Every major news outlet offers updates on infections, deaths, testing, and other metrics related to COVID-19. They also link to various models, such as those on HealthData.org, from The Institute for Health Metrics and Evaluation (IHME), an independent global health research center at the University of Washington. Politicians, corporate executives, and other leaders rely on these models (and many others) to make important decisions about reopening local economies, restarting businesses, and adjusting social distancing guidelines. Many of these models possess a shortcomingthey are not built with machine learning and AI.

Predictions and Coincidence

Given the sheer numbers of scientists and data experts working on predictions about the COVID-19 pandemic, the odds favor someone being right. Like the housing crisis and other calamitous events in the U.S., someone took credit for predicting that exact event. However, its important to note the number of predictors. It creates a multiple hypothesis testing situation where the higher number of trials increases the chance of a result via coincidence.

This is playing out now with COVID-19, and we will see in the coming months many experts claiming they had special knowledge after their predictions proved true. There is a lot of time, effort, and money invested in projections, and the non-scientists involved are not as eager as the scientists to see validation and proof. AI and machine learning technologies need to step into this space to improve the odds that the right predictions were very educated projections based on data instead of coincidence.

Modeling Meets its Limits

The models predicting infection rates, total mortality, and intensive care capacity are simpler constructs. They are adjusted when the conditions on the ground materially change, such as when states reopen; otherwise, they remain static. The problem with such an approach lies partly in the complexity of COVID-19s different variables. These variables mean the results of typical COVID-19 projections do not have linear relationships with the inputs used to create them. AI comes into play here, due to its ability to ignore assumptions about the ways the predictors building the models might assist or ultimately influence the prediction.

Improving Models with Machine Learning

Machine Learning, which is one way of building AI systems, can better leverage more data sets and their interrelated connections. For example, socioeconomic status, gender, age, and health status can all inform these platforms to determine how the virus relates to current and future mortality and infections. Its enabling a granular approach to review the impacts of the virus for smaller groups who might be in age group A and geographic area Z while also having a preexisting condition X that puts people in a higher COVID-19 risk group. Pandemic planners can use AI in a similar way as financial services and retail firms leverage personalized predictions to suggest things for people to buy as well as risk and credit predictions.

Community leaders need this detail to make more informed decisions about opening regional economies and implementing plans to better protect high-risk groups. On the testing front, AI is vital for producing quality data that are specific for a city or state and takes into account more than just basic demographics, but also more complex individual-based features.

Variations in testing rules across the states require adjusting models to account for different data types and structures. Machine learning is well suited to manage these variations. The complexity of modeling testing procedures means true randomization is essential for determining the most accurate estimates of infection rates for a given area.

The Automation Advantage

The pandemic hit with crushing speed, and the scientific community has tried to quickly react. Enabling faster movement with modeling, vaccine development, and drug trials is possible with automated AI and machine learning platforms. Automation removes manual processes from the scientists day, giving them time to focus on the core of their work, instead of mundane tasks.

According to a study titled Perceptions of scientific research literature and strategies for reading papers depend on academic career stage, scientists spend a considerable amount of time reading. It states, Engaging with the scientific literature is a key skill for researchers and students on scientific degree programmes; it has been estimated that scientists spend 23% of total work time reading. Various AI-driven platforms such as COVIDScholar use web scrapers to pull all new virus-related papers, and then machine learning is used to tag subject categories. The results are enhanced research capabilities that can then inform various models for vaccine development and other vital areas. AI is also pulling insights from research papers that are hidden from human eyes, such as the potential for existing medications as possible treatments for COVID-19 conditions.

Machine learning and AI can improve COVID-19 modeling as well as vaccine and medication development. The challenges facing scientists, doctors, and policy makers provide an opportunity for AI to accelerate various tasks and eliminate time-consuming practices. For example, researchers at the University of Chicago and Argonne National Laboratory collaborated to use AI to collect and analyze radiology images in order to better diagnose and differentiate the current infection stages for COVID-19 patients. The initiative provides physicians with a much faster way to assess patient conditions and then propose the right treatments for better outcomes. Its a simple example of AIs power to collect readily available information and turn it into usable insights.

Throughout the pandemic, AI is poised to provide scientists with improved models and predictions, which can then guide policymakers and healthcare professionals to make informed decisions. Better data quality through AI also creates strategies for managing a second wave or a future pandemic in the coming decades.

About the Author

PedroAlves is the founder and CEO of Ople.AI,a software startup that provides an Automated Machine Learning platform to empower business users with predictive analytics.

While pursuing his Ph.D. in ComputationalBiology from Yale University, Alves started his career as a data scientist and gained experience in predicting, analyzing, and visualizing data in the fields of social graphs, genomics, gene networks, cancer metastasis, insurance fraud, soccer strategies, joint injuries, human attraction, spam detection and topic modeling among others. Realizing that he was learning by observing how algorithms learn from processing different models, Alves discovered that data scientists could benefit from AI that mimics this behavior of learning to learn to learn. Therefore, he founded Ople to advance the field of data science and make AI easy, cheap, and ubiquitous.

Alves enjoys tackling new problems and actively participates in the AI community through projects, lectures, panels, mentorship, and advisory boards. He is extremely passionate about all aspects of AI and dreams of seeing it deliver on its promises; driven by Ople.

Related

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Breaking Down COVID-19 Models Limitations and the Promise of Machine Learning - EnterpriseAI

At Pew Research Center, we collect and analyze data in a variety of ways. Besides asking people what they think through surveys, we also regularly study things like images, videos and even the text of religious sermons.

In a digital world full of ever-expanding datasets like these, its not always possible for humans to analyze such vast troves of information themselves. Thats why our researchers have increasingly made use of a method called machine learning. Broadly speaking, machine learning uses computer programs to identify patterns across thousands or even millions of data points. In many ways, these techniques automate tasks that researchers have done by hand for years.

Our latest video explainer part of our Methods 101 series explains the basics of machine learning and how it allows researchers at the Center to analyze data on a large scale. To learn more about how weve used machine learning and other computational methods in our research, including the analysis mentioned in this video, you can explore recent reports from our Data Labs team.

Excerpt from:
What is machine learning, and how does it work? - Pew Research Center

With healthcare organizations around the world leveraging cloud technologies for key clinical and operational systems, the industry is building toward digitally enhanced, data-driven healthcare.

And unstructured healthcare data, within clinical documents and summaries, continues to remain an important source of insights to support clinical and operational excellence.

But there are countless nuggets of important unstructured data something that does not lend itself to manual search and manipulation by clinicians. This is where automation comes.

Arun Ravi, senior product leader at Amazon Web Services is co-presenting a HIMSS20 Digital presentation on unstructured healthcare data and machine learning, Accelerating Insights from Unstructured Data, Cloud Capabilities to Support Healthcare.

There is a huge shift from volume- to value-based care: 54% of hospital CEOs see the transition from volume to value as their biggest financial challenge, and two-thirds of the IT budget goes toward keeping the lights on, Ravi explained.

Machine learning has this really interesting role to play where were not necessarily looking to replace the workflows, but give essentially a superpower to people in healthcare and allow them to do their jobs a lot more efficiently.

In terms of how this affects health IT leaders, with value-based care there is a lot of data that is being created. When a patient goes through the various stages of care, there is a lot of documentation, a lot of data that is created.

But how do you apply the resources that are available to make it much more streamlined, to create that perfect longitudinal view of the patient? Ravi asked. A lot of the current IT models lack that agility to keep pace with technology. And again, its about giving the people in this space a superpower to help them bring the right data forward and use that in order to make really good clinical decisions.

This requires responding to a very new model that has come into play. And this model requires focus on differentiating a healthcare organizations ability to do this work in real time and do it at scale.

How you incorporate these new technologies into care delivery in a way that not only is scalable but actually reaches your patients and also makes sure your internal stakeholders are happy with it, Ravi said. And again, you want to reduce the risk, but overall, how do you manage this data well in a way that is easy for you to scale and easy for you to deploy into new areas as the care model continues to shift?

So why is machine learning important in healthcare?

If you look at the amount of unstructured data that is created, it is increasing exponentially, said Ravi. And a lot of that remains untapped. There are 1.2 billion unstructured clinical documents that are actually created every year. How do you extract the insights that are valuable for your application without applying manual approaches to it?

Automating all of this really helps a healthcare organization reduce the expense and the time that is spent trying to extract these insights, he said. And this creates a unique opportunity, not just to innovate but to build new products, he added.

Ravi and his co-presenter, Paul Zhao, senior product leader at AWS, offer an in-depth look into gathering insights from all of this unstructured healthcare data via machine learning and cloud capabilities in their HIMSS20 Digital session. To attend the session, click here.

Twitter:@SiwickiHealthITEmail the writer:bill.siwicki@himss.orgHealthcare IT News is a HIMSS Media publication.

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Machine learning can give healthcare workers a 'superpower' - Healthcare IT News

TLDR: The Complete 2020 Big Data and Machine Learning Bundle breaks down understanding and getting started in two of the tech eras biggest new growth sectors.

Its instructive to know just how big Big Data really is. And the reality is that its now so big that the word big doesnt even effectively do it justice anymore. Right now, humankind is creating 2.5 quintillion bytes of data every day. And its growing exponentially, with 90 percent of all data created in just the past two years. By 2023, the big data industry will be worth about $77 billion and thats despite the fact that unstructured data is identified as a problem by 95 percent of all businesses.

Meanwhile, data analysis is also the background of other emerging fields, like the explosion of machine learning projects that have companies like Apple scooping up machine learning upstarts.

The bottom is that if you understand Big Data, you can effectively right your own ticket salary-wise. You can jump into this fascinating field the right way with the training in The Complete 2020 Big Data and Machine Learning Bundle, on sale now for $39.90, over 90 percent off from TNW Deals.

This collection includes 10 courses featuring 68 hours of instruction covering the basics of big data, the tools data analysts need to know, how machines are being taught to think for themselves, and the career applications for learning all this cutting-edge technology.

Everything starts with getting a handle on how data scientists corral mountains of raw information. Six of these courses focus on big data training, including close exploration of the essential industry-leading tools that make it possible. If you dont know what Hadoop, Scala or Elasticsearch do or that Spark Streaming is a quickly developing technology for processing mass data sets in real-time, you will after these courses.

Meanwhile, the remaining four courses center on machine learning, starting with a Machine Learning for Absolute Beginners Level 1 course that helps first-timers get a grasp on the foundations of machine learning, artificial intelligence and deep learning. Students also learn about the Python coding languages role in machine learning as well as how tools like Tensorflow and Keras impact that learning.

A training package valued at almost $1,300, you can start turning Big Data and machine learning into a career with this instruction for just $39.90.

Prices are subject to change.

Read next: The 'average' Robinhood trader is no match for the S&P 500, just like Buffett

Read our daily coverage on how the tech industry is responding to the coronavirus and subscribe to our weekly newsletter Coronavirus in Context.

For tips and tricks on working remotely, check out our Growth Quarters articles here or follow us on Twitter.

Excerpt from:
Big data and machine learning are growing at massive rates. This training explains why - The Next Web

This weeks Top 5 comes from Teo Susnjaka computer scientistspecialising in machine learning. He is a Senior Lecturer in Information Technology at Massey University and is the developer behind GDPLive.

As always, we welcome your additions in the comments below or via email to david.chaston@interest.co.nz.

And if you're interested in contributing the occasional Top 5yourself, contact gareth.vaughan@interest.co.nz.

1. Covid-19 broke machine learning.

As the Covid-19 crisis started to unfold, we started to change our buying patterns. All of a sudden, some of the top purchasing items became: antibacterial soap, sanitiser, face masks, yeast and of course, toilet paper. As the demand for these unexpected items exploded, retail supply chains were disrupted. But they weren't the only ones affected.

Artificial intelligence systems began to break too. The MIT Technology Review reports:

Machine-learning models that run behind the scenes in inventory management, fraud detection, and marketing rely on a cycle of normal human behavior. But what counts as normal has changed, and now some are no longer working.

How bad the situation is depends on whom you talk to. According to Pactera Edge, a global AI consultancy, automation is in tailspin. Others say they are keeping a cautious eye on automated systems that are just about holding up, stepping in with a manual correction when needed.

Whats clear is that the pandemic has revealed how intertwined our lives are with AI, exposing a delicate codependence in which changes to our behavior change how AI works, and changes to how AI works change our behavior. This is also a reminder that human involvement in automated systems remains key. You can never sit and forget when youre in such extraordinary circumstances, says Cline.

Image source: MIT Technology Review

The extreme data capturing a previously unseen collapse in consumer spending that feeds the real-time GDP predictor at GDPLive.net, also broke our machine learning algorithms.

2. Extreme data patterns.

The eminent economics and finance historian, Niall Ferguson (not to be confused with Neil Ferguson who also likes to create predictive models) recently remarked that the first month of the lockdown created conditions which took a full year to materialise during the Great Depression.

The chart below shows the consumption data falling off the cliff, generating inputs that broke econometrics and machine learning models.

What we want to see is a rapid V-shaped recovery in consumer spending. The chart below shows the most up-to-date consumer spending trends. Consumer spending has now largely recovered, but is still lower than that of the same period in 2019. One of the key questions will be whether or not this partial rebound will be temporary until the full economic impacts of the 'Great Lockdown' take effect.

Paymark tracks consumer spending on their new public dashboard. Check it out here.

3. Wealth and income inequality.

As the current economic crisis unfolds, GDP will take centre-stage again and all other measures which attempt to quantify wellbeing and social inequalities will likely be relegated until economic stability returns.

When the conversation does return to this topic, AI might have something to contribute.

Effectively addressing income inequality is a key challenge in economics with taxation being the most useful tool. Although taxation can lead to greater equalities, over-taxation discourages from working and entrepreneurship, and motivates tax avoidance. Ultimately this leaves less resources to redistribute. Striking an optimal balance is not straightforward.

The MIT Technology Reviewreports thatAI researchers at the US business technology company Salesforce implemented machine learning techniques that identify optimal tax policies for a simulated economy.

In one early result, the system found a policy thatin terms of maximising both productivity and income equalitywas 16% fairer than a state-of-the-art progressive tax framework studied by academic economists. The improvement over current US policy was even greater.

Image source: MIT Technology Review

It is unlikely that AI will have anything meaningful to contribute towards tackling wealth inequality though. If Walter Scheidel, author of The Great Leveller and professor of ancient history at Stanford is correct, then the only historically effective levellers of inequality are: wars, revolutions, state collapses and...pandemics.

4. Bots and propaganda.

Over the coming months, arguments over what has caused this crisis, whether it was the pandemic or the over-reactive lockdown policies, will occupy much of social media. According to The MIT Technology Review, bots are already being weaponised to fight these battles.

Nearly half of Twitter accounts pushing to reopen America may be bots. Bot activity has become an expected part of Twitter discourse for any politicized event. Across US and foreign elections and natural disasters, their involvement is normally between 10 and 20%. But in a new study, researchers from Carnegie Mellon University have found that bots may account for between 45 and 60% of Twitter accounts discussing covid-19.

To perform their analysis, the researchers studied more than 200 million tweets discussing coronavirus or covid-19 since January. They used machine-learning and network analysis techniques to identify which accounts were spreading disinformation and which were most likely bots or cyborgs (accounts run jointly by bots and humans).

They discovered more than 100 types of inaccurate Covid-19-19 stories and found that not only were bots gaining traction and accumulating followers, but they accounted for 82% of the top 50 and 62% of the top 1,000 influential retweeters.

Image source: MIT Technology Review

How confident are you that you can tell the difference between a human and a bot? You can test yourself out here. BTW, I failed.

5. Primed to believe bad predictions.

This has been a particularly uncertain time. We humans don't like uncertainty especially once it reaches a given threshold. We have an amazing brain that is able to perform complex pattern recognition that enables us to predict what's around the corner. When we do this, we resolve uncertainty and our brain releases dopamine, making us feel good. When we cannot make sense of the data and the uncertainty remains unresolved, then stress kicks in.

Writing on this in Forbes, John Jennings points out:

Research shows we dislike uncertainty so much that if we have to choose between a scenario in which we know we will receive electric shocks versus a situation in which the shocks will occur randomly, well select the more painful option of certain shocks.

The article goes on to highlight how we tend to react in uncertain times. Aversion to uncertainty drives some of us to try to resolve it immediately through simple answers that align with our existing worldviews. For others, there will be a greater tendency to cluster around like-minded people with similar worldviews as this is comforting. There are some amongst us who are information junkies and their hunt for new data to fill in the knowledge gaps will go into overdrive - with each new nugget of information generating a dopamine hit. Lastly, a number of us will rely on experts who will use their crystal balls to find for us the elusive signal in all the noise, and ultimately tell us what will happen.

The last one is perhaps the most pertinent right now. Since we have a built-in drive that seeks to avoid ambiguity, in stressful times such as this, our biology makes us susceptible to accepting bad predictions about the future as gospel especially if they are generated by experts.

Experts at predicting the future do not have a strong track record considering how much weight is given to them. Their predictive models failed to see the Global Financial Crisis coming, they overstated the economic fallout of Brexit, the climate change models and their forecasts are consistently off-track, and now we have the pandemic models.

Image source:drroyspencer.com

The author suggests that this time "presents the mother of all opportunities to practice learning to live with uncertainty". I would also add that a good dose of humility on the side of the experts, and a good dose of scepticism in their ability to accurately predict the future both from the public and decision makers, would also serve us well.

Excerpt from:
Massey University's Teo Susnjak on how Covid-19 broke machine learning, extreme data patterns, wealth and income inequality, bots and propaganda and...

Telecommuters: tired of the constant embarrassment of showing up to video conferences wearing nothing but your underwear? Save the humiliation and all those pesky trips down to HR with Safe Meeting, the new system that uses the power of artificial intelligence to turn off your camera if you forget that casual Friday isnt supposed to be that casual.

The following infomercial is brought to you by [Nick Bild], who says the whole thing is tongue-in-cheek but we sense a certain degree of necessity is the mother of invention here. Its true that the sudden throng of remote-work newbies certainly increases the chance of videoconference mishaps and the resulting mortification, so whatever the impetus, Safe Meeting seems like a great idea. It uses a Pi cam connected to a Jetson Nano to capture images of you during videoconferences, which are conducted over another camera. The stream is classified by a convolutional neural net (CNN) that determines whether it can see your underwear. If it can, it makes a REST API call to the conferencing app to turn off the camera. The video below shows it in action, and that it douses the camera quickly enough to spare your modesty.

We shudder to think about how [Nick] developed an underwear-specific training set, but we applaud him for doing so and coming up with a neat application for machine learning. Hes been doing some fun work in this space lately, from monitoring where surfaces have been touched to a 6502-based gesture recognition system.

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Machine Learning Takes The Embarrassment Out Of Videoconference Wardrobe Malfunctions - Hackaday

As a Senior Product Manager for Revenue Management at Zalando Payments, you and your team of experienced researchers & engineers will work on cutting edge Machine Learning products to support our end to end Payments platform.WHERE YOUR EXPERTISE IS NEEDED

We celebrate diversity and are committed to building teams that represent a variety of backgrounds, perspectives and skills. All employment is decided on the basis of qualifications, merit and business need.

ABOUT ZALANDOZalando is Europe's leading online platform for fashion and lifestyle, connecting customers, brands and partners across 17 markets. We drive digital solutions for fashion, logistics, advertising and research, bringing head-to-toe fashion to more than 23 million active customers through diverse skill-sets, interests and languages our teams choose to use.

Our Payana Team includes of 12 highly motivated and skilled data scientists and research engineers. Our mission is to provide accurate and scalable prediction services for managing the payment risk of every checkout session and each order on the zalando platform. We work in groups, autonomously developing end-to-end solutions while following an agile process.

Please note that all applications must be completed using the online form - we do not accept applications via email.

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Senior Product Manager Payments - Machine Learning job with Zalando | 141053 - The Business of Fashion