Category Archives: Artificial Intelligence

Artificial intelligence (AI) in business is rapidly becoming a commonly-used competitive tool. Clearly, companies are past debating the pros and cons of AI. From better chatbots for customer service to data analytics to making predictive recommendations, deep learning and artificial intelligence in their many forms is seen by business leaders as an essential tool.

That puts AI in the short-list of technologies that your company should not just be watching, but actively exploring how to take advantage of. It joins leading emerging technologies like Machine Learning, cloud computing and Big Data.

If you aren't convinced that AI is ready to handle a growing number and range of tasks, consider IBM's Watson's 2011 winning performance on Jeopardy. Or consider the various ways you are likely already using AI-enabled devices and services in your personal life, like smart assistant apps or devices like Amazon's Alexa or Apple's Siri. Not to mention other AI-supercharged apps, such as whatever GPS app you use while driving.

Here's a quick look at how your competitors are already using AI in their business, and some advice on how to get on board.

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Results of a recent survey indicate that artificial intelligence can assist businesses in areas ranging from customer support to personalization.

Odds are you can't just call up your competitors and ask how they are using AI in their company. But thanks to the Internet, you can find out a lot of what they have said. For example, web-searching "how is Staples using AI" yields informative results from about how that company is putting artificial intelligence technology to work for itself.

Next, check your competitors' web sites and social media presences (notably LinkedIn and Facebook). Browse their press releases, news coverage, and blogs. You might even go old-school, and get any hardcopy newsletters, annual reports or other literature from the past year that might not be available online.

Then cast a wider net, with an industry search, like "how are hospitals using AI," "how are grocery stores using AI," or even a more general search.

For example, when I did a search for "using AI in my business," I got various hits talking about business uses for AI including:

Another suggestion: research how other parts of your supply chain parts, shipping, support, and the like are using AI.

Don't forget other IRL (In Real Life)/non-digital avenues. If you are going to an industry event, look for AI-related sessions. Chat up whoever you're standing or sitting near. And, of course, you could always read a book or two although, by definition, that advice will be at least six to twelve months out of date.

If you know anybody at one or more competitors who's informed and amenable, buy them a lunch and pick their brain.

Businesses have high hopes that AI can help them predict a wide array of activities.

Based on your research, you should be able to build a list and frame a sense of what AI can do for businesses in general, and for companies in your industry and of your size.

You'll find several major key areas:

Based on this list, your next step is to come up with a short list of how artificial intelligence can help your business specific tasks and use cases.

To help make this list:

Then, prioritize that list based on a mix of estimated costs, time to implement, risk/benefit, and overall value.

In parallel, select one or two smaller tasks for trying artificial intelligence for your business. This could be a small piece of a larger task. Important: start with a task that is not business-critical. Another quick tip: Start with tasks that aren't customer-facing.

Now it's time to identify potential technology vendors. There is no shortage of top artificial intelligence companies.

In order to find and compare vendors, you first have to assess how you might add artificial intelligence capabilities to your company's IT, which in turn depends on factors like:

Vendors for AI capabilities spans several categories:

For some of the AI you're looking for, your current vendors may already offer. In other cases, you may outsource. For still others, you may end up doing internally. It all depends on what you want, how much developer bandwidth you have in-house, and how you provision your IT operations.

Your best bet will be to find one or more AI experts, either internally, or outside consultants. For the latter, start with ones who aren't part of a vendor... unless the vendor is offering AI that is a match for your criteria.

Once you have identified your initial AI projects the real fun begins: implementation. Essential milestones:

Key: Be ready to revisit constantly.

Just because you have AI projects out of development and testing, and contributing to your business, that doesn't mean you're done. Just as provisioning infrastructure or updating your company's web and social presence is never done.

In addition to tracking your selected AI vendors for improvements, new features you want to stay on top of other AI developments. For example, what new capabilities have become available? What improvements in infrastructure performance or price make existing or new AI offerings now viable?

And, of course, you want to keep up with what others in your industry, and the AI vendors serving your industry, are doing or have on their road map.

Adding AI your company's operations and business is a big change, and likely a big transformation. Here's some quick advice to lessen the challenges:

Plus, focus on AI that's available as a supported product/service, rather than something still in development.

Although AI as an area within computer science dates back to the 1950's, it's only been within the past decade that many types of AI have become available to companies of all sizes.

This is thanks to factors like continuing hardware price/performance improvements, cloud computing, and advances in AI techniques. At the same time, computing trends like big data, IoT, self-driving vehicles, and speech and image recognition are generating more "targets" to point AI tools at.

In particular, Keep an eye on cloud costs and capabilities, along with what the various players are doing or talking about, AI-wise. Like nearly everything involving computer technology, many of the next cool capabilities can't be anticipated or predicted. Bottom line: talk to professionals in your field nothing will help you quite as much.

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Artificial Intelligence in Business: How to Use AI in Your ...

In the grand scheme of things, artificial intelligence (AI) is still in the very early stages of adoption by most organizations. However, most leaders are quite excited to implement AI into the companys business functions to start realizing its extraordinary benefits. While we have no way of knowing all the ways artificial intelligence and machine learning will ultimately impact business functions, here are 10 business functions that are ready to use artificial intelligence.

10 Business Functions That Are Ready To Use Artificial Intelligence

Marketing

If your company isnt using artificial intelligence in marketing, it's already behind. Not only can AI help to develop marketing strategies, but it's also instrumental in executing them. Already AI sorts customers according to interest or demographic, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it. Another way AI is used in marketing is through chatbots. These bots can help solve problems, suggest products or services, and support sales. Artificial intelligence also supports marketers by analyzing data on consumer behavior faster and more accurately than humans. These insights can help businesses make adjustments to marketing campaigns to make them more effective or plan better for the future.

Sales

There is definitely a side of selling products and services that is uniquely human, but artificial intelligence can arm sales professionals with insights that can improve the sales function. AI helps improve sales forecasting, predict customer needs, and improve communication. And intelligent machines can help sales professionals manage their time and identify who they need to follow-up with and when as well as what customers might be ready to convert.

Research and Development (R&D)

What about artificial intelligence as a tool of innovation? It can help us build a deeper understanding in nearly any industry, including healthcare and pharmaceuticals, financial, automotive, and more, while collecting and analyzing tremendous amounts of information efficiently and accurately. This and machine learning can help us research problems and develop solutions that weve never thought of before. AI can automate many tasks, but it will also open the door to novel discoveries, ways of improving products and services as well as accomplishing tasks. Artificial intelligence helps R&D activities be more strategic and effective.

IT Operations

Also called AIOps, AI for IT operations is often the first experience many organizations have with implementing artificial intelligence internally. Gartner defines the term AIOps as the application of machine learning and data science to IT operations problems. AI is commonly used for IT system log file error analysis, with IT systems management functions as well as to automate many routine processes. It can help identify issues so the IT team can proactively fix them before any IT systems go down. As the IT systems to support our businesses become more complex, AIOps helps the IT improve system performance and services.

Human Resources

In a business function with human in the name, is there a place for machines? Yes! Artificial intelligence really has the potential to transform many human resources activities from recruitment to talent management. AI can certainly help improve efficiency and save money by automating repetitive tasks, but it can do much more. PepsiCo used a robot, Robot Vera, to phone and interview candidates for open sales positions. Talent is going to expect a personalized experience from their employer just as they have been accustomed to when shopping and for their entertainment. Machine learning and AI solutions can help provide that. In addition, AI can help human resources departments with data-based decision-making and make candidate screening and the recruitment process easier. Chatbots can also be used to answer many common questions about company policies and benefits.

Contact Centers

The contact center of an organization is another business area where artificial intelligence is already in use. Organizations that use AI technology to enhance rather than replace humans with these tasks are the ones that are incorporating artificial intelligence in the right way. These centers collect a tremendous amount of data that can be used to learn more about customers, predict customer intent, and improve the "next best action" for the customer for better customer engagement. The unstructured data collected from contact centers can also be analyzed by machine learning to uncover customer trends and then improve products and services.

Building Maintenance

Another way AI is already at work in businesses today is helping facilities managers optimize energy use and the comfort of occupants. Building automation, the use of artificial intelligence to help manage buildings and control lighting and heating/cooling systems, uses internet-of-things devices and sensors as well as computer vision to monitor buildings. Based upon the data that is collected, the AI system can adjust the building's systems to accommodate for the number of occupants, time of day, and more. AI helps facilities managers improve energy efficiency of the building. An additional component of many of these systems is building security as well.

Manufacturing

Heineken, along with many other companies, uses data analytics at every stage of the manufacturing process from the supply chain to tracking inventory on store shelves. Predictive intelligence can not only anticipate demand and ramp production up or down, but sensors on equipment can predict maintenance needs. AI helps flag areas of concern in the manufacturing process before costly issues erupt. Machine vision can also support the quality control process at manufacturing facilities.

Accounting and Finance

Many organizations are finding the promise of cost reductions and more efficient operations the major appeal for artificial intelligence in the workplace, and according to Accenture Consulting, robotic process automation can produce amazing results in these areas for the accounting and finance industry and departments. Human finance professionals will be freed-up from repetitive tasks to be able to focus on higher-level activities while the use of AI in accounting will reduce errors. AI is also able to provide real-time status of financial matters to organizations because it can monitor communication through natural language processing.

Customer Experience

Another way artificial intelligence technology and big data are used in business today is to improve the customer experience. Luxury fashion brand Burberry uses big data and AI to enhance sales and customer relationships. The company gathers shopper's data through loyalty and reward programs that they then use to offer tailored recommendations whether customers are shopping online or in brick-and-mortar stores. Innovative uses of chatbots during industry events are another way to provide a stellar customer experience.

For more on AI and technology trends, see Bernard Marrs bookArtificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problemsand his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.

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10 Business Functions That Are Ready To Use Artificial Intelligence - Forbes

Moving up the charts with AI

There is increasing awareness that the greatest problems with artificial intelligence are not primarily technical, but rather how to achieve value from the technology. This was a growing problem even in the booming economy of the last several years, but a much more important issue in the current pandemic-driven recessionary economic climate.

Older AI technologies like natural language processing, and newer ones like deep learning, work well for the most part and are capable of providing considerable value to organizations that implement them. The challenges are with large-scale implementation and deployment of AI, which are necessary to achieve value. There is substantial evidence of this in surveys.

In an MIT Sloan Management Review/BCG survey, seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years.This number improves to 3 out of 5 when we include companies that have made significant investments in AI. Even so, this means 40% of organizations making significant investments in AI do not report business gains from AI.

NewVantage Partners 2019 Big Data and AI Executive surveyFirms report ongoing interest and an active embrace of AI technologies and solutions, with 91.5% of firms reporting ongoing investment in AI. But only 14.6% of firms report that they have deployed AI capabilities into widespread production. Perhaps as a result, the percentage of respondents agreeing that their pace of investment in AI and big data was accelerating fell from 92% in 2018 to 52% in 2019.

Deloitte 2018 State of Enterprise AI surveyThe top 3 challenges with AI were implementation issues, integrating AI into the companys roles and functions, and data issuesall factors involved in large-scale deployment.

In a 2018 McKinsey Global Survey of AI, most respondents whose companies have deployed AI in a specific function report achieving moderate or significant value from that use, but only 21 percent of respondents report embedding AI into multiple business units or functions.

In short, AI has not yet achieved much return on investment. It has yet to substantially improve the lives of workers, the productivity and performance of organizations, or the effective functions of societies. It is capable of doing all these things, but is being held back from its potential impact by a series of factors I will describe below.

Whats Holding AI Back

Ill describe the factors that are preventing AI from having a substantial return in terms of the letters of our new organization: the ROAI Institute. Although it primarily stands for return on artificial intelligence, it also works to describe the missing or critical ingredients for a successful return:

ReengineeringThe business process reengineering movement of the 1980s and early 90s, in which I wrote the first article and book (admittedly by only a few weeks in both cases) described an opportunity for substantial change in broad business processes based on the capabilities of information technology. Then the technology catalyst was enterprise systems and the Internet; now its artificial intelligence and business analytics.

There is a great opportunitythus far only rarely pursuedto redesign business processes and tasks around AI. Since AI thus far is a relatively narrow technology, task redesign is more feasible now, and essential if organizations are to derive value from AI. Process and task design has become a question of what machines will do vs. what tasks are best suited to humans.

We are not condemned to narrow task redesign forever, however. Combinations of multiple AI technologies can lead to change in entire end to end processesnew product and service development, customer service, order management, procure to pay, and the like.

Organizations need to embrace this new form of reengineering while avoiding the problems that derailed the movement in the past; I called it The Fad that Forgot People. Forgetting people, and their interactions with AI, would also lead to the derailing of AI technology as a vehicle for positive change.

Organization and CultureAI is the child of big data and analytics, and is likely to be subject to the same organization and culture issues as the parent. Unfortunately, there are plenty of survey results suggesting that firms are struggling to achieve data-driven cultures.

The 2019 NewVantage Partners survey of large U.S. firms I cite above found that only 31.0% of companies say they are data-driven. This number has declined from 37.1% in 2017 and 32.4% in 2018. 28% said in 2019 that they have a data culture. 77% reported that business adoption of big data and AI initiatives remains a major challenge. Executives cited multiple factors (organizational alignment, agility, resistance), with 95% stemming from cultural challenges (people and process), and only 5% relating to technology.

A 2019 Deloitte survey of US executives on their perspectives on analytical insights found that most executives63%do not believe their companies are analytics-driven. 37% say their companies are either analytical competitors (10%) or analytical companies (27%). 67% of executives say they are not comfortable accessing or using data from their tools and resources; even 37% of companies with strong data-driven cultures express discomfort.

The absence of a data-driven culture affects AI as much as any technology. It means that the company and its leaders are unlikely to be motivated or knowledgeable about AI, and hence unlikely to build the necessary AI capabilities to succeed. Even if AI applications are successfully developed, they may not be broadly implemented or adopted by users. In addition to culture, AI systems may be a poor fit with an organization for reasons of organizational structure, strategy, or badly-executed change management. In short, the organizational and cultural dimension is critical for any firm seeking to achieve return on AI.

Algorithms and DataAlgorithms are, of course, the key technical feature of most AI systemsat least those based on machine learning. And its impossible to separate data from algorithms, since machine learning algorithms learn from data. In fact, the greatest impediment to effective algorithms is insufficient, poor quality, or unlabeled data. Other algorithm-related challenges for AI implementation include:

InvestmentOne key driver of lack of return from AI is the simple failure to invest enough. Survey data suggest most companies dont invest much yet, and I mentioned one above suggesting that investment levels have peaked in many large firms. And the issue is not just the level of investment, but also how the investments are being managed. Few companies are demanding ROI analysis both before and after implementation; they apparently view AI as experimental, even though the most common version of it (supervised machine learning) has been available for over fifty years. The same companies may not plan for increased investment at the deployment stagetypically one or two orders of magnitude more than a pilotonly focusing on pre-deployment AI applications.

Of course, with any technology it can be difficult to attribute revenue or profit gains to the application. Smart companies seek intermediate measures of effectiveness, including user behavior changes, task performance, process changes, and so forththat would precede improvements in financial outcomes. But its rare for these to be measured by companies either.

A Program of Research and Structured Action

Along with several other veterans of big data and AI, I am forming the Return on AI Institute, which will carry out programs of research and structured action, including surveys, case studies, workshops, methodologies, and guidelines for projects and programs. The ROAI Institute is a benefit corporation that will be supported by companies and organizations who desire to get more value out of their AI investments

Our focus will be less on AI technology-though technological breakthroughs and trends will be considered for their potential to improve returnsand more on the factors defined in this article that improve deployment, organizational change, and financial and social returns. We will focus on the important social dimension of AI in our work as wellis it improving work or the quality of life, solving social or healthcare problems, or making government bodies more responsive? Those types of benefits will be described in our work in addition to the financial ones.

Our research and recommendations will address topics such as:

Please contact me at tdavenport@babson.edu if you care about these issues with regard to your own organization and are interested in approaches to them. AI is a powerful and potentially beneficial technology, but its benefits wont be realized without considerable attention to ROAI.

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Return On Artificial Intelligence: The Challenge And The Opportunity - Forbes

NEW YORK--(BUSINESS WIRE)--Ninety One, Inc., an augmented intelligence company developing innovative software and data science solutions designed to automate cardiac remote monitoring and further Precision Medicine, announced today, announced today that Dr. Thomas Hong will be the first in the state of Texas to utilize the technology that combines state-of-the-art surveillance with early warning detection capabilities.

Ninety One, Inc. utilizes a cloud-native platform that automates the collection of data and reports from implanted cardiac devices and wearables digitizes, structures, and analyzes them with applied data science in an single-point, easy-to-use interface for patient care and innovation in research. Ninety Ones Global team of data scientists, software engineers, and modern mathematicians utilize artificial intelligence on vast amounts of data produced by these devices to predict disease episodes and disease progression. Ninety Ones ability to improve patient's quality of life, improve mortality rates, and accelerate decision making in real-time impacting patient outcomes is game-changing for cardiology, said Dr. Thomas Hong.

We are extremely excited to have our technology being used for the first time in the State of Texas. Dr. Hong has long been an innovator working in the forefront of technology and research to identify treatment pathways that lead to better patient experiences and outcomes, said Matthew Werner, Chief Commercial Officer at Ninety One.

About Dr. Thomas Hong

Dr. Hong is a cardiac electrophysiologist, specializing in treating patients with heart rhythm disorders and has served as Assistant Professor of Clinical Cardiology at Baylor College of Medicine and Houston Methodist Hospital. He has published in several peer-reviewed journals including Heart Rhythm, Journal of Cardiovascular Electrophysiology, and American Journal of Medicine.

About Ninety One

Ninety One is a privately-held, data science and native-cloud technology company, focusing on clinical advancement in predictive analytics and Precision Medicine, and has established key, exclusive partnerships with leading research and healthcare institutions in the United States, Europe, and Asia. Pursuing this mission with vigorous commitment and passion, while leveraging innovations in science, Ninety One aspires to make a material impact on disease diagnosis, treatment, and prevention.

For more information please visit https://www.91.life

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Houston Cardiologist Becomes the First in State to Use Ninety One Inc.'s Artificial Intelligence and Precision Medicine Platform - Business Wire

The agency is looking at how to best apply curated data sets to new use cases.

The Department of Veterans Affairs is closer to expanding its use of artificial intelligence and developing novel use cases.

In looking back on the early stages of the VAs newly launched artificial intelligence program, the department's Director of AI Gil Alterovitz noted ongoing questions about how to best leverage AI data sets for secondary uses.

One of the interesting challenges is often that data is collected for maybe one reason, and it may be used for analyzing and finding results for that one particular reason. But there may be other uses for that data as well. So when you get to secondary uses you have to examine a number of challenges, he said at AFCEA's Automation Transformation conference.

Some of the most pressing concerns the VAs AI program hasencountered include questions of how to best apply curated data sets to newfound use cases, as well as how to properly navigate consent of use for proprietary medical data.

Considering the specificity of use cases, particularly for advanced medical diagnostics and predictive analytics, Alterovitz has proposed releasing broader ecosystems of data sets that can be chosen and applied depending on the demands of specific AI projects.

Theres a lot to think about data sets and how they work together. Rather than release one data set, consider releasing an ecosystem of data sets that are related," he said."Imagine, for example, someone is searching for a trial you have information about. Consider the patient looking for the trial, the physician, the demographics, pieces of information about the trial itself, where its located. Having all that put together makes for an efficient use case and allows us to better work together."

Alterovitz also discussed the value of combining structured and unstructured data sets in AI projects, a methodology that Veterans Affairs has found to provide stronger results than using structured data alone.

When you look at unstructured data, there have been a number of studies in health care looking at medical records where if you look at only structured data or only unstructured data individually, you dont get as much of a predictive capability whether it be for diagnostics or prognostics as by combining them, he said.

Beyond refining and expanding these data applications methodologies, the VA also appears attentive to how to best leverage proprietary medical data while protecting personally identifying information.

The solution appears to lie in creating synthetic data sets that mimic the statistical parameters and overall metrics of a given data set while obscuring the particularities of the original data set it was sourced from.

How do you make data available considering privacy and other concerns?" Alterovitz said."One area is synthetic data, essentially looking at the statistics of the underlying data and creating a new data set that has the same statistics, but cant be identified because it generates at the individual level a completely different data set that has similar statistics."

Similarly, creating select variation within a given data set can serve to remove the possibility of identifying the patient source, You can take the data, and then vary that information so that its not the exact same information you received, but is maybe 20% different. This makes it so you can show its statistically not possible to identify that given patient with confidence.

Going forward, the VA appears intent on solving these quandaries so as to best inform expanded AI research.

A lot of the data we have wasnt originally designed for AI. How you make it designed and ready for use in AI is a challenge and one that has a number of different potential avenues, Alterovitz concluded

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VA Looking to Expand Usage of Artificial Intelligence Data - GovernmentCIO Media

ARTIFICIAL Intelligence (AI) is the wave of the future. This area of computer science emphasising the creation of intelligent machines that work and react like humans is heavily influencing and taking over the way we get on with daily life.

Artificial Intelligence is revolutionising industries and improving the way business is conducted.

More importantly, it is revolutionising industries and improving the way business is done, being already widely used in applications including automation, data analytics and natural language processing.

On a bigger spectrum, from self-driving cars to voice-initiated mobile phones and computer-controlled robots, the presence of AI is seen and felt almost everywhere.

As more industries shift towards embracing the science of incorporating human intelligence in machines so the latter can function, think and work like humans, the demand for human capital with the relevant skill and expertise correspondingly increases.

As such, the question is, how do engineering students ride this wave and make the most of it?

AI has a high learning curve but the rewards of a career in AI far outweigh the investment of time and energy.

Unlike most conventional careers, AI is still in its infancy stage although several modern nations have fully embraced the Fourth Industrial Revolution.

Taking this into account, UCSI University has taken the initiative to develop the Bachelor of Computer Engineering (Artificial Intelligence) programme.

The nations best private university for two years in a row, according to the two recent QS World University Rankings exercises, proactively defines its own AI curriculum to offer educational content that can help increase the supply of AI engineers with job-ready graduates and real world experiences.

The AI programme at UCSI consists of a number of specialisations and several overlapping disciplines, including mathematical and statistical methods, computer sciences and other AI core subjects to provide a conceptual framework in providing solutions for real-world engineering problems.

The first two years covers core theoretical knowledge such as mathematics and statistics, algorithm design and computer programming, as well as electrical and electronics.

Students will progress to the AI subfields by selecting the specialisation elective tracks covering emerging areas such as machine learning, decision-making and robotics, perception and language and human-AI interaction, among others.

We aim to nurture the new generation workforce with the right skills set and knowledge on smart technologies to accelerate Malaysias transformation into a smart and modern manufacturing system, says Ang.

UCSI Faculty of Engineering, Technology and Built Environment dean Asst Prof Ts Dr Ang Chun Kit pointed out that AI was unavoidably the way forward.

We aim to nurture the new generation workforce with the right skills set and knowledge on smart technologies to accelerate Malaysias transformation into a smart and modern manufacturing system.

This programme was developed with a vision to provide the foundation for future growth in producing more complex and high-value products for industry sectors in Malaysia, he said.

Leading the faculty in which 46 of its members have PhDs, Ang emphasised the university focuses on research attachment abroad and has established partnerships with key industry players.

The faculty also stands out in terms of receiving grants to advance high impact projects.

Students from the faculty are also annually selected for researches at world-renowned universities such as Imperial College London and Tsinghua University.

The faculty also strives to give students field experience through internships at various top companies.

An example would be Harry Hoon Jian Wen, an Electrical and Electronic Engineering student. He was selected to go to the University of Queensland for a research attachment while also successfully completing his internship at Schneider Electric.

For further details, visit http://online.ucsiuniversity.edu.my/ or email info.sec@ucsiuniversity.edu.my

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The future through Artificial Intelligence - The Star Online

The Spanish government is planning to test 80,000 people a day for coronavirus with the roll-out of robot testers.

Technology will be used to speed up testing of people in Spain, one of the countries hardest hit by the Covid-19 outbreak, with more than 200 deaths so far. According to Bloomberg, Spanish authorities now plan to increase daily testing from about 20,000 a day to 80,000, by using four robots to apply artificial intelligence (AI) to testing.

Speaking at a conference on Saturday 21 March, Raquel Yotti, head of Madrids health institute, said: A plan to automate tests through robots has already been designed and Spain has committed to buying four robots that will allow us to execute 80,000 tests per day.

Because of the ease that coronavirus spreads from person to person, testing has been identified as one of the best ways to control the disease. But testing has cost and resource limitations. Applying AI and robot technology could help overcome these problems, while reducing medical practitioners exposure to the virus.

No further details have been given about how the robots will work, but AI is increasingly being designed to work in the healthcare industry by automating some of the work of medical staff, giving them more time to treat patients.

The technology has proved successful in medical trials, including identifying cancer in breast scans.

A research paper from Google Health, published inNaturemagazine, has reported that machine learning, based on Googles TensorFlow algorithm, can be used to reduce false positives in breast cancer scans. A false positive is when a mammogram scan is incorrectly identified as cancerous, and a false negative is when it is wrongly diagnosed as not being cancerous.

In the Google Health paper, based on training an AI algorithm to identify breast cancer using a large representativedataset from the UK and the US, the researchers reported an absolute reduction of 5.7% in false positives in the US dataset, while the UK dataset showed a 1.2% reduction in false positives.

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Coronavirus: Spain to use artificial intelligence to automate testing - ComputerWeekly.com

Fraud mitigation is one of the most sought-after artificial intelligence (AI) services because it can provide an immediate return on investment. Already, many companies are experiencing lucrative profits thanks to AI and machine learning (ML) systems that detect and prevent fraud in real-time.

According to a new report, Highmark Inc.s Financial Investigations and Provider Review (FIPR) department generated $260 million in savings that would have otherwise been lost to fraud, waste, and abuse in 2019. In the last five years, the company saved $850 million.

We know the overwhelming majority of providers do the right thing. But we also know year after year millions of health care dollars are lost to fraud, waste and abuse, said Melissa Anderson, executive vice president and chief audit and compliance officer, Highmark Health. By using technology and working with other Blue Plans and law enforcement, we have continually evolved our processes and are proud to be among the best nationally.

FIPR detects fraud across its clients services with the help of an internal team made up of investigators, accountants, and programmers, as well as seasoned professionals with an eye for unusual activity such as registered nurses and former law enforcement agents. Human audits performed to detect unusual claims and assess the appropriateness of provider payments are used as training data for AI systems, which can adapt and react more rapidly to suspicious changing consumer behavior.

As fraudulent actors have become increasingly aggressive and cunning with their tactics, organizations are looking to AI to mitigate rising threats.

We know it is much easier to stop these bad actors before the money goes out the door then pay and have to chase them, said Kurt Spear, vice president of financial investigations at Highmark Inc.

Elsewhere, Teradata, an AI firm specialized in selling fraud detection solutions to banks, claims in a case study that it helped Danske Bank reduce its false positives by 60% and increased real fraud detection by 50%.

Other service operators are looking to AI fraud detection with a keen eye, especially in the health care sector. A recent survey performed by Optum found that 43% of health industry leaders said they strongly agree that AI will become an integral part of detecting telehealth fraud, waste, or abuse in reimbursement.

In fact, AI spending is growing tremendously with total operating spending set to reach $15 billion by 2024, the most sought-after solutions being network optimization and fraud mitigation. According to theAssociation of Certified Fraud Examiners (ACFE)inauguralAnti-Fraud Technology Benchmarking Report,the amount organizations are expected to spend on AI and machine learning to reduce online fraud is expected to triple by 2021.

Mitigating fraud in healthcare would be a boon for an industry that is plagued with many structural inefficiencies.

The United States spends about $3.5 trillion on healthcare-related services every year. This staggering sum corresponds to about 18% of the countrys GDP and is more than twice the average among developed countries. However, despite this tremendous spending, healthcare service quality is lacking. According to a now-famous 2017 study, the U.S. has fewer hospital beds and doctors per capita than any other developed country.

A 2019 study found that the countrys healthcare system is incredibly inefficient, burning through roughly 25% of all its finances which basically go to waste thats $760 billion annually in the best case scenario and up to $935 billion annually.

Most money is being wasted due to unnecessary administrative complexity, including billing and coding waste this alone is responsible for $265.6 billion annually. Drug pricing is another major source of waste, account for around $240 billion. Finally, over-treatment and failure of care delivery incurred another $300 billion in wasted costs.

And even these astronomical costs may be underestimated. According to management firm Numerof and Associates, the 25% waste estimate might be conservative. Instead, the firm believes that as much as 40% of the countrys healthcare spending is wasted, mostly due to administrative complexity. The firm adds that fraud and abuse account for roughly 8% of waste in healthcare.

Most cases of fraud in the healthcare sector are committed by organized crime groups and a fraction of some healthcare providers that are dishonest.

According to the National Healthcare Anti-Fraud Association, the most common types of healthcare frauds in the United States are:

Traditionally, the most prevalent method for fraud management has been human-generated rule sets. To this day, this is the most common practice but thanks to a quantum leap in computing and Big Data, AI-based solutions based on machine learning algorithms are becoming increasingly appealing and most importantly practical.

But what is machine learning anyway? Machine learning refers to algorithms that are designed learn like humans do and continuously tweak this learning process over time without human supervision. The algorithms output accuracy can be improved continuously by feeding them data and information in the form of observations and real-world interactions.

In other words, machine learning is the science of getting computers to act without being explicitly programmed.

There are all sorts of various machine learning algorithms, depending on the requirements of each situation and industry. Hundreds of new machine learning algorithms are published on a daily basis. Theyre typically grouped by:

In a healthcare fraud analytics context, machine learning eliminates the use of preprogrammed rule sets even those of phenomenal complexity.

Machine learning enables companies to efficiently determine what transactions or set of behaviors are most likely to be fraudulent, while reducing false positives.

In an industry where there can be billions of different transactions on a daily basis, AI-based analytics can be an amazing fit thanks to their ability to automatically discover patterns across large volumes of data.

The process itself can be complex since the algorithms have to interpret patterns in the data and apply data science in real-time in order to distinguish between normal behavior and abnormal behavior.

This can be a problem since an improper understanding of how AI works and fraud-specific data science techniques can lead you to develop algorithms that essentially learn to do the wrong things. Just like people can learn bad habits, so too can a poorly designed machine learning model.

In order for online fraud detection based on AI technology to succeed, these platforms need to check three very important boxes.

First, supervised machine learning algorithms have to be trained and fine-tuned based on decades worth of transaction data to keep false positives to a minimum and improve reaction time. This is harder said than done because the data needs to be structured and properly labeled depending on the size of the project, this could take staff even years to solve.

Secondly, unsupervised machine learning needs to keep up with increasingly sophisticated forms of online fraud. After all, AI is used by both auditors and fraudsters. And, finally, for AI fraud detection platforms to scale, they require a large-scale, universal data network of activity (i.e. transactions, filed documents, etc) to scale the ML algorithms and improve the accuracy of fraud detection scores.

According to a new market research report released earlier this year, the healthcare fraud analytics market is projected to reach $4.6 billion by 2025 from $1.2 billion in 2020.

This growth is attributed to more numerous and complex fraudulent activity in the healthcare sector.

In order to tackle rising healthcare fraud, companies offer various analytics solutions that flag fraudulent activity some are rule-based models, but AI-based technologies are expected to form the backbone of all types of analytics used in the future. These include descriptive, predictive, and prescriptive analytics.

Some of the most important companies operating today in the healthcare fraud analytics market include IBM Corporation (US), Optum (US), SAS Institute (US), Change Healthcare (US), EXL Service Holdings (US), Cotiviti (US), Wipro Limited (Wipro) (India), Conduent (US), HCL (India), Canadian Global Information Technology Group (Canada), DXC Technology Company (US), Northrop Grumman Corporation (US), LexisNexis Group (US), and Pondera Solutions (US).

That being said, there is a wide range of options in place today to prevent fraud. However, the evolving landscape of e-commerce and hacking pose new challenges all the time. To keep up, these challenges require innovation that can respond and react rapidly to fraud. The common denominator, from payment fraud to abuse, seems to be machine learning, which can easily scale to meet the demands of big data with far more flexibility than traditional methods.

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Artificial intelligence for fraud detection is bound to save billions - ZME Science

Innovation leap watchfree bridge

The major challenges facing maritime transport include coping with the growing volume of trade, improving maritime safety, economic efficiency and environmental friendliness.

In the course of advances in information technology, these challenges have led to the rapid development of autonomous technologies. Within the framework of the BMWifunded research project B ZERO, the Fraunhofer CML is now developing a sensor and navigation system in cooperation with Wrtsil SAM, Hoppe Bordmesstechnik, NautilusLog, the Bernhard Schulte Group, the Federal Maritime and Hydrographic Agency and the Fraunhofer FKIE. The system should be able to guide a ship autonomously between defined departure and arrival points, so that manning the bridge around the clock is not necessary.

The Fraunhofer CML will develop an artificial intelligence for autonomous navigation by using reinforcement learning in B ZERO. With reinforcement learning a system can train meaningful de- cision guidelines without prior knowledge, only by results or responses to its actions. Reinforcement Learning is already used at CML in the fields of object recognition and robotics, and supports the anticipatory avoidance of collisions and grounding in nautical situations. The AI, which will later take over autonomous navigation in B ZERO, is trained at the CML by simulating nautical scenarios with different parameters such as number of approaching ships, sea area, visibility and weather conditions. The decision component to be trained, e.g. collision avoidance, knows the required state of the- se given conditions and reacts with the learned, appropriate voyage and/ or course changes to ensure a safe passage on a route. The expected result is a prototype system, which will be further developed in the simulation laboratory environment of the CML and validated by future tests on board a cargo ship.

Efficiency boost in image recognition

Great potential for maritime logistics results from the use of AI- supported image recognition, or computer vision in short. In addition to the acquisition of digital images, it enables their processing into highly compressed numerical information that can be further processed by machines. Computer vision is thus a key technology for the automated observation of conditions and the detection of changes. These capabilities enable a wide range of applications in the maritime sector. In maritime shipping, for example, many autonomous manoeuvres depend on the permanent, simultaneous and reliable situational awareness that computer vision enables. Gradual changes, such as erosion of quay walls or deformations of a ships hull, can be detected by computer vision, as can the position of cargo units on board or at the terminal.

The CML supports companies in the maritime industry in identifying and exploring the individual possibilities of computer vision. As part of the COOKIE project, which is funded by the IHATEC programme, a visual damage recognition and image-based repair prognosis of empty containers is being developed using artificial intelligence. This will not only ensure compliance with applicable security standards, but also make inspection procedures at the terminal gate more efficient.

In addition to computer vision, the CML has a broad spectrum of expertise in the field of machine learning and offers comprehensive solutions for AIsupported forecasting and assistance systems, from proof of concept to implementation.Source: CML Fraunhofer

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Artificial Intelligence Promotes Automation On Board And In Ports - Hellenic Shipping News Worldwide

Russ Altman (Image credit: Courtesy Russ Altman)

The impact of COVID-19 on society and the way artificial intelligence can be leveraged to increase understanding of the virus and its spread will be the focus of an April 1 virtual conference sponsored by the Stanford Institute for Human-Centered Artificial Intelligence (HAI).

COVID-19 and AI: A Virtual Conference, which is open to the public, will convene experts from Stanford and beyond. It will be livestreamed to engage the broad research community, government and international organizations, and civil society.

Russ Altman, one of the conference chairs, is an associate director of HAI and the Kenneth Fong Professor and professor of bioengineering, of genetics, of medicine, of biomedical data science, and, by courtesy, of computer science. He is also the host of the Sirius radio show The Future of Everything. He discusses the aims of the conference.

What was the idea behind the conference?

At HAI, we felt this was an opportunity to use our unique focus on AI and humanity to serve the public in a time of crisis. The issues involved in the pandemic are both nuanced and complex. Approaching it from multiple fields of expertise will help speed us toward solutions. The goal is to make leading-edge and interdisciplinary research available, bringing together our network of experts from across different schools and departments.

We have a world-class set of doctors and biological scientists at Stanford Medical School and theyll, of course, be involved. Well also have experts on AI, as well as the social sciences and humanities, to give their scholarly perspective on the implications of this virus, now and over time. The conference will be entirely virtual with every speaker participating remotely, providing an unpolished but authentic window into the minds of thinkers we respect.

What useful information will come out of the conference?

Were asking our speakers to begin their presentation by talking about the problem theyre addressing and why it matters. They will present the methods theyre using, whether scientific or sociological or humanistic, the results theyre seeing even if their work is preliminary and the caveats to their conclusions. Then theyll go into deeper detail that will be very interesting to academic researchers and colleagues. Importantly, we intend to have a summary of key takeaways afterward along with links to information where people can learn more.

We will not give medical advice or information about how to ensure personal safety. The CDC and other public health agencies are mobilized to do that.

What do you think AI has to offer in the fight over viruses like COVID-19?

AI is extremely good at finding patterns across multiple data types. For example, were now able to analyze patterns of human response to the pressures of the pandemic as measured through sentiments on social media, and even patterns in geospatial data to see where social distancing may and may not be working. And, of course, we are using AI to look for patterns in the genome of the virus and its biology to see where we can attack it.

This interdisciplinary conference will show how the availability of molecular, cellular and genomic data, patient and hospital data, population data all of that can be harnessed for insight. Weve always examined these data sources through more traditional methods. But now for the first time, and at a critical time of global crisis, we have the ability to use AI to look deeper into data and see patterns that were otherwise not visible previously, including the social and cultural impact of this pandemic. This is what will enable us to work together as a scholarly, scientific community to help the future of humankind.

Who do you hope will attend?

The core audience is scholars and researchers. We want to have a meaningful discussion about the research challenges and opportunities in the battle against this virus. Having said that, we know that there are many people with an interest in how scientists, researchers, sociologists and humanists are helping in this time of crisis. So were making the conference open to anyone interested in attending. It will be a live video stream from a link on our website, and available as a recording afterward.

What kind of policy effect do you hope the conference can have?

Good policy is always informed by good research. A major goal of HAI is to catalyze high-quality research that we hope will be heeded by policymakers as they work to craft responses to COVID-19 and future pandemic threats. So this will give insights to policymakers on what will be published in the coming months.

Register for the April 1 conference.

Learn more about the Stanford Institute for Human-Centered AI (HAI).

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Stanford virtual conference to focus on COVID19 and artificial intelligence | Stanford News - Stanford University News