Tag Archives | Artificial Intelligence

How Will Car Maintenance Evolve In the Autonomous Vehicles Era?

driverless cars predictive maintenance AV training design ethics

Imagine the day when your car not only diagnoses its own maintenance needs, but schedules its own appointment and then drives itself to the shop, leaving you with plenty of free time to do as you please.

This scenario is a distinct possibility in the autonomous vehicles (AV) era. Artificial intelligence (AI) will be used to create AVs, and also to enable them to self-diagnose.

In addition to the regular maintenance required in a traditional vehicle, there’s an abundance of equipment involved in building and operating AVs, which will also require upkeep. Waymo, the Google self-driving car, features radar that enables cruise control, ultrasound used for assisted parking, cameras for lane-keeping and back-up assistance, GPS systems to determine a car’s position, and sensors that help with navigation when satellite signals are blocked. And then there’s Waymo’s Light Detection and Ranging (LIDAR) technology, which gives the driver a 360 degree view. The sensors and chips for this car are outrageously expensive, and repairs will cost you.

Tesla, another contender in the race to create fully self-driving cars, is considering bundling the cost of maintenance and insurance with its AV sales, so you won’t necessarily feel it up front, and you won’t have to suddenly come up with the cash at the time maintenance is required.

That’s if you’re even in the market for your own AV.

How AV Rides Will Save

Don’t give up on AVs just yet. Although unexpected car repairs are the most frequent financial upset to family budgets, the future of transportation lies in shared, electric AVs, which will save riders the hassle and cost of vehicle maintenance. According to a May 2017 RethinkX report, the use of fleet-operated autonomous vehicles will help the average family save $5,600 per year on transportation.

[Editor Note: There’s a graphic available at showing that major car repairs were the biggest shock to families in 2015.]  

In urban areas at least, car ownership will lie with fleet operators rather than individuals. You’ll call for an AV – likely via a smartphone app – it will arrive at your location, you’ll get in and enter your destination, and you’ll head for the highway, simple as that. Not only will riders never have to think about maintenance, but they’ll never have to worry about refueling, paying parking tickets or parking fees, cleaning, or buying car insurance.

Additionally, riders won’t need to worry about the cost of a car accident, should one occur. AV manufacturers like Volvo, Google and Mercedes Benz have already pledged to accept responsibility if their product causes an accident.

AI Beyond AV

Transportation is just one industry being impacted by AI technology. Read more about how this technology will permeate various industries in the very near future, providing improved efficiencies and costs.

Prepare your company now by ordering Artificial Intelligence and Ethics in Design, IEEE’s exclusive 5-course training program, and learn how aligning technology with ethical values can help advance innovation, for AVs and more.


Amblard, Marc. (25 Jan 2018). Autonomous Cars Will Need “Autonomous Maintenance” Solutions. ReadWrite.

Kucharczyk, Sasha. (18 Apr 2017). How will maintenance change with the autonomous vehicle? Readwrite

Malarkey, Daniel. (16 Jan 2018). Part1: Your Car of the Future Is No Car At All. Sightline Institute.

Rosenberg, David J. and Pasciullo, Nicholas A. (29 Aug 2017). Autonomous Vehicles Predicted to Change Car Ownerships, Insurance Industry. The Legal Intelligencer.

Technology and Costs. Google’s Autonomous Vehicle.

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The Difference Between AI Ethics in Theory and in Practice

AI and ethics difference between theory and practical application artificial intelligence images

As we move forward in the age of artificial intelligence, the ethics of AI become crucial to product design. What seems ethical when developing AI products – programming machines with the ability to choose right from wrong – often changes when putting those products into practice.
With body cameras worn by police officers, AI technology allows for facial recognition that could assist in suspect identification. After all, memory can become foggy over time, while what a camera sees may be hard evidence. But what happens when facial recognition leads to bias and prejudice? 

While humans must program machines with ethics in mind, machines lack human reasoning–important when deciding what happens when things don’t go exactly as planned. When AI technology fails, as in the case of the autonomous vehicle that struck and killed a pedestrian, an innocent person is the victim.

David Danks, Carnegie Mellon University philosophy and psychology professor, says the people developing the technology must take into account both – ethics and the business goal – and realize “it is not a zero sum gain. It’s not ethical or profitable, where those are mutually exclusive. It’s not ethical or fast, where those are mutually exclusive.”

Many companies will consider ethical implications of AI when designing products, but only occasionally create dedicated ethics groups to focus on questionable uses of the technology by humans when it’s in practice. Of course, technological flaws and failures must also be taken into account.

Basic AI Design Considerations

Many companies will consider ethical implications of AI when designing products, but only occasionally create dedicated ethics groups to focus on questionable uses of the technology by humans when it’s in practice. Of course, technological flaws and failures must also be taken into account.

Controls must be built in to identify biases, show attribution, and enable course correction as needed. To that end, Constellation Research, a technology research and advisory firm based in Silicon Valley, suggests instilling these five design pillars for AI ethics in all projects:

  1. Transparent: Algorithms, attributes, and correlations should be open to inspection for all participants.
  2. Explainable: Humans should be able to understand how AI systems come to their contextual decisions.
  3. Reversible: Organizations must be able to reverse the learnings and adjust as needed.
  4. Trainable: AI systems must have the ability to learn from humans and other systems.
  5. Human-led: All decisions should begin and end with human decision points.

Learn More About Ethics in Design

Mark your calendar and register today for IEEE’s free webinar on Artificial Intelligence and Ethics in Design, taking place at 1:00 p.m. EST, May 9, 2018. You’ll learn how to help your organization apply the theory of ethics to the design and business of AI systems.

The webinar is the perfect companion to Artificial Intelligence and Ethics in Design, a five-course training created to educate and empower professionals to practically implement ethical considerations when developing intelligent and autonomous products and services.


Bhavsar, Vrajesh, ARM. (25 Jan 2018). The development of AI ethics must keep pace with innovation. VentureBeat.

Cook, John. (9 Feb 2018). The ethics of AI: Robots will rise, but will they rule us all? GeekWire.

Fingas, Jon. (26 April 2018). Axon opens ethics board to guide its use of AI in body cameras. Engadget UK.

Wang, R “Ray.” (26 Mar 2018). Designing Five Pillars for Level 1 Artificial Intelligence Ethics. Enterprise Irregulars.

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New Course Program Now Available: AI and Ethics in Design

Artificial Intelligence and Ethics in Design from IEEE Educational ActivitiesBe among the first to bring Part 1 of IEEE’s Artificial Intelligence and Ethics in Design to your employees!

Led by thought leaders in AI, philosophy and policy management fields, Artificial Intelligence and Ethics in Design is a five-course training from IEEE Continuing Professional Education. It will educate and empower professionals to practically implement ethical considerations when developing intelligent and autonomous products and services.

Why Choose This Training?

As technology advances, the need for ethical guidance will only increase. Technologists, engineers and programmers will be challenged with balancing the protection of privacy, autonomy and other values against the need for innovation and technological progress.

In every industry, we need ethical principles to navigate the challenges of artificial intelligence. According to Mark MacCarthy, SVP, Public Policy, Software & Information Industry Association (SIIA), and a panelist at the recent Global Privacy Summit of the International Association of Privacy Professionals (IAPP), “AI must be developed in a way that promotes transparency and fairness, that’s available to all, and that doesn’t reinforce existing inequalities.”

Artificial Intelligence and Ethics in Design is intended to help professionals leverage the power of AI while minimizing the risk. Participants will explore:

How To Enroll Now

Taught by the world’s leading experts, Artificial Intelligence & Ethics in Design is perfect for industry professionals focused on integrating artificial intelligence and autonomous systems within their companies. Upon successful completion, participants earn CEUs/PDHs for maintaining engineering licenses. 

If you’re an individual, learn more here. For corporate packages and pricing, learn more about speaking with an IEEE Content Specialist.


Lat, David. (4 Apr 2018). Machine Learning and Human Values: Can They Be Reconciled? Above the Law.

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Preparing Your Business for the Post-AI World (a.k.a. Right Now)

IEEE ai ethics artificial intelligence image

The world of Artificial Intelligence (AI) isn’t on the horizon; it’s right here, happening right now. AI is the technology behind personal digital assistants like Apple’s Siri and Google’s Alexa, targeted online advertising, more informed medical diagnoses, high-frequency trading, and HR specialists’ resume review processes.

AI has applications in virtually every industry. It has enormous power and potential to disrupt, innovate, enhance and, in many cases, totally transform a business. With the right strategy in place, an investment in AI pays off in big ways, including:

  • Cost reductions
  • Higher productivity
  • Increased revenue and profits
  • Richer customer experiences
  • Working-capital optimization
  • Broader business success

7 Steps to AI Prep

If your business isn’t quite there yet, no need to worry. Simply follow the steps below to help your business jump on the AI train and make the most of the great opportunities this technology presents.

1. Lay the Groundwork: 

  • Familiarize yourself with AI and what it can do for your business. AI technology is constantly evolving, so be sure to keep up with the latest developments and the impact they can have on your industry.
  • Determine and prioritize the most important areas in which AI can benefit your business. Start with the outcomes you want to achieve and work your way back, looking for places where AI can help.
  • Make sure you have a solid IT infrastructure that can handle the change.

2. Unlock Data Silos

Clearing obstacles created by functional silos or disconnected technologies will allow a flow of data within your organization, which is crucial for successful AI implementation.

AI IEEE professional development courses continuing education artificial intelligence3. Label Business Data

AI has limited ability to analyze data and produce insightful information without labels.

4. Feed AI Algorithms with Data and Context

Most AI algorithms are proficient at determining correlations, but they need context. The algorithms don’t understand the information surrounding the data, which may or may not be relevant.

5. Assess Existing Processes

Thoroughly evaluate all departments within your organization and all processes within each. Automating some of the tasks may help ensure that your personnel focuses on tasks that deliver more value.

To determine the areas of great opportunities and eliminate time- and effort-consuming responsibilities, ask your employees:

  • What are the low-value aspects of jobs that could be removed?
  • Which repeatable tasks take a lot of time?

6. Communicate What’s Coming

Change management will do wonders for your AI strategy implementation. It will be particularly important to communicate the end goal, as well as how employees’ jobs will change.

7. Invest in Your Employees’ AI Education:

Jody Kochansky, head of the Aladdin Product Group at financial services firm BlackRock, explains, “Ultimately, the combination of humans plus computers is more powerful than humans alone, and certainly more powerful than computers alone.”

Given the potential of AI to complement human intelligence, it’s vital for top-level executives to be educated about reskilling possibilities. However, according to the 2017 “Is Your Business AI Ready” report from Genpact, 82 percent of companies surveyed plan to implement AI in the next three years, but only 38 percent say they currently provide their employees with reskilling opportunities.

Ready to Make the Investment?

Ensuring opportunities for your employees to become AI-knowledgeable will increase the chances of successful adoption of the new technology and guarantee competitive advantage in the long-term. Train workers who are being moved from jobs that are automated by AI to jobs in which their work is augmented by AI. With the right pieces in place, workforces will not just survive but also thrive alongside automation.

A great place to start your employees’ AI education is with IEEE. Our new AI and Ethics in Design training course will help your organization apply the theory of ethics to the design and business of AI systems. Pre-order Part 1 today and train everyone in your organization for one low price.


Spektor, Nancy. (4 April 2018). How to Prepare Your Business Data for Artificial Intelligence. Smart Data Collective.

Is Your Business AI-Ready? Genpact.

Harrist, Margaret. (28 February 2017). Prepare Your Business For The AI Future. Forbes.

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Artificial Intelligence: Friend or Foe of the Healthcare Industry?

AI ethics and design

Sometime in the future, Artificial Intelligence (AI) will disrupt healthcare as we know it, but not in the ways most people think. Many fear machines will replace or even turn on humans. But the speed with which computer intelligence is advancing offers far more opportunities than dangers.

AI Variants

Today, AI is shorthand for any task a computer can perform just as well as, if not better than, humans. But there are different forms of AI to consider:

  • Most computer-generated solutions now emerging in healthcare rely on human-created algorithms for analyzing data and recommending treatments, not on independent computer intelligence.
  • Machine learning relies on neural networks (a computer system modeled on the human brain), to simulate and even expand on the way the human mind processes data. As a result, not even the programmers can be sure how their computer programs will derive solutions.
  • In deep learning, which is becoming increasingly useful in healthcare, software learns to recognize patterns in distinct layers. Because each neural-network layer operates both independently and in concert – separating aspects such as color, size and shape before integrating the outcomes – these newer visual tools hold the promise of transforming diagnostic medicine and can even search for cancer at the individual cell level.

Is it All Hype?

AI has been around since 1956, but has made precious few contributions to medical practice. Only recently has the hype begun to merge with reality.

AI hype includes a host of sophisticated new solutions from nurse-bots to AInsurance (insurance powered by AI), to AI wearables for the elderly, to name a few. In general, they’re algorithmic and not true machine-learning approaches. Nearly all have failed to move the needle on quality outcomes or life expectancy.

However, if computer speeds double another five times over the next 10 years, machine-learning tools and inexpensive diagnostic software could soon become as essential to physicians as the stethoscope was in the past.

Deep learning could be the very thing that catapults American healthcare into the future, helping to clarify the best care approaches, creating new approaches for diagnosing and treating hundreds of medical problems, and measuring doctor adherence without the faulty biases of the human mind.

The Hard Truth

Just as Uber and Lyft impacted the taxi industry and robotics the manufacturing industry, technology will have an impact on healthcare.

If technology is going to improve quality and lower costs in healthcare, some healthcare jobs will disappear. According to one study, AI is set to take over 47% of the US employment market within 20 years. Though blue-collar jobs have been in technology’s cross hairs for some time, doctors and other healthcare professionals are starting to feel the pressure, as well.

Over time, patients will be able to use a variety of AI tools to care for themselves, just as they manage so many other aspects of their lives today. But, fortunately for doctors, computers have yet to demonstrate the kind of empathy and compassion that millions of patients rely on in their medical care.

To learn more about AI and how aligning technology with ethical values can help advance innovation, explore IEEE’s new Artificial Intelligence and Ethics in Design Part I course program, available for pre-order now. Upon successful completion, engineers receive valuable CEUs/PDHs that can be used to maintain their licenses. Pre-order for your company now.


Pearl, Robert. (13 Mar 2018). Artificial Intelligence In Healthcare: Separating Reality From Hype. Barron’s.

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The Race to the Edge of the Network

iot industry 4.0 concept,industrial engineer using software (augmented, virtual reality) in tablet to monitoring machine in real time.Smart factory use Automation robot arm in automotive manufacturing

Edge computing is a distributed, open IT architecture that features decentralized processing power, enabling mobile computing and Internet of Things (IoT) technologies. In edge computing, data is processed by the device itself or by a local computer or server, rather than being transmitted to a data center.

Allowing large amounts of data to be processed near the source, edge computing helps reduce internet bandwidth usage. This efficient data processing both eliminates costs and ensures that applications can be used effectively in remote locations. Plus, the ability to process data without ever putting it into a public cloud adds a useful layer of security for sensitive data.

what is edge computing, how does edge computing work, 5G cloud computing, edge computing conceptDriven by a need to overcome cloud overhead in latency and bandwidth and a demand for more local processing, edge computing is poised to enable billions of new IoT end-points and real-time local artificial intelligence/machine learning (AI/ML) for autonomous systems. Edge computing allows smart applications and devices to respond to data almost instantaneously, as it’s being created, eliminating lag time, which is critical for technologies like self-driving cars.

How Client Devices Will Become Smarter

Robert Cihra, Managing Director and Senior Analyst at Guggenheim Securities, LLC, Research Division, says self-driving cars, smartphones and other client devices will become smarter in order to handle more local processing. According to Cihra, this is how:

  • Making machines smarter via real-time on-board AI/ML
  • Making thin-client smartphones fatter, as they need more processing and storage for on-device ML and virtual/augmented reality (VR/AR)
  • Pushing smartphone configurations/BOM costs and thereby ASPs even higher
  • Enabling more frictionless user interfaces (UIs) headlined by Voice and Vision vs. Keyboard and Screen
  • Enabling data input from devices that increasingly involve no human interaction at all (e.g., cameras, IoT sensors for location, vibration, temperature, etc.)
  • Favoring vertically-integrated vendors (hardware and software) particularly early on (e.g., Apple; Tesla; Google now building hardware; GM’s acquisition of Cruise Automation)

The Self-Driving Car Race

One of the hottest topics in edge computing is self-driving cars, because a self-driving car can’t be programmed to drive, but must think and act for itself, and it certainly cannot rely on the cloud and risk lag time.

The ability to process streams of sensor data and complex neural net pipelines in real-time is crucial. An autonomous car will require 50-100X the processing power and >10X the Dynamic Random Access Memory (DRAM) and Not And (NAND) technology of an Advanced Driver Assistance Systems (ADAS) car today.

Interior of Tesla Model S 90D car. Tesla Motors is an American company that designs manufactures and sells cutting edge electric cars.

Interior of Tesla Model S 90D car.

Cihra thinks Tesla, a pioneer in the American development of electric vehicles, is ahead of the curve in making automobiles an edge computing device. The company has used its connected fleet of customer cars for shared ML and building an in-house model that adds complexity, risk and cost, but also ultimate leverage.

As the perfect edge computing device, the automobile must be fully integrated, in terms of hardware and software development. And that’s why Cihra sees Apple either making a car itself or getting out of the market all together. Right now, Apple is investing in autonomous driving but has not yet committed to a car.

And This is Only the Beginning

Edge computing presents an incredible incremental growth opportunity for IoT development and data processing. To learn more about IoT, check out the IEEE Guide to the Internet of Things, our series of eight training courses designed to give your organization critical foundational knowledge.


Ray, Tiernan. (2 Mar 2018). Apple, Tesla to Lead ‘Edge’ Computing, Says Gugenheim. Barron’s.

What is Edge Computing? Hewlett Packard Enterprise.

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CES 2018: News To Know About

CES 2018 Updates from IEEEThe Consumer Electronics Show (CES) is the annual place to be for technology professionals, experts, and enthusiasts. There are literally hundreds of new product launches, and sometimes it’s hard to cut through all the clutter to find out what’s actually worth knowing about.

Of the technological solutions for home and business that this year’s event spawned, here’s what piqued our interest the most:

  • Cable industry behemoth, Comcast, put a major stake in the Internet of Things (IoT) ground by unveiling its new “Works with Xfinity” smart-home IoT platform. Keen on keeping existing customers due to simple inertia, tech experts and casual observers alike will wait and see how the company provides controls for hundreds of devices to Xfinity Internet customers at no extra cost—a key promise of this new offering. (Variety)
  • It’s worth noting that CES had an IoT device for everything. Even pets. (Wall Street Journal)
  • Nvidia was busy announcing a plethora of new stuff at this year’s expo, much of it centered around self-driving cars. In a partnership with Uber, its autonomous vehicle computing platforms will power the ride sharing company’s self-driving vehicles. Nvidia Xavier was also under consideration for a “Best of CES” Engadget award. (TechCrunchIGN; Engadget)
  • Self-driving cars might have gotten the lion’s share attention, but did you see the self-driving luggage from Travelmate? (Economic Times)
  • The big impact of artificial intelligence and machine learning in the enterprise is in cybersecurity, and especially in securing data center networks. Serena VM, provider of IT cyber capabilities, brought Fortune 500 cyber security to small offices with its virtual managed box for organizations that have remote or brand offices. (@SerenaVMUS)
  • Over in Eureka Park, which Leigh Christie, Director, Isobar NowLab Americas deemed “the best place for innovation at CES,” we learned how crucial low latency will be for virtual reality and augmented reality. (MediaPost)

And did you catch the IEEE booth at CES? Check out our show coverage on IEEE Transmitter!

What has been the most fascinating news or product announcement you’ve come across since CES started? Share with us in the comments below!

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Is Your Industry Ripe for Artificial Intelligence Disruption?

Health industry using AI artificial intelligence healthcare

Artificial intelligence (AI) is poised to make an indelible mark on how business is done in a variety of fields. The AI industry will have tremendous impact on the way people and programs work together effectively. Here are the industries that are primed to see the most tremendous improvements in efficiency and cost savings this year.


AI is now contributing to the practicality of disease management and empowering people to make better lifestyle choices. It’s also creating a better pathway between patients and their healthcare providers. Plus, in a world that’s increasingly vulnerable to cyber security threats, AI can play a vital role in safeguarding the industry overall. It’s not an overstatement to say that the AI industry is crucial for healthcare.

For medical practitioners and providers, AI has led to a breakthrough in emerging drug discovery platforms. Additionally, many treatment centers are using AI-based diagnostics as a first-tier of clinical diagnostics, leveraging AI for enabling personalized medicine.

According to Mark Michalski, executive director of Massachusetts General Hospital and Brigham and Women’s Center for Clinical Data Science, by the end of 2018, half of the leading healthcare systems could adopt some form of AI within their diagnostic groups. It’s anticipated that 2018 will be “the year AI becomes real for medicine.” (Michalski, Dec 2017).

Automotive artificial intelligence AI industry cars AI car industry auto industryAutomotive

There seems to be a new headline about self-driving vehicles permeating every news cycle as of late. Not only is AI “driving” driver-less cars, it can help build smart cars less expensively, ones that are capable of detecting and self-navigating various people, places, and things while in operation. The AI industry and the autonomous vehicles industry really go hand-in-hand.

The most prevalent of AI applications in the auto world is machine learning, which does much of what it sounds. Machines use data to teach themselves how to make real-time decisions in split seconds, similar to how humans learn and improve over time. Thus far, machine learning has been highly useful in developing advanced driving assistance systems. Outside of the vehicles themselves, machine learning has brought huge improvements to the sales and after service functions of the automobile life cycle.

Real Estate

The real estate industry hasn’t had a major breakthrough in decades, making it ripe for disruption. As Value Walk points out, AI has the ability to “reduce operational costs, improve customer service, improve efficiency, and reduce resource wastage” within real estate. AI bots in particular are revolutionizing other industries, and could be one of the first dominant AI technologies that both agencies and property owners adopt. Largely due to their relative ease of integration as compared to other facets of AI, AI bots are able to field queries about leasing, footage, and other common prospective buyer questions that come up during virtual tours.

While the disruption has already begun, Inc. predicts the largest single change that will come to real estate will be AI replacing a realtor for some people.

Bottom Line

The AI industry is expected to expand exponentially in the coming year, completely transforming these and other industries. Programmers, engineers, and technologists need to understand and implement globally accepted ethical considerations at the heart of AI as it continues its fundamental shift in the way business is done. Potential impediments to successfully using this technology, however, includes the practical applications of AI, as well as ethics in design.

This year, IEEE Continuing Professional Education is offering a two part online course program on the subject: Artificial Intelligence and Ethics in Design.  If you’re interested in group discounts for the AI ethics in design courses for your organization, please contact an IEEE Content Specialist today.

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Achieve Ethical AI Design with New IEEE Course Program

Ethical AI Design: AI and Ethics in Design course program from IEEE now available for pre-order

In an effort to prioritize ethical considerations in the design and development of Artificial Intelligence (AI) and Autonomous Systems (AS), the IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems (AI/AS) was formed. Representing the input of more than 100 experts in AI, law, ethics, and policy, it released a ground-breaking document, Ethically Aligned Design: A Vision for Prioritizing Human Well-being with Autonomous and Intelligent Systems, that encourages professionals to heed ethical AI design techniques when creating and proliferating of these technologies.

Practical Course Teaching Ethical AI Design

In support of the second version of this publication, EADv2, announced yesterday, IEEE Continuing Professional Education is offering a two part course, Artificial Intelligence and Ethics in Design, based on this document and developed in cooperation with the IEEE Global Initiative. Intended to educate and empower professionals to practically implement ethical considerations when developing intelligent and autonomous products and services, this course provides easily-digestible, practical content that offers CEUs/PDHs for successful course completion. Led by global thought leaders in AI, philosophy, and policy and management fields, part one of this course for ethical AI design is now available for purchase.

Pre-Order Artificial Intelligence and Ethics in Design Now

With the advancement of autonomous and intelligent systems, programmers, engineers, and technologists need to understand and implement globally accepted ethical considerations at the heart of AI and AS. Technical professionals love IEEE online training courses because they offer cutting-edge education on the latest technologies, taught by the world’s leading experts. If you’re interested in group discounts for the AI ethics in design courses for your organization, please contact an IEEE Content Specialist today.



Brown, M. (2017, 12 Dec). These New A.I. Guidelines Will Usher in a World of Ethical Robots. Inverse.

Gohd, C. (2017, 20 Nov). A Powerful Tech Organization Is Working to Protect Us From AI. Futurism.

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Managing IoT on the Edge

edge computing internet of things (IoT)As Internet of Things (IoT) devices proliferate, so does the data that they produce. As more and more data is produced, organizations are finding it to be a costly endeavor to send, process, and store all of this data in the cloud. In fact, some organizations don’t even have the connectivity within their infrastructure to send large amounts of data to the cloud. (Anderson, 2017) Reliance on cloud computing alone also slows down device performance, as bandwidth constraints constrict how much data can be sent and received. Cloud computing alone cannot efficiently handle the IoT. This is why edge computing is becoming a critical factor in IoT deployments.

Edge computing moves data processing from the cloud to hardware on the “edge” of the network. By keeping the data processing local, latency is minimized, which is critical for deployments where real-time processing and time-sensitive decisions are mission critical. Artificial intelligence can help parse data and trigger local actions, such as scheduling maintenance with a facility that has the necessary parts, or deciding when a safety alert needs to be sent. Monitoring, diagnostics, performance optimization, and predictive maintenance are all functions that benefit from an edge computing solution.

In addition to reduced latency, there’s another key benefit to edge computing. It is estimated that the cost of a combined edge and cloud infrastructure is 1/3 of the cost of a cloud-only solution. (Gaunt, 2017) Most of this cost savings is realized through the reduction of bandwidth requirements and computing resources.

Of course, when moving to the edge, security is a critical factor. Cyber security must be deployed in both hardware and software to protect an organization’s data and hardware. As the cyber security of Internet of Things devices continues to develop, it is essential that IoT device manufacturers take into account the fact that some processing will occur on the edge, and build in security measures, including the ability to install updates as needed. Yet the edge may even be more secure than the cloud, as it is by its very nature decentralized. It is more complex for a cyber attacker to hack the decentralized edge, than the more centralized cloud.

Edge computing offers exciting opportunities for IoT development, and the intelligent processing of data that these devices produce. Investment of infrastructure to support the edge will continue, and should yield strong returns for organizations, both financially, as well as through better use of data.

To learn more about the Internet of Things, check out our online course program IEEE Guide to the Internet of Things.



Gaunt, T. (20 Nov, 2017). Pushing IoT to the Edge. Networks Asia. 

Anderson, J. (17 Nov, 2017). Managing IoT with Edge Computing. Network Computing.

Figueredo, K. (15 Nov, 2017). Edge Computing and AI: From Theory to Implementation. IoT Agenda.

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