and ensuring precision in the results. These apps also help in offering personalized medicine, cancer detection, rendering personalized treatment, and in other areas. Machine learning chatbots is another benefit this technology gives to healthcare. With the help of such chatbots, medical facilities can build a patient support system, where they can get answers to various queries. IBM Watson is an example of an ML-based application that can access and analyze thousands of cancer cases to diagnose a patient.

With increasing awareness about health and fitness among people, there has been observed a spike in the applications rendering home workout, online personal trainer, and other such services. ML, when integrated with fitness applications, can provide personalized training and offer health or diet-related suggestions by analyzing users data.

Machine Learning has the potential to change the future of the finance industry by enabling applications to predict future market trends, crashes, and bubbles. Such apps can help in reducing operational cost with process automation, enhancing user experience, and improve compliance.Apart from the aforementioned domains, machine learning also helps in advancing data mining, security, audio and video recognition, image and object recognition, crime and security, and many other applications.

Machine learning is enabling digital transformation by advancing the mobile application development to minimize human efforts, reduce cost, and bring accurate outcomes. The vast use of ML-based algorithms in todays mobile applications is sheer proof that the technology is here to stay. To make the most of machine learning and deliver the best experience to your customers, you can integrate it into your existing business applications or get a new mobile app powered with machine learning. In both cases, it is advised to reach out to a reliable and experienced machine learning app development company so that you can get the best value of your money.

Read More: How Startups are Creating Disruptions Using Artificial Intelligence?

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How does Machine Learning Revolutionizing the Mobile Applications? - Customer Think

Key Dynamics of Machine Learning and Intelligent Automation in Contemporary Market – Analytics Insight

Key Dynamics of Machine Learning and Intelligent Automation in Contemporary Market

Automation has generated great buzz across many industries globally. And as more and more organizations are shifting their focus to digital transformation and innovation, they are adopting automation technologies to increase their business efficiency by reducing human errors. Moreover, when mixed with machine learning capabilities, automation tends to serve with an attractive proposition to an organization and its services across the market. The combination is popularly known as intelligent automation.

Intelligent automation as a blend of innovative AI capabilities and automation is extensively applicable to the more sophisticated end of the automation-aided workflow continuum. The potential benefits of ML-enabled intelligent automation capabilities, in terms of additional insights and financial impact, can be greatly augmented.

Today, to stay relevant, competitive, and efficient, organizations need to contemplate their business processes with the addition of machine learning and automation. Together they can provide great advantages to organisations. Being substantially different technologies, together they have the ability to evaluate the process and make cognitive decisions.

To make your automation process more dynamic, the successful integration of machine learning is key. Moreover, intelligent automation as an amalgamation is a two-way improvement strategy, where automation tools are exposed to huge amounts of data, and machine learning can be leveraged to determine how robots can be programmed to store and filter useful data.

Individually, both technologies are very fast-growing markets. The global machine learning market size is expected to reach US$96.7 billion by 2025, according to market reports, expanding at a CAGR of 43.8% from 2019 to 2025. Also, the global automation market size is expected to reach US$368.4 billion in 2025, from US$190.2 billion in 2017 growing at a CAGR of 8.8% from 2018 to 2025.

Moreover, the intelligent process automation market was valued at US$6.25 billion in 2017 and is projected to reach US$13.75 billion by 2023, at a CAGR of 12.9% from 2018 to 2023.

Organizations are becoming more open today, allowing their products and technologies to be better integrated and share data and this trend has given rise to innovative technology like intelligent automation.

With the incorporation of machine learning capabilities, intelligent automation possesses the ability to empower humans with advanced smart technologies and agile processes to enable fast and informative decisions. It also caters to a wide array of business operations with key benefits including increasing process efficiency and customer experience, better optimization of back-office operations, reduction in costs, and minimizing risk factors. Intelligent automation also optimizes the workforce productivity with better and effective monitoring and fraud detection. It also enables a more comprehensive product and service innovation.

Being an undeniable catalyst to progress, moreover, intelligent automation is no threat to human jobs. Rather its incorporation in a collaborative manner can help employees reshape their skills and creatives. Intelligent automation has the core benefit to extensively improve and digitalize business processes along with human judgment.

Therefore, the time has arrived when companies should consider investing strategically in automation and ML capabilities in order to understand and meet the expectations of customers which eventually leads to improved productivity and low-cost scalability.

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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Evolve your career with upGrads Machine Learning and Cloud program in association with IIT Madras – Economic Times

Amongst technologies that have revolutionised industries in the last two decades, Machine Learning holds a significant place. Machine Learning has not only made its way into versatile industry applications but has also allowed businesses to transform their operations by reducing costs, boosting efficiency, and transforming customer experience. Currently, Machine Learning is at a crucial crossroad where research is on to take automation to a stage where it requires no human intervention at all. This will pave the path towards a fully automated workflow which is achievable by integrating it with Cloud Computing. For predictive analysis to take over industries, the vast amount of data that has to be processed in Machine Learning models need a scalable distributed system for storage. This is where the relevance of Cloud comes in. ML, when paired with Cloud, forms an Intelligent Cloud that becomes a suitable destination for all Machine Learning projects and becomes handy for data collection, data optimization, data distribution, and managing a data transport network and deployment of Machine Learning models. With almost every business looking to deploy AI in their operations in the near future, the demand for skilled ML and Cloud professionals is more than ever before. A report by the World Economic Forum also suggests that this industry will create about 58 million new jobs by 2022. This clearly indicates the importance of upskilling oneself with a strongly connected ML and Cloud program.To cater to this growing demand and to help young professionals understand and develop packaged ML solutions, upGrad has collaborated with IIT Madras to develop an Advanced Certification in Machine Learning and Cloud program. The 9-month long program recognises the importance of taking ML to Cloud to realise full-scale AI implementations across verticals. upGrad understands the relevance of data and insights in business operations. The program covers the deployment of advanced Machine Learning models on Cloud, giving individuals an opportunity to cater to data demands across multiple industry domains like e-commerce, retail, healthcare, banking, manufacturing, transport, NBFC, and finance among others.'; var randomNumber = Math.random(); var isIndia = (window.geoinfo && window.geoinfo.CountryCode === 'IN') && (window.location.href.indexOf('outsideindia') === -1 ); //console.log(isIndia && randomNumber A Highly Selective & Exclusive ProgramTo ensure that the program is exciting as well as challenging, upGrads Advanced Certification in Machine Learning and Cloud is highly selective & exclusive and admits only 70 individuals in one cohort to ensure focused learning and individual growth. For this, applicants have to go through the All India Aptitude Test from IIT Madras, a comprehensive entrance test, an interview round, and a final panel selection before they are allowed admittance to the program. This ensures that each academic batch consists of highly skilled individuals who are capable of carrying the IIT batch forward and can later help their employers take high-stake data risks with confidence. The time investment for this program on a weekly basis is about 12-14 hours which further makes it an ideal upskilling programme for working individuals.Learn from the best in the business

With data being the operative word for every sector, every organization is currently scaling up its AI and ML workforce. upGrads Advanced Certification in Machine Learning and Cloud is helping learners become vital to their companys success by training them efficiently. upGrad learners deploy machine learning models using PySpark on Cloud and they get an opportunity to learn from a set of experienced Machine Learning faculty and industry leaders. The prestigious program also has about 300+ hiring partners, ensuring that learners can land up in the industry of their choice by the end of the program. The program has been largely successful in building employability of learners and boosting their annual packages. The current demand for ML engineers is at an all-time high, with even freshers getting hired at astounding pay packages. Considering this shift, upGrads Advanced Program in Machine Learning and Cloud is the best way to flag off ones ML journey.

Specifically designed for data analysts, business analysts, cloud engineers, software engineers, application developers, and product managers among others, the program will be highly beneficial in learning about the following aspects:Programming: Learn core and necessary languages like Python, which is required for ML operations and SQL, which is a vital language of the Cloud along with deployment of Machine Learning models using Cloud.

Machine learning concepts: Learn both basic and advanced subjects within ML. This will help learners to understand the application of appropriate ML algorithms to categorize unknown data or make predictions about it. The program also helps learners modify and craft algorithms of their own.

Foundations of Cloud and Hadoop: Learn about Hadoop, Hive, and HDFS along with the implementation of ML algorithms in the cloud on Spark/ PySpark (AWS/ Azure/ GCP).

Why choose upGrad?upGrads Advanced Certification Program in Machine Learning and Cloud will provide learners with a PG Certification from IIT Madras, one of Indias top IITs. This teaching panel includes faculty from IIT Madras and leading industry experts who seamlessly integrate online lectures, offline engagement, case studies, and interactive networking sessions. It provides 360-degree support to young professionals by taking care of career counselling, dedicated student success mentors, resume feedback, interview preparation, and job assistance. Over the years, the program has seen 500+ career transitions, with an average salary hike of 58%. Many of these learners have been placed in companies like KPMG, Uber, Big Basket, Bain & Co, Pwc, Zivame, Fractal Analytics, Microsoft etc. with impressive salary shifts.

upGrads Advanced Certification in Machine Learning and Cloud is also one of the most cost-effective methods for professionals looking to hop onto the Machine Learning bandwagon. The program fee is 2,00,000 and it is also available at a no cost EMI of 29,166/- per month. By uniting upGrads data expertise with IIT Madras academic excellence, it provides a unique opportunity to learners to scale up.

If you want to fast-track your career and make yourself readily employable, its time you take the All India Test for the Advanced Certification in Machine Learning and Cloud. The program commences on June 30, 2020, with admissions closing on June 7, 2020, owing to a mandatory pre-prep course spanning across 3 weeks before the start of the program. Its time to take the big leap with upGrad. Apply for the All India Aptitude Test today.

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Evolve your career with upGrads Machine Learning and Cloud program in association with IIT Madras - Economic Times

Assessing the Fallout From the Coronavirus Pandemic Machine Learning Software Market Current and Future Trends, Leading Players, Industry Segments…

The Machine Learning Software market research encompasses an exhaustive analysis of the market outlook, framework, and socio-economic impacts. The report covers the accurate investigation of the market size, share, product footprint, revenue, and progress rate. Driven by primary and secondary researches, the Machine Learning Software market study offers reliable and authentic projections regarding the technical jargon.All the players running in the global Machine Learning Software market are elaborated thoroughly in the Machine Learning Software market report on the basis of proprietary technologies, distribution channels, industrial penetration, manufacturing processes, and revenue. In addition, the report examines R&D developments, legal policies, and strategies defining the competitiveness of the Machine Learning Software market players.The report on the Machine Learning Software market provides a birds eye view of the current proceeding within the Machine Learning Software market. Further, the report also takes into account the impact of the novel COVID-19 pandemic on the Machine Learning Software market and offers a clear assessment of the projected market fluctuations during the forecast period.

Get Free Sample PDF (including COVID19 Impact Analysis, full TOC, Tables and Figures) of Market Report @ https://www.marketresearchhub.com/enquiry.php?type=S&repid=2601984&source=atm

The key players covered in this studyMicrosoftGoogleTensorFlowKountWarwick AnalyticsValohaiTorchApache SINGAAWSBigMLFigure EightFloyd Labs

Market segment by Type, the product can be split intoOn-PremisesCloud BasedMarket segment by Application, split intoLarge EnterprisedSMEs

Market segment by Regions/Countries, this report coversNorth AmericaEuropeChinaJapanSoutheast AsiaIndiaCentral & South America

The study objectives of this report are:To analyze global Machine Learning Software status, future forecast, growth opportunity, key market and key players.To present the Machine Learning Software development in North America, Europe, China, Japan, Southeast Asia, India and Central & South America.To strategically profile the key players and comprehensively analyze their development plan and strategies.To define, describe and forecast the market by type, market and key regions.

In this study, the years considered to estimate the market size of Machine Learning Software are as follows:History Year: 2015-2019Base Year: 2019Estimated Year: 2020Forecast Year 2020 to 2026For the data information by region, company, type and application, 2019 is considered as the base year. Whenever data information was unavailable for the base year, the prior year has been considered.

Do You Have Any Query Or Specific Requirement? Ask to Our Industry [emailprotected] https://www.marketresearchhub.com/enquiry.php?type=E&repid=2601984&source=atm

Objectives of the Machine Learning Software Market Study:To define, describe, and analyze the global Machine Learning Software market based on oil type, product type, ship type, and regionTo forecast and analyze the Machine Learning Software market size (in terms of value and volume) and submarkets in 5 regions, namely, APAC, Europe, North America, Central & South America, and the Middle East & AfricaTo forecast and analyze the Machine Learning Software market at country-level for each regionTo strategically analyze each submarket with respect to individual growth trends and their contribution to the global Machine Learning Software marketTo analyze opportunities in the market for stakeholders by identifying high growth segments of the global Machine Learning Software marketTo identify trends and factors driving or inhibiting the growth of the market and submarketsTo analyze competitive developments, such as expansions and new product launches, in the global Machine Learning Software marketTo strategically profile key market players and comprehensively analyze their growth strategiesThe Machine Learning Software market research focuses on the market structure and various factors (positive and negative) affecting the growth of the market. The study encloses a precise evaluation of the Machine Learning Software market, including growth rate, current scenario, and volume inflation prospects, on the basis of DROT and Porters Five Forces analyses. In addition, the Machine Learning Software market study provides reliable and authentic projections regarding the technical jargon.

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After reading the Machine Learning Software market report, readers can:Identify the factors affecting the Machine Learning Software market growth drivers, restraints, opportunities and trends.Examine the Y-o-Y growth of the global Machine Learning Software market.Analyze trends impacting the demand prospect for the Machine Learning Software in various regions.Recognize different tactics leveraged by players of the global Machine Learning Software market.Identify the Machine Learning Software market impact on various industries.

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Analysis on Impact of COVID-19- Cryptocurrency Mining Hardware Market 2020-2024 | Rising Popularity Of Mining Pools to Boost Growth | Technavio -…

LONDON--(BUSINESS WIRE)--Technavio has been monitoring the cryptocurrency mining hardware market and it is poised to grow by USD 2.80 bn during 2020-2024, progressing at a CAGR of over 7% during the forecast period. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment.

Technavio suggests three forecast scenarios (optimistic, probable, and pessimistic) considering the impact of COVID-19. Please Request Latest Free Sample Report on COVID-19 Impact

The market is fragmented, and the degree of fragmentation will accelerate during the forecast period. Advanced Micro Devices Inc., ASICminer Co., Baikal Miner, Bitfury Group Ltd., BitMain Technologies Holding Co., Canaan Inc., Cynosure Technologies Co. Ltd., Halong Mining, INNOSILICON Technology Ltd., and Shenzhen MicroBT Electronics Technology Co. Ltd. are some of the major market participants. To make the most of the opportunities, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Rising popularity of mining pools has been instrumental in driving the growth of the market. However, declining cost of mining hardware might hamper market growth.

Cryptocurrency Mining Hardware Market 2020-2024 : Segmentation

Cryptocurrency Mining Hardware Market is segmented as below:

To learn more about the global trends impacting the future of market research, download a free sample: https://www.technavio.com/talk-to-us?report=IRTNTR43766

Cryptocurrency Mining Hardware Market 2020-2024 : Scope

Technavio presents a detailed picture of the market by the way of study, synthesis, and summation of data from multiple sources. Our cryptocurrency mining hardware market report covers the following areas:

This study identifies increasing popularity of ICOs as one of the prime reasons driving the cryptocurrency mining hardware market growth during the next few years.

Cryptocurrency Mining Hardware Market 2020-2024 : Vendor Analysis

We provide a detailed analysis of around 25 vendors operating in the cryptocurrency mining hardware market, including some of the vendors such as Advanced Micro Devices Inc., ASICminer Co., Baikal Miner, Bitfury Group Ltd., BitMain Technologies Holding Co., Canaan Inc., Cynosure Technologies Co. Ltd., Halong Mining, INNOSILICON Technology Ltd., and Shenzhen MicroBT Electronics Technology Co. Ltd. Backed with competitive intelligence and benchmarking, our research reports on the cryptocurrency mining hardware market are designed to provide entry support, customer profile and M&As as well as go-to-market strategy support.

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Cryptocurrency Mining Hardware Market 2020-2024 : Key Highlights

Table Of Contents :

Executive Summary

Market Landscape

Market Sizing

Five Forces Analysis

Market Segmentation by Product

Customer Landscape

Geographic Landscape

Market Drivers

Market Challenges

Market Trends

Vendor Landscape

Vendor Analysis

Appendix

About Us

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavios report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavios comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

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Analysis on Impact of COVID-19- Cryptocurrency Mining Hardware Market 2020-2024 | Rising Popularity Of Mining Pools to Boost Growth | Technavio -...

Is 2020 the year to invest in cryptocurrency? – About Manchester

Over a decade since Bitcoin was first launched, there are now myriad cryptocurrencies on the market, such as NEO, Litecoin and Ethereum, but Bitcoin still remains the most well-known. Cryptocurrency is a form of digital currency, which requires no central banking system. It sits on a platform called blockchain, and Bitcoins are mined in exchange for Bitcoin rewards. Anyone can mine Bitcoin, and because the transactions have to be verified by several individuals, there is no need for a central bank to control it, it is decentralised. But you dont have to mine Bitcoin in order to own it, many people are now simply investing in cryptocurrencies through trading platforms.

But is cryptocurrency a good investment? And if so, will 2020 be a good year to invest? Its certainly been an interesting year so far, and a rocky ride in terms of many investments, with prices fluctuating, largely due to the Covid-19 pandemic. The value of Bitcoin has risen as high as $9,000 and seen a low of $4,000, before gaining ground to $6,600, marking the greatest fluctuations since 2017.

The most recent rise in Bitcoins value, as well as other cryptocurrencies, may have been triggered by US Federal Reserve quantitative easing, an attempt to reduce the damage Coronavirus could cause to the economy. This has led some to move investments into Bitcoin, and other cryptocurrencies, to hedge against the potential devaluing of currency caused by quantitative easing. As there is a finite number of Bitcoin on the market, some believe it should not be susceptible to such devaluing, as the amount of new Bitcoin being mined is always reducing. The increase in demand, and the reduction in supply, should drive up the value, in keeping with the principles of supply and demand, according to experts such as Simon Peters, a crypto analyst at eToro.

Cryptocurrencies first became popular after the economic crisis of 2008, when the value of other traditional shares and investments took a major hit. Similarly, since news of the Coronavirus outbreak first hit, transaction volumes on trading platforms seemed to have increased.

Cryptocurrency trading platforms Binance and MyEtherWallet have also seen increased investment and significant growth. It certainly appears that quantitative easing has been the catalyst for investors to seek alternative options.

But theres another reason to consider cryptocurrency investment in 2020 the Bitcoin halving this May, meaning the number of Bitcoin available will halve. This means less supply, and with the pandemic pushing up demand, some are anticipating a bull run.

If past performance is any indication, a halving is likely to push Bitcoin values up. The first halving in 2012 saw a whopping 8,000% increase in the value of Bitcoin over the following year, and the second one in 2016 saw Bitcoins value rise by 2,000% in the subsequent 18 months.

With no clear end in sight for the current lockdown situation, many businesses are losing value, if they survive at all, so traditional stocks and shares are taking a battering. Could cryptocurrencies be considered a safe haven in 2020? It is a fluctuating market, but steely investors may be prepared to take a punt.

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Bitcoin halving Q&A: what it’s all about and what it means for the cryptocurrency – The Conversation US

Bitcoin, the first and leading cryptocurrency in terms of trading volume and market capitalisation, went through its third halving on May 11 2020. This major adjustment to how the cryptocurrency operates has only happened twice before and happens every four years. But what does this actually mean and what impact will it have?

Q: how does bitcoin work?

Bitcoin is a digital currency that makes use of blockchain technology to store and record all transactions. First proposed in a white paper published online in 2008 by a mysterious person (or group of people) called Satoshi Nakamoto. The unique features of bitcoin compared to fiat currencies like dollars or pounds are that there is no central authority or bank. Each member of the network has equal power. This decentralised network is completely transparent and all transactions can be read on the blockchain. At the same time it offers privacy in terms of who owns the cryptocurrency.

Bitcoins are created (or mined) by so-called miners who contribute computing power to securing the network, as well as processing transactions on the network by solving complex mathematical puzzles through computational power. These miners are rewarded for their work processing the transactions on the blockchain with bitcoins. But to combat inflation, Nakamoto wrote into the code that the total number of bitcoins that will ever exist will be 21 million. Right now there are 18.38 million.

The first ever block recorded on the bitcoin blockchain was on January 3 2009 where Nakamoto received 50 bitcoins. In the white paper, Nakamoto specified that after every 210,000 blocks the reward for miners will half. So the first halving took place on November 28 2012 where the miners reward was reduced from 50 bitcoins to 25 bitcoins. The second halving was on July 9 2016 and the miners reward was reduced from 25 bitcoins to 12.5 bitcoins. And the third, most recent halving on May 11 2020 means bitcoin miners now receive 6.25 bitcoins.

Q: Why does bitcoin halve?

Nakamoto has never explained explicitly the reasons behind the halving. Some speculate the halving system was designed to distribute coins more quickly at the beginning to incentive people to join the network and mine new blocks. Block rewards are programmed to halve at regular intervals because the value of each coin rewarded is deemed likely to increase as the network expanded. However, this may lead to users holding bitcoin as a speculative asset rather than using it as a medium of exchange.

Q: What impact does halving have on bitcoin?

The obvious impact is that the amount of newly mined bitcoins per day will fall from about 1,800 to 900 bitcoins and the daily revenue of miners will reduce by half. This decrease in the rate of bitcoin creation tightens supply and some argue will lead to a bullish market and an increase in the price of bitcoin.

Meanwhile, the reduction of revenue for miners may squeeze out miners who are least efficient and therefore the computing power connected to the Bitcoin network may fall significantly.

The previous two halvings led to the most dramatic bull runs in Bitcoins history, although initially there was a brief sell-off. Marcus Swanepoel, co-founder and CEO of Luno, a cryptocurrency wallet which lets you store and carry out bitcoin transactions, believes that bitcoin may achieve a growth of 270% between this and the fourth halving in 2024.

Q: How is coronavirus affecting things?

Although bitcoin has gained more than 20% since the beginning of the year, where this halving may differ from its predecessors is the volatile and uncertain economic environment that it has taken place in. The International Monetry Fund predicted a 3% shrinking of global growth in its April forecast and this is expected to fall further. In the UK, the Bank of England has projected a decrease of 30% in the countrys GDP during the first half of 2020.

Some argue that bitcoins scarcity makes it a potential hedge against fiat currencies that are vulnerable to devaluation in times of economic crisis. But others believe the halving wont necessarily boost its price as people knew the halving was going to happen so it should be already priced into the market activity.

The only certainty is that the growth of new bitcoins has halved. It remains to be seen what impact this will have on the price and interest of this cryptocurrency.

Correction: a previous version of this article incorrectly said Michael Dubrovsky speculated the halving system was designed to distribute coins more quickly at the beginning to incentive people to join the network and mine new blocks.

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Bitcoin halving Q&A: what it's all about and what it means for the cryptocurrency - The Conversation US

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Learn how to invest in the stock market and cryptocurrency - New York Post