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.

Today’s residential and business sectors have never been more dependent on the reliable flow of electricity. From consumers demanding instantaneous internet connectivity—both at home and on-the-go— to the vast majority of businesses both large and small relying on uninterrupted electric power, electricity is critical to keep operations running, communications intact, and more.

However, while electricity is key to much of daily life, numerous developments have recently put a strain on the electric grid and stand to impede the continuous and reliable flow of electricity. Some of the factors that have led to issues of both resiliency and sustainability for electric users include:

• the ever-expanding electricity demands of a growing population
• the increased frequency of powerful storms and other natural disasters that cause power outages and damage utility assets
• aging grid infrastructure that drives inefficiencies and power quality losses
  

One solution that could offer a win-win solution to these dilemmas is the emergence of microgrid technologies.

Benefitting From Self-Sufficient Energy Systems

According to industry sources Microgrid Knowledge and the U.S. Department of Energy, microgrids are self-sufficient energy systems that support a defined community of users. Such communities could be a university campus, a hospital, a corporate center, or a residential neighborhood.

Typically driven or supported by solar power, wind turbines, battery energy storage systems (BESS), generators, fuel cells, and other renewable energy sources, microgrids can operate independently from the main electric grid if needed. Essentially, the microgrid becomes an energy “island” that’s impervious to power disruptions experienced by the main grid. At the same time, microgrid use of local generation reduces power losses that are inherent in the traditional long-distance transmission and distribution (T&D) of electricity. For example, across the United States’ nearly six million miles of T&D lines, losses can be up to 15%  and some European Union countries experience up to 17% power losses.

Through sophisticated software, microgrids can also be “intelligent” in terms of their ability to optimize use of multiple energy resources to achieve any of a number of specific goals. Common goals include securing the least expensive energy (perhaps by purchasing energy from the main grid if that’s the cheapest source on a particular day), producing the greenest energy or the most reliable supply, as well as other objectives.

A Solution of Choice

Based on these powerful capabilities and benefits, microgrid technologies have been the solution of choice for a range of critical projects.

For example, energy infrastructure provider AlphaStruxure recently announced its plan to create and operate an 11.34 MW microgrid that will transform JFK International Airport’s new terminal into “the first fully resilient airport transit hub in the New York region that can function off-grid during power disruptions.”

Back in 2011, a hurricane and snowstorm knocked out power to 750,000 area homes in Hartford, Connecticut for nearly two weeks. But thanks to a microgrid recently built by the city of Hartford, power now reliably flows to a number of the city’s most critical and life-sustaining environments, including a healthcare facility, school, gas station, and grocery store.

Similar microgrids built in the past five to ten years are helping to sustain operations at college campuses including Princeton University in New Jersey and New York University in Manhattan, as well as at Co-Op City, a housing development that’s home to over 50,000 residents in The Bronx, New York. Additionally, Microsoft recently announced its intention to build a new data center microgrid in San Jose, California.

International examples include planned microgrids at The Royal Mint in Wales and the Chub Cay Resort Marina in The Bahamas. The World Bank also plans to fund six microgrid projects in rural Nigeria.

According to Annette Clayton, CEO of Schneider Electric North America, the organization which will be providing microgrid technology, software, and services to AlphaStruxure’s microgrid installation at JFK Airport, “microgrids solve two of the most serious challenges — resilience and decarbonization — with a single solution.” 

Get Up to Speed on Microgrid Technologies

Whether you’re a city planner, an energy service provider, operate a mission-critical facility that’s reliant on the continuous flow of electricity, or are a savvy energy user or professional, it behooves you to learn more about the operation, benefits, and inner workings of microgrids.

The IEEE Academy on Smart Grid Microgrids offers a solid overview of microgrid technologies and their integration with renewable energy sources and energy management systems. Upon completing this five-hour online training, learners will gain a better understanding of the latest trends, technologies, solutions, and applications for microgrids. Learners will also explore the benefits, challenges, best practices, and insights related to microgrid modeling, analysis, protection, and control.

For more information or to enroll in this program, please visit the IEEE Learning Network (ILN)

 

Resources

Office of Electricity. The Role of Microgrids in Helping to Advance the Nation’s Energy System. U.S. Department of Energy.

Wood, Elisa. (28 March 2020). What is a Microgrid?. Microgrid Knowledge.

Brush, Kate. (Accessed 30 August 2022). DEFINITION: finite element analysis (FEA). TechTarget. 

Innovation & Policy: Energy Efficiency. T&D Europe.

AlphaStruxure to Design, Construct, and Operate JFK’s New Terminal One Microgrid, Creating the Largest Rooftop Terminal Solar Array in the U.S. (26 January 2023). AlphaStruxure/PR Newswire.

Gies, Erica. (4 December 2017). Microgrids Keep These Cities Running When the Power Goes Out. Inside Climate News.

Wood, Elisa. (15 June 2022). Enchanted Rock to Build California’s Largest RNG Microgrid for Microsoft. Microgrid Knowledge.

Wood, Elisa. (11 January 2022). 22 intriguing microgrid projects to watch in 2022. Microgrid Knowledge.

By 2025, there will be over 23 billion connections on the Internet of Things (IoT) compared to 15.1 billion in 2021, according to a recent report from GSMA, an industry organization that represents the interests of mobile network operators worldwide. 

The Internet of Things is a network of interlinked devices that harness the internet to continuously capture and process data and analytics from physical objects. As IoT adoption increases and it becomes more integrated, global supply chains are expected to reap major benefits. For example, IoT devices create multiple interaction points along supply chains that provide advanced data collection, factory automation, GPS shipment tracking, and enhanced communication between machines and people. 

According to Dipti Parmar, writing in CIO, there are two major ways that IoT – combined with artificial intelligence, wireless sensor networks, 5G, and big data – will make supply chains smarter, faster, and more efficient:

Eliminates dependency on complicated infrastructure:

Traditional tracking systems for supply chain analytics are expensive, time consuming, overly complex, and often lead to dependency on vendors. IoT-based data loggers, which can be attached to shipments and send data to cloud-based servers, can solve these issues. Once attached to shipments, these loggers can monitor thousands of goods traveling across the supply chain. This level of detail gives everyone involved valuable insight into any problems —such as temperature changes or container tilting — that could be affecting the shipments in real time. The loggers are also more affordable than traditional hardware used to track shipments, and can provide enhanced analytics for enhanced decision making.

Provides equal access to data:

In an IoT-enhanced supply chain, data is accessible in real time to everyone involved in the shipment of goods. With improved monitoring and visibility, manufacturers, suppliers, distributors, and retailers can: 

• make better and faster decisions
• save time and money
• improve forecasting
• reduce waste
• take more calculated risks
• increase revenue

IoT Depends on Advanced Cloud Technology

While the Internet of Things has the power to transform supply chains, its success will depend heavily on cloud computing technology. This is because IoT devices must be able to connect and send information to the cloud in a centralized location, which allows devices to communicate with one another. As such, organizations that want to adapt the benefits of IoT also need to embrace advanced cloud technology. 

“The cloud helps in this operation by streamlining and optimizing machine-to-machine communications and facilitating this across interfaces,” writes Ritesh Sutaria, Director of Prompt Softech, a custom forward development company, in IoT for All. “With the increased interactions between many connected devices and immense volumes of data generated, organizations will have to find a cost-effective way to store, process, and access data from their IoT solutions.”

Ongoing Disruptions Will Drive IoT Adoption

Despite its many benefits, industries have been hesitant to adopt IoT. However, ongoing challenges in the supply system, such as disruptions caused by the COVID-19 pandemic, will likely encourage more organizations to start adopting the technology in coming years. 

“Speed and reliability have always been and will continue to be the driving factors of the supply chain for the foreseeable future,” writes Parmar. “The next few months will be critical for companies that bank on data to improve their supply chains. They have a never-before opportunity to build on the momentum and insights gained as a result of COVID-related disruptions by adopting newer technology and systems. The ones that fail to adapt to changing realities will likely be left behind by more agile competitors.”

As organizations adopt IoT, they will increasingly depend on technical professionals who understand this complex technology. Learning the applications, principles, and trends behind the technology is a great way to make your skills more relevant. 

Want to Improve Your IoT Skills? Check out the IEEE Academy on IoT

Are you a professional engineer interested in improving your understanding of the Internet of Things? IEEE has created a new academy that combines existing IoT educational materials with the latest research and developments to help guide technical professionals in this expanding field. 

IEEE Academies are primarily for technical professionals who need to understand new technical information quickly so they can apply it to their work. In addition to gaining new skills and knowledge, participants will also earn a certificate upon their completion. There are two IoT learning paths from which to choose— and both are available on the IEEE Learning Network (ILN)!

IEEE Academy on Internet of Things (IoT): Communications Standards

Communication technology is an essential part of the Internet of Things as it allows devices to connect to each other. This learning path covers the basic principles of communication technology and practical usage of standardized communication. Learn more. 

IEEE Academy on Internet of Things (IoT): Computing Platforms

IoT computing platforms are essential to the development and deployment of IoT applications. This learning path covers all these aspects by providing an overview of the current state-of-art and future trends on computing platforms for IoT applications. Learn more.

What Are IEEE Academies?

IEEE Academies are designed to teach in-demand technical concepts in a new way to IEEE members working in industry. This new learning format at IEEE will help members understand a technical concept without needing a deep background in that technology. This will ensure they understand the fundamental concepts so they can apply them in the context of their general work and technical needs. Learn more about IEEE Academies.

Resources

Sutaria, Ritesh. (8 April 2022). Unveiling the Potential Relationship between IoT and Cloud Computing. IoT for All. 

Parmar, Dipti. (12 April 2022). How data from IoT devices is changing supply chain analytics. CIO. 

The Mobile Economy 2022. GSMA.