Recent advances in edge computing and edge artificial intelligence (AI) are revolutionizing a broad range of industries. They are enabling a new age in predictive analysis and operational performance. So what exactly is edge AI, and how is it changing the way businesses operate?
Edge Artificial Intelligence
Edge AI refers to AI computations performed near the user at the “edge” of a network and close to where the data is located. This could be a retail store, a workplace, or an actual device such as a phone or a traffic light. It contrasts with processing at long distances away in a central cloud computing facility or private data center. Recent advances in machine learning and high-speed computing have facilitated this change. Additionally, the worldwide adoption of Internet of Things (IoT) devices contributes to faster and more reliable connectivity. As a result, AI models are increasingly deployed at the edge.
Ultimately, AI has been successful when paired with edge computing because modern-day AI algorithms are sensitive to real-world issues. They handle conditions across diverse fields, from healthcare to agriculture. AI is highly effective in edge applications because it recognizes patterns and trends. Deploying it in a centralized cloud or private data center would be less feasible. This is due to issues related to latency, bandwidth, and privacy.
Because edge technology performs analyses on data locally through decentralized capabilities, it can respond to user needs much quicker. It also significantly reduces networking costs for an organization due to requiring less internet bandwidth. Furthermore, data processing isn’t reliant on internet access. Thus, mission-critical and time-sensitive AI applications can enjoy greater access and reliability. These edge computing benefits, combined with the expanding flexibility and “intelligence” of AI neural networks, are allowing organizations to capitalize on real-time insights. They can do so at a lower cost and with greater security and privacy.
Edge AI Use Cases
Edge AI is being recognized as a pivotal technology that will continue to impact new product development. It will streamline processes and enhance user experience across many industries.
In the utility industry, for example, edge AI models combine historical data, weather patterns, and other inputs. They aim to more efficiently generate and distribute energy to customers.
In manufacturing, sensor data analyzed by edge AI technology is helping predict machine failures. It helps factories avoid costly downtime.
Edge AI-enabled surgical tools in healthcare are assisting doctors. They support real-time assessments in the operating room that improve surgical outcomes.
In retail, edge AI enhances customer service. It enables voice-based ordering by customers via smart speakers or other intelligent devices.
In transportation, where real-time decisions are crucial, edge AI adjusts traffic lights. It helps to regulate traffic flow and reduce congestion.
And in security across numerous organizations, edge AI’s real-time analysis of video footage can identify unwarranted activity and immediately inform authorities.
The Power of Edge AI and Nanotechnology in Semiconductor Applications
According to the authors of Artificial Intelligence in Nanotechnology, an academic white paper on AI in nanotechnology, AI plays a significant role in development at the nano scale. It leads to exciting research and development called “AI-nanotechnology.”
Thanks to the big data that AI analyzes, semiconductors benefit from combining edge AI and nanotechnology. They lead to the design of more efficient chips, speeding up market entry.
Semiconductors, or chips, are components used to conduct or block electric current. They drive a bevy of modern-age devices, including mobile phones, computers, TVs, washing machines, LED bulbs, medical equipment, and more.
Edge AI enables semiconductor manufacturers to optimize their product’s power, performance, and area (or “PPA”). It helps design advanced new chips and cheaply overhauls older designs. This occurs without needing to update fabrication equipment. By integrating nanotechnology, they can design with materials at nano scales. They create robust semiconductors with improved functionality cost-effectively.
While both fields face hurdles—ethics, privacy, and bias for AI, and regulatory issues for nanotechnology—experts believe combining these technologies can spur innovation. They hold immense promise for revolutionizing various aspects of science, technology, and everyday life.
Stay on the Cutting Edge of Continuing Education
A new five-course program from IEEE, Integrating Edge AI and Advanced Nanotechnology in Semiconductor Applications, explores the intersection of AI, edge computing, and nanotechnology. It covers real-life applications and future trends. From AI nanoinformatics fundamentals to semiconductor design specifics, learners will acquire skills. They’ll be able to navigate the complexities of modern computing.
To learn more about accessing these courses for your organization, contact an IEEE Content Specialist today.
Interested in the course program for yourself? Visit the IEEE Learning Network.
Resources
Yeung, Tiffany. (17 February 2022). What is Edge AI and How Does It Work? NVIDIA.
(16 November 2023). Bringing AI to the Edge: How Edge AI is Revolutionizing Industries. Sintrones.
Agrawal, Radheyshree, Tilak Paras, Devand, Aryan, Bhatnagar, Archana, and Gupta, Piyush. (17 March 2024). Artificial Intelligence in Nanotechnology. Springer Nature.
Nanotechnology. National Geographic.
Brode, Bernie. (21 March 2022). AI and Nanotechnology are Working Together to Solve Real-World Problems. Stack Overflow Blog.
2023 Edge AI Technology Report. Chapter I: Overview of Industries & Application Use Cases. Wevolver.
Experts, commentators, and pundits alike have been saying it for years: Data is the new oil. The phrase is widely credited to mathematician Clive Humby, who also said, “Like oil, data is valuable, but if unrefined, it cannot really be used. It has to be changed into gas, plastic, and chemicals to create a valuable entity that drives profitable activity. Data must be broken down and analyzed for it to have value.”
Artificial intelligence and automation technology offer new ways to target potential customers, personalize messaging, and recommend products, thereby making data an essential resource for modern enterprises and business decision-making. Companies around the globe collect and analyze volumes of data daily. This highly valued commodity needs to be protected, but so do the individuals who provide it.
For modern companies, navigating data privacy can seem overwhelming. Different regions may be subject to varying legislation levels. Additionally, citizens of a particular region may still be protected by those laws no matter where they’re presently located. As data privacy regulations grow, companies face constantly changing data management requirements to secure the correct opt-in permission and ensure compliance. Recent data breaches and hacks of Uber, Verizon, Meta, and Microsoft demonstrate how sophisticated hackers have become.
Flawed Practices Lead to Consumer Mistrust
Inferior consumer privacy practices expose businesses to real repercussions, such as an increase in consumer data breaches. In 2021, there were more than 130,000 personal data breaches. These instances led to material losses like fines, but more importantly, loss of trust for current and prospective customers. According to a recent report, 87% of consumers “would not do business with a company if they had concerns about its security practices.” Investments in data protection and privacy fosters consumer loyalty and trust in a company’s products.
Data Security is Paramount
Without a solid data security platform, your company risks financial penalties for violating data privacy regulations and jeopardizing your company’s reputation. A recent article in Apple Magazine provides six tips for better data security in the workplace:
- Make sure all employees have strong passwords
- Have disaster recovery plans in place
- Create strong firewall and antivirus software policies
- Monitor and analyze your users’ online habits
- Encrypt your data whenever possible
- Invest in employee training programs
Technologies such as artificial intelligence, machine learning, the Internet of Things, virtual reality, and facial and biometric recognition all use or generate personal data. Protecting that data should be a top priority—and training your organization in data privacy can provide a critical competitive advantage. Does your company place a priority on data privacy skills?
Privacy by Design is the Future
Companies should consider both data privacy and security issues daily. According to Lindy Cameron, CEO of the UK National Cyber Security Centre (NCSC), a secure-by-design approach is vital to protecting the growing Internet of Things (IoT) and consumer-connected devices. She goes on to explain how the last decade has seen an increase in significant security risks as “the scale of consumer-, enterprise-, and city-level IoT has exploded in the last decade,” along with a growing dependency on connected technology.
Data privacy is not the domain of just IT departments anymore. Protecting personal data should start in product development—ensuring that every product team member understands privacy by design. For effective results, privacy should be layered throughout the product development lifecycle.
Enhance Your Data Privacy Skills
Engineering and technology professionals must increasingly consider data privacy and security when designing products and systems. As the world becomes more automated, it’s crucial for your organization to understand how to protect its data and devices.
Cyber Security Tools for Today’s Environment, an online 11-course program from IEEE, helps businesses improve their security techniques. Contact an IEEE Account Specialist today to get access to the course program for your organization. Interested in learning about getting access to the course for yourself? Visit the IEEE Learning Network to learn more.
Protecting Privacy in the Digital Age, brought to you by IEEE Educational Activities in collaboration with IEEE Digital Privacy, is a four-course program that provides a framework on how to operationalize privacy in an organizational context, how to make it usable for end users, and how to address emerging technical challenges to protecting digital privacy. Connect with an IEEE Content Specialist today to learn how to get access to this program for your organization. Interested in access for yourself? Visit the IEEE Learning Network (ILN).
Resources
Drapkin, Aaron. (18 October 2022). Data Breaches That Have Happened in 2022 So Far. Tech.co.
Hill, Michael. (24 October 2022). Security by design vital to protecting IoT, smart cities around the world. CSO.
Huang, Helen. (18 October 2022). Putting privacy first: A global approach to data governance. Treasure Data.
Newsroom AppleMagazine.com. (24 October 2022). 6 Tips for better data security in the workplace. AppleMagazine.com.
Talagala, Nisha. (2 March 2022). Data as The New Oil Is Not Enough: Four Principles for Avoiding Data Fires. Forbes.
Robicquet, Alexandre. (19 October 2022). Why Businesses Don’t Need More Data—They Need Better Data. Forbes.
Big data is creating exciting new opportunities for artificial intelligence (AI). According to Arvind Krishna, Chairman and Chief CEO of IBM, 2.5 quintillion bytes of data are produced each day. To analyze, distribute, and make use of this data, many organizations are combining AI with hybrid cloud technology.
“The economic opportunity behind these technologies is enormous, given that business is only about 10 percent of the way to realizing A.I.’s full potential,” writes Krishna in Inc.com. “Fortunately, we are making steady progress, with the number of organizations poised to integrate A.I. into their business processes and workflows growing rapidly. A recent IBM study showed that more than a third of the companies surveyed were using some form of A.I. to save time and streamline operations.”
However, for artificial intelligence programs to work effectively, organizations need to successfully manage their data. According to Andrew P. Ayres, a Senior Specialist with HPE’s Enterprise Services practice in the United Kingdom, writing in CIO, you can achieve this by:
- making “data-centric AI” and “AI-centric data” part of your data management strategy. Metadata and “data fabric” should be the foundational elements of this strategy.
- establishing policy requirements that include minimum AI data quality to prevent “bias, mislabeling, or irrelevance”
- determining the right “formats, tools, and metrics for AI-centric data” early on. This way you don’t have to develop new techniques as your AI evolves.
- ensuring that the data, algorithms, and people within your AI supply chain are diverse. This diversity helps to stay in line with your ethical values.
- appointing or hiring the right experts internally and externally to oversee data management. These experts are capable of developing effective processes and deployments for your AI.
How to Choose an AI Program That Works Best For Your Employees
As you develop your AI program, keep in mind that while AI can augment your organization in terms of speed and efficiency, it is not necessarily a substitute for human intelligence.
While AI is good at analyzing data and recognizing patterns, it still has a tendency to miss important context that humans easily spot. This can have potentially devastating consequences if, for example, an AI makes a critical error when analyzing medical documentation. As such, you need to consider how to make your AI work with your human employees in the most effective way possible.
According to experts from Boston Consulting Group, writing in Fortune, organizations can do this by following the following principles:
- Know your options in terms of how you can combine humans with AI: Depending on your organization’s unique needs, do you need your AI to act as an illuminator, recommender, decider, or automator? Knowing the difference can help you choose the best AI system for your organization. Choose whether it’s an AI that can make predictions or one that can help you automate operations remotely.
- Create a decision tree: A decision tree constitutes the questions you will ask in a sequence. This helps you clearly understand your objectives (goals), context (resources in terms of data), and outcomes (results in terms of deploying AI vs employees). This will help you determine what type of AI system (illuminator, recommender, decider, or automator) you need.
- Continuously assess and revise your human-AI combinations: Your needs for an AI program may evolve overtime and, as such, so will its relationship to your employees. For this reason it’s important to return to the decision tree occasionally to determine if you need to revise your model.
Knowing how to manage your organization’s data and determining the right AI program are important steps. However, you also need to ensure that your employees are equipped to work with this increasingly complex technology.
Bringing Ethics to the Forefront at Your Organization
An online five-course program, AI Standards: Roadmap for Ethical and Responsible Digital Environments, provides instructions for a comprehensive approach to creating ethical and responsible digital ecosystems.
Contact an IEEE Content Specialist to learn more about how this program can help your organization create responsible artificial intelligence systems.
Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!
Resources
Krishna, Arvind. (18 May 2022). Why Artificial Intelligence Creates an Unprecedented Era of Opportunity in the Near Future. Inc.
Candelon, Francois, Ding, Bowen, Gombeaud, Matthieu. (6 May 2022). Getting the balance right: 3 keys to perfecting the human-A.I. combination for your business. Fortune.
Ayres, Andrew P. (29 April 2022). Don’t Fear Artificial Intelligence; Embrace it Through Data Governance. CIO.
A number of new laws – recently passed in Europe, China, the U.S., and Brazil – are presenting an urgent need for organizations to develop data privacy policies. Not only are these laws creating compliance concerns, they are also compelling organizations to start embracing data privacy as a core value.
How Can Organizations Establish Data Privacy Policies As A Core Value?
According to Kevin Shepherdson, CEO and Founder of Straits Interactive, a leading data privacy consultancy in Singapore, transformation around data privacy needs to start with an organization’s leadership. Senior leaders need to clarify that their organizations take data privacy seriously. They should provide the necessary resources to institute a data protection management program (DPMP). This also should include training their staff around such programs.
“We often see data breaches being described as ‘human error’, which is unacceptable to regulators and should not happen where there is sufficient staff training and strong ‘tone at the top,’” Shepherdson writes in CPO Magazine. “As important as initiating the DPMP is sustaining it. The organization must maintain compliance efforts by educating stakeholders about its data protection policies. This includes conducting regular data privacy audits and regular risk assessments.”
How Can Organizations Successfully Implement a Data Privacy Program?
Stu Sjouwerman, founder and CEO of KnowBe4, which develops security awareness training and simulated phishing platforms, offers the following four recommendations for organizations that want to implement a successful data privacy program, which he originally outlined in Security Magazine:
- Be inclusive of every department in your organization: Data security impacts every facet of your organization. Each department likely processes data in its own way, so it’s important to include each department, process, and vendor in your data privacy plans.
- Track your practices using documentation: Documenting your data privacy practices as you go along will give you valuable perspective into how your practices deliver value and risk. “Map out your entire data lifecycle (using data flow diagrams) and the process each department uses to collect, store, access, use and share consumer data,” writes Sjouwerman. “Outline the organization’s legal and contractual obligations and the process with which end users can manage their privacy rights.”.
- Go Beyond Compliance: Organizations have a tendency to see legal and compliance obligations as “a checklist of items that need to be crossed.” According to Sjouwerman, this is a common mistake. Instead, he suggests looking at privacy as your users’ fundamental right. Your organization’s compliance practices must work to uphold this right.
- Continuously re-assess your data privacy practices: No organization stays the same. Departments, processes, vendors, products, and people change over time. As such, it’s important to regularly assess your data privacy practices to ensure they are evolving with your organization. According to Sjouwerman, this involves undergoing a Data Protection Impact Assessment. He says this will help “identify risks proactively and reduce the likelihood of any impact to the organization or its customers.”
With data privacy laws becoming more common, privacy policies are no longer a consideration – they are a necessity. Is your organization equipped with the knowledge to implement a successful data privacy program?
Data Privacy by Design
Privacy has emerged to be a critical aspect of our increasingly digitized world. Technological innovations are progressively becoming more intrusive into our personal lives attempting to extract sensitive personal information. This is often detrimental to an individual when any breach or spillage of data leads to a severe impact such as financial loss or identity theft.
Protecting Privacy in the Digital Age, brought to you by IEEE Educational Activities in collaboration with IEEE Digital Privacy, is a four-course program. It provides a framework on how to operationalize privacy in an organizational context. It also covers how to make it usable for end users, and how to address emerging technical challenges to protecting digital privacy. Connect with an IEEE Content Specialist today to learn how to get access to this program for your organization. Interested in access for yourself? Visit the IEEE Learning Network (ILN).
Resources
Sjouwerman, Stu. (22 March 2022). Data privacy in 2022: Four recommendations for businesses and consumers. Security Magazine.
Shepherdson, Kevin. (18 March 2022). Data Privacy in 2022: Navigating the Ever-shifting Terrain. CPO Magazine.
As artificial intelligence (AI) systems are built on larger and larger quantities of data, the potential threat that they can pose to the public also grows. For example, automated systems could be equipped with biased algorithms that discriminate against women and minority groups. Additionally, AI-based software, such as facial recognition technology, can jeopardize the privacy of millions of people. To get ahead of the problem, governments are beginning to regulate rapidly advancing AI. Organizations that develop these systems will eventually be required to comply.
In Europe, a law known as the General Data Protection Regulation (GDPR) oversees data privacy for EU citizens and residents. While the United States has yet to pass specific AI regulations, the country is expected to begin rolling out state and federal regulations in the coming years. One example is the Algorithmic Accountability Act, which would mandate that organizations examine and repair potentially harmful flaws in computer algorithms. Similar to the General Data Privacy Regulation (GDPR), the Algorithmic Accountability Act would make impact assessments mandatory for automated decision and information systems that are high risk.
Additionally, many businesses are taking steps to establish their own AI standards. These efforts ensure their systems are ethical and safe, as well as protect them from potential liability.
Here are four expert-recommended principles organizations can consider for their AI standards.
What Should AI Standards Include?
Transparency:
Huma Abidi, senior director of AI software products at Intel, recommends that AI developers define and create clear, quantifiable standards. These should include processes that can be measured in terms of quality and robustness. She told Venture Beat that ethical AI systems should be “fair, transparent, [and] explainable.”
One example is a paper titled “Datasheet for Datasets.” The paper focuses on a standardized process for machine learning. It documents datasets, which state that “every dataset [should] be accompanied with a datasheet.” This datasheet documents its motivation, composition, collection process, recommended uses, and so on.” Another example is a machine learning documentation project called “Model Cards for Model Reporting.” The paper explains: “Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information.”
According to Abibi, these “basic principles” should be built into workflows.
“My point is that like any other software product, you want to make sure it’s robust and all that. However, for AI, you especially—besides having standards and processes—you need to add these additional things,” she told Venture Beat.
A cautious, iterative approach to AI development:
According to Rashida Hodge, VP of North America Go-to-Market, Global Markets, at IBM, businesses should develop cautious, iterative approaches to AI development. The process should include a lifecycle that forces organizations to return to it regularly as the data evolves. They should tailor their AI models to any changes as necessary.
“Just like how we as humans process information and process nuance, as we read more information, as we go visit a different place, we have different perspectives. And we bring nuance to how we make decisions; we should look at AI applications in the exact same way,” Hodge told Venture Beat.
Oversight:
Organizations should avoid siloing their analytics teams. This can inadvertently lead to “analytic city states” that make streamlining technology and ideas challenging. Scott Zoldi, Chief Analytics Officer at FICO recommends organizations appoint a single chief analytic officer. This officer would be responsible for creating and enforcing organizational standards.
“You can safely build more houses when you don’t have to draft a new building code for every house. Likewise, you shouldn’t have to worry about rolling the dice as to which artist will be building your model,” Zoldi wrote in Enterprise AI News.
Professionalization:
It’s important that everyone in an organization is aware of how their job impacts AI development, even if their role is small. As discussed in a previous post, “tactics of professionalization” is one way organizations can standardize AI development broadly across their enterprises. According to these principles, AI developers should set up committed multidisciplinary teams. They should train all their employees and clearly define who within the organization is accountable for the consequences of their AI systems.
Establishing AI Standards for Your Organization
Artificial intelligence continues to spread across various industries, including healthcare, manufacturing, transportation, and finance among others. It’s vital to keep in mind rigorous ethical standards designed to protect the end-user when leveraging these new digital environments. AI Standards: Roadmap for Ethical and Responsible Digital Environments, is a new five-course program from IEEE that provides instructions for a comprehensive approach to creating ethical and responsible digital ecosystems.
Contact an IEEE Content Specialist to learn more about how this program can benefit your organization.
Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!
Resources
Colaner, Seth. (3 January 2020). Evolve: Operationalizing diversity, equity, and inclusion in your AI projects. Venture Beat.
Brumfield, Cynthia. (8 December 2020). New AI privacy, security regulations likely coming with pending federal, state bills. CSO.
Lucini, Fernando. (24 September 2020). Getting AI results by “going pro.” Accenture Research Report.
Zoldi, Scott. (6 November 2020). It’s Time To Set Industry Standards for AI. Enterprise AI.
With news that vaccines to control the spread of COVID-19 have been developed and approved, the next step will be the enormous undertaking of administering them to the public. For the current vaccines developed by Moderna and Pfizer-BioNTech to be long-lasting and effective, individuals must take two separate doses three to four weeks apart (the length of time depends on which vaccine is used)—bringing the total to about 15 billion doses.
“[It’s] a level of undertaking that is just beyond anything we have done as a society,” Mark Treshock, Blockchain Solutions Leader for Healthcare and Life Sciences at IBM, told mobihealthnews. “To confound that, it’s the fact that these vaccines are all different, and they are not interchangeable. So even though they treat or vaccinate against the same virus, they are different vaccines.”
Blockchain Technology
Blockchain technology, a decentralized digital ledger of transactions that records data in a way that prevents hacking and data altercation, may be able to help medical professionals, manufacturers, distributors, and patients stay on top of these vaccines in a secure manner. Not only can blockchain be used to track vaccines over long distances in order to ensure they are temperature controlled and safe for use upon delivery, it can also help medical professionals and patients maintain vaccination records. This could help patients as they may need to prove to authorities that they are safe to travel or to verify that they can be in an indoor office environment. Blockchain can also be used to solidify immunization records about a patient. This process can ensure a patient is receiving the correct pair of COVID-19 vaccines, which they may also need for verification purposes.
“The two-dose challenge,” said Treshock. “Where you need two doses, they need to be within a set time window, let’s say 30 days, and they need to be from the same manufacturer. So, if your first dose is Pfizer, your second dose has to be Pfizer as well. They aren’t interchangeable. When we start administering this vaccine at scale, it is going to be very challenging coordinating that.”
How Can Blockchain Help Manage Health Data?
Blockchain has the power to transform the healthcare industry. Whereas much of our online data is currently in the hands of private companies like Facebook, blockchain can give individuals control over their personal data. Data collected on the Internet is a kind of virtual representation of every user. However, many individuals have no real ownership over their data, which can create problems when it comes to security, access, monetization, privacy, and advocacy.
“That identity is now yours, but the data that comes from its interaction in the world is owned by someone else,” Carlos Moreira, CEO of WISeKey, told Harvard Business Review.
Not only is blockchain decentralized, it’s also immutable. This means transactions cannot be changed or undone without approval. Blockchain keeps digital identity safe in a “digital wallet” that gathers and protects all the data, which can include health information. For example, this “digital wallet” could house personal health records or health information captured by a smart watch, and then give an individual control over how that data is used.
Some organizations are already using blockchain to successfully manage health data, including:
- Canada’s University Health Network (UHN) created a patient control-and-consent platform designed to make clinical research easier. Created in partnership with IBM, UHN uses blockchain to amass and secure patient data throughout the network. It receives and records consent from each patient in order for their data to be shared with researchers.
- MiPasa, an initiative founded by the start-up Hacera, is a platform designed to capture pandemic data on an international scale from the Center for Disease Control, the World Health Organization, licensed private facilities, local public health agencies, and individuals—without identifying them. The platform aggregates data through Hacera’s Unbounded Network, a decentralized blockchain supported by Hyperledger Fabric. It then uses IBM’s blockchain and cloud platforms to stream the data.
- The blockchain startup Shivom is developing an international project that gathers and shares virus host data. The platform uses blockchain to actively maintain patient consent, and to securely and privately share genomic information and data analysis with third parties without offering access to patients’ raw genomic data.
Blockchain has the potential to revolutionize health care, but requires a transformation in the rules for defining and assigning data ownership.
Understand Enterprise Blockchain for Your Industry
What other industries can benefit from blockchain technology? Get Enterprise Blockchain for Healthcare, IoT, Energy, and Supply Chain, a five-course program from IEEE, to find out. Developed by leading experts in blockchain technology, this advanced program provides business use cases across key industries and sectors. It’s ideal for managers, professional engineers, and business leaders.
Contact an IEEE Content Specialist to learn more about how this program can benefit your organization.
Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!
Resources
Goodnough, Abby. Zimmer, Carl. Robbins, Rebecca. Mandavilli, Apoorva. Thomas, Denise Grady Katie. Parker-Pope, Tara. Weiland, Noah. Singer, Natasha, Leonhardt., David, Rabin. Roni Caryn. Bosman, Julie. Abelson, Reed. Pérez-Peña, Richard. (14 December 2020). Answers to Your Questions About the New Covid Vaccines in the U.S. New York Times.
Lovett, Laura. (25 November 2020). Blockchain could be the key to vaccine distribution, says IBM. mobihealthnews.
Tapscott, Don and Tapscott, Alex. (12 June 2020). What Blockchain Could Mean for Your Health Data. Harvard Business Review.
A future with widespread autonomous vehicle (AV) technology could include less traffic, safer roads, and interconnected vehicles that allow drivers to sit back and enjoy the ride. Expected to reach $556.67 billion USD by 2026, the market place for AV technology is growing quickly. However, the industry still has a long way to go. In order for autonomous vehicle technology to properly function, it must work in conjunction with other areas. The five most relevant are listed below.
Five Use Cases
5G
An autonomous vehicle is expected to generate 2 Petabytes (2 million GB) of data every year. It would take the best Wi-Fi available months to be able to transfer that amount of information. The nearly real-time speeds of 5G are 10 times faster than 4G. With its infrastructure and dense network, 5G makes the future of autonomous vehicles possible.
Latency
Decreased latency, another characteristic of 5G, can also benefit autonomous vehicles. 4G currently has a latency of 50 milliseconds, which can be seen as a large delay when it comes to passenger safety.
Smart Cities and the Internet of Things (IoT)
In order for an autonomous vehicle to make smart decisions, it requires information about its environment. Smart cities, which are IoT-ready, allow for that. A city that can report on traffic, signals, etc., can help a self-driving car move smarter and more easily navigate its way around town.
Data Management
Analyzing the amount of data a self-driving car produces takes time. With the potential of nearly 10 million cars hitting the road, edge computing can help streamline this analysis by examining it closer to the source.
V2X
Vehicle-to-everything (V2X) allows the information from autonomous vehicle sensors and other sources to travel through high-bandwidth, high-reliability, and low-latency channels. It creates an ecosystem that enables cars to communicate both with each other and with infrastructures including parking lots and traffic lights.
Not only can this improve vehicle safety, but it also gives drivers or passengers information about road conditions ahead, so that they can appropriately respond. When combined with Artificial Intelligence (AI), a self-driving car will be able to make that decision itself.
Roadblocks
A study from NAMIC found that 42% of surveyed consumers said that no matter how long the technology was available, they would refuse to ride in fully automated vehicles. Similarly, 46% of respondents were skeptical about using fully automated vehicles for ride-sharing services. In order to gain public trust, the right infrastructure needs to be in place.
Data management challenges, safety concerns, and high manufacturing costs are roadblocks that can prevent widespread autonomous vehicle adoption. However, as large manufacturers and automotive organizations continue to enhance and improve the technology, the potential for an autonomous future continues to grow.
Train Your Team in Autonomous Vehicle Technology
Prepare your organization for the latest developments in AV technology with training in foundational and practical applications of autonomous, connected, and intelligent vehicle technologies. Developed by leading experts in the field, the IEEE Guide to Autonomous Vehicle Technology is a seven-course training program offered online.
Interested in purchasing the program just for yourself? View it on the Learning Network, a new learning management platform!
Resources
(18 October 2019). Who Will Use Self-Driving Cars?. PYMNTS.
Zoria, Sophie. (1 November 2019). 5 Striking Uses For Autonomous Driving Technology. Customer Think.