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The book Design, Launch, and Scale IoT Servicesclassifies the components of IoT services into technical modules. One of the most important of these is Artificial Intelligence (AI). This article is intended to supplement the book by providing insight into AIand its applications for IoT.

After many years in the wilderness, AIis back on the hype curve and will change the world again. Or, will it? AIhas always been interesting, but what has changed to justify the current hype?

There are several contributing factors. The volumes of data that will be produced by many IoT services suggest that this data cannot be managed by humans with traditional analytics tools. Therefore, AIcan offer opportunities for IoT services to extract maximum value from the data. IoT cloud platforms are now offering AIservices via APIs and application development tools, making AI more accessible for many IoT services. Now, AIcan be incorporated without requiring extensive development or excessive costs.

AI can perform the Treble A actions automatically but there is a cost associated with every step in the lifecycle, therefore business owners should ask themselves why they should introduce AI. Understanding the end-goal is the starting point. Its not suitable for all services and requires evaluation to understand when and how it should be introduced.

The following questions can provide a useful starting point for evaluating the introduction of AI:

The majority of IoT services include (or claim to include) some aspect of AIin their solution. This is due to a wide diversity in AI definitions (supervised/unsupervised, reinforced/deep learning) and the hype surrounding AI. (Note: All IoT services should take advantage of this hype while it lasts.)

Lets look at the most common AI features and IoT industries to consider how IoT service owners can best evaluate AI and answer the questions above.

IoT cloud platform providers are offering powerful AIvisual recognition APIs. For example, developing a human visual recognition tool has now become a trivial exercise for developers, and the cost of using visual recognition in IoT services has reduced drastically. These tools are best used for use cases recognizing humans and objects, but may not be useful for very precise recognition use cases. Developing specific visual recognition capabilities proves too expensive for most services, but it does make the service more attractive for end-users.

Robotics is a branch of AIthat, for many, implies a 2-armed, 2-legged machine that communicateswith humans using visual or voice recognition. However, the most important use cases for IoT robotics involve the collection of data from sensors or extracted from robot programs. This data can be used by IoT services as input for AImachine learning algorithms to increase robot efficiency, implementing features such as predictive fault management or adaptive positioning. AIcan be used to increase productivity with robotic systems as part of Industrial IoT services that will become vital for many Industry 4.0 use cases.

Natural Language Processing (NLP) and voice recognition features have become widely available in mobile phones and CRM (customer relationship management) systems. They can be implemented via IoT cloud service APIs. This will be an option for many IoT services without requiring significant investment. It will make most services more attractive, implyingmore sales.However, we are probably quite far off from the stage where NLP is fundamental for IoT services. Its available on many mobile apps, but most users still prefer to use a touch screen. The main use cases for voice control systems will most likely involve voice to text transcription for operational or CRM activities to reduce cost but may increase frustration for end-users.(Note: Cloud providers are also introducing AIaudio recognition APIs for fault detection that can be used to replace or augment visual recognition features.)

Smart factories offer numerous opportunities for implementing use cases that can increase efficiency via visual inspection, checking for faulty components or assembly processes errors. The analysis required should include cost vs benefits. If visual inspection slows the production process, it may be counterproductive to introduce in a manufacturing processthat has a low fault rate.

For example, lets say that a smart factory is creating 5,000components per day averaging 50 faulty components. The introduction of a visual inspection may reduce the components to 0 faults. However, if it slows the manufacturing process to produce only 4,000components per day, is it worthwhile? The process owner will have to calculate if the reduction in throughput outweighs the benefits of a reduction in faulty components. This is an example of real-time fault detection that can used for industrial IoT services. (Note: Many of the IoT Cloud platform providers offer the possibility to implement AIon edge devices, thus increasing the number use cases for real-time AI.)

Many industrial IoT solutions suggest that visual recognition will be used to determine thecurrent health and emotional status of machine operators. This would require quite advanced featuresto be beneficial, and therefore,its unlikely to be relevant for most IoT services.

Visual inspection shows great promise in detecting cancer and other ailments using advanced AItechniques and is improving the accuracy of diagnosis in many IoT health use cases. Very often, visual inspection requires large volumes of sample cases and training sets to ensure that the performance is acceptable. Genome technology generates billions of data items mapping our DNA that cannot be handled by humans and analytics tools. The introduction of AIoffers the possibility to predict future health issues. Using data volumes of this magnitude requires unsupervised learning techniques, such as clustering. This may prove too complex and expensive for the majority of current IoT use cases. Again, cloud service providers provide options facilitating the management of training models and data with tools such as Google Cloud AutoML. However, its likely this will only be cost-effective for a limited number of IoT services.

Its surprising that we havent yet seen the widespread deployment of AIin the management of intelligent hospitals. As with any complex logistical processes, AIcan create significant efficiencies with relatively low investment.

Many smart home IoT services will implement voice recognition that connectswith smart speakers. These are widely available from providers such as Amazon, Google and Apple.They can communicate with most smart home devices without significant complexity. Its likely that voice recognition will be an add-on for the majority of IoT services; nice to have, but not fundamental. Therefore, in most cases, IoT business owners may have to budget for this as a premium service.

The potential of AI in transportation is very exciting (i.e. driverless cars.) There will be a lot of innovation with AIfor drivers, but new IoT service owners will have to carve out a niche in this market. Although the technology is available, we may still be quite a way off from many use cases being acceptable for drivers. Imagine all the cars on the road communicating with each other and learning from one another as they hit the road.

One example to consider: Car A detects ice on the road,informs other cars and they all proceed to automatically adjust speed and brakes based on performance data from the other cars. This may seem futuristic, but the technology is currently available and AI offers the possibility of increased performance and decision making.

Analytics is closely interlinked with AI. When utilizing AI, its typical to ask yourself if you need analytics tools or if analytics will die due to the implementation of AI. The answer? Not quite.Most IoT services employ analytics, and therefore the data required by AI will already be available. AIshould be able to replace a lot of the activities performed by humans using analytics tools. Or, the output of analytics can be the starting point of AIsintroduction in many IoT services. The latter doesnt imply analytics are a prerequisite. If the data is available, expert systems can be developed without analytics.

Now, were starting to see augmented analytics. This is where AI assists analytics with intelligent searching and other tasks. This may not be necessary for most IoT services, but we can be sure that its being used by the massive tech companies around the world. Unfortunately, most IoT services wont generate enough data to be cost-effective to introduce.

Analytics, statistics and lies are often interchangeable. These wont be solved by AI. One challenge for many IoT services is that neural networks and deep learning AI techniques cannot explain why theyre making decisions. This can reduce customer confidence and will be unsuitable for IoT services where a clear understanding of a decision-making process is important.

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Artificial Intelligence Applications: Is Your Business Implementing AI Smartly? - IoT For All

How Is Connected World Defining The Future Of Security

Theres a lot of hype around the term digital transformation. For some, its the integration of digital technology into everyday tasks. For others, its the incorporation of innovative processes aimed at making business optimization easier.In most cases, digital transformation will fundamentally change how an organization operates and delivers value to its customers. And within the security realm, the age of digital transformation is most certainly upon us. Technology is already a part of our day-to-day lives, with smart devices in our homes and the ability to perform tasks at our fingertips now a reality. No longer are the cloud, Internet of Things (IoT) and smart cities foreign and distant concepts full of intrigue and promise.Enhancing business operationsWere increasingly seeing devices become smarter and better able to communicate with each otherThese elements are increasingly incorporated into security solutions with each passing day, allowing enterprises the chance to experience countless benefits when it comes to enhancing both safety and business operations. The term connected world is a derivative of the digital transformation, signifying the increasing reliance that we have on connectivity, smart devices and data-driven decision-making. As we become more familiar with the advantages, flaws, expectations and best practices surrounding the connected world, we can predict what issues may arise and where the market is heading.Were increasingly seeing devices become smarter and better able to communicate with each other through the IoT to achieve both simple goals and arduous tasks. Within our homes, were able to control a myriad of devices with commands (Hey Google... or Alexa...), as well as recall data directly from our mobile devices, such as receiving alerts when someone rings our doorbell, theres movement in our front yard or when a door has been unlocked.Analytics-Driven solutionsThe focus is now shifting to the business impacts of connectivity between physical devices and infrastructures, and digital computing and analytics-driven solutions. Within physical security, connected devices can encompass a variety of sensors gathering massive amounts of data in a given timeframe: video surveillance cameras, access control readers, fire and intrusion alarms, perimeter detection and more.As the data from each of these sensors is collected and analyzed through a central platform, the idea of a connected world comes to fruition, bringing situational awareness to a new level and fostering a sense of proactivity to identifying emerging threats. The connected world, however, is not without its challenges, which means that certain considerations must be made in an effort to protect data, enhance structured networking and apply protective protocols to developing technology.Physical security systemsWe can expect to see the conversations regarding data privacy and security increase as wellAs the use of connected devices and big data continue to grow, we can expect to see the conversations regarding data privacy and security increase as well. Connectivity between devices can open up the risk of cyber vulnerabilities, but designing safeguards as technology advances will lessen these risks. The key goal is to ensure that the data organizations are using for enhancement and improvements is comprehensively protected from unauthorized access.Manufacturers and integrators must be mindful of their products' capabilities and make it easy for end users to adhere to data sharing and privacy regulations. These regulations, which greatly affect physical security systems and the way they're managed, are being implemented worldwide, such as the European Union's General Data Protection Regulation (GDPR). In the United States, California, Vermont and South Carolina have followed suit, and it can be expected that more countries and U.S. states develop similar guidelines in the future.Technology is already a part of our day-to-day lives, with smart devices in our homes and the ability to perform tasks at our fingertips now a realityAutomatic security updatesMitigating the concerns of the connected world extends beyond just data privacy. IoT technology is accelerating at such a pace that it can potentially create detrimental problems for which many organizations may be ill-prepared - or may not even be able to comprehend. The opportunities presented by an influx of data and the IoT, and applying these technologies to markets such as smart cities, can solve security and operational problems, but this requires staying proactive when it comes to threats and practicing the proper protection protocols.As manufacturers develop devices that will be connected on the network, integrating standard, built-in protections becomes paramount. This can take the form of continuous vulnerability testing and regular, automatic security updates. Protocols are now being developed that are designed to ensure everything is encrypted, all communications are monitored and multiple types of attacks are considered for defensive purposes to provide the best security possible.IoT-Connected devicesHackers wishing to do harm will stop at nothing to break into IoT-connected devicesBuilt-in protection mechanisms send these kinds of systems into protection mode once they are attacked by an outside source. Another way for manufacturers to deliver solutions that are protected from outside threats is through constant and consistent testing of the devices long after they are introduced to the market.Hackers wishing to do harm will stop at nothing to break into IoT-connected devices, taking every avenue to discover vulnerabilities. But a manufacturer that spends valuable resources to continue testing and retesting products will be able to identify any issues and correct them through regular software updates and fixes. IoT has become a common term in our vocabularies and since its more widely understood at this point and time, it's exciting to think about the possibilities of this revolutionary concept.Providing critical insightsThe number of active IoT devices is expected to grow to 22 billion by 2025 a number that is almost incomprehensible. The rise of 5G networks, artificial intelligence (AI) and self-driving cars can be seen on the horizon of the IoT. As more of these devices are developed and security protocols are developed at a similar pace, connected devices stand to benefit a variety of industries, such as smart cities.Smart cities rely on data communicated via the IoT to enhance processes and create streamlined approachesSmart cities rely on data communicated via the IoT to enhance processes and create streamlined approaches to ensuring a city is well-run and safe. For example, think of cameras situated at a busy intersection. Cameras at these locations have a variety of uses, such as investigative purposes in the event of an accident or for issuing red-light tickets to motorists. But there are so many other possible purposes for this connected device, including providing critical insights about intersection usage and traffic congestion. These insights can then be used to adjust stoplights during busy travel times or give cities valuable data that can drive infrastructure improvements.Physical security marketThe impact of connected devices on cities doesnt stop at traffic improvement. The possibilities are endless; by leveraging rich, real-time information, cities can improve efficiencies across services such as transportation, water management and healthcare. However, stringent protections are needed to harden security around the networks transmitting this kind of information in an effort to mitigate the dangers of hacking and allow this technology to continuously be improved.Whether you believe were in the midst of a digital transformation or have already completed it, one thing is certain: businesses must begin thinking in these connectivity-driven terms sooner rather than later so they arent left behind. Leveraging smart, connected devices can catapult organizations into a new level of situational awareness, but adopting protections and remaining vigilant continues to be a stalwart of technological innovation within the physical security market and into the connected world.

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Verizon Connect Integrated Video Utilizes Artificial Intelligence | Security News - SecurityInformed

12 Mar 2020: The advent of 4G and 5G internet of things (IoT)-based connectivity is spurring the online migration of smart-city applications, helping generate a more than sevenfold increase in smart-city artificial intelligence (AI) software revenue by 2025.

The global smart-city AI software market is set to soar to $4.9 billion in 2025, up from $673.8 million in 2019, according to Omdia. Wireless data communications standards are enabling smart-city applications to move into the online realm, where they can capitalize on the latest AI innovations. The growing capabilities of AI are enabling data and insights collected through IoT networks to be monitored, analyzed and acted upon.

From video surveillance, to traffic control, to street lighting, smart-city use cases of all types are defined by the collection, management and usage of data, said Keith Kirkpatrick, principal analyst for AI at Omdia. However, until recently, connecting disparate components and systems together to work in concert has been challenging due to the lack of connectivity solutions that are fast, cost effective, low latency and ubiquitous in coverage. These challenges now are being overcome by leveraging advances in AI and connectivity.

The arrival of 4G and 5G wireless data technologies is making it easier to collect and manage data, promoting the migration of smart-city AI software to the online realm. AI allows data to be analyzed more deeply than ever before. The technology can identify patterns or anomalies within that data, which then can be employed for tasks that allow machines to mimic what humans might consider to be intelligence.

Using the power of AI, smart-city systems can create municipal systems and services that not only operate more efficiently, but also provide significant benefits to workers and visitors. These benefits can come in many forms, including reduced crime, cleaner air, more orderly traffic flow and more efficient government services, as detailed by the latest Omdia AI research report Artificial Intelligence Applications for Smart Cities.

One example of how smart cities are leveraging AI is in the video surveillance realm.

When hosting public events, some cities are beginning to use video cameras that are mated to AI-based video analytics technology. The goal is to have AI algorithms scan the video and look for behavioral or situational anomalies that could indicate that a terrorist act or other outbreaks of violence may be about to occur.

However, cities are increasingly employing cloud-based AI systems that can search footage from most closed-circuit TV (CCTV) systems, allowing the platform and technology to be applied to existing camera infrastructure. Furthermore, video surveillance can be combined with AI-based object detection to perform tasks including learning patterns in an area; detecting faces, gender, heights and moods; reading license plates; and identifying anomalies or potential threats, such as unattended packages.

As the use of surveillance cameras has exploded, AI-based video analytics now represent the only way to extract value in the form of insights, patterns, and action from the plethora of video data generated by smart cities.

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Global smart-city artificial intelligence software revenue set to rise sevenfold by 2025, spurred by advancing AI and connectivity technologies -...

Wednesday, March 11, 2020

Stephanie Colombini/American Homefront

Credit: Stephanie Colombini/American Homefront

Above: Drs. Andrew Borkowski (left) and Stephen Mastorides analyze slides under a microscope to spot cancer in tissue samples in this undated photo.

Aired 3/11/20 on KPBS News

Listen to this story by Stephanie Colombini.

Inside a laboratory at the James A. Haley Veterans' Hospital in Tampa, Fla., machines are rapidly processing tubes of patients' body fluids and tissue samples. Pathologists examine those samples under microscopes to spot signs of cancer and other diseases.

But distinguishing certain features about a cancer cell can be difficult, so Drs. Stephen Mastorides and Andrew Borkowski, decided to get a computer involved.

In a series of experiments, they uploaded hundreds of images of slides containing lung and colon tissues into artificial intelligence software. Some of the tissues were healthy, while others had different types of cancer, including squamous cell and adenocarcinoma.

Then they tested software with more images the computer had never seen before.

"The module was able to put it together, and it was able to differentiate, 'Is it a cancer or is it not a cancer?'" Borkowski said. "And not only that, but it was also able to say what kind of cancer is it."

The doctors were harnessing the power of what's known as machine learning. Software pre-trained with millions of images, like dogs and trees, can learn to distinguish new ones. Mastorides, chief of pathology and laboratory medicine services at the Tampa VA, said it took only minutes to teach the computer what cancerous tissue looks like.

The two VA doctors recently published a study comparing how different AI programs performed when training computers to diagnose cancer.

"Our earliest studies showed accuracies over 95 percent," Mastorides said.

Enhance, not replace

The doctors said the technology could be especially useful in rural veterans clinics, where pathologists and other specialists aren't easily accessible, or in crowded VA emergency rooms, where being able to spot something like a brain hemorrhage faster could save more lives.

Borkowski. the chief of the hospital's molecular diagnostics section, said he sees AI as a tool to help doctors work more efficiently, not to put them out of a job.

"It won't replace the doctors, but the doctors who use AI will replace the doctors that don't," he said.

The Tampa pathologists aren't the first to experiment with machine learning in this way. The U.S. Food and Drug Administration has approved about 40 algorithms for medicine, including apps that predict blood sugar changes and help detect strokes in CT scans.

The VA already uses AI in several ways, such as scanning medical records for signs of suicide risks. Now the agency is looking to expand research into the technology.

The department announced the hiring of Gil Alterovitz as its first-ever Artificial Intelligence Director in July 2019 and launched The National Artificial Intelligence Institute in November. Alterovitz is a Harvard Medical School professor who co-wrote an artificial intelligence plan for the White House last year.

He said the VA has a "unique opportunity to help veterans" with artificial intelligence.

As the largest integrated health care system in the country, the VA has vast amounts of patient data, which is helpful when training AI software to recognize patterns and trends. Alterovitz said the health system generates about a billion medical images a year.

He described a potential future where AI could help combine the efforts of various specialists to improve diagnoses.

"So you might have one site where a pathologist is looking at slides, and then a radiologist is analyzing MRI and other scans that look at a different level of the body," he said. "You could have an AI orchestrator putting together different pieces and making potential recommendations that teams of doctors can look at."

Alterovitz is also looking for other uses to help VA staff members make better use of their time and help patients in areas where resources are limited.

"Being able to cut the (clinician) workload down is one way to do that," he said. "Other ways are working on processes, so reducing patient wait times, analyzing paperwork, etc."

Barriers to AI

But Alterovitz notes there are challenges to implementing AI, including privacy concerns and trying to understand how and why AI systems make decisions.

Last year, DeepMind Technologies, an AI firm owned by Google, used VA data to test a system to predict deadly kidney disease. But for every correct prediction, there were two false positives.

Those false results may cause doctors to recommend inappropriate treatments, run unnecessary tests, or do other things that could harm patients, waste time, and reduce confidence in the technology.

"It's important for AI systems to be tested in real-world environments with real-world patients and clinicians, because there can be unintended consequences," said Mildred Cho, the Associate Director of the Stanford Center for Biomedical Ethics.

Cho also said it's important to test AI systems with a variety of demographics, because what may work for one population may not for another. The DeepMind study acknowledged that more than 90 percent of the patients in the dataset it used to test the system were male veterans, and that performance was lower for females.

Alterovitz said the VA is taking those concerns into account as the agency experiments with AI and tries to improve upon the technology to ensure it is reliable and effective.

This story is part of our American Homefront Project, a public media collaboration on in-depth military coverage with funding from the Corporation for Public Broadcasting and The Patriots Connection.

KPBS' daily news podcast covering local politics, education, health, environment, the border and more. New episodes are ready weekday mornings so you can listen on your morning commute.

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The VA Has Embraced Artificial Intelligence To Improve Veterans' Health Care - KPBS

ATLANTA, March 11, 2020 (GLOBE NEWSWIRE) -- Jvion, a leader in Clinical Artificial Intelligence (AI), today announced the release of its innovative Social Determinants of Health (SDOH) solution that identifies socioeconomic barriers driving an individuals health risk and opportunities for investment in community benefit programs to address gaps in care. Leveraging Jvions peer-reviewed analytics layer and Microsoft Azure Maps, the solution empowers providers and health systems to address underserved populations and inequalities in existing healthcare delivery. Jvion goes beyond helping providers better understand the impact of SDOH by offering individualized interventions that aid in aligning community benefits more effectively.

Providers and healthcare executives recognize the growing role of socioeconomic insights in healthcare, especially in meeting the needs of underserved populations. To date, capturing that data and turning it into meaningful and actionable intelligence has proved elusive for many, said Shantanu Nigam, CEO of Jvion. Our unique approach turns socioeconomic, environmental, and behavioral data into real clinical value that drives higher engagement, more tailored interventions, and greater alignment between need and risk, resulting in better outcomes for individuals and the community as a whole.

As alignment and access to community benefit programs continue to be the cornerstone of building healthier communities, providers need appropriate insight into their populations and individual healthcare needs. Hospitals spent $95 billion on community benefits in the most recent year data is available (American Hospital Association), and increasingly both federal and state regulators are seeking clarity on what benefits are being provided to communities with this spend and their impact. Jvions SDOH solution not only fulfills the federal and state assessment needs for healthcare organizations, but also strategically informs providers where to allocate their community benefit spend to have the greatest level of impact.

Jvions SDOH solution requires limited input from providers and none from patients, largely relying on its high-performing AI approach, which leverages a global instance of de-identified patients to power the inferential outputs of the solution. Through this approach, the community inherits the attributes of the individual versus traditional methods, which apply community qualities to the individual. The SDOH solution features an interactive map interface built using Microsoft Azure maps and a web-based portal.

Were pleased that this technology collaboration is helping healthcare organizations in transforming patient care and their businesses. The Microsoft platform helps responsibly unify people, devices, apps and information by prioritizing compliance, security and trust, said Gareth Hall, director of business strategy for Worldwide Healthcare at Microsoft. Our partners are critical in helping healthcare organizations use technology to address industry challenges and seize opportunities to impact peoples lives in a positive way. The combination of the Microsoft platform and partner innovation is key to helping our industry transform.

The Jvion SDOH solution is now available. Register here to schedule a virtual demo. Additional information is available at https://jvion.com/jvionclinicalai/.

The latest KLAS report, Healthcare AI 2019 - Actualizing the Potential of AI, recognized Jvion as having by far the largest client base in the healthcare AI market," and "the largest offering of pre-built healthcare content for machine learning models/vectors." Additionally, Jvion was featured in the CB Insights Digital Health 150, showcasing the most promising private digital healthcare companies in the world.

About JvionJvion enables healthcare organizations to prevent avoidable patient harm and lower costs through its AI-enabled prescriptive analytics solution. An industry first, the Jvion Machine goes beyond simple predictive analytics and machine learning to identify patients on a trajectory to becoming high risk and for whom intervention will likely be successful. Jvion determines the interventions that will more effectively reduce risk and enable clinical action. And it accelerates time to value by leveraging established patient-level intelligence to drive engagement across hospitals, populations, and patients. To date, the Jvion Machine has been deployed across about 50 hospital systems and 300 hospitals, who report average reductions of 30% for preventable harm incidents and annual cost savings of $6.3 million. For more information, visit http://www.jvion.com.

Jvion PR Contact:Lexi Herosianlexi@scratchmm.com

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Jvion Tackles Socioeconomic Barriers to Care with Industry-Leading Artificial Intelligence - GlobeNewswire