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Category Archives: Machine Learning
For all the potential benefits of artificial intelligence and machine learning, one of the biggest and, increasingly most publicized challenges with the technology is the potential for algorithmic bias.
But an even more basic challenge for hospitals and health systems looking to deploy AI and ML can be the skepticism from frontline staff a hesitance to use predictive models that, even if they aren't inherently biased, are certainly hard to understand.
At Delaware-based Christiana Care Health System, the past few years have seen efforts to "simplify the model without sacrificing precision," says Dr. Terri Steinberg, its chief health information officer and VP of population health informatics.
"The simpler the model, the more human beings will accept it," said Steinberg, who will talk more about this notion in a March 12 presentation at HIMSS20.
When it comes to pop health programs, the data sets used to drive the analytics matter, she explains. Whether it's EHR data, social determinants of health, claims data or even wearables information, it's key to select the most relevant data sources, use machine learning to segment the population and then, crucially, present those findings to care managers in a way that's understandable and fits their workflow.
At HIMSS20, Steinberg, alongside Health Catalyst Chief Data Scientist Jason Jones, will show how Christiana Care has been working to streamline its machine learning processes, to ensure they're more approachable and thus more liable to be embraced by its care teams.
Dr. Terri Steinberg, Christiana Care Health System
They'll explain how to assign relative value to pop health data and discuss some of the challenges associated with integrating them; they'll show how ML can segment populations and spotlight strategies for using new data sources that will boost the value and utility of predictive models.
"We've been doing this since 2012," said Steinberg. And now we have significant time under our belts, so we wanted to come back to HIMSS and talk about what we were doing in terms of programming for care management and, more important, how we're segmenting our population with machine learning."
"There are a couple of patterns that we've seen repeated across engagements that are a little bit counter to how people typically go about building these models today, which is to sort of throw everything at them and hope for the best," said Jones, of Health Catalyst, Christiana Care's vendor partner.
At Christiana Care, he said, the goal instead has been to "help people understand as much as they would like about how the models are working, so that they will trust and actually use them.
"We've found repeatedly that we can build technically fantastic models that people just don't trust and won't use," he added. "In that case, we might as well not bother in the first place. So we're going to go through and show how it is that we can build models in such a way that they're technically excellent but also well-trusted by the people who are going to use them."
In years past, "when we built the model and put it in front of our care managers and said, 'Here you go, now customize your treatment plans based on the risk score,' what we discovered is that they basically ignored the score and did what they wanted," Steinberg explained.
But by simplifying a given model to the "smallest number of participants and data elements that can be," that enables the development of something "small enough for people to understand the list of components, so that they think that they know why the model has made a specific prediction," she said.
That has more value than many population health professionals realize.
"The goal is to simplify the model as much as you can, so human beings understand the components," said Steinberg.
"People like understanding why a particular individual falls into a risk category," she said. "And then they sometimes would even like to know what the feature is that has resulted in the risk. The take home message is that the more human beings understand what the machine is doing, the more likely they are to trust the machine. We want to personalize the black box."
Steinberg and Jones will talk more about making machine learning meaningful at a HIMSS20 session titled "Machine Learning and Data Selection for Population Health." It's scheduled for Thursday, March 12, from 10-11 a.m. in room W414A.
Seton Hall Announces New Courses in Text Mining and Machine Learning – Seton Hall University News & Events
Professor Manfred Minimair, Data Science, Seton Hall University
As part of its online M.S. in Data Science program, Seton Hall University in South Orange, New Jersey, has announced new courses in Text Mining and Machine Learning.
Seton Hall's master's program in Data Science is the first 100% online program of its kind in New Jersey and one of very few in the nation.
Quickly emerging as a critical field in a variety of industries, data science encompasses activities ranging from collecting raw data and processing and extracting knowledge from that data, to effectively communicating those findings to assist in decision making and implementing solutions. Data scientists have extensive knowledge in the overlapping realms of business needs, domain knowledge, analytics, and software and systems engineering.
"We're in the midst of a pivotal moment in history," said Professor Manfred Minimair, director of Seton Hall's Data Science program. "We've moved from being an agrarian society through to the industrial revolution and now squarely into the age of information," he noted. "The last decade has been witness to a veritable explosion in data informatics. Where once business could only look at dribs and drabs of customer and logistics dataas through a glass darklynow organizations can be easily blinded by the sheer volume of data available at any given moment. Data science gives students the tools necessary to collect and turn those oceans of data into clear and readily actionable information."
These tools will be provided by Seton Hall in new ways this spring, when Text Mining and Machine Learning make their debut.
Text MiningTaught by Professor Nathan Kahl, text mining is the process of extracting high-quality information from text, which is typically done by developing patterns and trends through means such as statistical pattern learning. Professor Nathan Kahl is an Associate Professor in the Department of Mathematics and Computer Science. He has extensive experience in teaching data analytics at Seton Hall University. Some of his recent research lies in the area of network analysis, another important topic which is also taught in the M.S. program.
Professor Kahl notes, "The need for people with these skills in business, industry and government service has never been greater, and our curriculum is specifically designed to prepare our students for these careers." According to EAB (formerly known as the Education Advisory Board), the national growth in demand for data science practitioners over the last two years alone was 252%. According to Glassdoor, the median base salary for these jobs is $108,000.
Machine LearningIn many ways, machine learning represents the next wave in data science. It is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. The course will be taught by Sophine Clachar, a data engineer with more than 10 years of experience. Her past research has focused on aviation safety and large-scale and complex aviation data repositories at the University of North Dakota. She was also a recipient of the Airport Cooperative Research Program Graduate Research Award, which fostered the development of machine learning algorithms that identify anomalies in aircraft data.
"Machine learning is profoundly changing our society," Professor Clachar remarks. "Software enhanced with artificial intelligence capabilities will benefit humans in many ways, for example, by helping design more efficient treatments for complex diseases and improve flight training to make air travel more secure."
Active Relationships with Google, Facebook, Celgene, Comcast, Chase, B&N and AmazonStudents in the Data Science program, with its strong focus on computer science, statistics and applied mathematics, learn skills in cloud computing technology and Tableau, which allows them to pursue certification in Amazon Web Services and Tableau. The material is continuously updated to deliver the latest skills in artificial intelligence/machine learning for automating data science tasks. Their education is bolstered by real world projects and internships, made possible through the program's active relationships with such leading companies as Google, Facebook, Celgene, Comcast, Chase, Barnes and Noble and Amazon. The program also fosters relationships with businesses and organizations through its advisory board, which includes members from WarnerMedia, Highstep Technologies, Snowflake Computing, Compass and Celgene. As a result, students are immersed in the knowledge and competencies required to become successful data science and analytics professionals.
"Among the members of our Advisory Board are Seton Hall graduates and leaders in the field," said Minimair. "Their expertise at the cutting edge of industry is reflected within our curriculum and coupled with the data science and academic expertise of our professors. That combination will allow our students to flourish in the world of data science and informatics."
Learn more about the M.S. in Data Science at Seton Hall
In 1950, Alan Turing developed the Turing test to answer the question can machines think? Since then, machine learning has gone from being just a concept, to a process relied on by some of the worlds biggest companies. Here Sophie Hand, UK country manager at industrial parts supplier EU Automation, discusses the applications of the different types of machine learning that exist today.
Machine learning is a subset of artificial intelligence (AI) where computers independently learn to do something they were not explicitly programmed to do. They do this by learning from experience leveraging algorithms and discovering patterns and insights from data. This means machines dont need to be programmed to perform exact tasks on a repetitive basis.
Machine learning is rapidly being adopted across several industries according to Research and Markets, the market is predicted to grow to US$8.81 billion by 2022, at a compound annual growth rate of 44.1 per cent. One of the main reasons for its growing use is that businesses are collecting Big Data, from which they need to obtain valuable insights. Machine learning is an efficient way of making sense of this data, for example the data sensors collect on the condition of machines on the factory floor.
As the market develops and grows, new types of machine learning will emerge and allow new applications to be explored. However, many examples of current machine learning applications fall into two categories; supervised learning and unsupervised learning.
A popular type of machine learning is supervised learning, which is typically used in applications where historical data is used to develop training models predict future events, such as fraudulent credit card transactions. This is a form of machine learning which identifies inputs and outputs and trains algorithms using labelled examples. Supervised learning uses methods like classification, regression, prediction and gradient boosting for pattern recognition. It then uses these patterns to predict the values of the labels on the unlabelled data.
This form of machine learning is currently being used in drug discovery and development with applications including target validation, identification of biomarkers and the analysis of digital pathology data in clinical trials. Using machine learning in this way promotes data-driven decision making and can speed up the drug discovery and development process while improving success rates.
Unlike supervised learning, unsupervised learning works with datasets without historical data. Instead, it explores collected data to find a structure and identify patterns. Unsupervised machine learning is now being used in factories for predictive maintenance purposes. Machines can learn the data and algorithms responsible for causing faults in the system and use this information to identify problems before they arise.
Using machine learning in this way leads to a decrease in unplanned downtime as manufacturers are able to order replacement parts from an automation equipment supplier before a breakdown occurs, saving time and money. According to a survey by Deloitte, using machine learning technologies in the manufacturing sector reduces unplanned machine downtime between 15 and 30 per cent, reducing maintenance costs by 30 per cent.
Its no longer just humans that can think for themselves machines, such as Googles Duplex, are now able to pass the Turing test. Manufacturers can make use of machine learning to improve maintenance processes and enable them to make real-time, intelligent decisions based on data.
The rest is here:
What is the role of machine learning in industry? - Engineer Live
It seems like AI is everywhere these days, from the voice recognition software in our personal assistants to the ads that pop up seemingly at just the right time. But believe it or not, the field is still in its infancy.
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BlackBerry combines AI and machine learning to create connected fleet security solution – Fleet Owner
Fleet Owner returned to CES, the annual mega technology show in Las Vegas, in search of potential transportation technology that could help fleets of the future. Here are some news and notes from the more than a million square feet of exhibit space. You can read our coverage of other news out of CES here: (DOT on autonomous vehicles,Peterbilt,Kenworth and Dana,Bosch and ZF,BlackBerry, andmore).
Plus.ai announced at CES 2020 it will expand testing of its self-driving trucks to cover all permissible continental states in the U.S. by the end of 2020. This will include closed-course testing and public road testing, with a safety driver and operations specialist onboard to assume manual control if needed.
"We want to build a technology solution that is applicable across different weather, terrains, and driving scenarios, said Shawn Kerrigan, COO and co-founder ofPlus.ai. Testing our trucks readiness means we need to put them through stringent safety tests, on every highway in the country.
Plus.ai has conducted autonomous truck testing in 17 states: Arizona, California, Colorado, Illinois, Indiana, Kansas, Minnesota, Missouri, Nevada, New Mexico, Ohio, Pennsylvania, South Dakota, Texas, Utah, West Virginia and Wyoming.
"Thesmart mobility ecosystem weve established in Ohiois a premier testing ground for autonomous vehicles," said Patrick Smith, interim executive director ofDriveOhio. Ohio is excited to welcome leading autonomous trucking companies like Plus.ai to test at our state-of-the-art facilities and infrastructure.
Plus.ai expects that the new testing sites and states will be selected by the spring and implementation will take place through the rest of the year.
Ryder's outdoor booth at CES 2020 featured a Nikola Two truck.Josh Fisher/Fleet Owner
Ryder System was among the trucking and logistics companies exhibiting at CES this year. And helping the company catch the eye of attendees was the Nikola Two tractor on display at its outdoor booth that focused on the future of transportation logistics and equipment.
Ryder is showing current and potential leasing customers what is available now and around the corner in electric and automated trucks and how they can help increase supply chain efficiency.
Bridgestone made its first appearance at CES, and highlighted its mobility solutions that look toward an autonomous future focused on extended mobility, improved safety and increased efficiency.
The company showed off its future airless tires, smart tire technology and its Webfleet Solutions platform. That platform uses data and analytics to move millions of vehicles as efficiently as possible.
"Bridgestone has a nearly 90-year history of using technology and research to develop advanced products, services and solutions for a world in motion," said TJ Higgins, global chief strategic officer of Bridgestone. As we look to the future, we are combining our core tire expertise with a wide range of digital solutions to deliver connected products and services that promote safe, sustainable mobility and continue contributing to society's advancement."
The company's CES showcase demonstrated how airless tires from Bridgestone combine a tire's tread and wheel into one durable, high-strength structure. This design eliminates the need for tires to be filled and maintained with air.
The company also showed how its digital twin and connected tire technology can be used to generate specific, actionable predictions that can enhance the precision of vehicle safety systems.
The Bosch Virtual Visor uses LCD and AI technology to keep a driver's eyes in the shade.Bosch Global
Bosch unveiled what is called the most drastic improvement to the 100-year-old sun visor.
The Virtual Visor links an LCD panel with a driver or occupant-monitoring camera to track the suns casted shadow on the drivers face.
The system uses artificial intelligence to locate the driver within the image from the driver-facing camera. It also utilizes AI to determine the landmarks on the face including where the eyes, nose and mouth are located so it can identify shadows on the face.
The algorithm analyzes the drivers view, darkening only the section of the display through which light hits the drivers eyes. The rest of the display remains transparent, no longer obscuring a large section of the drivers field of vision.
We discovered early in the development that users adjust their traditional sun visors to always cast a shadow on their own eyes, said Jason Zink, technical expert for Bosch in North America and one of the co-creators of the Virtual Visor. This realization was profound in helping simplify the product concept and fuel the design of the technology.
This use of liquid crystal technology to block a specific light source decreases dangerous sun glare, driver discomfort and accident risk; it also increases driver visibility, comfort and safety.
The World Economic Forum and Deepen AI unveiled Safety Pool, a global incentive-based brokerage of shared driving scenarios and safety data for safe autonomous driving systems.
Aptiv was one of the first publicly announced members of the initiative.
"At Aptiv, we believe that our industry makes progress by sharing, especially when it comes to safety. We are proud to be part of the World Economic Forum's Safety Pool, and we are confident that with continued collaboration, we will deliver the safer and more accessible mobility solutions our communities deserve," said Karl Iagnemma, Aptivs president of autonomous mobility.
Safety Pool is gathering a vast and diverse set of driving scenarios and safety data from the major industry players developing ADAS systems and autonomous driving technology, it was announced at CES 2020. Data will be accessible by the members while an incentive scheme ensures the right value is taken and given by every Safety Pool participant, regardless of their size, level of funding, or years of operations.
According to Deepen, WEF and the first publicly announced Safety Pool pioneering members, sharing this data on such a large scale will generate tremendous positive externalities for the whole industry.
Each company developing ADAS systems and autonomous driving technology will have the chance to tap into a massive, common and shared database of driving scenarios on which to train and validate their machine learning models. In this way, the overall safety of operations will drastically increase, accelerating time to deployment.
Raleys has brought artificial intelligence to pricing not to necessarily to go toe-to-toe with competitors, but to differentiate from them, President and CEO Keith Knopf said.
Speaking in a presentation at the National Retail Federation show in New York, Knopf described how the West Sacramento, Calif.-based food retailer is using machine learning algorithms from partner Eversight to help manage its price perception amid larger, and often cheaper, competitorswhile optimizing revenue by driving unit share growth and margin dollars. That benefit is going toward what he described as a differentiated positioning behind health and wellness.
This is not just about pricing for the sake of pricing. This is pricing within a business strategy to differentiateand afford the investment in price in a way that is both financially sustainable and also relevant to the customer, Knopf said.
Raleyshas been working with Eversight for about four years, and has since invested in the Palo Alto, Calif.-based provider of AI-led pricing and promotion management. Knopf described using insights and recommendations derived from Eversights data crunching to support its merchants, helping to strategically manage the Rubiks Cube of pricing and promoting 40,000 items, each with varying elasticity, in stores with differing customer bases, price zones and competitive characteristics.
Raleys, Knopf said, is high-priced relative to its competitors, a reflection of its sizeand its ambitions. Were a $3 billion to $4 billion retailer competing against companies much larger than us, with much greater purchasing power and so for us, [AI pricing] is about optimization within our brand framework. We aspire to be a differentiated operator with a differentiated customer experience and a differentiated product assortment, which is guided more toward health and wellness. We have strong position in fresh that is evolving through innovation. But we also understand that we are a high-priced, high-cost retailer.
David Moran, Eversights co-founder, was careful to put his companys influence in perspective. Algorithms don't replace merchants or set a strategy, he said, but can support them by bringing new computing power that exceeds the work a merchant could do alone and has allowed for experimentation with pricing strategies across categories.In an example he shared, a mix of price changessome going up, others downhelped to drive overall unit growth and profits in the olive oil category.
The merchants still own the art: They are still the connection between the brand positioning, the price value perception, and they also own the execution, Knopf said. This technology gets us down that road much faster and with greater confidence.
Knopf said he believes that pricing science, in combination with customer relationship management, will eventually trigger big changes in the nature of promotional spending by vendors, with a shift toward so-called below the line programs, such as everyday pricing and personalized pricing, and less above the line mass promotions, which he believes are ultimately ineffective at driving long-term growth.
Every time we promote above the line, and everybody sees what everybody else does, no more units are sold in totality in the marketplace, it's just a matter of whos going to sell this week at what price, Knopf said.I believe that its in in the manufacturers best interest, and the retailers best interest, to make pricing personalized and relevant, and the dollars that are available today will shift from promotions into a more personalized, one-on-one, curated relationship that a vendor, the retailer and the customer will share.