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.

In the rapidly evolving digital era, internet users have become increasingly aware of how their information is collected and used online. According to Norton LifeLock, 85% of adults want to do more to protect their online privacy. As consumers express concern and global regulations tighten, it is important to understand the premise of digital privacy and how to comply with it. 

Data Privacy or Digital Privacy?

Despite similar names and concepts, there is a stark distinction between data privacy and digital privacy. Data privacy refers to when a company or website properly handles sensitive user information such as personal contacts, medical records, financial history, and intellectual property. Data privacy works to prevent unauthorized access to confidential information by governing how data is collected, used, and shared. This concept pertains to both the digital and non-digital realms.

On the other hand, digital privacy focuses specifically on protecting our own information that we knowingly or unknowingly share online. An astonishing 90% of the world’s data was generated in the last two years alone! Most of that information was created or provided by individuals while using the internet. Safeguarding this user data mitigates the risk of web-based attacks, further promoting a more secure and trustworthy cyberspace. Without maintaining digital privacy, bad actors could easily monitor online activities, such as conversations and transactions, leading to harmful interceptions and breaches.

The concepts of data privacy and digital privacy both exist to protect individuals and their private information. It is crucial for internet-based systems to satisfy the level of security required by each of these measures.

Engineering Digital Privacy for All

The responsibility of creating a technical framework that fosters digital privacy falls heavily on engineers. Concurrently, existing and emerging laws have brought big changes to the technical engineering landscape. Soon enough, digital privacy regulations will cover 75% of the world’s population.

By not paying close attention to these laws, companies could be risking data breaches, harsh financial penalties from violations, and jeopardizing their reputation within the industry.

Adapting to changing data regulations has resulted in the creation of the Privacy by Design concept, which incorporates the idea of including privacy in every aspect of the engineering and product development cycle. The emerging role of privacy engineer implements this concept, ensuring that data privacy considerations are integrated into the product design.

Gather the Tools to Operationalize Internet Privacy

Is your team up-to-date on the latest privacy technologies and ethics?

Get ahead with Protecting Privacy in the Digital Age, brought to you by IEEE Educational Activities in collaboration with IEEE Digital Privacy. This four-course program 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

(2022). 2022 Norton Cyber Safety Insights Report: Special Release— Online Creeping. Norton LifeLock.

(3 March 2021). What is Digital Privacy? Definition and Best Practices. Microanalytics.

What is Data Privacy? SNIA.

Privacy By Design. Deloitte.

The Growing Role of Data Privacy Engineering on Technology. IEEE.

Artificial intelligence (AI) continues to dominate headlines, thanks to its potential to revolutionize countless industries. From manufacturing and healthcare to banking and retail, AI is streamlining automatable and administrative tasks across the board.

Beyond efficiency, AI plays a critical role in high-impact applications. It helps detect cybersecurity threats, prevent retail fraud, and improve autonomous vehicle navigation by recognizing driver patterns and predicting accidents. Additionally, AI enhances customer experiences by personalizing marketing and service interactions.

In essence, machines are now replicating, and even expanding, the capabilities of the human mind. As a result, AI is reshaping the future of business.

A New Industrial Era

Because of its transformative power, the World Economic Forum has dubbed AI part of the “fourth industrial revolution.” This new era merges the physical, digital, and biological worlds, following earlier revolutions driven by steam, electricity, and computing.

Forbes contributor Bernard Marr calls it the “Intelligence Revolution,” underscoring AI’s sweeping impact on society and industry.

AI: A Double-Edged Sword

Although often used interchangeably, AI actually falls into two distinct categories: artificial general intelligence (AGI) and generative artificial intelligence (generative AI).

AGI refers to the ability of machines to understand, learn, and perform intellectual tasks as humans would based on the processing of demonstrated customer patterns. Examples of this include:

• personalized product recommendations provided by Amazon
• customized workouts and health goals suggested by apps (such as the MyFitnessPal app formerly owned by sports apparel and gear provider Under Armour) that base their recommendations on collected health data for physical activity, sleep, and diet
• smart assistants like Alexa and Siri that can control home technology, dial the telephone upon request, and more

Generative AI refers to a form of artificial intelligence that learns the patterns and structure of inputted data and responds by generating text, images, or other media with similar characteristics. An example includes the much-publicized ChatGPT, a chatbot introduced in November 2022 by OpenAI, that can produce output of a desired length, format, style, level of detail, and language on most any topic.

Experts confirm that AI can help businesses enhance their productivity by leaps and bounds. For example, research firm Gartner estimates that AI can save companies around the world over 6 billion employee-hours annually. On an economic level, a recent study by global management consulting firm McKinsey & Company predicts that the analytics enabled by AI could add US $13 trillion to our global GDP by 2030.

At the same time, however, AI also raises its share of issues and ethical concerns. Among them, generative AI can lend itself to the alteration of text, images, and video in the form of inaccurate, misleading, manipulative, and/or potentially dangerous “deep fake” or fraudulent content. It also raises questions about ownership rights of created content and its eligibility for copyright protection.

Helping Industry Navigate the Complex Field of AI

Recognizing both the unprecedented importance and complexity of artificial intelligence, IEEE offers several course programs in AI and machine learning designed to help navigate these exciting, complicated, and rapidly-evolving technologies.

• Machine Learning: Predictive Analysis for Business Decisions— Ideal for computer engineers, business executives, industry executives, industry leaders, business leaders, technical managers, data scientists, and data engineers, this five-course program provides an overview of the different types of machine learning that are fueling businesses today, how these forms of AI use software, algorithms, and models in their design, and how attendees can deploy scalable machine learning into their own processes to achieve their business goals. 
• Artificial Intelligence and Ethics in Design— Ideal for data engineers, AI/ML engineers, design engineers, computer engineers, security engineers, electrical engineers, software engineers, UX designers, engineering managers, technical leaders, functional consultants, business users, research engineers, robotics engineers, machine learning engineers, and computer vision engineers, this five-course program covers such topics as law, compliance, and ethics in artificial intelligence, ethical challenges in data protection and safety, and responsible design in the algorithmic era. 
• Artificial Intelligence and Ethics in Design: Responsible Innovation— This five-course program is designed to help learners understand the ethics specifications that must be met when designing AI systems for European (and other) markets. Topics include causes of bias, transparency and accountability for robots and AI systems, and legal and implementation issues of enterprise AI.

To discover more IEEE courses about artificial intelligence, browse the IEEE Learning Network catalog.

 
Resources:

Forbes Technology Council. (13 January 2022). 16 Industries and Functions That Will Benefit from AI In 2022 and Beyond. Forbes.

Fourth Industrial Revolution. World Economic Forum.

Marr, Bernard. (10 August 2020). What Is the Artificial Intelligence Revolution and Why Does It Matter To Your Business? Forbes.

Schroer, Alyssa. (19 May 2023). What Is Artificial Intelligence? Built In.

Mohan, Malethy. (22 March 2023). The Difference Between Generative AI and Traditional AI. LinkedIn.

Kanade, Vijay. What Is General Artificial Intelligence (AI)? Definition, Challenges, and Trends. Spiceworks.com.

Rajagopalan, Ramesh. 10 Examples of Artificial Intelligence in Business. Online Degrees.

Elliott, Timo. (9 March 2020). The Power of Artificial Intelligence Vs. the Power Of Human Intelligence. Forbes.

Dilmegani, Cem. (22 April 2023). Generative AI Ethics: Top 6 Concerns. AIMultiple.

Deep learning is having a moment. There was a time where we could only dream of partially autonomous vehicles and voice-activated assistants. Today, however, these inventions are a regular part of our lives. A subfield of machine learning (ML) and artificial intelligence (AI), deep learning algorithms are designed to learn like a human brain. Deep learning continually analyzes data using an advanced technology known as “artificial neural networks,” which are operated by a series of algorithms that can perceive complex relationships in data sets. These neural networks allow computers to see, hear, and speak—it is the reason we can talk to our phones and dictate emails to our computers. 

Algorithms have always been part of the digital world, where they are trained and developed in perfectly simulated environments. The current wave of deep learning facilitates AI’s leap from the digital to the physical world. While the applications are endless—from manufacturing to agriculture—there are still challenges of accuracy, clean data, and reinforcement learning.

Deep Learning in the Real World

AI researchers are working to introduce deep learning to our physical, three-dimensional world. Experts anticipate that deep learning will advance several sectors over the next few years, including:

• Self-Driving vehicle capabilities: The handling of novel situations is the main problem for autonomous vehicle engineers. With growing exposure to millions of scenarios, a deep learning algorithm’s regular cycle of testing and implementation ensures safe driving. Global industry growth for autonomous cars is 16% a year. The global autonomous vehicle market reached nearly US$106 billion in 2021, and one forecast projects it will grow to US$2.3 trillion by 2030.
• Fraud news detection and news aggregation: Deep learning is heavily utilized in news aggregation, which attempts to tailor news to consumers’ preferences. Reader personas are defined with greater complexity to filter out content based on a reader’s interests, as well as geographical, social, and economic factors. Furthermore, there is always room for improvement in filtering out fake news and misinformation.
• Natural Language Processing (NLP): One of the most challenging things for computers to learn is how to comprehend the complexity of human language, including its syntax, semantics, tonal subtleties, expressions, and even sarcasm. The global market for Natural Language Processing (NLP) is expected to reach US$25.7 billion by 2027.
• Healthcare: Some of the deep learning projects gaining traction in the healthcare industry include assisting with the quick diagnosis of life-threatening diseases, addressing the shortage of qualified doctors and healthcare providers, and standardizing pathology results and treatment plans. By 2026, artificial intelligence has the potential to save the clinical healthcare business more than US$150 billion.

Getting “Data-Centric AI” with Deep Learning

Andrew Ng is among the pioneers of deep learning and, according to Fortune, he’s also one of the most thoughtful AI experts on how real businesses are using the technology. Ng has become a champion for what he calls “data-centric AI.” Ng believes developers and businesses should be asking questions like: What data is used to train the algorithm? How is it gathered and processed? How is it governed? 

Data-centric AI is the practice of “smartsizing” data so that a successful system can be built using the least amount of data possible. If data is carefully prepared, a company may need far less of it than they think—saving both time and money .Calling it as important as the shift to deep learning that occurred over the past decade, Ng believes that the shift to data-centric AI is the most important shift businesses need to make today. 

Be Prepared for Future of Deep Learning

As deep learning facilitates AI’s leap from the digital to the physical world, it is important to stay current with the latest technology advances. The IEEE Academy on Artificial Intelligence is designed for those who work in industry and need to understand new technical information quickly so they can apply it to their work. Learn more about the program>>

Interested in enrolling? Visit the IEEE Learning Network

 

Resources:

Placek, Martin. (16 January 2023). Size of the global autonomous vehicle market in 2021 and 2022, with a forecast through 2030. Statista.

Carsurance. (20 February 2022). 24 Self-Driving Car Statistics & Facts. Carsurance.

Global Industry Analysts, Inc. (April 2021). Natural Language Processing (NLP) – Global Market Trajectory & Analytics. Research and Markets

Gordon, Nicholas. (30 July 2021). Don’t buy the ‘big data’ hype, says cofounder of Google Brain. Fortune. 

Ingle, Prathamesh. (9 July 2022). Top Deep Learning Applications in 2022. Marktechpost.

Fine, Ken. (15 January 2022). How digital experiences are fueling the new digital economy. VentureBeat. 

Todorov, Georgi. (20 April 2022). 92 Stunning Artificial Intelligence Stats, Facts and Figures in 2022. Thrive My Way.

Woertman, Bert-Jan. (30 April 2022). Deep learning is bridging the gap between the digital and the real world. VentureBeat.

World Economic Forum. (20 July 2022). Is AI the only antidote to disinformation? The European Sting.

When many people hear the phrase “blockchain technology”, they immediately think of cryptocurrencies. However, blockchain is much more than cryptocurrencies. At its core, blockchain technology is a chain of records that store data and information. It is a tamper-proof and decentralized digital ledger, which provides full control to the user and eliminates governmental or third-party dominance. 

The global expenditure on blockchain solutions is anticipated to reach US$11.7 billion this year, and the number of individuals working in the blockchain sector has increased 76% as of June 2022. By 2024, it is anticipated that the worldwide blockchain technology market would generate US$20 billion in revenue. When blockchain technology is implemented correctly, it can solve problems in several sectors—with applications in the automotive, financial services, voting, and healthcare industries.

The Promise of Blockchain for Healthcare

Healthcare professionals and institutions are already capitalizing on blockchain technology by using early solutions to reduce costs, increasing the availability of authentic information, streamlining medical records, and providing secure and fast access to data. 

There are numerous applications of using blockchain in healthcare:

• Storage and Data Accessibility – Medical professionals can collaborate effectively, improving the opportunities and experiences for patients by using blockchain technology to access, store, and share data securely.
• Analysis and Data Collection – Using a data-driven, scalable, and patient-centric blockchain-based system will prove helpful in collecting sensitive data to train machine learning software. 
• Health Supply Chain Management – The blockchain provides practical solutions to streamline supply chain operations through less expensive, reliable, authentic, and easier methods.
• Drug Tracking – Blockchain technology provides a reliable way to ensure drug validity by providing the ability to trace every medicine back to its source.
• Remote Monitoring – Once uploaded to the blockchain, electronic medical records can be viewed and shared instantly and securely throughout the world. 

Protecting Sensitive Data

Hospital cyber security breaches hit an all-time high in 2021, with 45 million individuals affected by healthcare cyber attacks. The implications of these attacks can have a variety of consequences, ranging from the shutdown of hospital operations, diversion of non-emergency patients, a loss of confidentiality, exposure of patient data and information, and infrastructure damage.

Kali Durgampudi, the chief technology officer of healthcare payments company Zelis, believes that blockchain implementation is vital for protecting patients’ sensitive data from cyber criminals. He says that  because hackers cannot modify or copy the data, “blockchain technology vastly reduces security risks, giving hospital and healthcare IT organizations a much stronger line of defense against cyber criminals.”

Blockchain technology has the potential to alleviate many of these concerns. Any time the information is changed or shared, a new block is created to document the transaction. Strung together, these blocks create an impenetrable chain. Since the information cannot be modified or copied, blockchain technology vastly reduces security risks.

Challenges for Blockchain in Healthcare

Like most advances, there are limits to the promise of blockchain technology. Currently, blockchain’s scalability is low, with transaction speeds not up to the standard of being reliable for massive amounts of immediate transactions. Blockchain ecosystems can also require high energy consumption, making it expensive to manage over large amounts of data and networks. Finally, it is important to note that healthcare often lags behind other industries in adopting new and cutting-edge technologies. Regulations and infrastructure issues tend to prevent fast-paced growth in medical devices, newer drug development platforms, and adopting scalable technologies.

Blockchain Solutions for the Future

Get practical guidance for how to design a blockchain solution with the IEEE five-course program, A Step-by-Step Approach to Designing Blockchain Solutions. Developed by experts, this course program recaps the basics of the technology, the expected benefits of a blockchain solution, how a solution would benefit a prospect company, and more.

Contact an IEEE Account 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

Durgampudi, Kali. (18 July 2022). The Potential of Blockchain Technology To Address Healthcare’s Biggest Challenges. Forbes.

Encila, Jet. (15 August 2022). Blockchain Industry Workforce Grows 80% This Year, Study Shows. Bitcoinist.

Garg, Amit & Shuang, Sharon. (16 August 2022). Blockchain & Healthcare- Where Are We? DateDrivenInvestor.

Hoffmann, Sofia. (9 August 2022). What Benefits Blockchain can Bring to Healthcare. HealthTECH Zone.

Linken, Scott. (12 August 2022). Making sense of bitcoin, cryptocurrency and blockchain. PWC.

Quarmby, Brian. (22 July 2022). Blockchain’s use in healthcare ‘essential’ to protect sensitive data: Zelis CTO. Cointelegraph. 

Siwicki, Bill. (20 July 2022). Debunking some of healthcare’s biggest blockchain myths. HealthcareITNews. 

Not so long ago, the perception of virtual and augmented reality technologies was confined to science fiction. Movies like Avatar, The Matrix, and Total Recall painted a picture of what could be possible. Today’s virtual reality (VR) and augmented reality (AR) technology is not quite as immersive as these examples, but it is advancing rapidly. Today, many businesses are recognizing the benefits of using augmented and virtual realities to improve their operations. AR and VR are now being used for everything from prototyping and design to marketing, customer service, training, and productivity. 

While experts are split about the evolution of a truly immersive “metaverse,” they do expect that augmented and mixed-reality enhancements will become more useful in people’s daily lives. This is especially true when it comes to smart cities that commonly use Internet of Things (IoT) technologies. However, according to Jamie Cameron, director of digital solutions at building security company Johnson Controls, “connectivity and technology are not the end goal for smart cities—they are the means to improving the quality of life for city residents.” And with virtual and augmented reality technologies, smart cities could be much smarter.

Making Smart Cities More Sustainable

With the UN projecting 68% of the world population to live in urban areas by 2050, the combined carbon footprint of the world’s different cities is only set to grow. Connected communities have an advantage because they can use IoT technology to understand the problem and then help solve it. 

Smart cities can collect a wealth of data by installing different sensors around the community. These sensors can range from measuring air quality, as used by the London Air Quality Network; or detecting leaking water pipes, as Vodafone has recently partnered with SES Water to do. After collecting the data, the smart cities can decide what to do with all that information. A model of a city known as a digital twin can be used to simulate how different policies may affect a city. It can also provide insight into progress being made towards sustainability targets.

Creating Safer Smart Cities

The infrastructure and systems needed to successfully collect, analyze, and transmit information across a city are complex and comprehensive. Smart cities may represent a better way to plan and manage urban living, but they also serve as attractive new targets for cyber criminals. Digital enhancement enhances digital risk. To keep a smart city running smoothly, governments need tech-enabled support desks to help resolve problems. Smart cities are built from data, but what information is collected, who has access to it, and how it may be used are all highly contentious areas impacting public trust.

Enhancing the Quality of Life in Smart Cities

To improve the quality of life in urban spaces, city councils, urban planners, and developers are exploring cutting-edge digital solutions that can potentially power smart cities. Augmented reality technology is a promising solution. AR works by overlaying digital information in real-world environments: all you need is a smartphone and AR can provide constant feedback within smart cities, allowing everyone to make informed decisions in their day-to-day life. AR can make urban spaces more people centered and improve urban mobility, public safety, public health, and tourism.

Keep up with AR/VR Technology

Information and communication technologies have made smart cities a reality. However, augmented reality and virtual reality technologies have shifted the smart city paradigm. Practical Applications of Virtual and Augmented Reality in Business and Society: The Case of Smart Cities will help keep you current with AR/VR technology.

Interested in the course? Visit the IEEE Learning Network.

Resources:

Anderson, Janna and Raine, Lee. (30 June 2022). The Metaverse in 2040. Pew Research Center. 

Dumbell, Katherine. (18 July 2022). How smart technology can make cities more sustainable? Verdict. 

Galil, Eran. (16 July 2022). Improving the customer experience with virtual and augmented reality. VentureBeat.

Imperial College London. (21 July 2022). About Londair. LondonAir.

Lee, Giacomo. (6 January 2022). Meet the tech CEO who survived a flood and built a digital twin of Earth. Verdict.

Manser, James. (5 June 2020). How IoT tech could save the UK three billion litres of water a day. Vodafone.

Milewa, Gergana. (12 September 2021). How Smart Cities Can Use Augmented Reality Technology. AR Post. 

Rosenburg, Louis. (28 December 2021). Why AR, not VR, will be the heart of the metaverse. VentureBeat.

Open Access News. (15 November 2021). Connectivity: The fundamental ingredient of a successful smart city. Open Access Government.

Open Access News. (28 June 2022). The cities of the future are smart – but we must also make them secure. Open Access Government.

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.

Cloud technology is entering the era of the multi-cloud. When using multiple clouds supported by various cloud providers, organizations can reap the best features of each, thereby making their cloud infrastructure far more flexible.

During his opening keynote at Dell Technologies World Conference in May, CEO Michael Dell expressed his views on multi-clouds as the future of cloud technology. According to Dell, multi-cloud ecosystems will harness the combined power of edge computing with artificial intelligence (AI) to “process and deliver data across 5G networks in highly automated environments.” 

Multi-cloud technology is already expanding rapidly. According to Dell, 90 percent of his company’s customers currently have both on-premise and public cloud environments, while 75 percent are using three to four different clouds. However, he also noted that multi-cloud technology is creating larger amounts of data and security challenges in the process.

“Anything you want to do in today’s world, from [decentralized finance] to blockchain to metaverse, and autonomous vehicles, and robotics, smart everythings, based exploration, AI, disaster recovery, AR/VR — all these things consume and create tremendous amounts of distributed data and distributed computing power,” he said. “And because workloads follow data, the distributed future will be much bigger than you can imagine, and so will the attack surface. Ransomware attacks are the No. 1 threat for most organizations, and are occurring every 11 seconds, with an average cost of $13 million per occurrence.”

Despite some challenges, multi-cloud technology holds huge promises for organizations. When paired with hybrid cloud, in which an organization splits its data between a cloud and an on-premise datacenter, multi-cloud infrastructure can create a truly decentralized cloud platform. This allows an organization to not depend on any singular data center or provider. According to Entrepreneur, this approach allows organizations to customize their technological environment to their specific needs.

How Cloud Technology Is Already Advancing Health Care

One industry that is already gaining benefits from cloud technology is health care. According to Forbes, these benefits include expanding access to telehealth, which has already begun under the COVID-19 pandemic. Telehealth is working to make it possible for more people in both rural and urban areas to access physicians. 

Other benefits include faster drug testing and manufacturing. For example, vaccine maker Moderna was able to speed approval for its COVID-19 vaccines with support from cloud computing through Amazon Web Services. By using cloud computing, the company was able to build a technology to rapidly test vaccines. 

“Moderna runs its Drug Design Studio on AWS’s highly scalable compute and storage infrastructure to quickly design mRNA sequences for protein targets. It then uses analytics and machine learning to optimize those sequences for production so that the company’s automated manufacturing platform can successfully convert them into physical mRNA for testing,” state Moderna and AWS.

Understanding Challenges of the Cloud

Organizations are only beginning to realize the benefits of cloud computing. However, before they adopt the cloud, they must first understand the challenges that come with embracing this rapidly advancing technology.

To learn more about the benefits and challenges of cloud computing and how it pertains to your organization, check out Cloud Computing on the IEEE Learning Network. This online course program includes 25 self-paced courses focused on various aspects of cloud computing technologies.

Interested in getting access for your organization? Contact an IEEE Content Specialist for more details.

Resource

Kuehne, Joe. (9 May 2022). Dell Tech World: Michael Dell Proclaims That the Future Is Multicloud. BizTech.

Montoya, Sergio Ramos. (10 May 2022). This is how cloud computing advances, a valuable resource for companies. Entrepreneur.

Schnitfink, Theo. (10 May 2022). How Technology Puts The ‘Care’ In Healthcare: The Role Of The Cloud During The Pandemic. Forbes.

Press Release. AWS Powers Moderna’s Digital Biotechnology Platform to Develop New Class of Vaccines and Therapeutics. Businesswire.

Hewlett Packard Enterprise (HPE) recently announced the launch of innovative platforms. These platforms are expected to speed the development of machine learning models. The first, the HPE Machine Learning Development System, combines machine learning software with compute, accelerators, and networking. This combination shortens the time it takes to get results from building and training machine learning models from months to just days.

“Enterprises seek to incorporate AI and machine learning to differentiate their products and services. However, they are often confronted with complexity in setting up infrastructure required to build and train accurate AI models at scale,” stated Justin Hotard, executive vice president and general manager, HPC and AI, at HPE, in a press release. “The HPE Machine Learning Development System combines our proven end-to-end HPC solutions for deep learning with our innovative machine learning software platform into one system. This provides a performant out-of-the-box solution to accelerate time to value and outcomes with AI.”

The second platform, HPE Swarm, combines blockchain technology with the revolutionary learning methods “federated learning” and “swarm learning.” 

What Are Federated Learning and Swarm Learning?

Unlike traditional artificial intelligence (AI) models trained on centralized datasets, federated learning trains models on decentralized datasets. For example, let’s say a model is learning from data on a phone. Here, the model runs on the phone’s data but does not send the actual data to a central server — only insights gleaned from it. This method is much more secure for the owner of the phone. It also makes the system faster and more efficient, because it does not have to send large amounts of data back and forth to a central server. 

However, HP Swarm takes federated learning to a new level by using swarm learning, a subset of federate learning. Instead of relying on a central server, swarm learning uses blockchain. Blockchain is a decentralized digital ledger of transactions that records data by duplicating transactions and dispersing them to “nodes” across the network. As such, swarm learning makes the learning process even more decentralized, secure, and resilient. 

This technology could accelerate machine learning while advancing a large number of applications, particularly within healthcare. As Ledger Insights reported, it could allow cancer research centers across the world to collect valuable data with one another without having to share the actual data. 

“Swarm learning is an important movement in the AI market, with broad support across the public and private sectors. It serves to combine the power of expanding data sets with the innovation and insights from organizations across the globe,” Hotard told VentureBeat.

HP Swarm provides users with containers that are easily integrated with AI models via the HPE swarm API. Users can then instantly share AI model learnings with peers both inside and outside their organization. This enhances training without having to share the original data – making it far more secure. 

Swarm learning holds great potential for businesses. It can enable them to make faster decisions with better results, protect the privacy of their customers, share learnings with other organizations, and advance their data governance.  

Is Your Company Embracing Machine Learning?

It’s important for organizations developing and deploying machine learning to understand the concepts and techniques necessary for driving machine learning-enabled business insights.

Connect with an IEEE Content Specialist today to learn more about this program and how to get access to it for your organization.

Interested in the program for yourself? Visit the IEEE Learning Network.

Resources

(29 April 2022). HPE’s new platform lets customers build machine learning models quickly and at scale. TechCentral.ie. 

(29 April 2022). HPE launches Swarm Learning using blockchain for AI, machine learning. Ledger Insights.

Plumb, Taryn. (27 April 2022). HPE looks to deliver the power of ‘swarm learning’. VentureBeat.

Press Release. (27 April 2022). Hewlett Packard Enterprise accelerates AI journey from POC to production with new solution for AI development and training at scale. hpe.com 

Press Release. (27 April 2022). Hewlett Packard Enterprise ushers in next era in AI innovation with Swarm Learning solution built for the edge and distributed sites. hpe.com 

Despite a rush of new data privacy regulations around the world, many organizations have yet to transform the way they collect user data. However, due to the digitization and interconnectedness of modern-day businesses, those that wait to transform their policies may soon find themselves in trouble.

“Waiting even a year or two to start building out a compliant data privacy and management program will cost more, take longer, and be more disruptive to your business operations than having to adapt strong, existing processes to legislative and cultural changes,” wrote Jodi Daniels, CEO of Red Clover Advisors, an organization that assists companies in simplifying their data privacy practices, in Inc.com.

Alternatively, organizations that start building the new regulations into data privacy programs “have a unique opportunity to market themselves as a forward-thinking, consumer-friendly industry leader,” she added.

Three Rules That Should Replace Your Current Data Privacy Practices

As organizations come under increasing pressure — both from regulators and the public — to transform their practices around data collection, they will need to start adapting new rules. Writing in Harvard Business Review, Hossein Rahnama, an associate professor at Ryerson University in Toronto, and Alex “Sandy” Pentland, the Toshiba Professor of Media Arts and Sciences at MIT, recommend that organizations should put:

1. Trust before transactions: Many organizations currently collect troves of consumer data without obtaining user permission. However, as regulations become the norm, “data collected with meaningful consent” will become the most valuable data— given that it will become the only data that organizations will be allowed to use. As such, organizations will need to start creating processes in which they obtain explicit permission to obtain data, as well as a plan that clearly communicates with customers how their data will be used.
2. Insight before identity: Organizations also need to make data transfer processes between themselves and other organizations more secure. Instead of transferring data through traditional data agreements, they should consider adopting technology like federated learning and trust networks that use algorithms to obtain insight from data without having to transfer the actual data.
3. Flows before silos: Currently, chief information officers and chief data officers tend to work in silos. However, making the above changes should help them be able to break free of silos. By working with each other, they can better achieve a shared goal of obtaining the best possible insight from customer data.
   
   “For instance, a bank’s mortgage unit can secure a customer’s consent to help the customer move into their new house by sharing the new address with service providers such as moving companies, utilities, and internet providers,” explain Rahnama and Pentland. “The bank can then act as a middleman to secure personalized offers and services for customers, while also notifying providers of address changes and move-in dates. The end result is a data ecosystem that is trustworthy, secure, and under customer control.”

Is your organization ready to deal with the growing onset of new data privacy regulations? While you may think it’s smarter to watch and wait, preparing for them in advance is the best way to avoid potential problems in the future.

Data Privacy Training for Your Organization

As privacy grows in importance, the need for technical professionals to possess strong knowledge in the area also grows.

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

Daniels, Jodi. (3 March 2022). Why You Shouldn’t Wait to Build Out Your Company’s Data Privacy Function. Inc.com. 

Rahnama, Hossein and Pentland, Alex “Sandy.” (25 February 2022). The New Rules of Data Privacy. Harvard Business Review.