C-Suite Network™

Best Practices Entrepreneurship Industries Skills Technology

Smart Construction: How AI and Machine Learning Will Change the Construction Industry

These days, seemingly everyone is applying Artificial Intelligence (AI) and machine learning. I have written about disruptions in the manufacturing industry, such as Industry 4.0, while illustrating the Hard Trends that indicate where improvements will be made in the future.

The construction industry, which makes up 7% of the global workforce, should already have applied these technologies to improve productivity and revolutionize the industry. However, it has actually progressed quite slowly.

Growth in the construction industry has only been 1% over a few decades while manufacturing is growing at a rate of 3.6%. With the total worker output in construction at a standstill, it is no surprise that the areas where machine learning and AI could improve such statistics were minimal. Yet, those technologies are finally starting to emerge in the industry.

Artificial Intelligence (AI) is when a computer mimics specific attributes of human cognitive function, while machine learning gives the computer the ability to learn from data, as opposed to being specifically programmed by a human. Here are ten ways that AI and machine learning will transform the construction and engineering industries into what we’ll call “smart construction.”

  1. Cost Overrun Prevention and Improvement

Even efficient construction teams are plagued by cost overruns on larger-scale projects. AI can utilize machine learning to better schedule realistic timelines from the start, learning from data such as project or contract type, and implement elements of real-time training in order to enhance skills and improve team leadership.

  1. Generative Design for Better Design

When a building is constructed, the sequence of architectural, engineering, mechanical, electrical, and plumbing tasks must be accounted for in order to prevent these specific teams from stepping out of sequence or clashing. Generative design is accomplished through a process called “building information modeling.” Construction companies can utilize generative design to plot out alternative designs and processes, preventing rework.

  1. Risk Mitigation

The construction process involves risk, including quality and safety risks. AI machine learning programs process large amounts of data, including the size of the project, to identify the size of each risk and help the project team pay closer attention to bigger risk factors.

  1. More Productive Project Planning

A recent startup utilized 3D scanning, AI and neural networks to scan a project site and determine the progress of specific sub-projects in order to prevent late and over-budget work. This approach allowed management to jump in and solve problems before they got out of control. Similarly, “reinforcement learning” (machine learning based on trial and error) can help to collate small issues and improve the preparation phase of project planning.

  1. More Productive Job Sites

Professionals often fear machines will replace them. While intelligent machines will take over first repetitive and eventually more cognitively complex positions, this does not mean a lack of jobs for people. Instead, workers will transition to new, more fulfilling and highly productive roles to save time and stay on budget, and AI will monitor human productivity on job sites to provide real-time guidance on improving each operation.

  1. Safety First

Manual labor not only has the potential to be taxing on the body, but also to be incredibly dangerous. Presently, a general contractor is developing an algorithm that analyzes safety hazards seen in imagery taken from a job site, making it possible to hold safety briefings to eliminate elevated danger and improve overall safety on construction sites.

  1. Addressing Job Shortages

AI and machine learning have the capacity to plot out accurate distribution of labor and machinery across different job sites, again preventing budget overruns. One evaluation might reveal where a construction site has adequate coverage while another reveals where it is short staffed, thereby allowing for an efficient and cost-effective repositioning of workers.

  1. Remote Construction

When structures can be partially assembled off-site and then completed on-site, construction goes faster. The concept of using advanced robots and AI to accomplish this remote assembly is new. Assembly line production of something like a wall can be completed while the human workforce focuses on the finish work.

  1. Construction Sites as Data Sources

The data gathered from construction sites and the digital lessons learned by AI and advanced machines are all tools for improving the productivity of the next project. In this way, each construction site can contribute to a virtual textbook of information helpful to the entire industry.

  1. The Finishing Touches

Structures are always settling and shifting slightly. It would be beneficial to be able to dive back into data collated by a computer to track in real time the changes and potential problems faced by a structure — and AI and machine learning make this possible.

Given the inevitable changes on the horizon, and the potential for costs to drop up to 20% or more with increased productivity, professionals in the construction industry must pay attention to Hard Trends, become more anticipatory, and ultimately learn to turn disruption and change into opportunity and advantage.

Know What’s Next

Discover proven strategies to accelerate innovation with my latest book The Anticipatory Organization. Follow this link for a special offer.

Shape the Future–Before Someone Else Does It For You!

Marketing Personal Development Technology

AI Improves Your Website as More People Use It

Plenty has been written about how AI gets smarter with experience, the way people do. If you perform a task 100 times, you are faster and better at that task the 100th time than the first time–and AI models have that same quality. The more experience they have (usually expressed as the more data they have seen), the more patterns the models can recognize to make better sense of each new thing they see. I do a lot of work with AI models around website customer experience–often focused on how web users search and navigate company websites. The AI models reveal insights of where web users get stuck, or, more happily, which content seems to answer their needs.

That’s very powerful, but even more powerful is connecting AI models to automated actions. You see, if all the models do is to provide better insight to humans, those models are useful, but they will always be gated by the time and cost of humans taking actions on those insights. I have heard clients express to me in frustration that “the last thing [they] need is another dashboard”–even a smarter one populated with keen AI insights, because it still leaves them with more and more manual improvements to make.

What if the models could directly drive updates to the website that make it better?

My recent work with SoloSegment [full disclosure: I am a Senior Strategist and partner with SoloSegment] has opened my eyes to how AI can lead to immediate and continuous improvement of a website. You can use behavioral data to make searches on the site more successful. You can recommend content based on what has worked for others in the past. In other words, your website becomes more autonomous–a living, self-improving entity–that gets better the more people use it.

None of this means that you don’t need people to do the vast majority of the tasks of creating content, improving design, and all the rest of the things we do for our websites. But, for the first time, there are some things that humans don’t need to do, because the AI models, coupled with automated actions, can make some of the improvements in hands-free fashion. I don’t know about you, but this feels like a breakthrough to me, where we finally have linked the intelligence of the models to quickly and automatically improving the customer’s experience. And I can’t help but think there is much more to come.

Best Practices Culture Entrepreneurship Industries Leadership Skills Technology

Will A.I. Disrupt Your Profession?

Artificial intelligence (A.I.) is a technological advance for humankind that has some people excited and others terrified of what is to come. The main concern is rooted in what A.I. will do to jobs, and how we as human beings will be affected by changes in digital and mechanical techniques.

A.I. and other new forms of autonomous machine function are in the process of transforming our personal and professional lives, and this represents a Hard Trend that will happen and a subject I’ve discussed for decades now. We are just starting to see some incredible progression in the A.I. space, giving us a chance to pre-solve problems involved in real-world applications of A.I.

But while function is one thing, the newfound transformation we’ve watched come to fruition is coming from machine learning, a subset of A.I. that enables machines to become better at tasks that were previously dependent on human intelligence. With advances in a machine’s capability to think and learn like people, it’s easier than ever to pre-program physical functions so A.I. can take over menial or mundane tasks. Take, for example, a study conducted by legal tech startup LawGeex, which challenged 20 experienced lawyers to test their skills and knowledge against an A.I.-powered system the company built.

A lawyer is not often considered replaceable by technology or artificial intelligence. In this challenge, the task was to review risks contained in five nondisclosure agreements — a simple undertaking given the group of legal professionals, which included associates and in-house lawyers from Goldman Sachs, Cisco, and Alston & Bird, as well as general counsel and sole practitioners. This lineup should easily have triumphed over an A.I.-powered algorithm, right?


As a matter of fact, the study revealed that the A.I. system actually matched the top-performing lawyer for accuracy, as both achieved 94%. As a group, the lawyers managed an average of 85%, with the worst performer scoring a 67%.

But what about the speed of those decisions? When reviewing the nondisclosure agreements, the A.I. system far outpaced the group, taking just 26 seconds to review all five documents, compared to the lawyers’ average speed of 92 minutes. That is a tremendous spread when compared to the near-perfect accuracy the algorithm performed at in that time! The fastest review time of a single lawyer in the group was 51 minutes — over 100 times slower than the A.I. system! And the slowest time was nearly a standstill pace, as it clocked in at 156 minutes.

While reviewing documents is just one of several parts of the job of a lawyer, this data further proves the Hard Trend that I implore everyone to pay attention to in the years to come. Artificial intelligence is here to stay, and by using machine learning and deep learning techniques, new A.I. systems are learning how to think better and better every day. So the question remains: Are you anticipating how A.I. can be used to automate tasks and do things that might seem impossible today — in other words, disrupt your industry? Are you starting to learn more about A.I. so that you can become a positive disruptor rather than become the disrupted?   

For now, according to consultants, the fact remains that 23% of legal work can be easily performed using artificial intelligence; however, there are many aspects of a lawyer’s job, the obvious example being providing an emotional and compelling closing argument in court, that are currently beyond the capabilities of algorithms. While that may be the case today, what’s next? Using methods that I discuss in my latest book, The Anticipatory Organization, you can learn how to become an anticipatory thinker and be more entrepreneurial in the ways you apply A.I. technology to your profession.

Take the example of Alexa, which is utilized in an ever-growing number of applications, from ordering groceries to playing our favorite song during dinnertime. This device, enabled by A.I., has learned our routines and how to serve us better each day by listening to us ask it questions or give it tasks to accomplish.

Netflix and Spotify media streaming services are using A.I. to learn what we like to listen to or watch, and then, using this knowledge combined with their own databases, they can quickly suggest other songs or shows we may also enjoy. Over time they increasingly learn to understand the dynamics of what we like, recognizing our patterns enough to suggest new things to us we will most likely enjoy — very much like a best friend would introduce us to a new music group.

These are just two examples of many A.I.-enabled services that have been integrated into our lives, yet it was not too long ago that applications like these would have been viewed as an impossibility. In a relatively short amount of time they have become second nature in our lives. If A.I. can quickly accomplish a lawyer’s task today, then it can also learn how to accomplish many tasks in industries once thought untouchable by automation and machine learning, such as medicine, finance and design.

As an entrepreneur, it is increasingly important to understand what A.I. can do to create  business value. A.I. is presently forecast to reach nearly $4 trillion by 2022. Reacting to this opportunity will only keep you behind and disrupted. It’s time to learn to become anticipatory leaders in our fields, solving problems before they happen, and elevating our thinking to actively shape a positive future for ourselves and others.

If you would like to learn more about how you can better anticipate transformation in the professional world and developments in artificial intelligence, then be sure to pick up my latest book, The Anticipatory Organization. Let me help you take your career to the next level and remain indispensable in an ever-changing technological frontier.

Best Practices Culture Entrepreneurship Industries Management Personal Development Technology

Shaping the Future of A.I.

One of the biggest news subjects in the past few years has been artificial intelligence. We have read about how Google’s DeepMind beat the world’s best player at Go, which is thought of as the most complex game humans have created; witnessed how IBM’s Watson beat humans in a debate; and taken part in a wide-ranging discussion of how A.I. applications will replace most of today’s human jobs in the years ahead.

Way back in 1983, I identified A.I. as one of 20 exponential technologies that would increasingly drive economic growth for decades to come. Early rule-based A.I. applications were used by financial institutions for loan applications, but once the exponential growth of processing power reached an A.I. tipping point, and we all started using the Internet and social media, A.I. had enough power and data (the fuel of A.I.) to enable smartphones, chatbots, autonomous vehicles and far more.

As I advise the leadership of many leading companies, governments and institutions around the world, I have found we all have different definitions of and understandings about A.I., machine learning and other related topics. If we don’t have common definitions for and understanding of what we are talking about, it’s likely we will create an increasing number of problems going forward. With that in mind, I will try to add some clarity to this complex subject.

Artificial intelligence applies to computing systems designed to perform tasks usually reserved for human intelligence using logic, if-then rules, decision trees and machine learning to recognize patterns from vast amounts of data, provide insights, predict outcomes and make complex decisions. A.I. can be applied to pattern recognition, object classification, language translation, data translation, logistical modeling and predictive modeling, to name a few. It’s important to understand that all A.I. relies on vast amounts of quality data and advanced analytics technology. The quality of the data used will determine the reliability of the A.I. output.

Machine learning is a subset of A.I. that utilizes advanced statistical techniques to enable computing systems to improve at tasks with experience over time. Chatbots like Amazon’s Alexa, Apple’s Siri, or any of the others from companies like Google and Microsoft all get better every year thanks to all of the use we give them and the machine learning that takes place in the background.

Deep learning is a subset of machine learning that uses advanced algorithms to enable an A.I. system to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data, then using what has been learned to recognize new patterns contained in the data. Learning can be Human Supervised Learning, Unsupervised Learning and/or Reinforcement Learning like Google used with DeepMind to learn how to beat humans at the complex game Go. Reinforcement learning will drive some of the biggest breakthroughs.

Autonomous computing uses advanced A.I. tools such as deep learning to enable systems to be self-governing and capable of acting according to situational data without human command. A.I. autonomy includes perception, high-speed analytics, machine-to-machine communications and movement.  For example, autonomous vehicles use all of these in real time to successfully pilot a vehicle without a human driver.

Augmented thinking: Over the next five years and beyond, A.I. will become increasingly embedded at the chip level into objects, processes, products and services, and humans will augment their personal problem-solving and decision-making abilities with the insights A.I. provides to get to a better answer faster.

A.I. advances represent a Hard Trend that will happen and continue to unfold in the years ahead. The benefits of A.I. are too big to ignore and include:

  1. Increasing speed
  2. Increasing accuracy
  3. 24/7 functionality
  4. High economic benefit
  5. Ability to be applied to a large and growing number of tasks
  6. Ability to make invisible patterns and opportunities visible

Technology is not good or evil, it is how we as humans apply it. Since we can’t stop the increasing power of A.I., I want us to direct its future, putting it to the best possible use for humans. Yes, A.I. — like all technology — will take the place of many current jobs. But A.I. will also create many jobs if we are willing to learn new things. There is an old saying “You can’t teach an old dog new tricks.” With that said, it’s a good thing we aren’t dogs!

Start off The New Year by Anticipating disruption and change by reading my latest book The Anticipatory Organization. Click here to claim your copy!

Health and Wellness Marketing Technology

AI Automates Tasks, Not Jobs

I keep reading scare-mongering over how AI is going to make everyone unemployed. Some others say that it will give us a life of leisure. Maybe we should all stop to realize that both are saying the same thing–we are just learning who are optimists vs. pessimists.

But both of these points of view gloss over the real truth–AI doesn’t eliminate very many jobs completely. Yes, if self-driving cars come to pass, Uber drivers and truck drivers are at risk. But no matter how much automation is applied to Quickbooks, we will still need accountants–they just might not be entering and analyzing the transactions anymore.

AI, in general, automates tasks, not whole jobs.

The reason for that is that what we have today is called Narrow AI–it can be better than humans at discrete tasks, such as chess or Go or Jeopardy. It can make predictions within small spheres. But we are nowhere near General AI, where the judgement of a human across many spheres is possible. Humans need to be guiding all automation, especially AI, for the foreseeable future. So, while there will be some jobs that get largely automated away, we will likely still need humans to do parts of those jobs and there will likely be new jobs created we don’t even dream of yet.

In 1790, 90% of the US workforce were farmers. 200 years later, in 1990, less than three percent remained on the farm, yet we didn’t experience 87% unemployment. And that doesn’t even take into account the massive growth in population or the major expansion in the workforce as women joined.

We found other things to do.

No one knows what will happen in our AI future, but you can expect that it won’t be as bad as people fear nor as great as they expect. After all, if I had told you 20 years ago that you would willingly carry around a device 24×7 that allows your boss to call you any time of the day or night and know that you could be reached, you’d have labeled me a nutjob.

But we all carry our cell phones religiously and would fight to keep them if pressed. So, we are often better at seeing the downside of new technology than the upside and we should imagine that AI will probably turn out the same way.

Marketing Personal Development

Why Machine Learning Should be in Your Present, Not Just Your Future

I have spent the last 40 years on the cusp of various technologies. (It’s a trick. If you are on the cutting edge, there are no experts, so you get to call yourself one.) Now I am an expert in Marketing and AI. (See what I did there?)

I actually have been working in text analytics since the 80s and was first exposed to machine learning in IBM Research in the 90s, so I have been doing this for a while, if that counts for anything. So I am used to hearing people talk about how AI is the future. And it is.

But it’s also the present.

Sometimes, it’s just how you talk about it. I remember early in my career, I did what I thought was a knockout presentation on some new superpower technology, and as the audience was filing out, a few people came up to speak to me afterwards. They were all very excited and all agreed as one person breathlessly said to me, “Wow, you are really a visionary.”

Except that’s bad. Because that means that they didn’t think they needed to do anything about that technology for three years. So if every time you hear about machine learning it sounds to you like Big Data 5G Blockchain, then you are missing the power of the present.

Machine learning can take the data you are sitting on and start predicting outcomes that you needed to wait to have happen. We are working with clients to predict the bounce rates of new pages without having to wait three months to see what they are. You can imagine applying the same approach to exit rate, social shares, inbound links, and any other content metric.

Think about what an advantage that is. Rather than suffering with poorly-performing pages for months until the data stabilizes, you can make changes presuming that those pages will perform the way similar pages have in the past. So make them look like better-performing pages instead. But do it now, not months from now.

That is what machine learning does. It takes all the data that you already have and speeds up the correct decision. That speed is your competitive advantage. Or at least it is your competitive advantage if you are using machine learning now. Conversely, if you think AI is the future, then it might be your competitor’s advantage now.

Marketing Personal Development Technology

How to Build Human Trust in AI

To read the popular press, AI can outdo humans at anything, but the truth is far more complex. AI applications are typically to do just one thing well, such as when Watson took on all comers in the Jeopardy game show. But while many of us are fine with letting computers play games, polls show that many of us are distrustful of self-driving cars. And that trust is a key issue, because otherwise valuable applications will be slowed down or even stopped if people don’t trust the technology. Research also shows that when AI systems give incorrect answers too frequently or answers that make no sense to humans, it reduces their trust in the system.

So, how do you build human trust in AI?

Explain the system’s decisions. There are calls for explainable AI, where the system must provide an explanation of how it came to its decision. This technique is still R&D, because techniques today are notorious for being proverbial black boxes. The problem with the research is that when you force systems to use only those techniques that are explainable, they inevitably work worse. Someday, this might be the answer, but not today.

Improve the system’s accuracy. The reason you want AI to explain itself is so you understand how mistakes happen. If you can make it work well enough, maybe no one needs an explanation. After all, most is us don’t know how our cars work, but we trust when we apply the brakes that it will stop. Hardly anyone knows how Google’s AI works, but we trust that our searches will get us good results, so we keep using it.

Reduce the really big mistakes. Watson once gave a really bad answer to a Final Jeopardy question in the category US Cities, providing the response of “Toronto.” We call that a “howler”–an answer so bad that even if you don’t know the correct answer, you still know that response is wrong. You can actually tune the system to reduce howlers by scoring bad answers as worse responses than wrong answers that are “close.”

Put humans in the loop. This might be the simplest of all. Instead of treating every AI system as one to replace humans, maybe it is easier, safer, and more trustworthy to set up the AI to help the humans do their jobs. Watson is being used to diagnose diseases, but rather than replacing doctors, Watson shows the doctors possible diagnoses based on the symptoms, with the doctor making the final decision. When decisions are so high stakes, this might be the most prudent approach.

AI is no longer science fiction, but people are understandably nervous of this kind of powerful force that works in mysterious ways. We need to pay close attention to building human trust in the system to see AI used in the safest and most valuable ways possible.

Marketing Personal Development Technology

The Most Powerful AI Needs Human Judgement

I grow weary of reading the simplistic headlines around the impact of AI. Some people say that AI will put many of us into a new leisure class that doesn’t need to work. Others argue that AI will make us all unemployed. They are both saying the same thing, actually, so it is just a personality test to divide optimists from pessimists. But there is no technology that in the past had that kind of impact, so why is this one different? It probably isn’t.

What is much more likely is that as machines do more, we humans will do something else. Something machines can’t do yet. That’s the way it has always been, so I think that is the way to bet.

What fuels my belief that this is true is that the most powerful AI we see today depends on human judgement. No, I don’t mean the highly-paid data scientists and AI engineers that are all the rage these days. Sure, they are important, but I am talking about ordinary people doing ordinary jobs using judgement that computers just don’t have. This technique is called semi-supervised machine learning or active learning.

Here is how it works. Supervised machine learning is what most AI applications use. They need human judgement, too. But they use it only at the beginning. They ask humans to tell the system the right answer to a question–for example, whether a tweet has positive or negative sentiment. You pile up enough tweets with human answers and use that to train the AI system. So, far, so good. But that is where most systems stop.

The most powerful systems keep getting better, using semi-supervised machine learning. The secret is something called the confidence score. Most AI systems can do more than just answer the question. Beyond telling you that they think this tweet is positive or that tweet is negative, they can tell you how confident they are in that opinion. So, the system might be 90% confident that this tweet is positive and just 60% confident that another tweet is negative.  This provides some interesting possibilities for semi-supervision.

You can set up your system so that your system handles automatically any tweet with over 70% confidence. If it is that sure of itself, let it provide that answer on its own. But if it is less than 70% confident, you can refer that tweet to a human being to check its answer. Is that tweet negative–the one with 60% confidence? Checking the answers the system isn’t sure of is semi-supervision, and it has two benefits. The first is that the system is more likely to get the answers right if it can ask a human to check its work.

The second benefit is that each new human answer is new training data that the system can use to improve its model. By constantly asking for help with the answers it is least sure of, the system is improving itself as rapidly as possible. You can add more training data at any time to any machine learning system, but if your new training data is merely adding more examples of what the system is already doing well, it doesn’t cause any improvement. Only by adding new training data in the areas that the system is getting wrong does improvement happen.

So, yes, machine learning is very important. But semi-supervised machine learning is what provides that most rapid way of continuously improving your machine learning application. If your team isn’t using that approach, it might be time to ask why not.

Powered By MemberPress WooCommerce Plus Integration