Category Archives: Machine Learning
Looking at the most significant benefits of machine learning for software testing – The Burn-In
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.
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Looking at the most significant benefits of machine learning for software testing - The Burn-In
Leveraging AI and Machine Learning to Advance Interoperability in Healthcare – – HIT Consultant
(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
Educate Yourself on Machine Learning at this Las Vegas Event – Small Business Trends
One of the biggest machine learning events is taking place in Las Vegas just before summer, Machine Learning Week 2020
This five-day event will have 5 conferences, 8 tracks, 10 workshops, 160 speakers, more than 150 sessions, and 800 attendees.
If there is anything you want to know about machine learning for your small business, this is the event. Keynote speakers from Google, Facebook, Lyft, GM, Comcast, WhatsApp, FedEx, and LinkedIn to name just some of the companies that will be at the event.
The conferences will include predictive analytics for business, financial services, healthcare, industry and Deep Learning World.
Training workshops will include topics in big data and how it is changing business, hands-on introduction to machine learning, hands-on deep learning and much more.
Machine Learning Week will take place from May 31 to June 4, 2020, at Ceasars Palace in Las Vegas.
Click the red button and register.
Register Now
This weekly listing of small business events, contests and awards is provided as a community service by Small Business Trends.
You can see a full list of events, contest and award listings or post your own events by visiting the Small Business Events Calendar.
Image: Depositphotos.com
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Educate Yourself on Machine Learning at this Las Vegas Event - Small Business Trends
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
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Seton Hall Announces New Courses in Text Mining and Machine Learning - Seton Hall University News & Events
New Contest: Train All The Things – Hackaday
The old way was to write clever code that could handle every possible outcome. But what if you dont know exactly what your inputs will look like, or just need a faster route to the final results? The answer is Machine Learning, and we want you to give it a try during the Train All the Things contest!
Its hard to find a more buzz-worthy term than Artificial Intelligence. Right now, where the rubber hits the road in AI is Machine Learning and its never been so easy to get your feet wet in this realm.
From an 8-bit microcontroller to common single-board computers, you can do cool things like object recognition or color classification quite easily. Grab a beefier processor, dedicated ASIC, or lean heavily into the power of the cloud and you can do much more, like facial identification and gesture recognition. But the skys the limit. A big part of this contest is that we want everyone to get inspired by what you manage to pull off.
Wait, wait, come back here. Have we already scared you off? Dont read AI or ML and assume its not for you. Weve included a category for Artificial Intelligence Blinky your first attempt at doing something cool.
Need something simple to get you excited? How about Machine Learning on an ATtiny85 to sort Skittles candy by color? That uses just one color sensor for a quick and easy way to harvest data that forms a training set. But you could also climb up the ladder just a bit and make yourself a camera-based LEGO sorter or using an IMU in a magic wand to detect which spell youre casting. Need more scientific inspiration? Were hoping someday someone will build a training set that classifies microscope shots of micrometeorites. But wed be equally excited with projects that tackle robot locomotion, natural language, and all the other wild ideas you can come up with.
Our guess is you dont really need prizes to get excited about this one most people have been itching for a reason to try out machine learning for quite some time. But we do have $100 Tindie gift certificates for the most interesting entry in each of the four contest categories: ML on the edge, ML on the gateway, AI blinky, and ML in the cloud.
Get started on your entry. The Train All The Things contest is sponsored by Digi-Key and runs until April 7th.
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New Contest: Train All The Things - Hackaday
AFTAs 2019: Best New Technology Introduced Over the Last 12 MonthsAI, Machine Learning and AnalyticsActiveViam – www.waterstechnology.com
Following the global financial crisis, the banking industry has had to deal with more stringent risk capital requirements that demand agility, flexibility, speed, and ease of communication across traditionally siloed departments. Banks also needed a firm grasp of their enterprise-wide data to meet regulatory requirements, and also to ensure a return on capital. It is for this reason that Allen Whipple, co-founder and managing director at ActiveViam, says it makes sense for any regulatory solution to pivot from prescriptive to predictive analytics.
ActiveViam topped this category at this years AFTAs due to its FRTB Accelerator, part of a suite of Accelerator products that it launched in the past year. The products contain all the source code and formulae to meet a particular set of regulations and/or business requirements. In this case, it was those needed for the standardized approach (SA) and the internal model approach (IMA) risk framework, which stems from the Basel Committee on Banking Supervisions Fundamental Review of the Trading Book (FRTB).
The FRTB Accelerator includes capabilities such as the capital decomposition tool, which provides clients with the ability to deploy capital across an organization more precisely. This allows a client to take risk management a step further and perform predictive analysis, which can be applied to broader internal market risk scenarios, Whipple explains. He adds that banks can perform limit-monitoring and back-testing, which allows them to stay within the scope of their IMA status.
Looking ahead, ActiveViam will add a product for Python notebooks to facilitate data science work, reducing the time it takes to move from data to insight. Quants will no longer need to switch between notebooks, data visualization tools, and end-user business intelligence applications. Using the ActiveViam Python Library, they will be able to create dashboards and share them within the same environment. Coders can do everything in Jupyteror a Python notebook of choicefrom beginning to end, Whipple says.
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AFTAs 2019: Best New Technology Introduced Over the Last 12 MonthsAI, Machine Learning and AnalyticsActiveViam - http://www.waterstechnology.com
AI-System Flags the Under-Vaccinated in Israel – PrecisionVaccinations
An Israeli-based provider of machine learning-based solutions announced its flu complications algorithm has been selected as part of the Israeli healthcare organizations integrated strategy to enhance its vaccination campaign.
This innovative machine-learning (AI) program from Medial EarlySign is designed to facilitate more effective and targeted outreach to people in need of disease protection.
EarlySigns software applies advanced algorithms to ordinary patient data, collected over the course of routine care.
In a press release on January 14, 2020, EarlySign said its algorithm can flag individuals at high risk for developing flu-related complications and is being used as part of a clinical study undertaken by Maccabi Healthcare Services and EarlySign.
Varda Shalev, M.D., MPH, director of KSM Kahn-Sagol-Maccabi Research and Innovation Institute, founded by Maccabi Healthcare Services, said in this press release, Due to the late arrival of influenza vaccines in Israel this year, the time we have to vaccinate patients this flu season especially those at high risk for developing flu-related complications is much shorter than usual.
The influenza identification algorithm uses EMR generated data to identify and stratify unvaccinated individuals at high risk of developing flu-related complications, often requiring hospitalization.
This is good news since many areas in the Northern Hemisphere have reported increasing rates of influenza infections, according to the Global Flu Update #359, published by the World Health Organization (WHO).
On January 20, 2020, the WHO reported seasonal influenza A(H3N2) viruses have accounted for the majority of detections around the Northern Hemisphere this flu season.
Maccabis clinical study using EarlySigns flu complications algorithm supports the Israeli HMOs commitment to investigating and implementing machine learning-based solutions to improve the health of populations.
The program follows EarlySign collaboration with Geisinger Health System in December 2019, to apply advanced artificial intelligence and machine learning algorithms to Medicare claims data to predict and improve patient outcomes.
Approximately 4.3 million hospital readmissions occur each year in the U.S., costing more than $60 billion, with preventable adverse patient events creating additional clinical and financial burdens for both patients and healthcare systems, said David Vawdrey, Geisingers chief data informatics officer, in a related press release.
The AI vendor and Danville, Pennsylvania based healthcare provider intend to develop models that predict unplanned hospital and skilled nursing facility admissions within 30 days of discharge and adverse events such as respiratory failure, postoperative pulmonary embolism or deep vein thrombosis, as well as postoperative sepsis before they occur.
Maccabi Healthcare Services is Israels 2nd-largest HMO, covering approximately 2.3 million patients, operating 5 regional centers, including hundreds of branches and clinics throughout Israel.
Medial EarlySignenables healthcare providers to identify risks for critical threats, leading to potentially life-changing diagnoses for millions of patients every single day.
Machine-learning (AI) program news published by Precision Vaccinations.
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AI-System Flags the Under-Vaccinated in Israel - PrecisionVaccinations
New Program Supports Machine Learning in the Chemical Sciences and Engineering – Newswise
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Newswise The Camille and Henry Dreyfus Foundation announces the establishment of a new program for Machine Learning in the Chemical Sciences and Engineering. The goal of this program is to further the understanding and applications of machine learning throughout the chemical sciences. This program is open to academic institutions in the States, Districts, and Territories of the United States of America that grant a bachelors or higher degree in the chemical sciences, including biochemistry, materials chemistry, and chemical engineering. The deadline is April 2, 2020.
"In view of the increasing attention to and expectations for the profound impacts that artificial intelligence and data science will have on physical science and engineering, the Dreyfus Foundation plans to make strategic investments in machine learning for the chemical sciences and engineering, both to advance the field in these areas, and to help position the chemical sciences field to best avail itself of the broad agency opportunities for research support that are emerging. We are enthusiastic about the potential for machine learning to produce useful fundamental and practical insights in chemical research." -Richard N. Zare and Matthew V. Tirrell, Camille and Henry Dreyfus Foundation, Scientific Affairs Committee of the Board of Directors.
Below are some examples of areas this program may support:
Note that proposals are not restricted to the areas described above.
Please contact programs@dreyfus.org if you have questions regarding a grant application. Additional details are available at http://www.dreyfus.org
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New Program Supports Machine Learning in the Chemical Sciences and Engineering - Newswise
The Problem with Hiring Algorithms – Machine Learning Times – machine learning & data science news – The Predictive Analytics Times
Originally published in EthicalSystems.org, December 1, 2019
In 2004, when a webcam was relatively unheard-of tech, Mark Newman knew that it would be the future of hiring. One of the first things the 20-year old did, after getting his degree in international business, was to co-found HireVue, a company offering a digital interviewing platform. Business trickled in. While Newman lived at his parents house, in Salt Lake City, the company, in its first five years, made just $100,000 in revenue. HireVue later received some outside capital, expanded and, in 2012, boasted some 200 clientsincluding Nike, Starbucks, and Walmartwhich would pay HireVue, depending on project volume, between $5,000 and $1 million. Recently, HireVue, which was bought earlier this year by the Carlyle Group, has become the source of some alarm, or at least trepidation, for its foray into the application of artificial intelligence in the hiring process. No longer does the company merely offer clients an asynchronous interviewing service, a way for hiring managers to screen thousands of applicants quickly by reviewing their video interview HireVue can now give companies the option of letting machine-learning algorithms choose the best candidates for them, based on, among other things, applicants tone, facial expressions, and sentence construction.
If that gives you the creeps, youre not alone. A 2017 Pew Research Center report found few Americans to be enthused, and many worried, by the prospect of companies using hiring algorithms. More recently, around a dozen interviewees assessed by HireVues AI told the Washington Post that it felt alienating and dehumanizing to have to wow a computer before being deemed worthy of a companys time. They also wondered how their recording might be used without their knowledge. Several applicants mentioned passing on the opportunity because thinking about the AI interview, as one of them told the paper, made my skin crawl. Had these applicants sat for a standard 30-minute interview, comprised of a half-dozen questions, the AI could have analyzed up to 500,000 data points. Nathan Mondragon, HireVues chief industrial-organizational psychologist, told the Washington Post that each one of those points become ingredients in the persons calculated score, between 1 and 100, on which hiring decisions candepend. New scores are ranked against a store of traitsmostly having to do with language use and verbal skillsfrom previous candidates for a similar position, who went on to thrive on the job.
HireVue wants you to believe that this is a good thing. After all, their pitch goes, humans are biased. If something like hunger can affect a hiring managers decisionlet alone classism, sexism, lookism, and other ismsthen why not rely on the less capricious, more objective decisions of machine-learning algorithms? No doubt some job seekers agree with the sentiment Loren Larsen, HireVues Chief Technology Officer, shared recently with theTelegraph: I would much prefer having my first screening with an algorithm that treats me fairly rather than one that depends on how tired the recruiter is that day. Of course, the appeal of AI hiring isnt just about doing right by the applicants. As a 2019 white paper, from the Society for Industrial and Organizational Psychology, notes, AI applied to assessing and selecting talent offers some exciting promises for making hiring decisions less costly and more accurate for organizations while also being less burdensome and (potentially) fairer for job seekers.
Do HireVues algorithms treat potential employees fairly? Some researchers in machine learning and human-computer interaction doubt it. Luke Stark, a postdoc at Microsoft Research Montreal who studies how AI, ethics, and emotion interact, told the Washington Post that HireVues claimsthat its automated software can glean a workers personality and predict their performance from such things as toneshould make us skeptical:
Systems like HireVue, he said, have become quite skilled at spitting out data points that seem convincing, even when theyre not backed by science. And he finds this charisma of numbers really troubling because of the overconfidence employers might lend them while seeking to decide the path of applicants careers.
The best AI systems today, he said, are notoriously prone to misunderstanding meaning and intent. But he worried that even their perceived success at divining a persons true worth could help perpetuate a homogenous corporate monoculture of automatons, each new hire modeled after the last.
Eric Siegel, an expert in machine learning and author of Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, echoed Starks remarks. In an email, Siegel told me, Companies that buy into HireVue are inevitably, to a great degree, falling for that feeling of wonderment and speculation that a kid has when playing with a Magic Eight Ball. That, in itself, doesnt mean HireVues algorithms are completely unhelpful. Driving decisions with data has the potential to overcome human bias in some situations, but also, if not managed correctly, could easily instill, perpetuate, magnify, and automate human biases, he said.
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The Problem with Hiring Algorithms - Machine Learning Times - machine learning & data science news - The Predictive Analytics Times
Informed decisions through machine learning will keep it afloat & going – Sea News
2020,a new year and this one brings along a sea change for those who deal with thesea business. Keeping in view the business as well as the environmentalaspects, the authorities concerned, have rolled out several directions that thecompanies on and off-the shores need to follow.
Amidst the new rules and guidelines follow there are, of course, institutions growth targets. Recounting a few directions from the authorities concerned, with the Chief Operating Officer of Greensteam Learning Technology Simon Whitford, here we have his insights on how is Greensteam taking up the challenges.
Recently, the UN announced that global GHG emissions must be reduced by 7.6% every year for the next decade. With regard to GreenSteams fuel saving figures, Mr Whitford talks about solutions they have and explains:
GreenSteams ML platform calculates a 30% non-propulsion fuel use for an average non-optimised deep sea voyage. bad weather accounts for half of this fuel use (15%) and poor vessel optimisation makes up the rest (15%). Right now, GreenSteam has tools to measure and improve vessel optimisation. The 3 target areas for vessel optimisation are trim, speed and hull cleanliness. We have solutions for trim and hull cleanliness and we are beta testing a speed optimisation solution. That means we can address this 15% of fuel wastage.
To survive this period of upheaval and changein a sector already grappling with heightened security risks, recruitmentchallenges and rising operational costs, machine learning will be a vital toolin the arsenal of any shipping company, regardless of size, location, or modeof operation.
Embracing machine learning can no longer beregarded as a nice-to-have: in the post-2020 world, it is a Must-have.
Talking on the future strategies of the company, Mr Whitford says, On our roadmap we will apply our decade in development ML platform to route. Lets say this can help avoid 3% of poor weather losses. That would allow shipping companies to meaningfully address 18% of fuel use with our zero capex, zero down-time machine learning tools.
From 2020, shipping companies operatingoutside emissions control areas (ECAs) have to meet tighter regulations in theshape of the well-documented global 0.50% sulphur cap. The IMO 2020 has broughtthe current sulphur limit outside ECAs down from 3.5%. Technology, machinelearning to be more specific, has the solutions.
Machine learning is an advanced form ofartificial intelligence in which systems use powerful algorithms and logic tocut through the noise. By learning from experience, and by being able toidentify patterns and discover trends, decisions can be made with minimal humaninvolvement.
Mr Whitford , when asked about challenges replies with the term Not Exactly. He chooses to put it very differently. He states: There is an adoption period for the technology when the algorithm is learning about the ship and crew are learning about the technology. Legacy models have to discard 90% of (good) data to create their simple, shallow stereotype of each vessel. This creates a very poor image of the way the vessel will react in various conditions, and means they cannot measure fuel wastage with any accuracy, and forecasting performance is impossible.
He adds The difficulty with machine learning is the initial 3 month learning period to capture the behaviour of the ship across a wide variety of operating conditions but this is necessary to thoroughly understand how the individual vessel will operate, divide up the areas of fuel wastage and to enable speed, trim and fouling forecasts.
Theera of the machines is finally in full swing. There is proof all over that machinelearning models are highly adaptive. They are continuously revised and refined,becoming more accurate as new data enriches the dataset. Solutions based onmachine learning enable businesses solve complex problems quickly, and muchmore effectively, than has been possible traditionally.
To sum it up, its all about improving efficiency to survive during and beyond rapid and deep change. By helping human experts to make better-informed decisions, accurate predictive automated modelling frees up the creative human for alternative tasks, which may include responding to emergencies or other unforeseen events.
Sea News Feature, January 13
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Informed decisions through machine learning will keep it afloat & going - Sea News