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Category Archives: Machine Learning
Machine Learning Market Size 2020 by Top Key Players, Global Trend, Types, Applications, Regional Demand, Forecast to 2027 – LionLowdown
New Jersey, United States,- The report, titled Machine Learning Market Size By Types, Applications, Segmentation, and Growth Global Analysis and Forecast to 2019-2027 first introduced the fundamentals of Machine Learning: definitions, classifications, applications and market overview; Product specifications; Production method; Cost Structures, Raw Materials, etc. The report takes into account the impact of the novel COVID-19 pandemic on the Machine Learning market and also provides an assessment of the market definition as well as the identification of the top key manufacturers which are analyzed in-depth as opposed to the competitive landscape. In terms of Price, Sales, Capacity, Import, Export, Machine Learning Market Size, Consumption, Gross, Gross Margin, Sales, and Market Share. Quantitative analysis of the Machine Learning industry from 2019 to 2027 by region, type, application, and consumption rating by region.
Impact of COVID-19 on Machine Learning Market: The Coronavirus Recession is an economic recession that will hit the global economy in 2020 due to the COVID-19 pandemic. The pandemic could affect three main aspects of the global economy: manufacturing, supply chain, business and financial markets. The report offers a full version of the Machine Learning Market, outlining the impact of COVID-19 and the changes expected on the future prospects of the industry, taking into account political, economic, social, and technological parameters.
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In market segmentation by manufacturers, the report covers the following companies-
How to overcome obstacles for the septennial 2020-2027 using the Global Machine Learning market report?
Presently, going to the main part-outside elements. Porters five powers are the main components to be thought of while moving into new business markets. The customers get the opportunity to use the approaches to plan the field-tested strategies without any preparation for the impending monetary years.
We have faith in our services and the data we share with our esteemed customers. In this way, we have done long periods of examination and top to bottom investigation of the Global Machine Learning market to give out profound bits of knowledge about the Global Machine Learning market. Along these lines, the customers are enabled with the instruments of data (as far as raw numbers are concerned).
The graphs, diagrams and infographics are utilized to speak out about the market drifts that have formed the market. Past patterns uncover the market turbulences and the final results on the markets. Then again, the investigation of latest things uncovered the ways, the organizations must take for shaping themselves to line up with the market.
Machine Learning Market: Regional analysis includes:
?Asia-Pacific(Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia, and Australia)?Europe(Turkey, Germany, Russia UK, Italy, France, etc.)?North America(the United States, Mexico, and Canada.)?South America(Brazil etc.)?The Middle East and Africa(GCC Countries and Egypt.)
The report includes Competitors Landscape:
? Major trends and growth projections by region and country? Key winning strategies followed by the competitors? Who are the key competitors in this industry?? What shall be the potential of this industry over the forecast tenure?? What are the factors propelling the demand for the Machine Learning Industry?? What are the opportunities that shall aid in the significant proliferation of market growth?? What are the regional and country wise regulations that shall either hamper or boost the demand for Machine Learning Industry?? How has the covid-19 impacted the growth of the market?? Has the supply chain disruption caused changes in the entire value chain?
The report also covers the trade scenario,Porters Analysis,PESTLE analysis, value chain analysis, company market share, segmental analysis.
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Toward the tail end of pre-pandemic 2019, Mayo Clinic Chief Information Officer Cris Ross stood on a stage in California and declared, "This artificial intelligence stuff is real."
Indeed, while some may argue that AI and machine learning might have been harnessed better during the early days of COVID-19, and while the risk of algorithmic bias is very real, there's little question that artificial intelligence, evolving and maturing by the day for an array of use cases across healthcare.
Here are the most-read stories about AI during this most unusual year.
UK to use AI for COVID-19 vaccine side effects. On a day when vaccines, developed in record time, first begin to be administered in the U.S., it's worth remembering AI's crucial role in helping the world get to this hopefully pivotal moment.
AI algorithm IDs abnormal chest X-rays from COVID-19 patients. Machine learning has been a hugely valuable diagnostic tool as well, as illustrated by this story about a tool from cognitive computing vendor behold.ai that promises 'instant triage" based on lung scans offering faster diagnosis of COVID-19 patients and helping with resource allocation.
How AI use cases are evolving in the time of COVID-19. In a HIMSS20 Digital presentation, leaders from Google Cloud, Nuance and Health Data Analytics Institute shared perspective on how AI and automation were being deployed for pandemic response from the hunt for therapeutics and vaccines to analytics to optimize revenue cycle strategies.
Microsoft launches major $40M AI for Health initiative. The company said the the five-year AI for Health (part of its $165 million AI for Good initiative) will help healthcare organizations around the world deploy with leading edge technologies in the service of three key areas: accelerating medical research, improving worldwide understanding to protect against global health crises such as COVID-19 and reducing health inequity.
How AI and machine learning are transforming clinical decision support. "Todays digital tools only scratch the surface," said Mayo Clinic Platform President Dr. John Halamka. "Incorporating newly developed algorithms that take advantage of machine learning, neural networks, and a variety of other types of artificial intelligence can help address many of the shortcomings of human intelligence."
Clinical AI vendor Jvion unveils COVID Community Vulnerability Map. In the very early days of the pandemic, clinical AI company Jvion launched this intereactive map, which tracks the social determinants of health, helping identify populations down to the census-block level that are at risk for severe outcomes.
AI bias may worsen COVID-19 health disparities for people of color. An article in the Journal of the American Medical Informatics Association asserts that biased data models could further the disproportionate impact the COVID-19 pandemic is already having on people of color. "If not properly addressed, propagating these biases under the mantle of AI has the potential to exaggerate the health disparities faced by minority populations already bearing the highest disease burden," said researchers.
The origins of AI in healthcare, and where it can help the industry now. "The intersection of medicine and AI is really not a new concept," said Dr. Taha Kass-Hout, director of machine learning and chief medical officer at Amazon Web Services. (There were limited chatbots and other clinical applications as far back as the mid-60s.) But over the past few years, it has become ubiquitous across the healthcare ecosystem. "Today, if youre looking at PubMed, it cites over 12,000 publications with deep learning, over 50,000 machine learning," he said.
AI, telehealth could help address hospital workforce challenges. "Labor is the largest single cost for most hospitals, and the workforce is essential to the critical mission of providing life-saving care," noted a January American Hospital Association report on the administrative, financial, operational and clinical uses of artificial intelligence. "Although there are challenges, there also are opportunities to improve care, motivate and re-skill staff, and modernize processes and business models that reflect the shift toward providing the right care, at the right time, in the right setting."
AI is helping reinvent CDS, unlock COVID-19 insights at Mayo Clinic. In a HIMSS20 presentation, JohnHalamka shared some of the most promising recent clinical decision support advances at the Minnesota health system and described how they're informing treatment decisions for an array of different specialties and helping shape its understanding of COVID-19. "Imagine the power [of] an AI algorithm if you could make available every pathology slide that has ever been created in the history of the Mayo Clinic," he said. "That's something we're certainly working on."
Twitter:@MikeMiliardHITNEmail the writer:firstname.lastname@example.orgHealthcare IT News is a HIMSS publication.
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Top 10 AI and machine learning stories of 2020 - Healthcare IT News
Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy. – UroToday
Thanks to advancements in diagnosis and treatment, prostate cancer patients have high long-term survival rates. Currently, an important goal is to preserve quality of life during and after treatment. The relationship between the radiation a patient receives and the subsequent side effects he experiences is complex and difficult to model or predict. Here, we use machine learning algorithms and statistical models to explore the connection between radiation treatment and post-treatment gastro-urinary function. Since only a limited number of patient datasets are currently available, we used image flipping and curvature-based interpolation methods to generate more data to leverage transfer learning. Using interpolated and augmented data, we trained a convolutional autoencoder network to obtain near-optimal starting points for the weights. A convolutional neural network then analyzed the relationship between patient-reported quality-of-life and radiation doses to the bladder and rectum. We also used analysis of variance and logistic regression to explore organ sensitivity to radiation and to develop dosage thresholds for each organ region. Our findings show no statistically significant association between the bladder and quality-of-life scores. However, we found a statistically significant association between the radiation applied to posterior and anterior rectal regions and changes in quality of life. Finally, we estimated radiation therapy dose thresholds for each organ. Our analysis connects machine learning methods with organ sensitivity, thus providing a framework for informing cancer patient care using patient reported quality-of-life metrics.
Computers in biology and medicine. 2020 Nov 28 [Epub ahead of print]
Zhijian Yang, Daniel Olszewski, Chujun He, Giulia Pintea, Jun Lian, Tom Chou, Ronald C Chen, Blerta Shtylla
New York University, New York, NY, 10012, USA; Applied Mathematics and Computational Science Program, University of Pennsylvania, Philadelphia, PA, 19104, USA., Carroll College, Helena, MT, 59625, USA; Computer, Information Science and Engineering Department, University of Florida, Gainesville, FL, 32611, USA., Smith College, Northampton, MA, 01063, USA., Simmons University, Boston, MA, USA; Department of Psychology, Tufts University, Boston, MA, 02111, USA., Department of Radiation Oncology, The University of North Carolina, Chapel Hill, NC, 27599, USA., Depts. of Computational Medicine and Mathematics, UCLA, Los Angeles, CA, 90095-1766, USA., Department of Radiation Oncology, University of Kansas Medical Center, Kansas City, KS, 66160, USA., Department of Mathematics, Pomona College, Claremont, CA, 91711, USA; Early Clinical Development, Pfizer Worldwide Research, Development, and Medical, Pfizer Inc, San Diego, CA, 92121, USA. Electronic address: .
Machine-learning processes are invaluable at mining data for patterns in oil and gas production, but are generally limited in interpreting the information for decision-making needs.
Both a machine-learning algorithm and an engineer can predict if a bridge is going to collapse when they are given data that shows a failure might happen. Engineers can interpret the data based on their knowledge of physics, stresses and other factors, and state why they think the bridge is going to collapse. Machine-learning algorithms generally cant give an explanation of why a system would fail because they are limited in terms of interpretability based on scientific knowledge.
Since machine-learning algorithms are tremendously useful in many engineering areas, such as complex oil and gas processes, Petroleum Engineering Professor Akhil Datta-Gupta is leading Texas A&M Universitys participation in a multi-university and national laboratory project to reduce this limitation. The project began Sept. 2 and was initially funded by the U.S. Department of Energy (DOE). He and the other participants will inject science-informed decision-making into machine-learning systems, creating an advanced evaluation system that can assist with the interpretation of reservoir production processes and conditions while they happen.
Hydraulic fracturing operations are complex. Data is continually recorded during production processes so it can be evaluated and modeled to simulate what happens in a reservoir during the injection and recovery processes. However, these simulations are time-consuming to make, meaning they are not available during production and are more of a reference or learning tool for the next operation.
Enhanced by Datta-Guptas fast marching method, machine-learning systems can quickly compress data so they can render how fluid movements change in a reservoir during actual production processes.
Courtesy of Akhil Datta-Gupta
The DOE project will create an advanced system that will quickly sift data produced during hydraulic fracturing operations through physics-enhanced machine-learning algorithms, which will filter the outcomes using past observed experiences, and then render near real-time changes to reservoir conditions during oil recovery operations. These rapid visual evaluations will allow oil and gas operators to see, understand and effectively respond to real-time situations. The time advantage permits maximum production in areas that positively respond to fracturing, and stops unnecessary well drilling in areas that show limited response to fracturing.
It takes considerable effort to determine what changes occur in the reservoir, said Datta-Gupta, a University Distinguished Professor and Texas A&M Engineering Experiment Station researcher. This is why speed becomes critical. We are trying to do a near real-time analysis of the data, so engineering operations can make decisions almost on the fly.
The Texas A&M teams first step will focus on evaluating shale oil and gas field tests sponsored with DOE funding and identifying the machine-learning systems to use as the platform for the project. Next, they will upgrade these systems to merge multiple types of reservoir data, both actual and synthetic, and evaluate each system on how well it visualizes underground conditions compared to known outcomes.
At this point, Datta-Guptas research related to the fast marching method (FMM) for fluid front tracking will be added to speed up the systems visual calculations. FMM can rapidly sift through, track and compress massive amounts of data in order to transform the 3D aspect of reservoir fluid movements into a one-dimensional form. This reduction in complexity allows for the simpler, and faster, imaging.
Using known results from recovery processes in actual reservoirs, the researchers will train the system to understand changes the data inputs represent. The system will simulate everyday information, like fluid flow direction and fracture growth and interactions, and show how fast reservoir conditions change during actual production processes.
We are not the first to use machine-learning in petroleum engineering, Datta-Gupta said. But we are pioneering this enhancement, which is not like the usual input-output relationship. We want complex answers, ones we can interpret to get insights and predictions without compromising speed or production time. I find this very exciting.
Swami Sivasubramanian, vice-president of machine learning at AWS, spoke about the five tenets of innovation that AWS strives towards while announcing new machine learning tools, during AWS re:Invent
AWS vice-president of machine learning, Swami Sivasubramanian, announced new machine learning capabilities during re:Invent
As machine learning disrupts more and more industries, it has demonstrated its potential to reduce time spent by employees on manual tasks. However, training machine learning models can take months to achieve, creating excessive costs.
With this in mind, AWS vice-president of machine learning, Swami Sivasubramanian used his keynote speech at AWS re:Invent to announce new tools that aim to speed up operations and save costs. Sivasubramanian went through five tenets for machine learning that AWS observes, which acted as vessels for further explanations of use cases for the new tools.
Firstly, Sivasubramanian explained the importance of providing firm foundations, vital for freedom of creativity. The technology has provided foundations for autonomous vehicles and robotic communication, among other budding spaces. One drawback of machine learning, however, is that a single framework is yet to be established for all practitioners, with Tensorflow, Pytorch and Mxnet being the main three.
AWS SageMaker, the cloud service providers machine learning service, has been able to speed up training processes. During the keynote, availability of faster distribution training on Amazon SageMaker was announced, which is predicted to complete training up to 40% faster than before and can allow for completion in the space of a few hours.
This article explores the ways in which Kubernetes enhances the use of machine learning (ML) within the enterprise. Read here
From preparing and optimising data and algorithms to training and deployment, machine learning training can be time-consuming and costly. AWS released SageMaker in 2017 to break down barriers for budding data engineers.
Following its predecessor, SageMaker, Data Wrangler was launched during re:Invent to accelerate data preparation, which commonly takes up most of the time spent on training machine learning algorithms. This tool allows for the preparation of data from multiple sources without the need to write code. With more than 300 data transformations, Data Wrangler can cut the time taken to aggregate and prepare data from weeks to minutes.
To then make it even easier for builders to reach their project goals in the quickest time possible, the Sagemaker Feature Store was launched, which allows features to stay in sync with each other and aggregate data faster.
Sagemaker Pipelines is another new tool which allows developers to leverage end-to-end continuous integration and delivery.
There is also a need to understand and eradicate biases, and in response to this, AWS announced Sagemaker Clarify. This tool works in four steps; by detecting bias during analyses with algorithms before delivering a report which allows steps to be taken; models are checked for unbalanced data, and once deployed, a report is given for each input for prediction, which helps to provide information to customers. Bias detection can be carried out over time, with notifications being given if any bias is found.
As artificial intelligence becomes more prevalent throughout business and society, companies need to be mindful of human bias creeping into their machine models. Richard Downs, UK director at Applause discusses how businesses can use the wisdom of crowds to source the diverse set of data and inputs needed to train algorithms. Read here
John Loughlin, chief technologist in data and analytics at Cloudreach, said: The Clarify product really caught my eye, because bias is an important problem that we need to address, so that people maintain their trust in these kinds of technology. We dont want adoption to be impeded because models arent doing what theyre supposed to.
Also announced during the keynote was deep profiling for Sagemaker Debugger, which allows builders to monitor performance in order to move the training process along faster.
With the aim of making machine learning accessible to as many builders as possible, SageMaker Autopilot was introduced last year to provide recommendations on the best models for any project. The tool features added visibility, showing users how models are built, and ranking models using a leaderboard, before one is decided on.
Integration of this kind of technology for databases, data warehouses, data lakes and business intelligence (BI) tools were referred to as future frontiers that customers have been demanding, and machine learning tools were announced for Redshift and Neptune during the keynote. While capabilities for Redshift make it possible to get predictions for data warehouses starting from a SQL query, ML for Neptune can make predictions for connected datasets without the need for prior experience in using the technology.
Brad Campbell, chief technologist in platform development at Cloudreach, said: What stands out when I look at ML for Redshift is that what you have in Redshift, which you dont get in other data sources, is the true composite of your businesss end-to-end value chain in one place.
Typically when Ive worked in Redshift, there was a lot of ETL work to be done, but with ML, this can really unlock value for people who have all this end-to-end value chain data coalesced in a data warehouse.
Another recently launched tool, Amazon Quicksight ML, provides stories of data dashboards in natural language, cutting the time spent on gaining business intelligence information from days or weeks to seconds. The tool takes into consideration the different terms that various departments within an organisation may use, meaning that the tool can be used by any member of staff, regardless of the department they work in.
Kevin Davis, cloud strategist at Cloudreach, said: There is another push in this area to lower the bar of entry for ML consumption in the business space. There is a broadening of scope for people who can implement these services, and a lot of horizontal integration for ML capabilities, along with some deep vertical implementation capabilities.
Yair Green, CTO at GlobalDots, explains how artificial intelligence and machine learning changed the Software-as-a-Service industry. Read here
Without considering problems that the business needs to solve, no project can be truly successful. According to Sivasubramanian, any good machine learning problem to focus on is rich in data, impacts the business, but cant be solved using traditional methods.
AI-powered tools such as Code Guru, DevOps Guru, Connect and Kendra from AWS allow staff to quickly solve business problems that arise within DevOps, call centres and intelligent search services, which can range from performance issues to customer complaints.
During the keynote, the launch of Amazon Lookout for Metrics was announced, which will allow developers to find anomalies within their machine learning models, with the tool ranking them according to severity. This ensures that models are working as they should be.
The caveat I have around Lookout for Metrics is that its clearly directed, and intended to look at the most common business insights, said Davis.
In terms of generally lowering the bar of entry, you can potentially put this in the hands of business analysts that are familiar enough with SQL queries, and allow them to directly pull insights or anomalies from business data stores.
For the healthcare sector, AWS also announced the launch of Amazon Healthlake, which provides an analysis of patient data that would otherwise be difficult to make conclusions on due to its usually unstructured nature.
Commenting on the release of Amazon Healthlake, Samir Luheshi, chief technologist in application modernisation at Cloudreach, said: Healthlake stands out as very interesting. There are a lot of challenges around managing HIPAA and EU GDPR, and its not an easy lift, so Id be interested to see how extra layers can be applied to this to make it suitable for consumption in Europe.
Andrew Pellegrino, director of intelligent automation at DataRobot, analyses RPA and the rise of intelligent automation in healthcare. Read here
Just as algorithms need to be learned so that tasks can be automated effectively, the final tenet of ML discussed by Sivasubramanian calls for companies that deploy machine learning to encourage their engineers to continuously learn new skills and technologies, if they arent doing so already.
AWS has been looking to educate the next generation of builders through its own Machine Learning University, which offers solution-based machine learning training and certification, and where budding builders can learn from AWS practitioners. Learners can also develop skills specific to a particular job role, such as a cloud architect or cloud developer.
Furthermore, AWS DeepRacer, the cloud service providers 3D racing simulator, allows developers of any skill level to learn the essentials of reinforcement learning, and submit models in an aim to win races. The decision making of models can be evaluated with the aid of a 1/18th scale car thats driven by machine learning.
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How AWS's five tenets of innovation lend themselves to machine learning - Information Age
Biology 2.0: Combining machine-learning, robotics and biology to deliver drug discovery of tomorrow
Intelligent OMICS, Arctoris and Medicines Discovery Catapult test in silico pipeline for identifying new molecules for cancer treatment.
Medicines discovery innovators, Intelligent OMICS, supported by Arctoris and Medicines Discovery Catapult, are applying artificial intelligence to find new disease drivers and candidate drugs for lung cancer. This collaboration, backed by Innovate UK, will de-risk future R&D projects and also demonstrate new cost and time-saving approaches to drug discovery.
Analysing a broad set of existing biological information, previously hidden components of disease biology can be identified which in turn lead to the identification of new drugs for development. This provides the catalyst for an AI-driven acceleration in drug discovery and the team has just won a significant Innovate UK grant in order to prove that it works.
Intelligent OMICS, the company leading the project, use in silico (computer-based) tools to find alternative druggable targets. They have already completed a successful analysis of cellular signalling pathways elsewhere in lung cancer pathways and are now selectively targeting the KRAS signalling pathway.
As Intelligent OMICS technology identifies novel biological mechanisms, Medicines Discovery Catapult will explore the appropriate chemical tools and leads that can be used against these new targets, and Arctoris will use their automated drug discovery platform in Oxford to conduct the biological assays which will validate them experimentally.
Working together, the group will provide druggable chemistry against the entire in silico pipeline, offering new benchmarks of cost and time effectiveness over conventional methods of discovery.
Much has been written about the wonders of artificial intelligence and its potential in healthcare, says Dr Simon Haworth, CEO of Intelligent OMICS. Our newsflows are full of details of AI applications in process automation, image analysis and computational chemistry. The DeepMind protein folding breakthrough has also hit the headlines recently as a further AI application. But what does Intelligent OMICS do that is different?
By analysing transcriptomic and similar molecular data our neural networks algorithms re-model known pathways and identify new, important targets. This enables us to develop and own a broad stream of new drugs. Lung cancer is just the start we have parallel programs running in many other areas of cancer, in infectious diseases, in auto-immune disease, in Alzheimers and elsewhere.
We have to thank Innovate UK for backing this important work. The independent validation of our methodology by the highly respected cheminformatics team at MDC coupled with the extraordinarily rapid, wet lab validation provided by Arctoris, will finally prove that, in drug discovery, the era of AI has arrived.
Dr Martin-Immanuel Bittner, Chief Executive Officer of Arctoris commented:
We are thrilled to combine our strengths in robotics-powered drug discovery assay development and execution with the expertise in machine learning that Intelligent OMICS and Medicines Discovery Catapult possess. This unique setup demonstrates the next stage in drug discovery evolution, which is based on high quality datasets and machine intelligence. Together, we will be able to rapidly identify and validate novel targets, leading to promising new drug discovery programmes that will ultimately benefit patients worldwide.
Prof. John P. Overington, Chief Informatics Officer at Medicines Discovery Catapult:
Computational based approaches allow us to explore a top-down approach to identifying novel biological mechanisms of disease, which critically can be validated by selecting the most appropriate chemical modulators and assessing their effects in cellular assay technologies.
Working with Intelligent OMICS and with support from Arctoris we are delighted to play our part in laying the groundwork for computer-augmented, automated drug discovery. Should these methods indeed prove fruitful, it will be transformative for both our industry and patients alike.
If this validation is successful, the partners will have established a unique pipeline of promising new targets and compounds for a specific pathway in lung cancer. But more than that they will also have validated an entirely new drug discovery approach which can then be further scaled to other pathways and diseases.