By 2019, many industries have embraced the time and cost saving benefits of automation and robotics, but the unique spatial challenges and adaptive nature of the construction industry means that it has not yet caught up to the robot workplace revolution. This may soon change though as industry experts say that the construction field is poised for a robotic takeover.

According to the U.S. Bureau of Labour Statistics, almost 200, 000 construction jobs were left unfulfilled and the McKinsey report estimated that on average 98 percent of construction projects go over time and over budget. These labour and efficiency issues mean that year over year productivity growth for the construction industry has stalled significantly. In fact, the ten year analysis of the industry growth from 2005-2015 shows a 1.5% decrease in productivity for the construction industry. As a result, any added efficiency that can save time and labour costs on construction sites may potentially save hundreds of millions of dollars. Below is a list of some of the exciting new ways that robotics are changing the construction industry from beginning to end:

       1. Scouting with drones

While drones have been making an impact commercially in leisure activities, their benefits as an industrial tool are only just being discovered. Scouting construction sites that span large areas, tall buildings, or deep quarries used to take days of strenuous GPS work, but this can now be done in a matter of hours by a single pilot.

       2. Prefabrication

On site 3D printing robots can provide quick, on-demand, and adaptable prefabricated parts that will reduce or even eliminate the need for transporting various building materials. Should there be an issue with the size or shape of a component, a 3D printing robot could be tweaked to immediately print a new part instead of stalling the entire operation to order to correct fit.

        3. Exoskeletons

Companies like Ekso Bionics are working to augment human abilities via robotics with exoskeletons that increase worker mobility and strength. Moreover, this may drastically reduce the potential for work-related injuries on both a short term and long term chronic scale. For example, the EksoVest fits on a worker’s upper body to support the arms in an elevated position when accomplishing overhead projects. Not only does this reduce the potential for injury and chronic pain, it also allows the worker to more effectively focus on their craftsmanship.

        4. Repetitive work

While construction sites require creative problem solving and adaptability, there is still a significant amount of repetitive, time consuming labour, such as with brick laying. Companies like New York based Construction Robotics are tackling these kinds of common repetitive tasks with robots like SAM100, which can lay 2, 000 bricks per day in comparison to the average of 400 bricks per day per skilled mason.

          5. Demolition

Most demolition robots are currently slower than human demolition crews, but they have the benefit of cheaper cost and vastly reducing safety concerns. Considering the rate of injuries within the construction industry, this is an added layer of security for both companies and workers.

Construction sites can never fully be automated by robots, as the industry will always be reliant on humans to make quick judgements, adapt to environmental changes, and inject creativity and artistry into the works that they build. However, humans working alongside robots will drastically reduce time, costs, and risk of injuries in almost every part of the construction lifecycle. The construction industry is primed in the next few decades to begin building better, faster, and smarter with robotics.

While the images of artificial intelligence popularized in modern film and literature are still far from a reality today, subsets of the field like machine learning and deep learning continue to make significant advancements. Machine learning uses statistical models, patterns, and inference to teach computer systems to conduct tasks without requiring explicit instructions. By drawing from a set of training data, computer systems are then able to build up a mathematical model to make decisions and predictions without relying on strict programming. One of the most successful and pervasive achievements of this form of machine learning, or more specifically deep learning and deep neural networks, is automatic speech recognition, which now goes by many names, such as Alexa, Siri, or Cortana.

In addition to speech recognition, one of the areas that machine learning excels in is image analysis, which has major implications for medical imaging and diagnosis. Current imaging technologies, like CT scans, MRI scans, and X-rays can be time consuming in comparison to the image analysis conducting by machine learning algorithms. More efficient and accurate radiology tools aided by AI could also reduce the need for tissue samples, allowing for remote diagnosis for patients in rural areas.

For example, researchers at the Aichi Cancer Center Hospital and the Yokohama City University School of Medicine in Japan published their study on the ability of AI to effectively differentiate between malignant and benign cystic lesions. The team tested the AI’s deep learning by retroactively inputting a data set of 85 patients who underwent cyst fluid or surgical specimen analysis to diagnose malignancy and the AI performed significantly better in diagnosis than traditional methods. In a 2018 study of deep learning with chest radiograph diagnosis, researchers at Stanford University used an algorithm, called CheXNeXt, to interpret 420 images of pathologies like pneumonia, pleural effusion, pulmonary masses, and nodules. In comparison to a group of 9 radiologists who took 240 minutes to review the same images, the model analyzed the images in 1.5 minutes and with comparable accuracy.

One of the primary obstacles for deep learning in medicine is the huge amount of data that is required in order to build a model that will make accurate decisions and predictions, and this resource is limited by privacy laws for the use and distribution of patient data. There may also be issues with the format or quality of the collected data that would require more human effort to prepare the training sets for machine learning analysis. Furthermore, it is important to be cognizant of biases that may exist in data sets that do not include a diverse range of demographics.

Currently, most machine learning research remains small in scale and limited in scope because of these obstacles, but the implications for the world of image analysis and diagnosis in medicine are enormous. In the coming decades, machine learning may overwhelmingly replace the interpretive work of radiologists with more efficient and accurate diagnoses.