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How Will Car Maintenance Evolve In the Autonomous Vehicles Era?

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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 Sightline.org 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.

Resources

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.

Resources

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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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.

Resources

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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String of Setbacks Halts Several Autonomous Vehicle Tests

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While many believe the autonomous vehicle (AV) is a form of artificial intelligence (AI) that could ultimately help reduce the number of people killed and injured on the roads each year, it seems that fatalities are inevitable.

Last week, 49-year-old Elaine Hertzberg was struck and killed by an Uber self-driving vehicle in Tempe, Arizona. Although police are still investigating and haven’t determined whether the car was at fault, a video from the vehicle’s dashboard camera shows that the human safety driver present in the Uber vehicle was not watching the road and did not have his hands hovering above the steering wheel, as instructed in case there’s a need for intervention. This is the first death caused by a self-driving car.

Uber’s Struggling Self-Driving Cars

The crash was a major setback for Uber, although even months ago its self-driving car project was falling short of expectations. Among other issues, Uber’s autonomous vehicles were having trouble driving through construction zones and alongside tall vehicles, and its human safety drivers had to intervene far more frequently than the drivers of competing autonomous car projects.

As of March, Uber was struggling to meet its target of 13 miles per intervention in Arizona. Meanwhile, in tests on California roads last year, Waymo reports its cars went an average of nearly 5,600 miles before the driver had to take control. Waymo is now testing in Chandler, Arizona, with no safety drivers.

Testing Comes to a Screeching Halt

Sure that the market for self-driving cars could be worth trillions of dollars, tech companies like Uber and Waymo, as well as automakers like Toyota, Ford and General Motors, have spent billions in development.

In fact, just weeks before Hertzberg’s death, The Economic Times reported that Uber and Toyota had been collaborating on self-driving systems, negotiating a possible deal for Toyota to use Uber’s automated driving technology in one of their minivan models. A Toyota spokeswoman said the company had been regularly exchanging information about automated driving with Uber for some time.

However, since the incident, Uber’s autonomous vehicle trials across North America have been halted and Toyota has decided to pause their Chauffeur mode testing on public roads.

Ford has made no changes to testing operations and GM still plans to roll out its commercial service in 2019.

Resources

Kokalitcheva, Kia, and Fried, Ina. (20 March 2018). Some self-driving car companies hit brakes on tests after fatality. Axios.

Symons, Xavier. (25 Mar 2018). A self-driving car killed a pedestrian. What now? BioEdge.

Wakabayashi, Daisuke. (24 Mar 2018). Uber’s self-driving cars were struggling before Arizona crash. The Economic Times.

Reuters. (19 Mar 2018). Toyota in talks with Uber on self-driving tech. The Economic Times.

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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.

Resources

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.

Resources

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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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.

 

Resources:

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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