Cloud Hosting

EOSJul 01, 2020 14:54:08 IST

On land, its easy for us to see divisions between ecosystems: A rain forests fan palms and vines stand in stark relief to the cacti of a high desert. Without detailed data or scientific measurements, we can tell a distinct difference in the ecosystems flora and fauna.

But how do scientists draw those divisions in the ocean? A new paper proposes a tool to redraw the lines that define an oceans ecosystems, lines originally penned by the seagoing oceanographerAlan Longhurstin the 1990s. The paper uses unsupervised learning, a machine learning method, to analyze the complex interplay between plankton species and nutrient fluxes. As a result, the tool could give researchers a more flexible definition of ecosystem regions.

Using the tool on global modeling output suggests that the oceans surface has more than 100 different regions or as few as 12 if aggregated, simplifying the56 Longhurst regions. The research could complement ongoing efforts to improve fisheries management and satellite detection of shifting plankton under climate change. It could also direct researchers to more precise locations for field sampling.

A sea turtle in the aqua blue waters of Hawaii. Image: Rohit Tandon/Unsplash

Coccolithophores, diatoms, zooplankton, and other planktonic life-formsfloaton much of the oceans sunlit surface. Scientists monitor plankton with long-term sampling stations and peer at their colorsby satellitefrom above, but they dont have detailed maps of where plankton lives worldwide.

Models help fill the gaps in scientists knowledge, and the latest research relies on an ocean model to simulate where 51 types of plankton amass on the surface oceans worldwide. The latest research then applies the new classification tool, called the systematic aggregated ecoprovince (SAGE) method, to discern where neighborhoods of like-minded plankton and nutrients appear.

SAGE relies, in part, on a type of machine learning algorithm called unsupervised learning. The algorithms strength is that it searches for patterns unprompted by researchers.

To compare the tool to a simple example, if scientists told an algorithm to identify shapes in photographs like circles and squares, the researchers could supervise the process by telling the computer what a square and circle looked like before it began. But in unsupervised learning, the algorithm has no prior knowledge of shapes and will sift through many images to identify patterns of similar shapes itself.

Using an unsupervised approach gives SAGE the freedom to let patterns emerge that the scientists might not otherwise see.

While my human eyes cant see these different regions that stand out, the machine can, first author and physical oceanographerMaike Sonnewaldat Princeton University said. And thats where the power of this method comes in. This method could be used more broadly by geoscientists in other fields to make sense of nonlinear data, said Sonnewald.

A machine-learning technique developed at MIT combs through global ocean data to find commonalities between marine locations, based on how phytoplankton species interact with each other. Using this approach, researchers have determined that the ocean can be split into over 100 types of provinces, and 12 megaprovinces, that are distinct in their ecological makeup.

Applying SAGE to model data, the tool noted 115 distinct ecological provinces, which can then be boiled down into 12 overarching regions.

One region appears in the center of nutrient-poor ocean gyres, whereas other regions show productive ecosystems along the coast and equator.

You have regions that are kind of like the regions youd see on land, Sonnewald said.One area in the heart of a desert-like region of the ocean is characterized by very small cells. Theres just not a lot of plankton biomass. The region that includes Perus fertile coast, however, has a huge amount of stuff.

If scientists want more distinctions between communities, they can adjust the tool to see the full 115 regions. But having only 12 regions can be powerful too, said Sonnewald, because it demonstrates the similarities between the different [ocean] basins. The tool was published in arecent paperin the journalScience Advances.

OceanographerFrancois Ribaletat the University of Washington, who was not involved in the study, hopes to apply the tool to field data when he takes measurements on research cruises. He said identifying unique provinces gives scientists a hint of how ecosystems could react to changing ocean conditions.

If we identify that an organism is very sensitive to temperature, so then we can start to actually make some predictions, Ribalet said. Using the tool will help him tease out an ecosystems key drivers and how it may react to future ocean warming.

Jenessa Duncombe.Text 2020. AGU.

This story has been republished from Eosunder the Creative Commons 3.0 license.Read theoriginal story.

Find latest and upcoming tech gadgets online on Tech2 Gadgets. Get technology news, gadgets reviews & ratings. Popular gadgets including laptop, tablet and mobile specifications, features, prices, comparison.

Read more:
Machine learning finds use in creating sharper maps of 'ecosystem' lines in the ocean - Firstpost

JACKSONVILLE, Fla., July 1, 2020 /PRNewswire-PRWeb/ --The Decisions no-code workflow and rules platform was designed to enable businesses to automate and optimize their digital processes but do so in a way that is able to be done by non-programming staff. NLP Logix was founded with the mission to bring the power of machine learning to industry by becoming its customers outsourced data science team. With the combination of the Decisions platform and NLP Logix machine learning tools and team, the ability to quickly and affordably integrate artificial intelligence to workflows is now here.

"We were brought in to automate a number of financial processes for a very large non-profit," said Matt Berseth, Lead Data Scientist for NLP Logix. "They had already deployed the Decisions platform to automate their workflows and we were able to easily embed a number of machine learning models, one of which reviewed and approved financial applications, and the efficiency gains have been amazing."

A great example of the power of the new Partnership between Decisions and NLP Logix, is the loan origination process, which is almost entirely driven by rules and workflow and any human interactions are repetitive decisions based on experience. The Decisions platform automates the gathering and review of the loan application, while the machine learning models, which have been trained using years of application approval decisions by trained humans, make a final approval recommendation.

"After working with NLP Logix, we quickly realized that the addition of a trained data science team which can train and deploy machine learning models very quickly, accurately and at scale, was a very valuable addition to the Decisions platform" said Athena Harrell. "And to have a partner like NLP Logix that has the talent and team that can also implement the Decisions solution is icing on the cake."

About Decisions

Decisions is a leading provider of Business Process Management/Workflow/Rule Technology and is headquartered in Chesapeake, VA. Decisions technology is deployed as the basis of multiple commercial applications in medical, finance, logistics and operations software. In addition, Decisions technology is used directly by companies on almost all continents, ranging from small/mid-size companies to over a dozen Fortune 500. For more information go to http://www.decisions.com

About NLP Logix

NLP Logix is an artificial intelligence/machine learning product and automation solutions provider, which has evolved over the last nine years to one of the fastest growing teams of machine learning practitioners. Our team of experts have extensive experience leveraging natural language processing, computer vision, neural networks, and predictive modeling to help companies revolutionize how they operate. NLP Logix delivers automation and machine learning solutions to customers across a wide swath of industries, including financial services, energy, healthcare, government, human resources, and many more.

SOURCE NLP Logix

Original post:
Decisions and NLP Logix Announce Partnership to bring the Power of Machine Learning to Business Process Management - Benzinga

Prophecy Market Insights recently presented Machine Learning in Medical Imaging market report which provides reliable and sincere insights related to the various segments and sub-segments of the market. The market study throws light on the various factors that are projected to impact the overall dynamics of the Machine Learning in Medical Imaging market over the forecast period (2019-2029).

The Machine Learning in Medical Imaging research study contains 100+ market data Tables, Pie Chat, Graphs & Figures spread through Pages and easy to understand detailed analysis. This Machine Learning in Medical Imaging market research report estimates the size of the market concerning the information on key retailer revenues, development of the industry by upstream and downstream, industry progress, key highlights related to companies, along with market segments and application. This study also analyzes the market status, market share, growth rate, sales volume, future trends, market drivers, market restraints, revenue generation, opportunities and challenges, risks and entry barriers, sales channels, and distributors.

Get Sample Copy of This Report @ https://www.prophecymarketinsights.com/market_insight/Insight/request-sample/3599

Global Machine Learning in Medical Imaging market 2020-2030 in-depth study accumulated to supply latest insights concerning acute options. The report contains different predictions associated with Machine Learning in Medical Imaging market size, revenue, CAGR, consumption, profit margin, price, and different substantial factors. Along with a detailed manufacturing and production analysis, the report also includes the consumption statistics of the industry to inform about Machine Learning in Medical Imaging market share. The value and consumption analysis comprised in the report helps businesses in determining which strategy will be most helpful in expanding their Machine Learning in Medical Imaging market size. Information about Machine Learning in Medical Imaging market traders and distributors, their contact information, import/export and trade analysis, price analysis and comparison is also provided by the report. In addition, the key company profiles/players related with Machine Learning in Medical Imaging industry are profiled in the research report.

The Machine Learning in Medical Imaging market is covered with segment analysis and PEST analysis for the market. PEST analysis provides information on a political, economic, social and technological perspective of the macro-environment from Machine Learning in Medical Imaging market perspective that helps market players understand the factor which can affect businesss accomplishments and performance-related with the particular market segment.

Segmentation Overview:

By Type (Supervised Learning, Unsupervised Learning, Semi Supervised Learning, and Reinforced Leaning)

By Application (Breast, Lung, Neurology, Cardiovascular, Liver, and Others)

By Region (North America, Europe, Asia Pacific, Latin America, and Middle East & Africa)

Competitive landscape of the Machine Learning in Medical Imaging market is given presenting detailed insights into the company profiles including developments such as merges & acquisitions, collaborations, partnerships, new production, expansions, and SWOT analysis.

Machine Learning in Medical Imaging Market Key Players:

The research scope provides comprehensive market size, and other in-depth market information details such as market growth-supporting factors, restraining factors, trends, opportunities, market risk factors, market competition, product and services, product advancements and up-gradations, regulations overview, strategy analysis, and recent developments for the mentioned forecast period.

The report analyzes various geographical regions like North America, Europe, Asia-Pacific, Latin America, Middle East, and Africa and incorporates clear market definitions, arrangements, producing forms, cost structures, improvement approaches, and plans. Besides, the report provides a key examination of regional market players operating in the specific market and analysis and outcomes related to the target market for more than 20 countries.

Request Discount @ https://www.prophecymarketinsights.com/market_insight/Insight/request-discount/3599

The report responds to significant inquires while working on Global Machine Learning in Medical Imaging Market. Some important Questions Answered in Machine Learning in Medical Imaging Market Report are:

Contact Us:

Mr. Alex (Sales Manager)

Prophecy Market Insights

Phone: +1 860 531 2701

Email: [emailprotected]

Read more:
Machine Learning in Medical Imaging Market Strategies and Insight Driven Transformation 2020-2030 - Cole of Duty

As artificial technology gains traction in the enterprise, many on the business side remain fuzzy on AI techniques and how they can be applied to drive business value. Machine Learning and deep learning, for example, are two AI techniques that are often conflated. But machine learning can involve a wide variety of techniques for building analytics models or decision engines that don't involve neural networks, the mechanism for deep learning. And there is a whole range of AI techniques outside of machine learning as well that can be applied to solve business problems.

Business managers who recognize these distinctions will have a greater understanding of the business value of AI and be better prepared to have productive conversations with data scientists, data engineers, end users and executives about what's feasible and what's required. These distinctions can also guide discussions about the best way to implement AI applications.

Without a solid understanding of the various aspects and aims of AI techniques, businesses run the risk of not using AI to drive business value, experts in the field said.

Sanmay Das and Nicholas Mattei, chair and vice chair respectively of the Association for Computing Machinery's Special Interest Group on Artificial Intelligence think one of the biggest blind spots is failing to see machine learning as one component of AI.

"Some can argue with this characterization, but we think that it loses sight of so much more that is encompassed in the goal of AI, which is to build intelligent agents," Das and Mattei told TechTarget.

Focusing only on the learning aspect of machine learning loses sight of how learning fits into a larger AI loop of perception, reasoning, planning and action. This larger framework can guide managers in understanding how all these areas can be mixed and combined to create intelligent applications.

Even when people are specifically talking about machine learning, they are typically describing supervised learning problems. Das, an associate professor of computer science and engineering at Washington University in St. Louis, and Mattei, an assistant professor of computer science at Tulane University, argued that this narrow view of machine learning techniques leaves out many advances in unsupervised machine learning and reinforcement learning problems that can drive business value.

Managers often discover machine learning as a byproduct of the success of deep learning. Juan Jos Lpez Murphy, an AI and big data tech director lead at Globant, an IT consultancy, said the positive side of this trend is that it opens people up to considering how they might apply machine learning to their business. "The money might not always be where the mouth is, but now there's an ear to that mouth," he said.

The downside is that people conflate neural networks with all of machine learning. As a result, he said he hears managers asking questions like "Which deep learning framework are you using?" which is never the relevant aspect of machine learning for a given application.

This confusion also tends to encourage people to focus on AI's "it" technologies, like computer vision and natural language processing. These kinds of applications, while advanced and exciting, are more complex to develop and may not provide as much immediate business value. In many cases, more classical machine learning approaches to tasks -- such as forecasts, churn prediction, risk scoring and optimization -- are better suited to solving business problems.

It is important for business managers to know which AI and machine learning techniques to deploy for which business problems.

For AI implementations requiring transparency and explainability, companies may want to stay away from deep learning techniques, which can result in so-called black box algorithms that are difficult for humans to understand. In these cases, Globant's Lopez Murphy finds clients turning to decision trees or logistic regression algorithms for explicitly reporting the impact of a variable.

Recommender engines, employed to great effect by online giants Netflix and Amazon, are used not only to sell the next item or recommend a movie, but also for internal applications and reports that people look at in their jobs. These applications and reports can be tackled with neural networks, but there are many more suitable approaches, Lopez Murphy said. Forecast models are used to derive confidence intervals that will enable short-term planning or to detect a sudden change in behavior, like outliers or changes to a trend.

"Many of these techniques [e.g., recommender systems and decision trees] have been available and used before deep learning, but are as relevant today, if not more so than they were before," Lopez Murphy said. These types of applications are also able to take advantage of data that is generally more available, curated and relevant than what is required to build deep learning applications.

Debu Chatterjee, senior director of platform AI engineering at ServiceNow, said the IT services software company regularly uses a variety of machine learning capabilities outside of deep learning to drive business value from AI, including classification, identifying similarity between things, clustering, forecasting and recommendations. For example, in service management, incoming tickets are initially read and routed by humans who decide which team is best suited to work on them. Machine learning models trained from these results can automatically route tickets to the best qualified groups for resolution without human intervention. This type of application uses classic supervised machine learning techniques like logistic regression to generate a working model that provides this decision support for optimized work routing.

ServiceNow also uses machine learning for pattern recognition. During a major event, many people call the service organization, but each IT fulfiller only sees one incident at a time, making it nearly impossible to manually recognize the overall pattern. Chatterjee said clustering techniques using machine learning can recognize the overall patterns to identify a major incident automatically, which can help to reduce the overall time to resolve incidents and events.

A wide variety of machine learning algorithms use unsupervised learning, an approach where the training data has not been labeled beforehand. Muddu Sudhakar, CEO of Aisera, a predictive AI service management provider, said that supervised learning models are highly accurate and trustworthy, but they require extensive datasets for training to achieve that high level of accuracy. Conversely, unsupervised learning models are less accurate and trustworthy, but learning takes place in real time without the need of any training data.

The most popular applications of unsupervised learning techniques cluster data into self-organizing maps. Another family of popular unsupervised techniques helps to discover the relationships among objects extracted from the data. Sudhakar said these techniques are popular for market-basket or associative data analysis personalization (i.e., users who buy X and Y products are more likely to buy Z) and recommendation systems for browsing webpages.

Read the original here:
Learn the business value of AI's various techniques - TechTarget

Machine learning has become a disruptive trend in the technology industry with computers learning to accomplish tasks without being explicitly programmed. The manufacturing industry is relatively new to the concept of machine learning. Machine learning is well aligned to deal with the complexities of the manufacturing industry. Manufacturers can improve their product quality, ensure supply chain efficiency, reduce time to market, fulfil reliability standards, and thus, enhance their customer base through the application of machine learning. Machine learning algorithms offer predictive insights at every stage of the production, which can ensure efficiency and accuracy. Problems that earlier took months to be addressed are now being resolved quickly. The predictive failure of equipment is the biggest use case of machine learning in manufacturing. The predictions can be utilized to create predictive maintenance to be done by the service technicians. Certain algorithms can even predict the type of failure that may occur so that correct replacement parts and tools can be brought by the technician for the job.

Get Access to sample pages:https://www.trendsmarketresearch.com/report/sample/9906

Market Analysis

According to Infoholic Research, Machine Learning as a Service (MLaaS) Market will witness a CAGR of 49% during the forecast period 20172023. The market is propelled by certain growth drivers such as the increased application of advanced analytics in manufacturing, high volume of structured and unstructured data, the integration of machine learning with big data and other technologies, the rising importance of predictive and preventive maintenance, and so on. The market growth is curbed to a certain extent by restraining factors such as implementation challenges, the dearth of skilled data scientists, and data inaccessibility and security concerns to name a few.

Segmentation by Components

The market has been analyzed and segmented by the following components Software Tools, Cloud and Web-based Application Programming Interface (APIs), and Others.

Segmentation by End-users

The market has been analyzed and segmented by the following end-users, namely process industries and discrete industries. The application of machine learning is much higher in discrete than in process industries.

Segmentation by Deployment Mode

The market has been analyzed and segmented by the following deployment mode, namely public and private.

Regional Analysis

The market has been analyzed by the following regions as Americas, Europe, APAC, and MEA. The Americas holds the largest market share followed by Europe and APAC. The Americas is experiencing a high adoption rate of machine learning in manufacturing processes. The demand for enterprise mobility and cloud-based solutions is high in the Americas. The manufacturing sector is a major contributor to the GDP of the European countries and is witnessing AI driven transformation. Chinas dominant manufacturing industry is extensively applying machine learning techniques. China, India, Japan, and South Korea are investing significantly on AI and machine learning. MEA is also following a high growth trajectory.

Vendor Analysis

Some of the key players in the market are Microsoft, Amazon Web Services, Google, Inc., and IBM Corporation. The report also includes watchlist companies such as BigML Inc., Sight Machine, Eigen Innovations Inc., Seldon Technologies Ltd., and Citrine Informatics Inc.

Benefits

The study covers and analyzes the Global MLaaS Market in the manufacturing context. Bringing out the complete key insights of the industry, the report aims to provide an opportunity for players to understand the latest trends, current market scenario, government initiatives, and technologies related to the market. In addition, it helps the venture capitalists in understanding the companies better and take informed decisions.

Read more here:
Machine Learning As A Service In Manufacturing Market Augmented Expansion to Be Registered by 2018-2023 - 3rd Watch News