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Artificial Intelligence (AI)

Construction Project Management & Other Construction Trends to Look Out For in 2021

As the construction industry is getting more competitive, you have to look out for the following construction industry trends that will prevail in 2021 and beyond.

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After a turbulent year of adjusting to the pandemic’s changes and the changing online landscape, many businesses, including the construction business industry, are thriving to re-emerge and grow in 2021.

With the increased labour shortage and construction expenses, the construction industry is forced to integrate innovative tech solutions to stand out in the competitive industry niche and reduce the potential risks of error and construction-material waste. While more construction businesses are integrating construction project management software in their business model, they are also focusing on other aspects to improve their projects and meet the expectations of their clients.

With the latest tech-related adjustments, innovative technology trends continuously affect construction sites, improve construction projects, and boost sales and revenue. Certain construction trends are working at the core of construction businesses and frontline construction workers.

As the construction industry is getting more competitive, you have to look out for the following construction industry trends that will prevail in 2021 and beyond. Read on to learn more!

1. Safety Equipment

Safety comes first, particularly when it comes to dangerous occupations, such as working at construction sites. With innovative software and machinery, construction sites improve their safety measurements as the machinery can now detect safety issues before accidents can occur.

A strong internet connection and Wi-Fi are being made available that covers all areas of the construction site, which assists with the quick and immediate reporting of accidents if a worker falls or gets injured. Innovative machinery that is integrated with AI (artificial intelligence) has made its way to construction sites.

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This machinery helps with the transportation and moving of hazardous materials., in addition, you are also more likely to expect AI (artificial intelligence) working at the core of the construction sites that also lay bricks and assist with construction.

When it comes to safety equipment, you will find headsets as an essential element on construction sites. The headsets keep the workers protected from noise pollution, which is common to see on construction sites. While watching workers from noise, the headsets also help the workers stay in tune with their work surroundings.

It is necessary to note here that contrary to the myth that AI (artificial intelligence) in the form of robots will soon be replacing humans, it is the opposite case with construction. The robots that you will find on the construction sites won’t be there to replace humans. On the contrary, the use of AI on construction is only to benefit the workers, reduce their loads, and keep them safe. That said, AI and robots are making room for higher-level jobs and new talent.

2. Efficient Use of Technology

As we mentioned before, we are moving into a different era dominated by technology. The biggest differences that you will find in today’s construction projects and the previous construction projects are the use of technology and how it is integrated into construction sites. Besides the use of construction project management software, you will also find the inclusion of smart contracts. In other words, the pandemic has shifted construction projects to technology construction.

The pandemic has performed an essential role in causing increased reliance on technology. The construction project management software, for instance, helps in keeping all employees on the same page as the milestones are marked. It also allows managers to manage all steps of the construction project remotely and effortlessly.

You can also expect drones to fly on construction sites. One essential benefit of construction drones is that they can cover areas that can effectively report and analyze the construction sites.

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Artificial Intelligence (AI)

The Difference Between AI and Machine Learning?

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later.

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Do you ever read articles mentioning both Artificial Intelligence (AI) and Machine Learning (ML) and ask yourself how do these relate and how do they differ from each other? Don’t worry; you’re not alone in this struggle. Our global society is becoming increasingly digitised, and chatbots, speech processing devices and intelligent mobile app algorithms are all around us, so you better catch up with the trends. It is time to demystify the concepts of AI and ML and finally bring some clarity into the blurry picture.

From my practice working in a bespoke software development company, I hear and read about deep learning, neural networks and autonomous systems all the time. This article puts the main differences between those buzzwords in the spotlight and reveals what hides behind it all. Some companies only claim to use AI to market their products better, hoping that only true tech enthusiasts will notice the difference. While there are indeed lots of overlapping technologies behind AI and ML, here are the fundamental difference setting them apart:

1. The Core of AI and ML as Technologies

A lot of digital content discusses AI and ML by using them as synonyms, while the leading technology they focus on is supervised learning – a branch of ML, but more on that later. Artificial Intelligence is a subsector of Information Technology (IT) that focuses on developing machine algorithms that simulate natural human intelligence and automating them to behave in a way that exerts intelligence.

The main areas that concern AI is learning from experience and efficient problem-solving. As John McCarthy, the father of AI, once put it, AI mimics human intelligence but is not constrained by biologically observable methods.

Machine Learning, on the other hand, is a subset of AI that concentrates on developing computer algorithms that get machines closer to learning like actual humans. Here is a Venn diagram to make it simpler. The recognition of distinct patterns and pattern regularities and the subsequent derivation of suitable solutions are the tasks that this technology masters.

An algorithm needs to be fed existing databases to learn to recognise and follow the patterns for this to happen. The ML generates artificial knowledge derived from its previous experience. All the knowledge that is gained can be generalised and applied for solving other problems. This approach allows previously unknown data to be processed and used quickly.

2. Types of Artificial Intelligence

Generally speaking, there are two main types of AI: weak (narrow) and strong (general). Most of the AI-powered software solutions we come across are weak ones, and it is called like that because it can only perform several tasks well, meaning it has limited functionality.

The narrow type of AI can successfully handle simple problem-solving tasks after the appropriate training period. Some of the main areas where narrow AI can shine are text, speech or image recognition, navigation systems, streaming services etc.

The second type of AI – the strong one, is yet to be fully developed. It refers to programming machines to perform complete tasks, requiring general intelligence and human-like consciousness. These types of futuristic robots will be able to think autonomously without special training. They will most like show some level of self-awareness, while it is not expected that narrow AI will reach such a cognitive awareness state.

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3. Types of Machine Learning

While we wait for this general AI type to become more than a far-fetched concept, let’s discuss the main subdivisions of ML, shall we? There are three core ML types: Supervised Learning (SL), Unsupervised Learning (UL) and Reinforcement Learning (RL). SL is when software developers instruct the algorithm to learn something, e.g. differentiate between a snail and a turtle by designing training and data sets.

These include input values (e.g. object features) with labels and the desired outcome (e.g. proper classification). For example, supervised learning already helps automate X-ray readings, face recognition, malware detection or weather forecasting.

UL stands for using only input data without a previously defined goal or human supervision. For instance, ML experts use unlabeled dataset, such as animal pictures without the labels ‘’snail’’ and ‘’turtle’’ with the goal that the program makes meaning of the data and detects underlying structures on it own.

The last type of ML is reinforcement learning is based on the reward principle. It all starts with an initial state without any background information, and the program must perform an action, and the system receives negative or positive feedback. The process continues until the desired shape is reached.

4. Deep Learning and Neural Networks

When reading about AI, people often stumble upon two other buzzwords, causing a mild degree of confusion, so let’s also explore what deep learning and neural networks mean. First, to cast some light on the two concepts, I should mention that deep learning is a subfield of machine learning, and the most widely used method for deep learning is by utilising neural networks.

The neural network concept has its foundation in the human biological networks in the brain, which receive signals from nearby cells and decide whether this signal is important and if they should send it further. In the context of machines, the input, e.g. cat’s picture, plays the role of a signal, which is transmitted through different layers and to get to the output (result), which can be if the system decides to categorise the picture as a cat. Applied to more complex scenarios, deep learning using neural networks are behind self-driving cars or voice control systems.

5. What are the Use Cases of AI

I hope you now recognise the differences between AI and ML and that all the attention around these trends is well deserved. Once again, human beings prove that with the right knowledge, using the right tools and techniques, countless inspiring solutions can be developed in the future.

Some of the most promising business domains where AI is already improving core processes are supply chain management, automated quality control, self-driving vehicles, automated support processes (e.g. ticketing systems, chatbots etc.), predictive maintenance. Moreover, AI carries a huge potential to facilitate innovation in the R&D (Research and Development) sector as Big Data and Data Analytics become inseparable parts of obtaining powerful data insights and drive businesses forward.

Aleksandrina is a Content Author at Dreamix, a custom software development company, and is keen on innovative technological solutions with a positive influence on our world. Her teaching background, mixed with her interest in psychology, drives her to share knowledge. She is an avid reader and enthusiastic blogger, always looking for the next inspiration.

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