DEV Community: Brian Collins

What's Fueling the Spike in American Concerns Around AI?

Brian Collins — Thu, 25 Apr 2024 01:58:32 +0000

Recently the personal care brand, Dove (Unilever) released a new ad celebrating the 20th anniversary of their famous Campaign for Real Beauty that’s getting a lot of attention. The company isn’t sheepish about its stand against the marketing of “fake” beauty products, photoshopped fashion models, and social media images that market impossible beauty standards to young girls. With this latest ad, entitled “The Code”, Dove has doubled down its efforts and taken on the explosive market of AI-generated images, which it pledges to never use to “create or distort women’s images”.

The ad is flooding social media platforms, giving fresh oxygen and awareness to the hotly debated topics of social media impacts on young people, impossible standards of beauty, and the rising depression and anxiety levels among teenage girls. The ad also throws down the marketing gauntlet to firms and companies around the world, implicitly asking, “Will you follow “The Code” of conduct we’re committing to?”

However, the ad’s strong message and stance against AI comes on the heels of a recent spike in concern among Americans around AI and its influence on our daily lives. The fear of AI, or algorithmophobia, is a topic I covered in an earlier piece where I make the argument that our industry’s marketing strategy needs to better understand the causes of algorithmophobia so as to adapt its messaging to overcome common consumer fears.

In the article, I explored how popular culture often polarizes our view of AI, depicting it as either entirely benevolent or entirely malevolent. I argued that such extreme views could heighten anxiety about adopting new technologies. In this piece, however, I aim to delve deeper into the psychological factors that shape our attitudes towards technology and to examine the recent surge in concerns and their underlying causes.

In doing so, I do not wish to dismiss legitimate concerns about AI or AI-generated content and its potential impact on body image and mental health. There are valid reasons for caution, given AI’s unpredictable effects and recent emergence. Instead, my focus is on exploring the deeper-rooted anxieties that arise with the advent of technologies like AI—fears about the very concept of artificial intelligence and the apprehensions that typically accompany such disruptive innovations. Addressing these fears is crucial for us to fully leverage the benefits that AI offers.

Concerns Around AI Among Americans is Rising Sharply

A recent 2023 Pew Research Center Survey looked at how Americans felt about the role of artificial intelligence in their daily lives. The survey showed a sharp increase from the two previous years. When asked how the increased use of AI in daily life made them feel, 52% of respondents said they were more concerned than excited. In 2022, that number was 38%, and in 2021 it was 37%. So, what’s contributing to this bump in concern?

One factor is the explosive growth in AI’s development and deployment, along with the resulting increase in media coverage and debate. We hear, see, and read about AI everywhere now. The public’s awareness is rising and likely outpacing a fuller understanding of AI.

In fact, Pew reports that while most Americans (90%) are aware of AI, the majority are limited in their ability to identify many common uses of the tech (e.g. email spam detection or music playlist recommendations). Awareness without understanding promotes anxiety. It’s functional. Our ancestors (those who survived to procreate) tended to focus more on a general awareness of potential threats than our kin who felt compelled to gain a true understanding of what was making noises in the bushes over there!

To that point, the survey showed that education plays a role in attitudes. Adults with a college or postgraduate degree are more likely to be familiar with AI than those with less education. Also, Americans with higher levels of education tend to be more positive towards AI’s impact on their lives.

However, it’s also important to note that more educated Americans are more likely to work with AI. In 2022, 19% of American workers were in jobs that are most exposed to AI. Those jobs tend to be in higher-paying fields where higher education was beneficial. Working with AI on a practical level helps explain more positive attitudes in this population. Education and experience often allay fears.

Two competing perspectives of artificial intelligence dominate today: 1) AI as the supplanter, leaving humanity to wither towards extinction (left). 2) AI as an assistant to unleash unlimited potential (right).

The Function of the “Human Element” in our Attitudes

One of the more interesting take-aways from the Pew surveys was the shifting levels of trust in AI when it came to the absence or existence of the “human element”— those parts we tend to value (creativity, empathy, analytical thinking) and those we tend to avoid (bias, destructive impulses, impulsiveness).

However, it’s this ambivalence to the human side of AI that provides more valuable insights into how we form our attitudes. Consider these Pew Survey findings on health care:

65% of Americans say they would want AI to be used in their own skin cancer screening.
67% say they would not want AI to help determine the amount of pain meds they get.
Now, consider how U.S. teens responded to a Pew survey asking them about using ChatGPT for schoolwork:

39% said it was “acceptable” to use ChatGPT to solve math problems.
Only 20% said it was “acceptable” to use ChatGPT to write essays.
What explains the apparent contradiction here? What’s the difference between skin cancer screening and pain med management? Why did twice as many respondents find it acceptable to use AI to solve math problems but not write essays?

Part of the answer is we assign varying levels of subjectivity or “human-ness” to different tasks (even though humans do them all). In other words, it’s more “human” to compose an essay because writing is about expressing yourself. Math is about following strict rules. The same attitude usually applies even when you’re tasked with writing an objective, unbiased, and factual piece like a news article. However, aren’t these the same strategies you need to solve math problems?

To diagnose skin cancer successfully, it helps to be experienced, objective, and unbiased. AI skin cancer detection systems are trained on thousands of high-res images of skin lesions. These systems can remember and compare them all, dispassionately and efficiently. No ambiguity. No corrective lenses. No hangover from last night’s bar hopping.

On the other hand, accurately gauging someone’s pain level requires a person who feels pain, a human who’s shared the experience of pain, who has empathy and compassion. It’s likely it’s this lack of human connection that engenders distrust in AI on this issue.

Our Attitudes are Contextual

It’s also important to recognize our attitudes align with the aim of our motives. While most of us are capable of compassion and empathy, these faculties also make us easy to manipulate. In most instances, it’s easier to bullshit your way through a book report than a calculus problem. Also, most people would have more success convincing a human doctor to over-prescribe pain meds than an AI physician. If we need moral nuance or ethical wiggle room, we distrust AI to accept such gray areas. I may be able to convince you that 2 + 2 = 5, but I can’t “convince” my calculator.

What these surveys show is that our attitudes and trust in AI are highly contextual. They change based on the stakes of potential outcomes and the need for objectivity or subjectivity (perceived or actual). If the potential negative outcomes are low (a robot cleaning our house), we tend to trust more. However, we wouldn’t want a robot to make life-altering decisions about our health, unless that decision required high levels of objectivity and unbiased judgment…but only to a point. Thus, this back-and-forth “dance” with AI is our ambivalence at work.

Subjectivity and objectivity are both part of the human experience, yet we prioritize subjective faculties like compassion, empathy, trust, expression, and creativity as “truly human” qualities. The crux of the issue is that, while these aspects of ourselves are indeed some of our best features, they are also the source of our distrust of ourselves and, by extension, AI. Because we are compassionate, we can be manipulated. Because we are emotional, we can be biased. Because we are trusting, we can be deceived. This double-edged sword pierces the heart of our shifting attitudes towards AI.

Mechanisms That Shift Attitudes Towards Technology

The Pew Surveys show American’s trust of AI runs along a spectrum. Just as the temperature in a building is regulated by a thermostat, our individual and collective trust in AI is influenced by an array of ‘control mechanisms’—such as our education levels, occupations, media portrayals—that continuously modulate our comfort levels and perceptions of safety with the technology. So, what else adjusts the thermostat? I asked clinical psychologist Dr. Caleb Lack to help me identify some common factors that could influence public sentiment on AI.

Habituation

One common mechanism to calm nerves around technology is the simple passage of time. All big technologies—like automobiles, television, and the internet—follow a similar journey from resistance to full adoption. Given enough time, any new tech will go from being “magical” or “threatening” to being commonplace.

“I think what often happens with tech is people habituate to it,” explains Lack. “As with any new technology, you just become used to it. Rule changes in sports are a good example. People will often complain about them at first. Two seasons later, no one is talking about it. It’s how it is now, so that’s how it’s always been. Humans have very short memories and most of us don’t reflect much on the past.”

Social Media & Mental Health

We often hear mental health and technology experts sounding the alarm about the negative effects of smartphones and social media. And with good reason. Studies continue to show the negative effects of smartphones, social media, and algorithms on our mental health, especially in adolescents.

One 2018 study showed participants who limited their social media usage reported reductions in loneliness and depression compared to those who didn’t (Hunt, Melissa G., et al). Another found significant increases in depression, suicide rates, and suicidal ideation among American adolescents between 2010 and 2015, a period that coincides with increased smartphone and social media use (Twenge, Jean M., et al).

Few of us have failed to notice the irony that “social” media seems to be making us feel more alone. Many are asking will AI super charge social media, create something that further alienates and damages mental wellbeing of young people. The Dove ad is a product and a reflection of this collective anxiety.

The “Black Box” of Technology

The term “black box” refers to any kind of system or device where the inputs and outputs are visible, but the processes or operations inside are hidden from the user. For most of us, the world is one big black box, technologically speaking.

“Technology of all kinds has become impenetrable for most of the population,” says Lack. “I think that’s where we’re seeing a lot of concern around AI. To someone completely unfamiliar, DALL·E or ChatGPT look like magic. It’s completely incomprehensible. But, then again, how many of those people know how their car works, or their phone, or the internet even. We eventually habituate and just accept them as normal.”

Even if we normalize new technology and forget our previous concerns, an opaque world is still a strange one, and we don’t do well with strange for extended periods of time. We need resolution. We either find out what’s really making noise in the bushes over there, or we run away. Either way, we’re satisfied with our newly acquired knowledge of the real squirl we discovered or the imagined snake we avoided. Yet, how do we run away from an inscrutable technology that’s increasingly integral to our lives? Enter anxiety…

Skills Specialization

The jobs and tasks we do are getting more and more specialized. It’s a consequence of technology itself. We can’t know everything obviously, and specialization can increase efficiency, bolster innovation, and raise wages for workers. However, these benefits have social costs, according to Lack, who offers an insightful perspective on the connection between specialization and social isolation:

“Our knowledge and skills have become so siloed we don’t know what anyone else is doing. You only know your own skills and knowledge. These knowledge silos are keeping us from connecting with others. At the same time, we’ve also become more physically disconnected from people mostly because of technology. The resulting social isolation is a major source of anxiety for humans. If you socially isolate someone, the same areas of their brain become active as if you’re actively torturing them,” he cautions.

The Great Replacement Theory

As we’ve seen, algorithmophobia draws on other common parts of general tech phobia. However, it does seem to carry a unique feature not seen with other technologies. Lack refers to the feature as the “fear of supplantation” or the idea that AI could replace humanity altogether. The notion goes well beyond just taking jobs, but making humanity obsolete, a vestigial species.

“When cars came along,” he explains, “people weren’t worried Model T’s would become sentient, take over, and kill everyone. Today, that fear does exist. So, it’s not just worries around the safety of self-driving cars. It’s about safety, and the fear that humanity may not survive, that life will become Christine meets Terminator.”

Although supplantation is a unique fear with respect to technology and AI, it’s an anxiety as old as humanity itself. “We see fear of supplantation in racist tropes also,” Lack explains. “They’re coming to take my job. They’re coming to marry into my family. They’re coming to replace me. Humans are really good at creating Us vs Them mentalities.”

“Artificial Intelligence” as Competitor to Humanity

Previously, I’ve argued that how we market “artificial intelligence” is important to how consumers will respond to it. To quell anxieties in consumers, I’ve called for “Dehumanizing” AI, by minimizing its human side and “promoting it as a tool first and foremost.” There is a balance to be struck. Imbuing AI with too much “humanity” risks stoking fears of replacement. Too little may diminish the advantages of technology to automate our thinking.

Lack agrees that what we call AI has important psychological implications. In fact, he argues that the moniker “artificial intelligence” creates an antagonistic relationship by making it a direct competitor for resources.

“You’re setting AI up essentially as another species,” he explains. “Competition over resources has been one of the biggest drivers of conflict for our species. We have a hard enough time getting along with even slightly different members of our own.”

Conclusion

As AI continues to permeate every facet of our lives, it is essential to understand and address the multifaceted anxieties it generates among the public. The 2023 Pew Research Center survey serves as a bellwether for rising concerns, indicating a need for greater education and transparency about AI’s role and capabilities. By exploring the psychological underpinnings and societal impacts of AI integration, we can better navigate the complex dynamics of technology adoption.

Efforts to demystify AI through clearer communication, coupled with practical exposure, can alleviate fears and foster a more informed and balanced perspective. Ultimately, addressing the human element in technological development and ensuring ethical standards in AI applications will be pivotal in harmonizing our relationship with these advanced systems, ensuring they enhance rather than detract from societal well-being.

Works Cited

Twenge, Jean M., et al. “Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time.” Journal of Abnormal Psychology, vol. 127, no. 1, 2018, pp. 6-17. DOI:10.1037/abn0000336.

Hunt, Melissa G., et al. “No More FOMO: Limiting Social Media Decreases Loneliness and Depression.” Journal of Social and Clinical Psychology, vol. 37, no. 10, 2018, pp. 751-768. DOI:10.1521/jscp.2018.37.10.751.

Pew Research Center Articles

What the data says about Americans’ views of artificial intelligence

60% of Americans Would Be Uncomfortable With Provider Relying on AI in Their Own Health Care

Which U.S. Workers Are More Exposed to AI on Their Jobs?

Public Awareness of Artificial Intelligence in Everyday Activities

Growing public concern about the role of artificial intelligence in daily life

Caleb W. Lack, Ph.D. is a Professor of Psychology at the University of Central Oklahoma.

What’s the Cloud? A Non-Techie Explanation

Brian Collins — Sun, 06 Aug 2023 23:29:11 +0000

Cloud computing has been around for a while, but longevity never ensures understanding. I mean, the general theory of relativity is 108-years old, and I still don’t get it. So, I’ve challenged myself to use my skills at simplifying technical jargon to explain how “the cloud” works.

But this isn’t simply an exercise in explanatory writing. The cloud is important; it’s where most technology lives today, including building tech. It impacts our lives on a major level, and it doesn’t care if we “get it” or not. Plus, cloud architecture and processes are changing, and continue to change since their introduction in the 1960’s. The cloud as we know (or don’t know) it is evolving. So, if we don’t get the basics down now, we’ll soon be left further behind. In short, we need to get cirrus about the cloud. With that caveat and dad joke out of the way, let’s get started.

Computing Work (IPOS)

All computers perform four basic types of computation “work”: They input data, process it, output it, and store it. I’m typing this article into Word (input). The computer reads my keystrokes (process). The CPU outputs letters to my monitor (output). I save my doc on my PC’s hard drive (storage). That’s it. All computational work can be summed up with these four functions (aka “IPOS”)

Now, in my example, these four work functions were done on my PC (aka “Locally”). But they don’t have to be. In fact, almost all IPOS work can be done almost anywhere! This is an important point that some may not realize. It’s understandable. Our PCs, laptops, and phones are real. They’re on our desks and in our laps. We’re holding them in our hands. It makes sense we would assume this is where the “stuff” happens. But the truth is, tons of computation work that goes into running our devices and apps is done in a giant data center (in the “cloud”) probably thousands of miles away.

Cloud-as-a-Utility

Think of the cloud as a giant PC that you can use, but that someone else owns and runs. And it can IPOS the hell out of stuff. Commercial cloud services like AWS and Azure are built by Big Tech, so they’re big, fast, and secure.

You may have noticed about a decade ago that your favorite apps (e.g., Office, Adobe, etc.) moved from boxed software to a subscription-based model. That is, they became a software-as-a-service or “SaaS”. The change was part of a move to cloud hosting of apps by many tech companies. Instead of selling you a CD ROM or downloadable app, you simply bought a monthly or annual plan. You then downloaded the app to your PC or phone. These companies were now using cloud services, meaning most of the computation work was located there rather than your PC. Today, this is almost exclusively how apps are sold, stored, and operated.

For many companies today (including the building industry) cloud service works like a computational “utility” that they pay for much like their electric and gas services. It’s a type of infrastructure like an electric grid. Cloud service providers do the computation work; you pay them to do it. But why did this move to remote computational work happen? Answer: the benefits of course.

Benefits of Cloud Computing

To electrify our buildings, most of us don’t go and buy a generator; instead, we pay the electric company. Why? Generators are expensive. We must buy fuel to run them. If they break, we must repair them. We even need to guard them against theft. Rather than do all this work, we offload it to the electric company. The benefits of cloud computing services are similar. Here are a few of them:

Scalability: Cloud services can easily scale up or down based on demand, allowing businesses to adjust resources as needed without having to invest in and manage physical infrastructure.
Accessibility: Users can access cloud-based applications and data from anywhere with an internet connection, providing flexibility and enabling remote work.
Cost-effectiveness: Cloud computing reduces the need for upfront hardware and software investments, as users can pay for services on a subscription or pay-as-you-go basis.
Reliability: Cloud providers often offer high levels of reliability and uptime, with redundant systems and data backups to ensure continuity.
Security: Reputable cloud providers implement robust security measures to protect data from unauthorized access, and they often have dedicated teams focused on ensuring data privacy and security.
Updates and Maintenance: Cloud service providers handle system updates, maintenance, and security patches, freeing users from these tasks.

It’s important to remember that companies didn’t move to the cloud simply because they “felt like it.” They did it because it was cheaper, easier, and more reliable. Cloud services work at economies of scale, and proper market competition ensures they pass that savings on to the consumers.

Other Cloud Types
So far, we’ve discussed commercial cloud services like Amazon Web Services (AWS) and Google Cloud Platform (GCP). These are “public” third-party services because resources are often shared. However, other cloud types exist.

Private Clouds

Regular old companies can create their own cloud data centers housed in their buildings (i.e., on-prem) or get a third-party to create and manage one for them. Like public clouds, private clouds perform IPOS work. They can serve and manage apps for employees, store company data, and are remotely accessible.

Often with public clouds, data and resources are shared among other customers (For example, web hosts often run multiple customer sites on the same server). However, private clouds are dedicated to one entity. However, such exclusivity comes at a price, but it is often required for enhanced data security by organizations that have specific regulatory requirements, sensitive data, or those that need complete control over their resources.

Hybrid Cloud

As the same implies, a “hybrid cloud” is one that combines elements of both public and private clouds. A hybrid cloud environment allows an organization to use a mixture of public cloud services (e.g., servers) and private cloud infrastructure (e.g., storage) to meet specific needs.

Hybrid clouds increase flexibility by distributing the computational workload. For example, Company A has lots of data to store. Some of it is super sensitive, the bulk of it isn’t. The company could invest in enough equipment to store all the data in its private cloud, or it could store the less sensitive data in a public cloud and save time, space, and resources.

Hybrid clouds are growing in popularity because they offer superior flexibility and security to a standalone solution. That’s because computational work can happen in either cloud, whether it’s inputting, processing, or outputting. So, when the electricity goes out, you’ve still got your generator.

The choice between public, private, and hybrid cloud depends on an organization’s specific needs, including factors such as security, compliance, scalability, and cost considerations. Each cloud deployment model offers distinct advantages and challenges, allowing businesses to tailor their cloud strategy to their unique requirements.

Computation Workload Sharing

As was stated previously, many devices like smartphones share the computational workload with the cloud. To illustrate this, let’s look at a few of the steps in the “simple” process of using your phone to view an image saved in the cloud:

The cloud service authenticates your phone using your login credentials.
Your smartphone establishes an internet connection through cellular data or Wi-Fi.
The cloud transfers the image data to your smartphone.
Your smartphone’s CPU processes the data.
The data is temporarily stored in your smartphone’s RAM.
The cloud service monitors for errors or exceptions.
The smartphone’s OS and UI components render the photo on screen.
The cloud synchronizes any file changes between your phone and cloud storage.

Note how the computational work and resources shift from the device to the cloud. In a hybrid architecture, public and private clouds share in a similar way.

Conclusion

At this point, two ideas may be surfacing. 1) Hybrid clouds require some serious orchestration, and 2) The notion of a “computer” is expanding. Both ideas are driving the evolution of cloud computing and driving us to a new form of architecture called cloud-native. These changes have implications for how we operate and automate our buildings. The future of building automation will leverage these new technologies making them greener, cheaper, more responsive, and better places to live and work. However, we can only reap these benefits if we prepare our buildings to play well in the clouds.

Tech’s “Hidden” Industry: Smart Buildings

Brian Collins — Fri, 21 Apr 2023 00:21:09 +0000

While Silicon Valley is laying off thousands of workers, companies in the smart buildings industry (SBI) are facing serious workforce shortages in almost every field. Businesses of every size, from systems integrators (SI) to equipment manufacturers, are fighting over engineers and developers. It’s the perfect time for laid off, bored, or disillusioned workers to give the smart buildings industry a serious look.

One might assume: “Well, with such a worker shortage, the SBI must be a crap place to work.” Far from the truth. The SBI actually pays well and offers challenging, meaningful work. It also holds some of the largest companies in the world, yet it’s an industry often “hidden” from most people. That’s because building operation is one of those things that, when it works well, goes unnoticed. The air conditioning keeps us cool. The lights turn on. The toilets flush. It’s only when these events don’t happen that we notice…and we notice fast.

So, while the SBI doesn’t produce a “sexy” ride share app or disruptive social media platform, it’s an industry with far-reaching and more profound impacts on our lives by comparison. Far from being a boring vocation, building automation is an exciting, innovative industry that’s seeing significant acceleration due to the growth in AI, analytics, and digital twin technologies. In an attempt to bring SBI out of the shadows, here are some facts and ideas that shed light on one of the tech world’s best kept secrets.

Make a Real Contribution to Saving the Environment

Buildings are a big part of our lives. We live, work, and sleep within them (when not camping) about 90% of the time. Houses, malls, schools, hospitals, skyscrapers, and other facilities require enormous amounts of space and energy to operate. Together, the built environment has a major impact on the actual environment. And the impact is growing larger. Consider these stats:

Buildings make up nearly 40% of all greenhouse gas emissions world-wide.(1)
Of that number, 27% come from building operations alone.(1)
Global building floor area is expected to double by 2060.(1)
There are close to 6 million commercial buildings in the U.S. alone.(5)

But where there’s a big impact, there’s a big opportunity. By automating building systems like lighting, HVAC and security, we can operate facilities that use fewer resources and emit less carbon. Several studies estimate we could cut energy consumption within commercial buildings by 30% using smart building systems.2, 4 Think of the environmental implications. However, only 10% of the 6 million commercial buildings in the U.S. have a building automation system (BAS). 3

Tech adoption and innovation can solve the energy efficiency problems of wasteful buildings, and the potential energy savings is real. There is important work to be done, and the engineers and developers who design future smart buildings are key players in reducing GHG emissions. By choosing a career in the SBI, you’ll be on the ground floor of some of the most environmentally impactful work being done today.

The Smart Buildings Industry. A Smart Career Move. - YouTube

We interview Brad White, President of SES Consulting about what smart buildings are, what he looks for in a new hire, and why an engineer or developer might ...

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Help People with Accessibility Needs

Building automation is a critical part of giving people with special needs better access to facility services. Automatic doors activated by sensors provide easy access for people who have mobility issues. Elevator systems provide access to occupants who can’t use stairs. Assistive technology like automated voices and Braille labels help people with visual impairments navigate spaces.

However, these technologies pale in comparison to what’s possible once future technology matures. Imagine designing a smart building that could track the movements of a visually impaired person, guiding and opening doors throughout an entire multi-storied building.

Use Cutting Edge Tech

Smart building professionals work with cutting edge technologies like advanced data analytics, digital twin technology, AI/ML, and VR/AR. These advanced tools along with greater interoperability present endless possibilities for improving the safety and lives of building occupants.

Imagine designing an automated security system that could detect and respond to an active shooter situation. AI and security cameras could provide real-time analysis of a building’s exterior, using facial recognition or similar software to identify threats. The system would then command building access to lock down the facility, set off alarms, and lower lighting and window covers within exterior facing rooms. Real-time analysis could track first-responder locations and help guide occupants to safe exits away from intruders. Such a system would likely save lives.

The future of automation is exciting. Getting into building automation now will assure you’ll have a hand in shaping the future of how people live, work, and play.

Work with Open Systems

The name of the game in building automation is interoperability. Devices and software must integrate and share data seamlessly to optimize a building’s operation. Interoperability becomes difficult or impossible when specific companies limit customers to their specific tech. Similar challenges face the smart building industry. For decades, propriety software from a few manufacturers have limited customer choice for design and maintenance of building systems.

However, things have changed dramatically over the past few years, and the industry is moving aggressively towards more open systems and standards that make data transmission and device integration possible.

Enjoy Strong Job Security

Job security within the smart buildings industry is strong because of multiple factors, including industry growth and the high demand of workers. One report estimates that the global building automation system market will grow at an annual rate of 10% by 2026, from $75.0 billion in 2021 to $121.5 billion.(6)

Both developers and engineers are in high demand in the building automation industry, with some occupations like software developers growing at a projected rate of 25%.(7) The industry is also bracing for a large staffing turnover. Within the next five years, around 35% of the workforce will retire and create an even larger demand.(8) The result will be higher wages and greater work flexibility. It’s the perfect time to win leverage over negotiating your salary and benefits.

Career Opportunities Abound

Building automation offers plenty of career growth opportunities. Engineers and programmers can grow their careers rapidly as they gain experience and expertise in the field. Some examples of career growth opportunities within the building automation industry include:

Systems Engineer: Responsible for designing, implementing, and maintaining building automation systems. They work closely with clients, contractors, and other stakeholders to develop solutions that meet their needs, and are involved in all stages of the project, from design and implementation to testing and commissioning.

Software Engineer/Developer: Responsible for developing the software that runs building automation systems. They work closely with systems engineers to design and develop software that meets client needs and ensures the smooth operation of building automation systems.

Technical Sales: Responsible for selling building automation systems and services to clients. They work closely with clients to understand their needs and develop solutions that meet those needs. They’re often involved in the design and implementation of building automation systems.

Project Management: Responsible for overseeing the design, implementation, and commissioning of building automation systems. They work closely with clients, contractors, and other stakeholders to ensure that projects are completed on time, within budget, and to the client’s satisfaction.

Research and Development: There are also opportunities for engineers and programmers to research and develop new technologies and solutions for building automation systems.

Conclusion

Even though building automation is an “hidden” industry, it’s impact on our lives and environment is anything but. As a building automation tech, you have the skills and knowledge to shape the future of the built environment. Even though a career in building automation isn’t the flashy “dream job” of Silicon Valley, it is important work that focuses on the fundamental needs of humans—where they work and live. When we design dwellings to be safe, comfortable, and functional, we are directly impacting lives on a level from which everything else flows. That is real change.

Sources
1 WHY THE BUILT ENVIRONMENT?

2 (IS-2022-145) Energy Efficiency and Savings for Commercial Buildings

3 Small-and-Medium-Sized Commercial Building Monitoring and Controls Needs: A Scoping Study

4 Report Delves Into the Impacts of Commercial Building Controls on Energy Savings

5 Commercial buildings have gotten larger in the United States, with implications for energy

6 Building Automation Global Forecast to 2027

7 Bureau of Labor Statistics: Software Developers

8 “Monday Live!” January 16th, 2023. Melissa Boutwell, ASP

AI vs Machine Learning: What’s the Difference?

Brian Collins — Tue, 28 Mar 2023 23:28:19 +0000

AI and machine learning (ML) are often used interchangeably, but they’re not technically the same thing. However, the difference is smaller than you think, and once you understand it, you’ll never mistake the two again. The following is a very basic explanation and omits many technical aspects of AI and ML which go beyond the scope of the intended audience. The definitions and examples attempt to lay a foundation for further exploration around these topics.

Artificial Intelligence: The Entire Robot

Artificial intelligence (AI) is a broad term that refers to creating machines that can perform tasks that normally require human intelligence. Examples of such tasks include visual perception, speech recognition, decision-making, and language translation. There are many subsets and subfields of AI, each of which tries to solve a specific problem and/or takes a different approach to creating “intelligence”. Here are the five most recognized subsets of AI:

Natural Language Processing (NLP) focuses on enabling machines to understand, interpret, and generate human language. NLP is used in applications such as chatbots, voice assistants, and language translation. ChatGPT is an NLP.
Computer Vision is concerned with enabling machines to interpret and understand visual data from the world around them. Computer vision is used in applications such as object detection or facial recognition. Autonomous vehicles, like some Tesla models, use computer vision.
Robotics develops machines that can physically and autonomously interact with the world around them to perform tasks like assembly line work or rescue operations. Boston Dynamics focuses on robotics.
Expert Systems are designed to mimic the reason-based decision-making ability of an expert in a particular field, such as medical diagnosis or financial analysis. Expert systems are why you keep hearing about AI lawyers defending people in court.
Machine Learning involves feeding data into a machine learning algorithm and allowing it to learn from that data in order to make accurate predictions or classifications about new data. So, ML is a subset of AI. That’s the first big difference to note. While AI is a term that encompasses a wide range of technologies and techniques, ML is a specific approach to building AI systems.

It’s helpful to think of AI as the “entire robot”—a fully autonomous machine capable of thinking and acting like a human. However, each subset is only one part of the entire robot. Robotics attempts to develop the “body” for interacting with the environment. Computer vision gives the robot the ability to make visual sense of its world. NLP arms it with the power to communicate. ML bestows the faculty of learning. And expert systems send it to university. It’s a true Frankenstein’s monster of disparate parts, but when brought together will finally realize the goal of AI.

What’s Machine Learning?

You hear a lot about ML because it’s a critical step in creating the entire robot. Almost everything we consider to be alive must be able to learn. Birds do it. Bees to it. Heck, even amoebas do it. But despite its ubiquity in the world of the living, learning is incredibly complex. Therefore, ML is taking on one of the biggest challenges, but it’s a triumph that offers the biggest ROI. Once we create a machine that learns, we can train it to make better decisions. So how do you create a machine to learn?

ML uses statistical algorithms to enable machines to learn from data and improve their performance on specific tasks over time. ML algorithms analyze large amounts of data to identify patterns, which it uses to make predictions or decisions on new data. Like humans, ML is a process that requires that machines be “taught” by exposing them to information.

ML Example: House Price Estimator

Suppose you wanted to create a ML learning algorithm that predicts the price of a house based on its size and location. You would need two sets of data: a training set and a test set. First, we create a training set of data composed of recently sold houses with their sale price and location.

The ML then processes the training data to look for patterns. After some processing, let’s say it “learned” the following “rules”:

Houses larger than 2,000 sq ft sell for > $200K
Houses less than 2,000 sq ft sell for < $200K
Houses within 5 miles of the airport sell for < $100K
Homes within 5 miles of the lake sell for > $300K

The algorithm could then use this knowledge to predict the price of a house outside the training dataset (i.e., the test set). For example, a house that is:

2,500 sq ft and 3 miles from airport.

Since the new house is more than 2,000 sq ft, the algorithm would then apply the “> $200K” rule, but since the it’s also less than 5 miles of the airport, it would apply the “<$100K” rule. Therefore, the algorithm’s prediction would likely be “$150K”.

Next, the ML algorithm checks its guess against the actual price, which is $170K. It now has a $20,000 discrepancy it needs to resolve. It checks for more patterns and learns that, as houses of equal size get closer to the airport, they decrease in price. Through some calculations, the program can determine the changes in price by proximity and apply the data as a weighted value in its next prediction. For example, maybe each mile closer to the airport equates to a 10% decrease in price.

The machine uses this constant process of guessing and checking (called backpropagation) to improve its predictions. The more iterations and inputs, the “smarter” the algorithm gets.
“So what?”, you might ask, “Isn’t this simple logic? Why do we need a machine to do this?” Well, for one, ML can sift through data, find patterns, and test its guesses against real world data at an astonishing rate. In short, it can “learn” much quicker than humans. For another, it can juggle many more parameters than we ever could, so its guesses will inevitably we more accurate over time.

Think about all the factors that go into the price of a house besides size and location. There’s the house’s age, condition, number of rooms, the market conditions, and seller motivation just to name a few. But there are other less typical considerations like current interest rates, lot locations, or roof type. When you drill down further, you find that the real number of factors is enormous. Few sellers place a critical role on the color of a house when calculating an asking price, but what if it mattered more than we thought? What about the history of the house or the future of the neighborhood where it resides? The better our predictive capabilities, the more important these “lesser” considerations become.

ML can iterate much faster and with greater detail than we can, making it more efficient at locating “hidden” patterns. What if dark-colored houses sold for higher prices than light-colored ones? Maybe houses with more east-facing windows were cheaper than more west-facing ones. Machine learning can consider all these factors and then some—and do it in real time.
Finally, imaging adding to this learning algorithm the ability to search for, monitor, and collect house price information for a large region of the country. It would be a fully autonomous learning and predicting machine that would only get smarter the longer it worked. That’s where ML is at today.