Category Archives: Machine Learning
Machine learning is sub-categorized to three types:
Supervised Learning Train Me!
Unsupervised Learning I am self sufficient in learning
Reinforcement Learning My life My rules! (Hit & Trial)
Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a dataset which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.
The model learns through observation and finds structures in the data. Once the model is given a dataset, it automatically finds patterns and relationships in the dataset by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes.
Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based on some patterns and relationships it creates clusters and divides the dataset into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.
It is the ability of an agent to interact with the environment and find out what is the best outcome. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it.
The rest is here:
What is Machine Learning? | Types of Machine Learning ...
Vectorspace AI Datasets are Now Available to Power Machine Learning (ML) and Artificial Intelligence (AI) Systems in Collaboration with Elastic -…
SAN FRANCISCO, Jan. 22, 2020 /PRNewswire/ -- Vectorspace AI (VXV) announces datasets that power data engineering, machine learning (ML) and artificial intelligence (AI) systems. Vectorspace AI alternative datasets are designed for predicting unique hidden relationships between objects including current and future price correlations between equities.
Vectorspace AI enables data, ML and Natural Language Processing/Understanding (NLP/NLU) engineers and scientists to save time by testing a hypothesis or running experiments faster to achieve an improvement in bottom line revenue and information discovery. Vectorspace AI datasets underpin most of ML and AI by improving returns from R&D divisions of any company in discovering hidden relationships in drug development.
"We are happy to be working with Vectorspace AI based on their most recent collaboration with us based on the article we published titled 'Generating and visualizing alpha with Vectorspace AI datasets and Canvas'. They represent the tip of the spear when it comes to advances in machine learning and artificial intelligence. Our customers and partners will certainly benefit from our continued joint development efforts in ML and AI," Shaun McGough, Product Engineering, Elastic.
Increasing the speed of discovery in every industry remains the aim of Vectorspace AI, along with a particular goal which relates to engineering machines to trade information with one another, connected to exchanging and transacting data in a way that minimizes a selected loss function. Data vendors such as Neudata.co, asset management companies and hedge funds including WorldQuant, use Vectorspace AI datasets to improve and protect 'alpha'.
Limited releases of Vectorspace AI datasets will be available in partnership with Amazon and Microsoft.
About Vectorspace AI (vectorspace.ai)
Vectorspace AI focuses on context-controlled NLP/NLU (Natural Language Processing/Understanding) and feature engineering for hidden relationship detection in data for the purpose of powering advanced approaches in Artificial Intelligence (AI) and Machine Learning (ML). Our platform powers research groups, data vendors, funds and institutions by generating on-demand NLP/NLU correlation matrix datasets. We are particularly interested in how we can get machines to trade information with one another or exchange and transact data in a way that minimizes a selected loss function. Our objective is to enable any group analyzing data to save time by testing a hypothesis or running experiments with higher throughput. This can increase the speed of innovation, novel scientific breakthroughs and discoveries. For a little more on who we are, see our latest reddit AMA on r/AskScience or join our 24 hour communication channel here. Vectorspace AI offers NLP/NLU services and alternative datasets consisting of correlation matrices, context-controlled sentiment scoring, and other automatically engineered feature attributes. These services are available utilizing the VXV token and VXV wallet-enabled API. Vectorspace AI is a spin-off from Lawrence Berkeley National Laboratory (LBNL) and the U.S. Dept. of Energy (DOE). The team holds patents in the area of hidden relationship discovery.
SOURCE Vectorspace AI
Red Hat Survey Shows Hybrid Cloud, AI and Machine Learning are the Focus of Enterprises – Computer Business Review
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The data aspect in particular is something that we often see overlooked
Open source enterprise software firm Red Hat now a subsidiary of IBM have conducted its annual survey of its customers which highlights just how prevalent artificial intelligence and machine learning is becoming, while a talent and skill gap is still slowing down companies ability to enact digital transformation plans.
Here are the top three takeaways from Red Hats customer survey;
When asked to best describe their companies approach to cloud infrastructure 31 percent stated that they run a hybrid cloud, while 21 percent said their firm has a private cloud first strategy in place.
The main reason cited for operating a hybrid cloud strategy was the security and cost benefits it provided. Some responders noted that data integration was easier within a hybrid cloud.
Not everyone is fully sure about their approach yet, as 17 percent admitted they are in the process of establishing a cloud strategy, while 12 percent said they have no plans at all to focus on the cloud.
When it comes to digital transformation there has been a notable rise in the amount of firms that undertaken transformation projects. In 2018; under a third of responders (31 percent) said they were implementing new processes and technology, this year that number has nearly doubled as 58 percent confirm they are introducing new technology.
Red Hat notes that: The drivers for these projects vary. And the drivers also vary by the role of the respondent. System administrators care most about simplicity. IT architects focus on user experience and innovation. For managers, simplicity, user experience, and innovation are all tied for top priority. Developers prioritize innovationwhich, overall, was cited as the most important reason to do digital transformation projects.
However, one in ten surveyed said they are facing a talent and skillset gap that is slowing down the pace at which they can transform their business. The skillset is being made worse by the amount of new technologies that are being brought to market such as artificial intelligence, machine learning and containerisation, the use of which is expected to grow significantly in the next 24 months.
Artificial intelligence, machine learning models and processes is the clear emerging technology for firms in 2019, as 30 percent said that they are planning to implement an AI or ML project within the next 12 months.
However, enterprises are worried about the compatibility and complexity of implementing AI or ML, with 29 percent stating they are worried about evolving software stacks.
One in five (22 percent) responders are worried about getting access to the right data. The data aspect in particular is something that we often see overlooked; obtaining relevant data and cleansing or transforming it in ways that its a useful input for models can be one of the most challenging aspects of an AI project, Red Hat notes.
Red Hats survey was created by compiling 876 qualified responses from Red Hat customers during August and September of 2019.
Both Millennials and Generation Z can be categorized as digital natives. The way in which they learn reflects that reality. From a learning perspective, a companys learning programs must reflect that also.
Utilizing technologies such as microlearning, which is usually delivered with mobile technology, or machine learning to can engage these individuals in the way they are accustomed to consuming information.
Microlearning is delivering learning in bite-sized pieces. It can take many different forms such an animation or a video. In either case, the information is delivered in a short amount of time; in as little as two to three minutes. In most cases, micro-learning happens on a mobile device or tablet.
When should micro-learning be used?
Think of it as a way to engage employees already on the job. It can be used to deliver quick bits of information that will become immediately relevant to their daily responsibilities. To be more pointed, microlearning is the bridge between formal training and application. At least one study shows after six weeks following a formal training, 85% of the content consumed will have been lost. Microlearning can deliver that information in the interim and can be used at the moment of application.
Microlearning shouldnt be used to replace formal training, but rather as a compliment which makes it perfect for developing and retaining high-quality talent.
Amnesty International piloted a microlearning strategy to launch its global campaign on Human Rights Defenders. The program used the learning approached to build a culture of human rights. It allowed Amnesty to discuss human rights issues in a quick, relevant, and creative manner. As such, learners were taught how to talk to people in everyday life about human rights and human rights defenders.
WEBINAR: L&Ds Role in Enabling the Future of Work with a Skills Focused Strategy
Dell has also used the strategy to implement a digital campaign to encourage 14,000 sales representatives around the world to implement elements of its Net Promoter Score methodology. Using mobile technology and personal computers, the company was able to achieve 11% to 19% uptake in desire among sales reps globally.
Machine learning can also be used as a strategy. Machine learning, which is a branch of artificial intelligence, is an application that provides systems the ability to automatically learn and improve from experience without being programmed to do so.
For the purpose of explanation, the example of an AI-controlled multiple-choice test is relevant. If a person taking the test marked an incorrect answer, AI would then give them a question a bit easier to answer. If the question was answered wrong again, AI would follow with a question lower in difficulty level. When the student began to answer questions correctly, the difficulty of the questions would increase. Similarly, a person answering questions correctly would continue to get more difficult questions. This allows the AI to determine what topics the student understands least. In doing so, learning becomes personalized and specific for the student.
But technology isnt the sole basis for disseminating information. Learning programs should also focus on creating more experience opportunities that offer development in either leadership or talent. Those programs should also prioritize retention. Programs such as mentoring and coaching are great examples.
Dipankar Bandyopadhyay led this charge when he was the Vice President of HR Global R&D and Integration Planning Lead Culture & Change Management for the Monsanto Company. Monsanto achieved this through itsGlobal Leadership Program For Experienced Hires.
A couple of years ago, we realized we had a need to supplement our talent pipeline, essentially in our commercial organization and businesses globally really building talent for key leadership roles within the business, which play really critical influence roles and help drive organizational strategy in these areas. With this intention, we created Global Commercial Emerging Leaders Program, Bandyopadhyay said. Essentially, what it does is focus on getting external talent into Monsanto through different industry segments. This allows us to broaden our talent pipeline, bringing in diverse points of view from very different industry segments (i.e., consumer goods, investment banking, the technology space, etc.) The program selects, onboards, assimilates and develops external talent to come into Monsanto.
Microlearning and machine learning are valuable in developing the workforce, but they are not the only ones available. Additionally, its important to note an organization cant simply provide development and walk away. There has to be data and analysis that tracks employee learning success. There also needs to be strategies in place to make sure workers are retaining that knowledge. Otherwise, it is a waste of money.
NEXT: How L&D Can Help Itself
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Five Reasons to Go to Machine Learning Week 2020 – Machine Learning Times – machine learning & data science news – The Predictive Analytics Times
When deciding on a machine learning conference, why go to Machine Learning Week 2020? This five-conference event May 31 June 4, 2020 at Caesars Palace, Las Vegas delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learnings commercial deployment, and the hottest topics and techniques. In this video, Predictive Analytics World Founder Eric Siegel spills on the details and lists five reasons this is the most valuable machine learning event to attend this year.
Note: This article is based on the transcript of a special episode of The Dr. Data Show click here to view.
In this article, I give five reasons that Machine Learning Week May 31 June 4, 2020 at Caesars Palace, Las Vegas is the most valuable machine learning event to attend this year. MLW is the largest annual five-conference blow-out part of the Predictive Analytics World conference series, of which I am the founder.
First, some background info. Your business needs machine learning to thrive and even just survive. You need it to compete, grow, improve, and optimize. Your team needs it, your boss demands it, and your career loves machine learning.
And so we bring you Predictive Analytics World, the leading cross-vendor conference series covering the commercial deployment of machine learning. By design, PAW is where to meet the whos who and keep up on the latest techniques.
This June in Vegas, Machine Learning Week brings together five different industry-focused events: PAW Business, PAW Financial, PAW Industry 4.0, PAW Healthcare, and Deep Learning World. This is five simultaneous two-day conferences all happening alongside one another at Caesars Palace in Vegas. Plus, a diverse range of full-day training workshops, which take place in the days just before and after.
Machine Learning Week delivers brand-name, cross-industry, vendor-neutral case studies purely on machine learning deployment, and the hottest topics and techniques.
This mega event covers all the bases for both senior-level expert practitioners as well as newcomers, project leaders, and executives. Depending on the topic, sessions and workshops are either demarcated as the Expert/practitioner level, or for All audiences. So, you can bring your team, your supervisor, and even the line-of-business managers you work with on model deployment. About 60-70% of attendees are on the hands-on practitioner side, but, as you know, successful machine learning deployment requires deep collaboration between both sides of the equation.
PAW and Deep Learning World also takes place in Germany, and Data Driven Government takes place in Washington DC but this article is about Machine Learning Week, so see predictiveanalyticsworld.com for details about the others.
Here are the five reasons to go.
Five Reasons to Go to Machine Learning Week June 2020 in Vegas
1) Brand-name case studies
Number one, youll access brand-name case studies. At PAW, youll hear directly from the horses mouth precisely how Fortune 500 analytics competitors and other companies of interest deploy machine learning and the kind of business results they achieve. More than most events, we pack the agenda as densely as possible with named case studies. Each day features a ton of leading in-house expert practitioners who get things done in the trenches at these enterprises and come to PAW to spill on the inside scoop. In addition, a smaller portion of the program features rock star consultants, who often present on work theyve done for one of their notable clients.
2) Cross-industry coverage
Number two, youll benefit from cross-industry coverage. As I mentioned, Machine Learning Week features these five industry-focused events. This amounts to a total of eight parallel tracks of sessions.
Bringing these all together at once fosters unique cross-industry sharing, and achieves a certain critical mass in expertise about methods that apply across industries. If your work spans industries, Machine Learning Week is one-stop shopping. Not to mention that convening the key industry figures across sectors greatly expands the networking potential.
The first of these, PAW Business, itself covers a great expanse of business application areas across many industries. Marketing and sales applications, of course. And many other applications in retail, telecommunications, e-commerce, non-profits, etc., etc.
The track topics of PAW Business 2020
PAW Business is a three-track event with track topics that include: analytics operationalization & management i.e., the business side core machine learning methods and advanced algorithms i.e., the technical side innovative business applications covered as case studies, and a lot more.
PAW Financial covers machine learning applications in banking including credit scoring insurance applications, fraud detection, algorithmic trading, innovative approaches to risk management, and more.
PAW Industry 4.0 and PAW Healthcare are also entire universes unto themselves. You can check out the details about all four of these PAWs at predictiveanalyticsworld.com.
And the newer sister event Deep Learning World has its own website, deeplearningworld.com. Deep learning is the hottest advanced form of machine learning with astonishing, proven value for large-signal input problems, such as image classification for self-driving cars, medical image processing, and speech recognition. These are fairly distinct domains, so Deep Learning World does well to complement the four Predictive Analytics World events.
3) Pure-play machine learning content
Number three, youll get pure-play machine learning content. PAWs agenda is not watered down with much coverage of other kinds of big data work. Instead, its ruthlessly focused specifically on the commercial application of machine learning also known as predictive analytics. The conference doesnt cover data science as a whole, which is a much broader and less well-defined area, that, for example, can include standard business intelligence reporting and such. And we dont cover AI per se. Artificial intelligence is at best a synonym for machine learning that tends to over-hype, or at worst an outright lie that promises mythological capabilities.
4) Hot new machine learning practices
Number four, youll learn the latest and greatest, the hottest new machine learning practices. Now, we launched PAW over a decade ago, so far delivering value to over 14,000 attendees across more than 60 events. To this day, PAW remains the leading commercial event because we keep up with the most valuable trends.
For example, Deep Learning World, which launched more recently in 2018 covers deep learnings commercial deployment across industry sectors. This relatively new form of neural networks has blossomed, both in buzz and in actual value. As I mentioned, it scales machine learning to process, for example, complex image data.
And what had been PAW Manufacturing for some years has now changed its name to PAW Industry 4.0. As such, the event now covers a broader area of inter-related work applying machine learning for smart manufacturing, the Internet of Things (IoT), predictive maintenance, logistics, fault prediction, and more.
In general, machine learning continues to widen its adoption and apply in new, innovative ways across sectors in marketing, financial risk, fraud detection, workforce optimization, and healthcare. PAW keeps up with these trends and covers todays best practices and the latest advanced modeling methods.
5) Vendor-neutral content
And finally, number five, youll access vendor-neutral content. PAW isnt run by an analytics vendor and the speakers arent trying to sell you on anything but good ideas. PAW speakers understand that vendor-neutral means those in attendance must be able to implement the practices covered and benefit from the insights delivered without buying any particular analytics product.
During the event, some vendors are permitted to deliver short presentations during a limited minority of demarcated sponsored sessions. These sessions often are also substantive and of great interest. In fact, you can access all the sponsors and tap into their expertise at will in the exhibit hall, where theyre set up for just that purpose.
By the way, if youre an analytics vendor yourself, check out PAWs various sponsorship opportunities. Our events bring together a great crowd of practitioners and decision makers.
Summary Five Reasons to Go
1) Brand-name case studies
2) Cross-industry coverage
3) Pure-play machine learning content
4) Hot new machine learning practices
5) Vendor-neutral content
and those are the reasons to come to Machine Learning Week: brand-name, cross-industry, vendor-neutral case studies purely on machine learnings commercial deployment, and the hottest topics and techniques.
Machine Learning Week not only delivers unique knowledge-gaining opportunities, its also a universal meeting place the industrys premier networking event. It brings together the whos who of machine learning and predictive analytics, the greatest diversity of expert speakers, perspectives, experience, viewpoints, and case studies.
This all turns the normal conference stuff into a much richer experience, including the keynotes, expert panels, and workshop days, as well as opportunities to network and talk shop during the lunches, coffee breaks, and reception.
I encourage you to check out the detailed agenda see all the speakers, case studies, and advanced methods covered. Each of the five conferences has its own agenda webpage, or you can also view the entire five-conference, eight-track mega-agenda at once. This view pertains if youre considering registering for the full Machine Learning Week pass, or if youll be attending along with other team members in order to divide and conquer.
Visit our website to see all these details, register, and sign up for informative event updates by email.
Or to learn more about the field in general, check out our Predictive Analytics Guide, our publication The Machine Learning Times, which includes revealing PAW speaker interviews, and, episodes of this show, The Dr. Data Show which, by the way, is generally about the field of machine learning in general, rather than about our PAW events.
This article is based on a transcript from The Dr. Data Show.
CLICK HERE TO VIEW THE FULL EPISODE
About the Dr. Data Show. This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics. Click here to view more episodes and to sign up for future episodes of The Dr. Data Show.
About the Author
Eric Siegel, Ph.D., founder of the Predictive Analytics Worldand Deep Learning World conference series and executive editor ofThe Machine Learning Times, makes the how and why of predictive analytics (aka machine learning) understandable and captivating. He is the author of the award-winning bookPredictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, the host of The Dr. Data Show web series, a former Columbia University professor, and a renowned speaker, educator, and leader in the field. Follow him at @predictanalytic.
Since October of last year I have had the opportunity to work with an startup working on automated machine learning and I thought that I would share some thoughts on the experience and the details of what one might want to consider around the start of a journey with a data scientist in a box.
Ill start by saying that machine learning and artificial intelligence has almost forced itself into my work several times in the past eighteen months, all in slightly different ways.
The first brush was back in June 2018 when one of the developers I was working with wanted to demonstrate to me a scoring model for loan applications based on the analysis of some other transactional data that indicated loans that had been previously granted. The model had no explanation and no details other than the fact that it allowed you to stitch together a transactional dataset which it assessed using a nave Bayes algorithm. We had a run at showing this to a wider audience but the palate for examination seemed low and I suspect that in the end the real reason was we didnt have real data and only had a conceptual problem to be solved.
The second go was about six months later when another colleague in the same team came up with a way to classify data sets and in fact developed a flexible training engine and data tagging approach to determining whether certain columns in data sets were likely to be names, addresses, phone numbers and email addresses. On face value you would think this to be something simple but in reality, it is of course only as good as the training data and in this instance we could easily confuse the system and the data tagging with things like social security numbers that looked like phone numbers, postcodes that were simply numbers and ultimately could be anything and so on. Names were only as good as the locality from which the names training data was sourced and cities, towns. Streets and provinces all proved to most work ok but almost always needed region-specific training data. At any rate, this method of classifying contact data for the most part met the rough objectives of the task at hand and so we soldiered on.
A few months later I was called over to a developers desk and asked for my opinion on a side project that one of the senior developers and architects had been working on. The objective was ambitious but impressive. The solution had been built in response to three problems in the field. The first problem to be solved was decoding why certain records were deemed to be related to one another when with the naked eye they seemed to not be, or vice versa. While this piece didnt involve any ML per se, the second part of the solution did, in that it self-configured thousands of combinations of alternative fuzzy matching criteria to determine an optimal set of duplicate record matching rules.
This was understandably more impressive and practically understandable almost self-explanatory. This would serve as a great utility for a consultant, a data analyst or a relative layperson to find explainability in how potential duplicate records were determined to have a relationship. This was specifically important because it immediately could provide value to field services personnel and clients. In addition, the developer had cunningly introduced a manual matching option that allowed a user to evaluate two records and make a decision through visual assessment as to whether two records could potentially be considered related to one another.
In some respects what was produced was exactly the way that I like to see products produced. The field describes the problem; the product management organization translates that into more elaborate stories and looks for parallels in other markets, across other business areas and for ubiquity. Once those initial requirements have been gathered it is then to engineering and development to come up with a prototype that works toward solving the issue.
The more experienced the developer of course the more comprehensive the result may be and even the more mature the initial iteration may be. Product is then in a position to pitch the concept back at the field, to clients and a selective audience to get their perspective on the solution and how well it matches the for solving the previously articulated problem.
The challenge comes when you have a less tightly honed intent, a less specific message and a more general problem to solve and this comes now to the latest aspect of machine learning and artificial intelligence that I picked up.
One of the elements with dealing with data validation and data preparation is the last mile of action that you have in mind for that data. If your intent is as simple as one of, lets evaluate our data sources, clean them up and makes them suitable for online transaction processing then thats a very specific mission. You need to know what you want to evaluate, what benchmark you wish to evaluate them against and then have some sort of remediation plan for them so that they support the use case for which theyre intended say, supporting customer calls into a call centre. The only areas where you might consider artificial intelligence and machine learning for applicability in this instance might be for determining matches against the baseline but then the question is whether you simply have a Boolean decision or whether in fact, some sort of stack ranking is relevant at all. It could be argued either way, depending on the application.
When youre preparing data for something like a decision beyond data quality though, the mission is perhaps a little different. Effectively your goal may be to cut the cream of opportunities off the top of a pile of contacts, leads, opportunities or accounts. As such, you want to use some combination of traits within the data set to determine influencing factors that would determine a better (or worse) outcome. Here, linear regression analysis for scoring may be sufficient. The devil, of course, lies in the details and unless youre intimately familiar with the data and the proposition that youre trying to resolve for you have to do a lot of trial and error experimentation and validation. For statisticians and data scientists this is all very obvious and you could say, is a natural part of the work that they do. Effectively the challenge here is feature selection. A way of reducing complexity in the model that you will ultimately apply to the scoring.
The journey I am on right now with a technology partner, focuses on ways to actually optimise the features in a way that only the most necessary and optimised features will need to be considered. This, in turn, makes the model potentially simpler and faster to execute, particularly at scale. So while the regression analysis still needs to be done, determining what matters, what has significance and what should be retained vs discarded in terms of the model design, is being all factored into the model building in an automated way. This doesnt necessarily apply to all kinds of AI and ML work but for this specific objective it is perhaps more than adequate and one that doesnt require a data scientist to start delivering a rapid yield.
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Adventures With Artificial Intelligence and Machine Learning - Toolbox
Software development is a massive part of the tech industry that is absolutely set to stay. Its importance is elemental, supporting technology from the root. Its unsurprisingly a massive industry, with lots of investment and millions of jobs that help to propel technology on its way with great force. Software testing is one of the vital cogs in the software development machine, without which faulty software would run amuck and developing and improving software products would be a much slower and much more inefficient process. Software testing as its own field has gone through several different phases, most recently landing upon the idea of using machine learning. Machine learnings importance is elemental to artificial intelligence, and is a method of freeing up the potential of computers through the use of data feeding. Effective machine learning can greatly improve software testing.
Lets take a look at how that is the case.
As well as realizing the immense power of data over the last decade, we have also reached a point in our technological, even sociological evolution in which we are producing more data than ever, proposes Carl Holding, software developer at Writinity and ResearchPapersUK. This is significant in relation to software testing. The more complex and widely adopted software becomes, the more data that is generated about its use. Under traditional software testing conditions, that amount of data would actually be unhelpful, since it would overwhelm testers. Conversely, machine learning computers hoover up vast data sets as fuel for their analysis and their learning pattern. Not only do the new data conditions only suit large machine learning computers, its also precisely what makes large machine learning computers most successful.
Everyone makes mistakes, as the old saying goes. Except, thats not true: machine learning computers dont. Machine learning goes hand in hand with automation, something which has become very important for all sorts of industries. Not only does it save time, it also gets rid of the potential for human mistakes, which can be very damaging in software testing, notes Tiffany Lee, IT expert at DraftBeyond and LastMinuteWriting. It doesnt matter how proficient a human being is at this task, they will always slip up, especially under the increased pressure put on them with the volume of data that now comes in. A software test sullied by human error can actually be even worse than if no test had been done at all, since getting misinformation is worse than no information. With that in mind, its always just better to leave it to the machines.
Business has always been about getting ahead, regardless of the era or the nature of the products and services. Machine learning is often looked to as a way to predict the future by spotting trends in data and feeding those predictions to the companies that want it most. Software is by no means an industry where this is an exception. In fact, given that it is within the tech sector, its even more important to software development than other industries. Using a machine learning computer for software testing can help to quickly identify the way things are shaping up for the future which means that you get two functions out of your testing process, for the price of one. This can give you an excellent competitive edge.
That machine learning computers save you time should be a fairly obvious point at this stage. Computers handle tasks that take humans hours in a matter of seconds. If you add the increased accuracy advantage over traditional methods then you can see that using this method of testing will get better products out more quickly, which is a surefire way to start boosting your sales figures with ease.
Overall, its a no-brainer. And, as machine learning computers become more affordable, you really have no reason to opt for any other method beyond it. Its a wonderful age for speed and accuracy in technology and with the amount that is at stake with software development, you have to be prepared to think ahead.
(Left- Wilson To, Head of Worldwide Healthcare BD, Amazon Web Services (AWS) & Patrick Combes, Worldwide Technical Leader Healthcare and Life Sciences at Amazon Web Services (AWS)- Right)
Navigating the healthcare system is often a complex journey involving multiple physicians from hospitals, clinics, and general practices. At each junction, healthcare providers collect data that serve as pieces in a patients medical puzzle. When all of that data can be shared at each point, the puzzle is complete and practitioners can better diagnose, care for, and treat that patient. However, a lack of interoperability inhibits the sharing of data across providers, meaning pieces of the puzzle can go unseen and potentially impact patient health.
The Challenge of Achieving Interoperability
True interoperability requires two parts: syntactic and semantic. Syntactic interoperability requires a common structure so that data can be exchanged and interpreted between health information technology (IT) systems, while semantic interoperability requires a common language so that the meaning of data is transferred along with the data itself.This combination supports data fluidity. But for this to work, organizations must look to technologies like artificial intelligence (AI) and machine learning (ML) to apply across that data to shift the industry from a fee-for-service where government agencies reimburse healthcare providers based on the number of services they provide or procedures ordered to a value-based model that puts focus back on the patient.
The industry has started to make significant strides toward reducing barriers to interoperability. For example, industry guidelines and resources like the Fast Healthcare Interoperability Resources (FHIR) have helped to set a standard, but there is still more work to be done. Among the biggest barriers in healthcare right now is the fact there are significant variations in the way data is shared, read, and understood across healthcare systems, which can result in information being siloed and overlooked or misinterpreted.
For example, a doctor may know that a diagnosis of dropsy or edema may be indicative of congestive heart failure, however, a computer alone may not be able to draw that parallel. Without syntactic and semantic interoperability, that diagnosis runs the risk of getting lost in translation when shared digitally with multiple health providers.
Employing AI, ML and Interoperability in Healthcare
Change Healthcare is one organization making strides to enable interoperability and help health organizations achieve this triple aim. Recently, Change Healthcareannounced that it is providing free interoperability services that breakdown information silos to enhance patients access to their medical records and support clinical decisions that influence patients health and wellbeing.
While companies like Change Healthcare are creating services that better allow for interoperability, others like Fred Hutchinson Cancer Research Center and Beth Israel Deaconess Medical Center (BIDMC) are using AI and ML to further break down obstacles to quality care.
For example, Fred Hutch is using ML to help identify patients for clinical trials who may benefit from specific cancer therapies. By using ML to evaluate millions of clinical notes and extract and index medical conditions, medications, and choice of cancer therapeutic options, Fred Hutch reduced the time to process each document from hours, to seconds, meaning they could connect more patients to more potentially life-saving clinical trials.
In addition, BIDMC is using AI and ML to ensure medical forms are completed when scheduling surgeries. By identifying incomplete forms or missing information, BIDMC can prevent delays in surgeries, ultimately enhancing the patient experience, improving hospital operations, and reducing costs.
An Opportunity to Transform The Industry
As technology creates more data across healthcare organizations, AI and ML will be essential to help take that data and create the shared structure and meaning necessary to achieve interoperability.
As an example, Cernera U.S. supplier of health information technology solutionsis deploying interoperability solutions that pull together anonymized patient data into longitudinal records that can be developed along with physician correlations. Coupled with other unstructured data, Cerner uses the data to power machine learning models and algorithms that help with earlier detection of congestive heart failure.
As healthcare organizations take the necessary steps toward syntactic and semantic interoperability, the industry will be able to use data to place a renewed focus on patient care. In practice, Philips HealthSuite digital platform stores and analyses 15 petabytes of patient data from 390 million imaging studies, medical records and patient inputsadding as much as one petabyte of new data each month.
With machine learning applied to this data, the company can identify at-risk patients, deliver definitive diagnoses and develop evidence-based treatment plans to drive meaningful patient results. That orchestration and execution of data is the definition of valuable patient-focused careand the future of what we see for interoperability drive by AI and ML in the United States. With access to the right information at the right time that informs the right care, health practitioners will have access to all pieces of a patients medical puzzleand that will bring meaningful improvement not only in care decisions, but in patients lives.
About Wilson To, Global Healthcare Business Development lead at AWS & Patrick Combes, Global Healthcare IT Lead at AWS
Wilson To is the Head Worldwide Healthcare Business Development at Amazon Web Services (AWS). currently leads business development efforts across the AWS worldwide healthcare practice.To has led teams across startup and corporate environments, receiving international recognition for his work in global health efforts. Wilson joined Amazon Web Services in October 2016 to lead product management and strategic initiatives.
Patrick Combes is the Worldwide Technical Leader for Healthcare Life & Sciences at Amazon (AWS) where he is responsible for AWS world-wide technical strategy in Healthcare and Life Sciences (HCLS). Patrick helps develop and implement the strategic plan to engage customers and partners in the industry and leads the community of technically focused HCLS specialists within AWS wide technical strategy in Healthcare and Life Sciences (HCLS). Patrick helps develop and implement the strategic plan to engage customers and partners in the industry and leads the community of technically focused HCLS specialists within AWS.
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Leveraging AI and Machine Learning to Advance Interoperability in Healthcare - - HIT Consultant
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