If not the most deadly, the novel coronavirus (COVID-19) is one of the most contagious diseases to have hit our green planet in the past decades. In little over three months since the virus was first spotted in mainland China, it has spread to more than 90 countries, infected more than 185,000 people, and taken more than 3,500 lives.

As governments and health organizations scramble to contain the spread of coronavirus, they need all the help they can get, including from artificial intelligence. Though current AI technologies arefar from replicating human intelligence, they are proving to be very helpful in tracking the outbreak, diagnosing patients, disinfecting areas, and speeding up the process of finding a cure for COVID-19.

Data science and machine learning might be two of the most effective weapons we have in the fight against the coronavirus outbreak.

Just before the turn of the year, BlueDot, an artificial intelligence platform that tracks infectious diseases around the world, flagged a cluster of unusual pneumonia cases happening around a market in Wuhan, China. Nine days later, the World Health Organization (WHO)released a statementdeclaring the discovery of a novel coronavirus in a hospitalized person with pneumonia in Wuhan.

BlueDot usesnatural language processingandmachine learning algorithmsto peruse information from hundreds of sources for early signs of infectious epidemics. The AI looks at statements from health organizations, commercial flights, livestock health reports, climate data from satellites, and news reports. With so much data being generated on coronavirus every day, the AI algorithms can help home in on the bits that can provide pertinent information on the spread of the virus. It can also find important correlations between data points, such as the movement patterns of the people who are living in the areas most affected by the virus.

The company also employs dozens of experts who specialize in a range of disciplines including geographic information systems, spatial analytics, data visualization, computer sciences, as well as medical experts in clinical infectious diseases, travel and tropical medicine, and public health. The experts review the information that has been flagged by the AI and send out reports on their findings.

Combined with the assistance of human experts, BlueDots AI can not only predict the start of an epidemic, but also forecast how it will spread. In the case of COVID-19, the AI successfully identified the cities where the virus would be transferred to after it surfaced in Wuhan. Machine learning algorithms studying travel patterns were able to predict where the people who had contracted coronavirus were likely to travel.

Coronavirus (COVID-19) (Image source:NIAID)

You have probably seen the COVID-19 screenings at border crossings and airports. Health officers use thermometer guns and visually check travelers for signs of fever, coughing, and breathing difficulties.

Now,computer vision algorithmscan perform the same at large scale. An AI system developed by Chinese tech giant Baidu uses cameras equipped with computer vision and infrared sensors to predict peoples temperatures in public areas. The system can screen up to 200 people per minute and detect their temperature within a range of 0.5 degrees Celsius. The AI flags anyone who has a temperature above 37.3 degrees. The technology is now in use in Beijings Qinghe Railway Station.

Alibaba, another Chinese tech giant, has developed an AI system that candetect coronavirus in chest CT scans. According to the researchers who developed the system, the AI has a 96-percent accuracy. The AI was trained on data from 5,000 coronavirus cases and can perform the test in 20 seconds as opposed to the 15 minutes it takes a human expert to diagnose patients. It can also tell the difference between coronavirus and ordinary viral pneumonia. The algorithm can give a boost to the medical centers that are already under a lot of pressure to screen patients for COVID-19 infection. The system is reportedly being adopted in 100 hospitals in China.

A separate AI developed by researchers from Renmin Hospital of Wuhan University, Wuhan EndoAngel Medical Technology Company, and the China University of Geosciences purportedly shows 95-percent accuracy on detecting COVID-19 in chest CT scans. The system is adeep learning algorithmtrained on 45,000 anonymized CT scans. According to a preprint paperpublished on medRxiv, the AIs performance is comparable to expert radiologists.

One of the main ways to prevent the spread of the novel coronavirus is to reduce contact between infected patients and people who have not contracted the virus. To this end, several companies and organizations have engaged in efforts to automate some of the procedures that previously required health workers and medical staff to interact with patients.

Chinese firms are using drones and robots to perform contactless delivery and to spray disinfectants in public areas to minimize the risk of cross-infection. Other robots are checking people for fever and other COVID-19 symptoms and dispensing free hand sanitizer foam and gel.

Inside hospitals, robots are delivering food and medicine to patients and disinfecting their rooms to obviate the need for the presence of nurses. Other robots are busy cooking rice without human supervision, reducing the number of staff required to run the facility.

In Seattle, doctors used a robot to communicate with and treat patients remotely to minimize exposure of medical staff to infected people.

At the end of the day, the war on the novel coronavirus is not over until we develop a vaccine that can immunize everyone against the virus. But developing new drugs and medicine is a very lengthy and costly process. It can cost more than a billion dollars and take up to 12 years. Thats the kind of timeframe we dont have as the virus continues to spread at an accelerating pace.

Fortunately, AI can help speed up the process. DeepMind, the AI research lab acquired by Google in 2014, recently declared that it has used deep learning to find new information about the structure of proteins associated with COVID-19. This is a process that could have taken many more months.

Understanding protein structures can provide important clues to the coronavirus vaccine formula. DeepMind is one of several organizations who are engaged in the race to unlock the coronavirus vaccine. It has leveraged the result of decades of machine learning progress as well as research on protein folding.

Its important to note that our structure prediction system is still in development and we cant be certain of the accuracy of the structures we are providing, although we are confident that the system is more accurate than our earlier CASP13 system, DeepMinds researchers wroteon the AI labs website. We confirmed that our system provided an accurate prediction for the experimentally determined SARS-CoV-2 spike protein structure shared in the Protein Data Bank, and this gave us confidence that our model predictions on other proteins may be useful.

Although its too early to tell whether were headed in the right direction, the efforts are commendable. Every day saved in finding the coronavirus vaccine can save hundredsor thousandsof lives.

This story is republished fromTechTalks, the blog that explores how technology is solving problems and creating new ones. Like them onFacebookhere and follow them down here:

Published March 21, 2020 17:00 UTC

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Why AI might be the most effective weapon we have to fight COVID-19 - The Next Web

The latest report titled, Global Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 unveils the value at which the Machine Learning in Retail industry is anticipated to grow during the forecast period, 2019 to 2024. The report estimates CAGR analysis, competitive strategies, growth factors and regional outlook 2024. The report is a rich source of an exhaustive study of the driving elements, limiting components, and different market changes. It states market structure and then further forecasts several segments and sub-segments of the global market. The market study is provided on the basis of type, application, manufacturer as well as geography. Different elements such as opportunities, drivers, restraints, and challenges, market situation, market share, growth rate, future trends, risks, entry limits, sales channels, distributors are analyzed and examined within this report.

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Emerging Trend of Machine Learning in Retail Market 2019 by Company, Regions, Type and Application, Forecast to 2024 - Bandera County Courier

By Davide Zilli, Client Services Director at Mind Foundry

Today in so many industries, from manufacturing and life sciences to financial services and retail, we rely on algorithms to conduct large-scale machine learning analysis. They are hugely effective for problem-solving and beneficial for augmenting human expertise within an organization. But they are now under the spotlight for many reasons and regulation is on the horizon, with Gartner projecting four of the G7 countries will establish dedicated associations to oversee AI and ML design by 2023. It remains vital that we understand their reasoning and decision-making process at every step.

Algorithms need to be fully transparent in their decisions, easily validated and monitored by a human expert. Machine learning tools must introduce this full accountability to evolve beyond unexplainable black box solutions and eliminate the easy excuse of the algorithm made me do it!"

Bias can be introduced into the machine learning process as early as the initial data upload and review stages. There are hundreds of parameters to take into consideration during data preparation, so it can often be difficult to strike a balance between removing bias and retaining useful data.

Gender for example might be a useful parameter when looking to identify specific disease risks or health threats, but using gender in many other scenarios is completely unacceptable if it risks introducing bias and, in turn, discrimination. Machine learning models will inevitably exploit any parameters such as gender in data sets they have access to, so it is vital for users to understand the steps taken for a model to reach a specific conclusion.

Removing the complexity of the data science procedure will help users discover and address bias faster and better understand the expected accuracy and outcomes of deploying a particular model.

Machine learning tools with built-in explainability allow users to demonstrate the reasoning behind applying ML to a tackle a specific problem, and ultimately justify the outcome. First steps towards this explainability would be features in the ML tool to enable the visual inspection of data with the platform alerting users to potential bias during preparation and metrics on model accuracy and health, including the ability to visualize what the model is doing.

Beyond this, ML platforms can take transparency further by introducing full user visibility, tracking each step through a consistent audit trail. This records how and when data sets have been imported, prepared and manipulated during the data science process. It also helps ensure compliance with national and industry regulations such as the European Unions GDPR right to explanation clause and helps effectively demonstrate transparency to consumers.

There is a further advantage here of allowing users to quickly replicate the same preparation and deployment steps, guaranteeing the same results from the same data particularly vital for achieving time efficiencies on repetitive tasks. We find for example in the Life Sciences sector, users are particularly keen on replicability and visibility for ML where it becomes an important facility in areas such as clinical trials and drug discovery.

There are so many different model types that it can be a challenge to select and deploy the best model for a task. Deep neural network models, for example, are inherently less transparent than probabilistic methods, which typically operate in a more honest and transparent manner.

Heres where many machine learning tools fall short. Theyre fully automated with no opportunity to review and select the most appropriate model. This may help users rapidly prepare data and deploy a machine learning model, but it provides little to no prospect of visual inspection to identify data and model issues.

An effective ML platform must be able to help identify and advise on resolving possible bias in a model during the preparation stage, and provide support through to creation where it will visualize what the chosen model is doing and provide accuracy metrics and then on to deployment, where it will evaluate model certainty and provide alerts when a model requires retraining.

Building greater visibility into data preparation and model deployment, we should look towards ML platforms that incorporate testing features, where users can test a new data set and receive best scores of the model performance. This helps identify bias and make changes to the model accordingly.

During model deployment, the most effective platforms will also extract extra features from data that are otherwise difficult to identify and help the user understand what is going on with the data at a granular level, beyond the most obvious insights.

The end goal is to put power directly into the hands of the users, enabling them to actively explore, visualize and manipulate data at each step, rather than simply delegating to an ML tool and risking the introduction of bias.

The introduction of explainability and enhanced governance into ML platforms is an important step towards ethical machine learning deployments, but we can and should go further.

Researchers and solution vendors hold a responsibility as ML educators to inform users of the use and abuses of bias in machine learning. We need to encourage businesses in this field to set up dedicated education programs on machine learning including specific modules that cover ethics and bias, explaining how users can identify and in turn tackle or outright avoid the dangers.

Raising awareness in this manner will be a key step towards establishing trust for AI and ML in sensitive deployments such as medical diagnoses, financial decision-making and criminal sentencing.

AI and machine learning offer truly limitless potential to transform the way we work, learn and tackle problems across a range of industriesbut ensuring these operations are conducted in an open and unbiased manner is paramount to winning and retaining both consumer and corporate trust in these applications.

The end goal is truly humble, honest algorithms that work for us and enable us to make unbiased, categorical predictions and consistently provide context, explainability and accuracy insights.

Recent research shows that 84% of CEOs agree that AI-based decisions must be explainable in order to be trusted. The time is ripe to embrace AI and ML solutions with baked in transparency.

About the author:

Davide Zilli, Client Services Director at Mind Foundry

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Originally posted here:
Keeping Machine Learning Algorithms Humble and Honest in the Ethics-First Era - Datamation

PHOTO:Charles

Lets say you have a customer who has taken a certain action: downloaded an ebook, filled out an application, added a product to their cart, called into your call center or walked into your branch office, to name a few. What content, offer or message should you deliver to them next? What next step should you recommend? How can you best add value for that individual, while nurturing the person, wherever they are in their relationship with your business?

Based on your history (or even lack of history) with a given individual, you and your company might also have questions such as: Whats the best product to upsell to this particular client? (and should I even try to upsell that person?); Whats the right promotion to show an engaged shopper on my ecommerce site? and Whats the right item to promote to someone logged into my application? The list goes on.

These types of questions are all important to businesses today, who often talk about next best actions. This customer-centric (often 1-to-1) approach and sequencing strategy can take a number of forms. But at a basic level, the concept means what it sounds like: determining the most relevant or appropriate next action (or offer, promotion, content, etc.) to show a person in the moment, based on their current and previous actions or other information youve gathered about them across your online and offline channels. Next best actions can also include triggering messages to call center agents or sales reps to alert them of important activity, or to suggest the next best action they should take with a customer.

Companies put awide variety of thought, time and effort into establishing sequencing paths from none at all (with a one-size-fits-all message, promotion, offer, etc.) to a lot. At a majority of organizations, though, determining the next best action for their customers is very important, involving multiple teams of people across functions and divisions.

There are teams of marketers and designers, for instance, who create elaborate promotions and offers with different media for different channels. And there are customer experience teams who devote many cycles to thinking about call-center scripts and next best actions.

So when it comes to deploying those next best actions, it can devolve into an inter-departmental war about who gets the prime real estate. For example, when new visitors hit the homepage or when customers log into the app, what gets displayed in the hero area?

Why all the effort and involvement? Its because next best actions are strategically important to engagement and the bottom line. Present the right, relevant offer or action to a customer or prospect, and youre helping elicit interest and drive conversions. Present the wrong (e.g., outdated, irrelevant, mismatched to sales cycle stage, etc.) one, and youre losing customer interest or even turning them off your brand.

Related Article: Good Personalization Hinges on Good Data

For many years, organizations have taken a rule-based approach to determining the right next best action for a particular customer in a particular channel or at a particular stage in their journey. Rules are manually created and structured with if-then logic (e.g., IF a person takes this action or belongs to this group, THEN display this next). They govern the experiences and actions for audience segments which can be broad or get very narrow.

Three types of rules are the most frequently applied to next-best-action decisioning. These can be used on their own or, typically, in concert:

Related Article: Why Personalization Efforts Fail

But one problem with rules is the more targeted and relevant you want to get, the greater the number of rules you need to make. With rules, personalization of the next best action is inversely correlated to simplicity. In other words, to deliver truly relevant and highly specific actions and experiences using rules only, you quickly enter a world of nearly unmanageable complexity.

Theres also the time factor to consider. As you have likely experienced, it takes a lot of hours to create and prescribe sequencing via rules for the multitude of scenarios customers can encounter and the paths they can take. And unraveling a heavily nested set of rules in order to make minor adjustments (and make them correctly) can take many more hours.

Another problem with rules is that they are just a human guessing. Suppose youre wrong in the next best action youve set up for a customer to receive in fact, it may actually be hurting revenues or customer loyalty.

So while rules do play a vital role in determining and displaying next best actions, a rules-only-based approach generally isnt optimal or scalable in the long-term.

Related Article: Refine Your Personalization Efforts by Ditching Tech-First Tendencies

Machine learning, a type of artificial intelligence (AI), can supplement rules and play a powerful role in prioritization and other next-best-action decisions: pulling in everything known about an individual in the channel of engagement and across channels, factoring in data from similar people, and then computing and displaying the optimal, relevant next best action or offer at the 1-to-1 level. Typically, this all occurs in milliseconds faster than you can blink an eye.

Across industries, theres an enormous amount of behavioral data to parse through to uncover trends and indicators of what to do next with any given individual. This can be combined with attribute and transaction data to build a rich profile and predictive intelligence. Machine-learning algorithms automate this process, make surprising discoveries and keep learning based on ever-growing data: from studying both the individual customer and customers with similar attributes and behaviors, and from learning from how customers are reacting to the actions being suggested to them.

In addition, when multiple promotions or next actions are valid, you can apply machine learning to decide on and display the truly optimal one, balancing whats best for the customer with whats best for your business.

Optimized machine-learning-driven next best actions outperform manual ones, even when what they suggest might seem counter-intuitive. For example, a banking institution might promote its most popular cash-back credit card offer to all new site visitors. But for return visitors located in colder climate regions, a continuous learning algorithm might determine that the banks travel rewards card offer performs much better. Only machine learning can pick up on behavioral signals and information at scale (including seemingly unimportant information) in a way that humans simply cannot.

Related Article: 5 Drivers of Personalized Experiences: A Walk Through the AI Food Chain

Determining and displaying next best actions involve integrations and interplay across channels. One system is informing another of an action a customer has taken and what to do next. For example: a customer who joined the loyalty program could be eligible to receive a certain promotion in their email. Or a shopper who browsed purses online can be push-notified a coupon code to use in-store, thanks to beacon technology. An alert might get triggered to a call center agent based on a customers unfinished loan application letting the agent know to provide information on interest rates or help set up an appointment at the customers local branch as that person is calling in.

Given the wide range of activity and vast quantities of data, its important to have a single system that can arbitrate all these actions, apply prioritization and act as the central brain. This helps keep customer information unified and up-to-date, and aids in real-time interaction management and experience delivery.

In the end, everything organizations do when communicating and relating to their customers could be viewed as next best actions. In fact, personalization and next best actions are closely intertwined, as two sides of the same coin. Its hard to separate a next best action from the personalization decisioning driving it, which is why the two areas should be (and sometimes are) tied together from a strategy and systems perspective.

By effectively determining and triggering personalized next steps, you can tell a cohesive and consistent cross-channel story that bolsters brand perception, improves the buyer journey and turns next best actions into must-take ones.

Karl Wirth is the CEO and co-founder of Evergage, a Salesforce Company and a leading real-time personalization and interaction management platform provider. Karl is also the author of the award-winning book One-to-One Personalization in the Age of Machine Learning.

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The Power of AI in 'Next Best Actions' - CMSWire