Category Archives: Machine Learning

The Global Machine Learning Operationalization Software Market analysis report published on Dataintelo.com is a detailed study of market size, share and dynamics covered in XX pages and is an illustrative sample demonstrating market trends. This is a latest report, covering the current COVID-19 impact on the market. The pandemic of Coronavirus (COVID-19) has affected every aspect of life globally. This has brought along several changes in market conditions. The rapidly changing market scenario and initial and future assessment of the impact is covered in the report. It covers the entire market with an in-depth study on revenue growth and profitability. The report also delivers on key players along with strategic standpoint pertaining to price and promotion.

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The Global Machine Learning Operationalization Software Market report entails a comprehensive database on future market estimation based on historical data analysis. It enables the clients with quantified data for current market perusal. It is a professional and a detailed report focusing on primary and secondary drivers, market share, leading segments and regional analysis. Listed out are key players, major collaborations, merger & acquisitions along with upcoming and trending innovation. Business policies are reviewed from the techno-commercial perspective demonstrating better results. The report contains granular information & analysis pertaining to the Global Machine Learning Operationalization Software Market size, share, growth, trends, segment and forecasts from 2020-2026.

With an all-round approach for data accumulation, the market scenarios comprise major players, cost and pricing operating in the specific geography/ies. Statistical surveying used are SWOT analysis, PESTLE analysis, predictive analysis, and real-time analytics. Graphs are clearly used to support the data format for clear understanding of facts and figures.

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Primary research, interviews, news sources and information booths have made the report precise having valuable data. Secondary research techniques add more in clear and concise understanding with regards to placing of data in the report.

The report segments the Global Machine Learning Operationalization Software Market as:Global Machine Learning Operationalization Software Market Size & Share, by Regions

Global Machine Learning Operationalization Software Market Size & Share, by ProductsCloud BasedOn Premises

Global Machine Learning Operationalization Software Market Size & Share, ApplicationsBFSIEnergy and Natural ResourcesConsumer IndustriesMechanical IndustriesService IndustriesPublice SectorsOther

Key PlayersMathWorksSASMicrosoftParallelMAlgorithmiaH20.aiTIBCO SoftwareSAPIBMDominoSeldonDatmoActicoRapidMinerKNIME

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Machine Learning Operationalization Software Market (2020-2026) | Where Should Participant Focus To Gain Maximum ROI | Exclusive Report By DataIntelo...

AI and machine learning are turning a corner, marking this year with new and improved platforms and use cases. However, database administrators dont always have the tools and skills necessary to manage this new minefield of technology.

DBTA recently held a webinar featuring Charlie Berger, senior director, product management, machine learning, AI, and, Cognitive Analytics, Oracle who discussed how to gain an attainable, logical, evolutionary path to add machine learning to users Oracle data skills.

Operational DBAs spend a lot of time on maintenance, security, and reliability, Berger said. The Oracle Autonomous Database can help. It automates all database and infrastructure management, monitoring, tuning; protects from both external attacks and malicious internal users; and protects from all downtime including planned maintenance.

The Autonomous Database removes tactical drudgery, allowing more time for strategic contribution, according to Berger.

Machine learning allows algorithms to automatically sift through large amounts of data to discover hidden patterns, new insights, and make predictions, he explained.

Oracle Machine Learning extends Oracle Autonomous Database and enables users to build AI applications and analytics dashboards. OML delivers powerful in-database machine learning algorithms, automated ML functionality, and integration with open source Python and R.

From a database developer to a data scientist, Oracle can transform the data management platform into a combined/hybrid data management and machine learning platform.

There are 6 major steps to becoming a data scientist that include:

An archived on-demand replay of this webinar is availablehere.

Read more:
Oracle Offers Machine Learning Workshop to Transform DBA Skills - Database Trends and Applications

Machine learning is one aspect of the AI portfolio of capability that has been with us in various forms for decades, so its hardly a product of science fiction. Its widely used as a means of processing high volumes of customer data to provide a better service and hence increase profits.

Yet things become more complex when the technology is brought into the public sector, where many decisions can greatly affect our lives. AI is often feared, particularly around removing the human touch that could lead to unfair judgements or decisions that could cause injury, death or even the complete destruction of humanity. If we think about medical diagnoses or the unfair denial of welfare for a citizen, its apparent where the first two fears arise. Hollywood can take credit for the final scenario.

Whatever the fear, we shouldnt throw the baby out with the bathwater. Local services in the UK face a 7.8 billion funding gap by 2025. With services already cut to the bone, central and local government organisations, along with the NHS, need new approaches and technologies to drive efficiency while also improving the service quality. Often this means collaboration between service providers, but collaboration between man and machine can also play a part.

Machine learning systems can help transform the public sector, driving better decisions through more accurate insights and streamlining service delivery through automation. Whats important, however, is that we dont try to replace human judgement and creativity with machine efficiency we need to combine them.

Theres a strong case to be made for greater adoption of machine learning across a diverse range of activities. The basic premise of machine learning is that a computer can derive a formula from looking at lots of historical data that enables the prediction of certain things the data describes. This formula is often termed an algorithm or a model. We use this algorithm with new data to make decisions for a specific task, or we use the additional insight that the algorithm provides to enrich our understanding and drive better decisions.

For example, machine learning can analyse patients interactions in the healthcare system and highlight which combinations of therapies in what sequence offer the highest success rates for patients; and maybe how this regime is different for different age ranges. When combined with some decisioning logic that incorporates resources (availability, effectiveness, budget, etc.) its possible to use the computers to model how scarce resources could be deployed with maximum efficiency to get the best-tailored regime for patients.

When we then automate some of this, machine learning can even identify areas for improvement in real-time and far faster than humans and it can do so without bias, ulterior motives or fatigue-driven error. So, rather than being a threat, it should perhaps be viewed as a reinforcement for human effort in creating fairer and more consistent service delivery.

Machine learning is also an iterative process; as the machine is exposed to new data and information, it adapts through a continuous feedback loop, which in turn provides continuous improvement. As a result, it produces more reliable results over time and ever more finely tuned and improved decision-making. Ultimately, its a tool for driving better outcomes.

The opportunities for AI to enhance service delivery are many. Another example in healthcare is Computer Vision (another branch of AI), which is being used in cancer screening and diagnosis. Were already at the stage where AI, trained from huge libraries of images of cancerous growths, is better at detecting cancer than human radiologists. This application of AI has numerous examples, such as work being done at Amsterdam UMC to increase the speed and accuracy of tumour evaluations.

But lets not get this picture wrong. Here, the true value is in giving the clinician more accurate insight or a second opinion that informs their diagnosis and, ultimately, the patients final decision regarding treatment. A machine is there to do the legwork, but the human decision to start a programme for cancer treatment, remains with the humans.

Acting with this enhanced insight enables doctors to become more efficient as well as effective. Combining the results of CT scans with advanced genomics using analytics, the technology can assess how patients will respond to certain treatments. This means clinicians avoid the stress, side effects and cost of putting patients through procedures with limited efficacy, while reducing waiting times for those patients whose condition would respond well. Yet, full-scale automation could run the risk of creating a lot more VOMIT.

Victims Of Modern Imaging Technology (VOMIT) is a new phenomenon where a condition such as a malignant tumour is detected by imaging and thus at first glance it would seem wise to remove it. However, medical procedures to remove it carry a morbidity risk which may be greater than the risk the tumour presents during the patients likely lifespan. Here, ignorance could be bliss for the patient and doctors would examine the patient holistically, including mental health, emotional state, family support and many other factors that remain well beyond the grasp of AI to assimilate into an ethical decision.

All decisions like these have a direct impact on peoples health and wellbeing. With cancer, the faster and more accurate these decisions are, the better. However, whenever cost and effectiveness are combined there is an imperative for ethical judgement rather than financial arithmetic.

Healthcare is a rich seam for AI but its application is far wider. For instance, machine learning could also support policymakers in planning housebuilding and social housing allocation initiatives, where they could both reduce the time for the decision but also make it more robust. Using AI in infrastructural departments could allow road surface inspections to be continuously updated via cheap sensors or cameras in all council vehicles (or cloud-sourced in some way). The AI could not only optimise repair work (human or robot) but also potentially identify causes and determine where strengthened roadways would cost less in whole-life costs versus regular repairs.

In the US, government researchers are already using machine learning to help officials make quick and informed policy decisions on housing. Using analytics, they analyse the impact of housing programmes on millions of lower-income citizens, drilling down into factors such as quality of life, education, health and employment. This instantly generates insightful, accessible reports for the government officials making the decisions. Now they can enact policy decisions as soon as possible for the benefit of residents.

While some of the fears about AI are fanciful, there is a genuine concern about the ethical deployment of such technology. In our healthcare example, allocation of resources based on gender, sexuality, race or income wouldnt be appropriate unless these specifically had an impact on the prescribed treatment or its potential side-effects. This is self-evident to a human, but a machine would need this to be explicitly defined otherwise. Logically, a machine would likely display bias to those groups whose historical data gave better resultant outcomes, thus perpetuating any human equality gap present in the training data

The recent review by the Committee on Standards in Public Life into AI and its ethical use by government and other public bodies concluded that there are serious deficiencies in regulation relating to the issue, although it stopped short of recommending the establishment of a new regulator.

SAS welcomed the review and contributed to it. We believe these concerns are best addressed proactively by organisations that use AI in a manner which is fair, accountable, transparent and explainable.

The review was chaired by crossbench peer Lord Jonathan Evans, who commented:

Explaining AI decisions will be the key to accountability but many have warned of the prevalence of Black Box AI. However, our review found that explainable AI is a realistic and attainable goal for the public sector, so long as government and private companies prioritise public standards when designing and building AI systems.

Todays increased presence of machine learning should be viewed as complementary to human decision-making within the public sector. Its an assistive tool that turns growing data volumes into positive outcomes for people, quickly and fairly. As the cost of computational power continues to fall, ever-increasing opportunities will emerge for machine learning to enhance public services and help transform lives.

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Continued here:
How can machine learning benefit the healthcare sector? - Open Access Government

Author: CapitalWatch Staff

Canaan Inc. (Nasdaq: CAN) became publicly traded in New York in late November. It raised $90 million in its IPO, which Canaan's founder, chairman, and chief executive officer,Nangeng Zhang modestly called "a good start." Since that time, the company has met significant milestones in its mission to disrupt the supercomputing industry.

Operating since 2013, Hangzhou-based Canaan delivers supercomputing solutions tailored to client needs. The company focuses on the research and development of artificial intelligence (AI) technology specifically, AI chips, AI algorithms, AI architectures, system on a chip (SoC) integration, and chip integration. Canaan is also known as a top manufacturer of mining hardware in China the global leader in digital currency mining.

Since IPO, Canaan has made strides in accomplishing new projects, despite the hard-hit cross-industry crisis Covid-19 has caused worldwide. In a recent announcement, Canaan said it has developed a SaaS product which its partners can use to operate a cloud mining platform. Cloud mining allows users to mine digital currency without having to buy and maintain mining hardware and spend on electricity a trend that has been gaining popularity.

A Chip of the Future

Earlier this year, Canaan participatedat the 2020 International Consumer Electronics Show in Las Vegas, the world's largest tech show that attracts innovators from across the globe. Canaan impressed, showcasing its Kendryte K210 the world's first-ever RISC-V-based edge AI chip. The chip was released in September 2018 and has been in mass-production ever since.

K210 is Canaan's first chip. The AI chip is designed to carry out machine learning. The primary functions of the K210 are machine vision and semantic, which includes the KPU for computing convolutional neural networks and an APU for processing microphone array inputs. KPU is a general-purpose neural network processor with built-in convolution, batch normalization, activation, and pooling operations. The next-generation chip can detect faces and objects in real-time. Despite the high computing power, K210 consumes only 0.3W while other typical devices consume 1W.

More Than Just Chipping Away at Sales

As of September 30, 2019, Canaan has shipped more than 53,000 AI chips and development kits to AI product developers since release.

Currently, the sales of K210 are growing exponentially, according to CEO Zhang .

The company has moved quickly to the commercialization of chips, and developed modules, products and back-end SaaS, offering customers a "full flow of AI solutions."

Based on the first generation of K210, Canaan has formed critical strategic partnerships.

For example, the company launched joint projects with a leading AI algorithm provider, a top agricultural science and technology enterprise, and a well-known global soft drink manufacturer to deliversmart solutionsfor variousindustrialmarkets.

The Booming Blockchain Industry

Currently, Canaan is working under the development strategy of "Blockchain + AI." The company has made several breakthroughs in the blockchain and AI industry, including algorithm development and optimization, standard unit design, low-voltage and high-efficiency operation, high-performance design system and heat dissipation, etc. The company has also accumulated extensive experience in ASIC chip manufacturing, laying the foundation for its future growth.

Canaan released first-generation products based on Samsung's 8nm and SMIC's 14nm technologies in Q4 last year. The former has been shipped in Q1 this year, while the latter will be shipped in Q2. In February, it launched the second generation of the product which is more efficient, more cost-effective and offers better performance.

Currently, TSMC's 5nm technology is under development. This technology will further improve the company's machines' computing power and ensure Canaan's leading position in the blockchain hardware space.

"We are the leader in the industry," says Zhang.

Canaan's Covid-19 Strategy

During the Covid-19 outbreak, Canaan improved the existing face recognition access control system. The new software can detect and identify people wearing masks. At the same time, the intelligent attendance system has been integrated to assist human resource management

Integrating mining machine learning and AI, the K210 chip has been used on Avalon mining machine, which can identify and monitor potential network viruses through intelligent algorithms. The company will explore more innovative integration in the future.

Second-Generation Gem

In terms of AI, the company will launch the second-generation AI chip K510 this year. The design of its architecture has been "greatly" optimized, and the computing power is several times more robust than the K210. Later this year, Canaan will use this tech in areas including smart energy consumption, smart industrial parks, smart driving, smart retail, and smart finance.

Canaan's Cash

In terms of operating costs and R&D, the company's last-year operating cost dropped 13.3% year-on-year. In 2018 and 2019, Canaan recorded R&D expenses of 189.7 million yuan and 169 million yuan, respectively347 million yuan were used to incentivize core R&D personnel.

In addition, the company currently has more than 500 million yuan in cash ($70.5 million), will continue to operate under the "blockchain + AI" strategy, with a continued focus on the commercialization of its AI technology.

A Fruitful Future

Canaan began as a manufacturer of Bitcoin mining machines, but it has become more than that. In the short term, the Bitcoin halving cycle is approaching (Estimated to occur on May 11, 2020 CW); this should promote the sales of company's mining machine, In the long term, now a global leader in ASIC technology, Canaan could be in a unique position to meet supercomputing demand.

"Blockchain is a good start, but we'll go beyond that," says Zhang. "When a seed grows up to be a big tree, it will bear fruit."

So far, it has done just that. Just how high that "tree" can get remains to be seen, but one thing is certain: The Kendryte K210 chip will be the driving force fueling the company's growth.

More here:
Canaan's Kendryte K210 and the Future of Machine Learning - CapitalWatch

We have all seen movies or read books about an apocalyptic world where humankind is fighting against a deadly pathogen, and researchers are in a race against time to find a cure for the same. But COVID-19 is not a fictional chapter, it is real, and scientists all over the world are frantically looking for patterns in data by employing powerful supercomputers with the hopes of finding a speedier breakthrough in vaccine discovery for the COVID-19.

A team of researchers from Penn State University has recently unearthed a solution that has the potential to expedite the process of discovering a novel coronavirus treatment that is by employing an innovative hybrid branch of research known as quantum machine learning. Quantum Machine Learning is the latest field that combines both machine learning and quantum physics. The team is led by Swaroop Ghosh, Joseph R., and Janice M. Monkowski Career Development Assistant Professor of Electrical Engineering and Computer Science and Engineering.

In cases where a computer science-driven approach is implemented to identify a cure, most methodologies leverage machine learning to focus on screening different compounds one at a time to see if they can find a bond with the virus main protease, or protein. And the quantum machine learning method could yield quicker results and is more economical than any current methods used for drug discovery.

According to Prof. Ghosh, discovering any new drug that can cure a disease is like finding a needle in a haystack. Further, it is an incredibly expensive, laborious, and time-consuming solution. Using the current conventional pipeline for discovering new drugs can take between five and ten years from the concept stage to being released to the market and could cost billions in the process.

He further adds, High-performance computing such as supercomputers and artificial intelligence canhelp accelerate this process by screeningbillions of chemical compounds quicklyto findrelevant drugcandidates.

This approach works when enough chemical compounds are available in the pipeline, but unfortunately, this is not true for COVID-19. This project will explorequantum machine learning to unlock new capabilities in drug discovery by generating complex compounds quickly, he explains.

The funding from the Penn State Institute for Computational and Data Sciences, coordinated through the Penn State Huck Institutes of the Life Sciences as part of their rapid-response seed funding for research across the University to address COVID-19, is supporting this work.

Ghosh and his electrical engineering doctoral students Mahabubul Alam and Abdullah Ash Saki and computer science and engineering postgraduate students Junde Li and Ling Qiu have earlier worked on developing a toolset for solving particular types of problems known as combinatorial optimization problems, using quantum computing. Drug discovery too comes under a similar category. And hence their experience in this sector has made it possible for the researchers to explore in the search for a COVID-19 treatment while using the same toolset that they had already developed.

Ghosh considers the usage of Artificial intelligence fordrug discovery to be a very new area. The biggest challenge is finding an unknown solution to the problem by using technologies thatare still evolving that is, quantum computing and quantum machine learning.Weare excited about the prospects of quantum computing in addressinga current critical issue and contributing our bit in resolving this grave challenge. he elaborates.

Based on a report by McKinsey & Partner, the field of quantum computing technology is expected to have a global market value of US$1 trillion by 2035. This exciting scope of quantum machine learning can further boost the economic value while helping the healthcare industry in defeating the COVID-19.

Excerpt from:
Recent Research Answers the Future of Quantum Machine Learning on COVID-19 - Analytics Insight

LONDON--(BUSINESS WIRE)--As a part of its new article series that analyzes COVID-19s impact across industries, Quantzig, a premier analytics services provider, today announced the completion of its recent article Why Online Food Delivery Companies are Betting Big on AI and Machine Learning

The article also offers comprehensive insights on:

Human activity has slowed down due to the pandemic, but its impact on business operations has not. We offer transformative analytics solutions that can help you explore new opportunities and ensure business stability to thrive in the post-crisis world. Request a FREE proposal to gauge COVID-19s impact on your business.

With machine learning, you dont need to babysit your project every step of the way. Since it means giving machines the ability to learn, it lets them make predictions and also improve the algorithms on their own, says a machine learning expert at Quantzig.

After several years of being confined to technology labs and the pages of sci-fi books, today artificial intelligence (AI) and big data have become the dominant focal point for businesses across industries. Barely a day passes by without new magazine and paper articles, blog entries, and tweets about such advancements in the field of AI and machine learning. Having said that, its not very surprising that AI and machine learning in the food and beverage industry have played a crucial role in the rapid developments that have taken place over the past few years.

Talk to us to learn how our advanced analytics capabilities combined with proprietary algorithms can support your business initiatives and help you thrive in todays competitive environment.

Benefits of AI and Machine Learning

Want comprehensive solution insights from an expert who decodes data? Youre just a click away! Request a FREE demo to discover how our seasoned analytics experts can help you.

As cognitive technologies transform the way people use online services to order food, it becomes imperative for online food delivery companies to comprehend customer needs, identify the dents, and bridge gaps by offering what has been missing in the online food delivery business. The combination of big data, AI, and machine learning is driving real innovation in the food and beverage industry. Such technologies have been proven to deliver fact-based results to online food delivery companies that possess the data and the required analytics expertise.

At Quantzig, we analyze the current business scenario using real-time dashboards to help global enterprises operate more efficiently. Our ability to help performance-driven organizations realize their strategic and operational goals within a short span using data-driven insights has helped us gain a leading edge in the analytics industry. To help businesses ensure business continuity amid the crisis, weve curated a portfolio of advanced COVID-19 impact analytics solutions that not just focus on improving profitability but help enhance stakeholder value, boost customer satisfaction, and help achieve financial objectives.

Request more information to know more about our analytics capabilities and solution offerings.

About Quantzig

Quantzig is a global analytics and advisory firm with offices in the US, UK, Canada, China, and India. For more than 15 years, we have assisted our clients across the globe with end-to-end data modeling capabilities to leverage analytics for prudent decision making. Today, our firm consists of 120+ clients, including 45 Fortune 500 companies. For more information on our engagement policies and pricing plans, visit: https://www.quantzig.com/request-for-proposal

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Quantzig Launches New Article Series on COVID-19's Impact - 'Understanding Why Online Food Delivery Companies Are Betting Big on AI and Machine...