Artificial Intelligence Archives

Based on the ability of artificial intelligence (AI) to automate repetitive tasks and process massive amounts of data, AI technology is revolutionizing many industries. Such industries range from healthcare and banking to cyber security, transportation, marketing, customer service, manufacturing, and more.

One industry that’s undergoing a particularly significant transformation at the hands of AI technology is the field of semiconductor design.

The Landscape for Semiconductors

Semiconductors, also called chips, microchips, or integrated circuits, are tiny components that enable electronic switching and serve as the foundation for all computer processing. As a result, semiconductors are integral to everything from smart phones and laptops to wind turbines, solar technology, wearable technology (like fitness trackers), electronic control systems and driverless capabilities in modern vehicles, implantable medical technology (like pacemakers and insulin pumps), gaming hardware, and many more technologies consider essential in today’s industrialized economies.

As sales of connected technologies continue to grow, so does demand for the next-generation semiconductors needed to fuel them. According to Statista, the global market for semiconductors is expected to grow by 13% to nearly US$590 billion in 2024. At the same time, the semiconductor industry is highly competitive. Taiwan, South Korea, and Japan currently lead the world in semiconductor production. However, experts expect the landscape will get even more competitive. The United States and European Union are vigorously ramping up their activity following their enactment of The CHIPS and Science Act and The European CHIPS Act in August 2022 and September 2023, respectively.

In the semiconductor industry’s ongoing quest for tools that can enhance engineering efficiency and accelerate speed to market, thereby giving manufacturers a competitive edge, the use of artificial intelligence and machine learning (ML) stand as game-changers in semiconductor design and manufacturing.

A New Paradigm in Design

Experts confirm that the use of AI enhances semiconductor (chip) design, or the process known as “electronic design automation” (EDA), in many ways.

Among them, AI automates complex processing tasks, thereby reducing the risk of human error. Artificial intelligence’s ability to analyze past patterns across huge quantities of data, identify efficient pathways, and optimize the space (or “real estate”) within semiconductors helps improve semiconductor performance and meet design criteria. It also reduces chip size, resources required, and cost. By being able to “learn” from past experiences, AI algorithms help semiconductor engineers predict and prevent potential design issues down the road that could otherwise result in the need for costly changes.

Ultimately, AI helps semiconductor manufacturers optimize power, performance, and area, or “PPA”– the three goals of chip design– by helping engineers to both design advanced new chips as well as efficiently and cheaply overhaul and shrink the many older-technology (65 nanometer process node or larger) chip designs on which much of the semiconductor industry has been predicated for the past decade without the need to update their fabrication equipment.

The future continues to look bright for the integration of AI in semiconductor design, with Deloitte experts noting that “some chips are getting so complex that advanced AI may soon be required.”

Learn the Ins and Outs of AI in Semiconductor Design from an Industry Expert

In today’s fast-paced technological landscape, AI and ML techniques are revolutionizing chip design methodologies. Integrated-circuit (IC) chip companies and engineers have unprecedented opportunities to use these technologies to enhance product quality across crucial dimensions such as speed, energy efficiency, and cost. This, in turn, enables the achievement of goals with reduced engineering resources and accelerated time-to-market.

Stay on top of the dynamic field of AI in semiconductor design through a two-day virtual training from IEEE, Artificial Intelligence and Machine Learning in Chip Design. It is presented by Andrew B. Kahng, an IEEE Fellow, Distinguished Professor of CSE and ECE at the University of California San Diego, and co-founder of Blaze DFM, Inc., an EDA software company that delivered new cost and yield optimizations at the IC design-manufacturing interface.

This comprehensive two-day virtual training session will equip engineers with:

The essential knowledge to leverage AI and ML effectively in chip design and EDA,
An understanding of the rationale behind these technological shifts to identifying high-value applications and selecting relevant AI and ML technologies, and
Insights into optimizing design methods and preparing for the future of chip design.

Attendees will also have the opportunity for first-hand interaction with Professor Kahng and ask him questions during the interactive question-and-answer portion of the training.

Successful completion of this training and assessment will earn attendees an IEEE Certificate of Completion bearing professional development hours (PDHs) and continuing education units (CEUs).

Don’t miss this opportunity to get your questions answered directly by a renowned subject matter expert in the industry! Save your seat today to secure your spot in this enlightening training session.

Interested in access for yourself? Visit the IEEE Learning Network (ILN).

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

Resources

Anirudh, VK. (10 February 2022). 10 Industries AI Will Disrupt the Most by 2030. Spiceworks.

(2 February 2024). How AI is Transforming the Semiconductor Industry in 2024 and Beyond. ACL Digital.

McCallum, Shiona. (3 August 2023). What Are Semiconductors and How Are They Used? BBC.

(29 March 2024). Generative AI: The Next S-Curve for the Semiconductor Industry? McKinsey & Company.

Loucks, Jeff, Stewart, Duncan, Simons, Christie, and Kulik, Brandon. (30 November 2022). AI in Chip Design: Semiconductor Companies are Using AI to Design Better Chips Faster, Cheaper, and More Efficiently. Deloitte.

Alsop, Thomas. (8 February 2024). Semiconductor Market Revenue Worldwide from 1987 to 2024. Statista.

Few technologies have been by equal measures as captivating and controversial as the ongoing emergence of artificial intelligence (AI).

Most recently, the tech universe was rocked following the November 2023 firing and unprecedented rehiring of Sam Altman, CEO of OpenAI, a leading artificial intelligence company and maker of ChatGPT. That same month, and roughly a year after its breakthrough introduction in November 2022, ChatGPT announced the release of a powerful upgraded version that, among other capabilities, allows users to make their own customized chatbots and include chatbot creations in the company’s new “GPT Store.” The latest version of ChatGPT also offers a new legal “shield” that reportedly protects professional users against claims of copyright infringement.

However, with the continued growth of AI and on the tail of the aforementioned developments, a host of repercussions and concerns have emerged. Among them, AIHungry.com’s research on Google Trends revealed that a search for the term “AI Taking Jobs” reached record-high levels in November 2023. Furthermore, that the term experienced a 400% increase in search activity between November 2022 and November 2023.

While these statistics reflect society’s unease with what it sees as a growing reality, experts agree that those who don’t understand AI or how to use it are at the greatest risk of being replaced by it. This underscores the importance of acquiring new skills as a way to gain a competitive advantage and future-proof yourself from workplace developments like automation.

The Pros and Cons of AI

According to a recent summary of key statistics and predictions collected from prominent industry sources, publications, and market research firms, the growth and evolution of AI will potentially drive a mixed bag of results. These results are expected to have both positive and negative ramifications on the future of society and the workplace. Among them:

AI could replace up to one billion jobs worldwide over the coming decade— though it may also create 97 million new jobs by 2025.
One out of three businesses surveyed in a recent study claimed to be replacing at least some human functions in their workflow with AI solutions.
Administrative/repetitive functions, as well as jobs in such fields as bookkeeping and proofreading, are most at risk of being replaced by AI solutions. Manual labor jobs, as well as those requiring creativity and/or interpersonal skills (such as writing and legal services), are reportedly at the least risk of being replaced by AI.
In a recent study, one in four employees surveyed in the U.S. believes that their job may be replaced by an AI solution in the next five years, with 37% expressing concern over the possibility of this displacement.
On the other hand, nearly 20% of workers in that study welcomed the growth of AI based on their belief that it will relieve them of some tedious/repetitive tasks. 85% of those surveyed support the move towards automation for “hazardous or unhealthy” jobs.
Three out of four employees surveyed in another study, however, believe that the widespread adoption of AI will end up driving inequality in the workplace, with women being at 10% greater risk of job loss due to automation than their male counterparts.

Government Oversight

As the debate over AI rages on between stakeholders worldwide to determine how the technology can best help– not hurt– citizens, companies, and employees, calls for governmental parameters around the use of artificial intelligence are growing louder.

As shared during an October 2023 hearing by the U.S. Senate Committee on Health, Education, Labor and Pensions’ Subcommittee on Employment and Workplace Safety, a joint World Economic Forum and Accenture report revealed that some 40% of the 19,000 individual tasks across 867 occupations studied could be impacted and/or replaced by the ‘large language model’ (LLM) tools used by AI. With generative AI expected to impact everything from the state of both existing and future jobs to privacy, legal, and ethical considerations and more, industry leaders in the U.S. are asking Congress to establish a “rational, risk-based” regulatory framework for AI that will take the needs of employers, employees, and other constituents into consideration.

The U.S. White House Office of Management of Budget supported this request in October 2023 by asking each of its executive agencies to designate a Chief AI Officer (CAIO) to be in charge of “advancing responsible AI innovation” and “managing risks from the use of AI.” According to the official White House memo, “Artificial intelligence (AI) is one of the most powerful technologies of our time [and] we must seize the opportunities AI presents while managing its risks….particularly those affecting the safety and rights of the public.”

Stay on the Cutting-Edge of AI

The world of AI remains a moving target. With AI systems “advancing so rapidly and unpredictably that even on the rare occasions lawmakers and regulators have tried to tackle them, their proposals quickly become obsolete,” according to New York Times journalists Karen Weise and Cade Metz.

The rapid forward motion of AI will have ramifications on the global labor pool. A summary of key statistics and predictions reports that 120 million workers worldwide will need “upskilling” in the next three years due to developments in artificial intelligence. The key to avoiding AI job automation, according to the report? “Creativity, emotional intelligence, and STEM skills.”

Are you on top of the full extent of AI’s direction, impact on society and business, and evolving design requirements? Are you shoring up your skill sets to minimize the risk of replacement by automation? AI-related course programs from IEEE are designed to keep learners abreast of the many opportunities, challenges, and considerations to be taken into account when developing, planning, using, or training for the expansion of artificial intelligence across its many applications.

Artificial intelligence-related courses from IEEE include:

Resources

(1 November 2023). US Senate Subcommittee Focuses on AI in the Workplace. IAPP.

(1 November 2023). White House OMB Issues AI Memorandum to Federal Agencies. IAPP.

Miller, Jim. (11 November 2023). AI Replacing Jobs Statistics: 40 Automation and AI Stats for 2023. AIHungry.com.

Weise, Karen and Metz, Cade. (8 December 2023). The Morning: AI’s Big Year. The New York Times.

Wilson, Mark. (November 2023). ChatGPT Gets its Biggest Update So Far – Here are 4 Upgrades That Are Coming Soon. TechRadar.

Perrigo, Billy. (22 November 2023). Sam Altman Returns as OpenAI CEO. Here’s How It Happened. Time.

(September 2023). Jobs of Tomorrow: Large Language Models and Jobs. World Economic Forum/Accenture.

Lufkin, Braun. (18 April 2022). What ‘Upskilling’ Means for the Future of Work. BBC.

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.

Technology has always presented numerous opportunities for improving and transforming healthcare. Such improvements include reducing human errors, improving clinical outcomes, facilitating care coordination, improving practice efficiencies, and tracking data over time. Machine learning (ML) has already proven effective at disease identification and prediction, recognizing patterns that are too subtle for the human eye to detect, guiding physicians towards better-targeted therapies and improved outcomes for patients. Researchers have also used ML as a tool to recognize signs of depression and suicidality by assessing patients’ voices, picking up changes in speech too subtle for a doctor to notice. Artificial intelligence (AI) and machine learning can expand our approach to mental health.

Mapping Mental Health

Researchers at Massachusetts General Hospital have developed an artificial intelligence model that generates ‘personalized maps’ to guide individuals toward improved mental well-being. In this study, the researchers developed a model based on deep learning, a type of machine learning that uses layered algorithmic architectures to analyze data. The researchers also identified the most depression-prone psychological configurations on the self-organizing maps, which they used to develop an algorithm to help individuals move away from potentially dangerous mental states.

Shortest Path to Human Happiness

Deep Longevity, in collaboration with Harvard Medical School, offers another deep learning approach to mental health. Researchers have created two digital models of psychology that work together to find a path to happiness.

The first model depicts the trajectories of the human mind as it ages. The second model is a self-organizing map that serves as the foundation for a recommendation engine for mental health applications. This learning algorithm splits all respondents into clusters depending on their likelihood of developing depression and determines the shortest path to mental stability for any individual.

Combining Technology & Therapy is Key

Anyone with a smartphone can access conversational agent phone apps, also known as chatbots, which are meant to help users cope with the anxieties of daily life. These language processing systems can imitate human discussion by simulating conversations with a therapist via text. They can be a gateway to therapy or can reinforce lessons from in-person sessions. Research has shown that some people prefer interaction with chatbots rather than with real humans.

With the help of AI and machine learning, researchers are hoping the brain can help identify mental health issues. By applying specially designed algorithms to brain scans, labs could identify distinctive features that determine a patient’s optimal treatment. Machine learning could also assist in suicide-prevention. Currently, doctors only have a slight advantage over random probability in recognizing this risk. But algorithms, using data that are easily accessible to health care providers, can predict attempts with significantly improved accuracy.

Stay Current with Technology Advances

From healthcare to security, machine learning plays a critical role in developing the technology that will determine our future. Covering machine learning models, algorithms, and platforms, Machine Learning: Predictive Analysis for Business Decisions, is a five-course program from IEEE.

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

Deep Longevity LTD. (2 July 2022). Harvard Developed AI Identifies the Shortest Path to Human Happiness. SciTechDaily.

Gavrilova, Yulia. (4 July 2022). AI Chatbots & Mental Healthcare. IOT for All.

Glick, Molly. (1 July 2022). Your Next Therapist Could Be a Chatbot App. Discover.

Kennedy, Shania. (28 June 2022). AI-Generated ‘Maps’ May Help Improve Mental Well-being. Health IT Analytics.

Kesari, Ganes. (24 May 2021). AI Can Now Detect Depression from Just Your Voice. Forbes.

Rutherford, Lucie. (18 February 2022). Medicine Meets Big Data: Clinicians Look to AI For Disease Prediction and Prevention. UVAToday.

Savage, Neil. (25 March 2020). How AI is improving cancer diagnostics. Nature.

Artificial intelligence (AI) is more present in our lives than ever. With varied uses, AI can predict what we want to see as we scroll through social media, as well as help to solve global challenges like hunger, environmental changes, and pandemics. This technology has countless applications in the real world. A McKinsey survey illustrates that AI adoption followed an upward trajectory in the year 2021 and continues to do so. According to the survey, “56 percent of all respondents report AI adoption in at least one function.”

However, AI technology is not always beneficial—AI can violate privacy, AI-generated output cannot always be explained, and AI can be biased. When the data feeding an AI system is not representative of the diversity and plurality of our societies, it can produce biased or discriminatory outcomes.

An often-cited example is facial recognition technology. Used to access mobile phones and bank accounts, it’s also being increasingly employed by law enforcement authorities. With emerging problems accurately identifying women and darker-skinned people, facial recognition is far from being perfected. This is not surprising when you look at how AI is developed: only 1 in 10 software developers worldwide are women. Furthermore, developers come overwhelmingly from western countries.

Hardcoding Ethics into AI

Humans can be biased, but people possess the ability to recognize how their conclusions may be biased, discriminatory, or unethical. While there is some recent debate over the “sentient” qualities of AI programs, they cannot “think” or “feel”. AI performance depends entirely on its coding. Because AI does not have this meta-cognitive ability, it is up to people to override unethical decisions when they arise. Unethical AI is not a consequence simply of programming deficiencies, but rather of not fully considering how ethical requirements should be incorporated into the learning algorithm during development.

Organizations using AI need to become more proactive and formulate actionable AI ethics policies by thinking about ethics from the start. This approach already is deemed essential to cyber security products, where “security by design” development principles drives the need to assess risks and hardcode security from the start. This mindset should be applied to the development of AI tools so these can be deployed responsibly and without bias. This process will be critical as societies and cultures change over time, and AI products should always reflect current values.

How to Create an AI Ethics Policy

Aligning AI ethics is not just a moral responsibility, it is also a business imperative. It requires action to build an AI ethics-aware culture. Reid Blackman, CEO of Virtue, recommends instilling actionable ethics into AI systems by following these seven guidelines:

Bring clarity to AI standards
Increase awareness among everyone in the organization
Thoroughly incorporate AI ethics into team culture
Make sure there are AI experts as part of an AI ethics committee
Introduce accountability
Measure everything— set key performance indicators (KPIs) to track whether your organization is meeting its goals for AI standard adoption
Gain executive sponsorship

Prepare for an AI Future

The AI market size is expected to grow and surpass US$1,597 billion by 2030. Organizations and technology professionals should prepare for a changing landscape when it comes to the future of AI.

Get a jumpstart on learning about ethics in artificial intelligence systems. Check out Artificial Intelligence and Ethics in Design, a five-course program from IEEE that provides the background knowledge needed to integrate AI and autonomous systems within their companies or to their customers and end users.

Contact an IEEE Account Specialist to get organizational access or check it out for yourself on the IEEE Learning Network.

Resources

Bedzow, Ira. (30 June 2022). What It Takes to Create and Implement Ethical Artificial Intelligence. Forbes.

Boston Consulting Group (BCG). (7 July 2022). 87% of Climate and AI Leaders Believe That AI Is Critical in the Fight Against Climate Change. PR Newswire.

Chui, Michael et al. (8 December 2021). The state of AI in 2021. McKinsey.

Henderson, Emily. (10 June 2022). Using artificial intelligence to discover new antivirals against COVID-19 and future pandemics. New Medical.

McKendrick, Joe. (10 June 2022). 7 Steps to More Ethical Artificial Intelligence. Forbes.

Mubarik, Abu. (20 June 2022). This is how former Wall Street trader Sara Menker from Ethiopia is using AI to remove world hunger. Face 2 Face Africa.

Precedence Research. (19 April 2022). Artificial Intelligence Market Size to Surpass Around US$ 1,597.1 Bn By 2030. GlobeNewswire.

Ramos, Gabriela and Koukku-Ronde, Ritva. (22 June 2022). A new global standard for AI ethics. UNESCO.

Smith, Wesley. (26 June 2022). Five Reasons AI Programs Are Not ‘Persons’. Mind Matters News.

Yu, Eileen. (30 June 2022). AI ethics should be hardcoded like security by design. ZD Net

Most of us can only dream of playing professional sports. However, augmented reality (AR) could soon turn these dreams into reality — that is, a virtual one.

This fall, the National Football League (NFL) will release its first officially licensed game for virtual reality platforms, ESPN reported. The “NFL PRO ERA” game will give players the ability to experience — in virtual reality — what it is like to play pro football. Using a special AR headset, players will be able to experience the game on a virtual field.

The game, which will be available on Meta Quest and PlayStation VR, and licensed by the NFL Players Association, will turn NFL fans into virtual NFL quarterbacks. It gives them the ability to try to make plays they have seen real-life NFL quarterbacks make on television.

“When we think about this experience, you’re finally immersing yourself as the professional athlete for the first time ever. You are seeing it in a way that you’ve never seen it,” Troy Jones, co-founder of StatusPRO, the company that is developing the game, told ESPN. “It is the future, and we look at it as the new era of gaming and the next step in the way people will consume sports.”

How AR Gaming Will Improve Cognitive and Mental Health

The impact of AR gaming will be felt beyond entertainment. According to CNET, one area where it is already having an impact is on exergaming, a type of gaming that incorporates physical movement. Exergaming has been around for a while — with well-known brands like DanceDance Revolution, which made its debut in 1999. Researchers are looking into how to take the technology a step further by combining it with AR.

For example, the Pacific Brain Health Center’s “FitBrain” program aims to boost the mental function of seniors. It does so by merging cognitive exercises and physical exercise through stationary bikes and treadmills combined with 2D tablets or 3D VR headsets.

“Physical exercise is probably one if not the most well-validated interventions to improve both general health and also brain health. Or brain function or both,” Dr. David A. Merrill, an adult and geriatric psychiatrist and director of the Brain Health Center, told CNET.

Technological Advancements in AR Will Create a More Realistic, Immersive Experience

While AR has been around since the 1990s, the technology has become much more advanced in recent years. Improvements include features that allow players to better interact with the virtual environment. According to CNN Business, researchers at Salzburg University developed an AR mask. Players can breathe into it, allowing them to blow out candles, blow up balloons, and more in a virtual world. Meanwhile, researchers from Carnegie Mellon University equipped an Oculus Quest 2 headset with ultrasonic transducers. These produce ultrasonic energy, which points at a wearer’s mouth to generate unique sensations. Such as the feeling of wind on your lips, PC Gamer reported.

These AR advancements are only the beginning. With the rollout of 5G networks across the world – a development that will allow for faster internet speeds and the transferring of enormous amounts of data necessary to support advanced AR gaming – it is only a matter of time before AR gaming becomes widespread.

Practical Applications of Virtual and Augmented Reality in Business and Society

One aspect of video gaming that makes it unique is that it has always been driven by the desire for maximum fun, not the need to solve a specific real-world problem. This has driven both the development and commercialization of new technologies.

Enroll in Practical Applications of Virtual and Augmented Reality in Business and Society: The Case of Gaming on the IEEE Learning Network to discover the impact of video games as a $160 billion USD industry on the evolution of real world intelligent and immersive realities.

In this online course, we review the history and relevance of gaming while discussing unexpected use cases. We’ll explore what makes gaming unique and show how gaming has impacted the development of multiple technologies that are fundamental to immersive reality.

Resources

Rice, Andrea. (17 May 2022). VR Exercise Games Could Offer Hope for Delaying Dementia. CNET.

Corrigan, Hope. (9 May 2022). Scientists add mouth haptics to VR, complete with spiders. PC Gamer.

Rothstein, Michael. (20 April 2022). NFL-licensed virtual reality game set for fall release. ESPN.

This mask makes breathing in virtual reality more realistic. CNN Business.

For the first time, autonomous vehicles (AVs) are now being tested on the streets of Austin, Texas and Miami, Florida, without drivers at the wheel. Designed by Argo AI, the vehicles are also being tested in Washington, D.C., Pittsburgh, Pennsylvania, Detroit, Michigan, and Palo Alto, California. The tests even extend to the German cities of Hamburg and Munich.

These tests are only the beginning. The company, whose autonomy platform uses lidar, sensors, and mapping software, is partnering with both ride-sharing service Lyft and Walmart’s delivery service. Together they aim to provide driverless taxi rides and autonomous grocery delivery.

Despite many advancements in AV technology, the road ahead remains uncertain. To replace human drivers, these vehicles need to be able to intuitively navigate roads. They must make split decisions the same way humans do. Current systems are still far from reaching this level of autonomy. However, some recent research breakthroughs may help engineers understand how to overcome this challenge.

Overly Conservative Decision Making Can Make AVs Easy to Fool

To make autonomous vehicles safer, engineers have traditionally designed them to be overly cautious. However, recent research from the University of California suggests this is part of the problem.

Since AVs cannot tell the difference between an object that makes its way onto a roadway by accident and an object placed intentionally, they can be tricked into making a wrong decision. For example, coming to a sudden stop in the middle of the road could potentially cause an accident.

Ironically, this problem is a result of engineers designing AV planning modules to operate with “an abundance of caution.” Ziwen Wan, a Ph.D. student in computer science at UC Irvine, explained this to the UC newsroom.

“But our testing has found that the software can err on the side of being overly conservative,” Wan said. “This can lead to a car becoming a traffic obstruction, or worse.”

New Machine Learning Technique Helps AVs Maintain Steady Flow at Intersections

Another obstacle for autonomous systems is knowing how to move together in busy intersections. A team of researchers from MIT recently discovered a machine learning technique that can help AVs navigate signalized intersections. This allows traffic to continue flowing uninterrupted, while making traveling faster and more fuel efficient.

Rather than relying on typical mathematical models to navigate complex intersections, the researchers turned to deep reinforcement learning. This model-free method uses trial-and-error, in which the control algorithm learns to make a sequence of decisions and is rewarded when it makes the right one. They refined this training by using another technique known as reward shaping. In this, they give the system some domain knowledge it would not be able to learn by itself. Using this method, the vehicle would be penalized if it stopped when it wasn’t supposed to brake. This helps the vehicle understand how to balance competing speed requirements. It allows it to both improve travel time and reduce emissions.

Using simulations, the researchers found that if every vehicle on the road is autonomous, their control system could reduce fuel consumption by 18 percent and carbon dioxide emissions by 25 percent. Additionally, travel speeds could increase by 20 percent.

These research findings are just the start. With every advancement in AV technology, engineers are one step closer to creating a world where traveling is easier, faster, and safer.

Preparing for Roadways of the Future

Learn about the latest developments in AV technology with training in foundational and practical applications through the IEEE Guide to Autonomous Vehicle Technology. Created by leading experts in the field, this online seven-course training program explores the latest strategies and business-critical research on autonomous, connected, and intelligent vehicle technologies

Connect with an IEEE Content Specialist today to learn more about purchasing the program for your organization.

Interested in purchasing the program for yourself? Access it now through the IEEE Learning Network (ILN)!

Resources

Bradbury, Rosie. (31 May 2022). There are now fully driverless cars with no human behind the wheel for safety on the roads of Miami and Austin. Business Insider.

Bell, Brian. (26 May 2022). Autonomous vehicles can be tricked into dangerous driving behavior. University of California.

Zewe, Adam. (17 May 2022). On the road to cleaner, greener, and faster driving. MIT News.

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

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.

Artificial intelligence (AI) systems are evolving fast. However, ethical standards that ensure these systems don’t harm the public, such those that aim to prevent unintentional biases based on the data these systems are trained on, have been less quick to evolve. According to a global survey conducted by MIT Sloan Management Review, which polled over 1,000 executives, 82% of managers in organizations with at least USD $100 million in annual revenues agreed or strongly agreed that responsible AI (RAI) should be included in their top management agenda. At the same time, only 50% reported that RAI is a part of their top management’s agenda.

How can organizations that develop or use artificial intelligence ensure RAI is not just an afterthought? A recent panel of global AI experts, organized by MIT Sloan Management Review and global consulting firm BCG, concluded with the following takeaways:

Leadership needs to understand why RAI is important to the organization’s strategy. Otherwise, RAI may never make it into the agendas of the organization’s major decision makers.
Determine whether RAI is part of your AI strategy or a part of your wider organizational goals, such as corporate responsibility. Without an understanding of this, leadership may not fully grasp that it should be integrated into their larger agenda.
Look at RAI as an urgent need that must be integrated now. Otherwise, you may miss valuable opportunities to prevent risk and harm down the line.

What are the Fundamental Principles of AI Ethics?

Understanding the core principles of AI is the first step to developing an effective AI standards framework. Such a framework should also align with an organization’s mission. It should also align with any regulations the organization may be affected by through its implementation of the AI system. According to TechTarget, the basic principles of ethical AI include:

Fairness: The AI system does not contain biases and functions equally well for all groups
Accountability: The AI system has ways to identify who is responsible across different stages of the AI life cycle if something goes wrong. It also provides ways for humans to supervise and control the system
Transparency: When the AI system makes a decision, it allows humans to understand why it came to that conclusion. This is essential for building trust
Safety: The AI system is equipped with effective security controls

Incorporating These Principles into AI Systems

During an interview with Analytics India Magazine, Layak Singh, CEO of Artivatic AI, an insurance platform, said the company reduces biases in AI by defining the business problems it wants to solve while considering end users. They then configure data collection methods to be able to incorporate diverse perspectives.

“We also ensure that we clearly understand our training data, as this is where most biases are introduced and can be avoided,” Singh said. “With that aim, we also ensure an ML [machine learning] team that’s assorted as they ask dissimilar queries and thus interact with the AI models in various ways. This leads to identifying errors before the model is underway in production. It is the best manner to reduce bias both at the beginning and while retraining models.”

Additionally, there is a major focus on feedback as his company keeps feedback channels, such as forum discussions, open in order to run continual audits and upgrades.

Ensuring AI systems are ethical is becoming essential to building trust with clients and customers. Don’t wait until that trust is already broken— start developing an ethical AI standards framework today.

Incorporating AI Standards 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 benefit your organization.

Interested in getting access for yourself? Visit the IEEE Learning Network (ILN) today!

Resources

Krishna, Sri. (20 April 2022). Talking Ethical AI with Artivatic’s Layak Singh. India Analytics Magazine.

Kiron, David, Renieris, Elizabeth, and Mills, Steven. (19 April 2022). Why Top Management Should Focus on Responsible AI. MIT Sloan Management Review.

Kompella, Kashyap. (1 April 2022). How AI ethics is the cornerstone of governance. TechTarget.