Learning to Master the Art of Your Career

It doesn’t matter what you do for a living — whether you work in medicine or retail, law or construction, software engineering or writing — there’s an art and science to every career. Each profession has its scientific aspects, those more mechanical facets, rules, and methods you must know to succeed. Yet no matter how dry, straightforward, or technical, these professions also have creative qualities that foster critical thinking.

It doesn’t matter what you do for a living — whether you work in medicine or retail, law or construction, software engineering or writing — there’s an art and science to every career. Each profession has its scientific aspects, those more mechanical facets, rules, and methods you must know to succeed. Yet no matter how dry, straightforward, or technical, these professions also have creative qualities that foster critical thinking.

This dichotomy is the reason no two professionals within the same industry are identical. These people may work within their careers for the same amount of time, possibly went to similar schools, or perhaps have the same position at the same company. However, they differentiate themselves in the ways they apply creativity and critical thinking to their jobs.

This idea impacts our personal lives as well. Consider medical professionals with the same specialty. If all dentists were the same by virtue of having identical skill sets and nothing more, you would have no preference for whom you go to for a root canal. But this isn’t the case; you prefer your dentist over one you have never been to due to their individual touch.

A real-world example occurred with one of my brothers, as some years back he struggled with pain in his legs. He visited three different orthopedic surgeons, all with identical skill sets and backgrounds. The doctors examined my brother. One suggested invasive surgery and the second proposed a more exploratory surgery. Both of these were unfavorable options. It wasn’t until we saw the third orthopedic surgeon that creative critical thinking took place. The doctor took one look at him and asked if he always wore his leather belt around his hips in the same place. When my brother answered in the affirmative, the doctor recommended he switch belts, replacing his leather one with a softer, more elastic material. With this change, his ailments were cured within a week.

All three doctors had the same impressive credentials and experience in the science behind their specialties; however, the third doctor utilized creative critical thinking to problem-solve.

Whether you’re training or in any level of schooling for a career, the “science” of that field is where the education lies. You’re receiving a hard, factual, standardized education, based on data and a proven methodology. Likewise, whether it’s accounting or food service, you’re also being schooled in the best practices of your industry.

Even in the creative fields, you still learn both the science and the art of your craft in order to find professional success in it. Writers must learn grammatical and syntactical convention, but they also have to learn how to write something everyone must read. Musicians need to learn scales, notation, and instrumental technique, but they also need to learn how to touch the hearts and souls of listeners to achieve musical greatness.

So where does the “art” come into these fields?

Artistic aspects of a career are picked up by professionals through years of experience and another, more flexible, less standardized type of “education,” one of induction. The first method of becoming more creative within your career through personal and professional experience is somewhat obvious — the longer you do something, you’ll become better at problem solving and thinking “outside the box.”

The second method, the nonstandard educational method of developing intuitive insights coupled with creativity, involves gleaning the best-kept secrets and most well-honed, time-honored methods, the knowledge and wisdom of your profession from other professionals. These should be people who’ve already distinguished themselves through their own creativity. You might seek these people out, like a musician choosing to take lessons from one of his favorite players, or an entrepreneur asking the advice of someone who’s already established herself as a success in business. You might also stumble into these people during the course of your life, like having a captivating, inspirational professor or being trained by a capable manager who knows the secrets to making your job fun and interesting.

You can learn the science of your job from books, manuals, and classroom lessons and know that you will be good at what you do — but you need to learn the art from the artists of your field to become exceptional. This knowledge and wisdom transfer is key not only to success, but to a rewarding career as well. Not only does it provide professionals an essential balance of skills, it’s what keeps industries thriving and innovative. It’s what pushes us to compete with others by bettering ourselves and, in doing so, to push our very professions forward.

Pick up a copy of my latest best selling book The Anticipatory Organization to help shape your future and accelerate your success.

Smart Construction: How AI and Machine Learning Will Change the Construction 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.”

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

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

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!

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