Currency
July 31, 2023
One industry that is on the early stages of the technology adoption cycle is construction – as it is one of the least digitalized industries in the world
It’s no exaggeration to say that generative AI will have a transformative effect on global industry. With the ability to generate new outputs based on algorithms and trained data, it represents the next step in artificial intelligence and a new level of sophistication for machine learning. Predictions are already appearing around its economic value. According to the FT, it’s set to provide a boost of 7% to global GDP, affecting over 300 million jobs.
One industry that is on the early stages of the technology adoption cycle is construction – as it is one of the least digitalized industries in the world, with almost stagnant productivity over the past two decades - growing at only 1%. With construction being a whopping 6% of GDP for countries like the US and the UK, gains in productivity through better data and adoption of technology through the design process would not only increase productivity but also help build more sustainably.
Take the design of a building for example. Whilst the true impact of generative AI is yet to be seen, for architects in the design process, it marks a turning point in the sector’s “digital journey”. Increased efficiencies, improved 3D modelling, and enhanced user experiences are just some of the ways in which it will benefit.
AI language tools are sure to play a key role in processes, such as improving helpdesk support and customer service response times. Yet the technology’s potential extends much further. Through improved deep-machine learning, it could be used to draw upon existing data sets and product information for a quicker, more efficient construction design process. It can help automate how architects and designers can visualize plans, alter to build-in more sustainable materials, compliant with regulatory standards, and improve the efficiency of buildings over the lifetime.
So, what’s next? Well for one, we should recognize that “natural language” processing tools (NLP), such as ChatGPT, aren’t putting jobs at risk. Instead, I see these tools being used to support, rather than replace. Research by OpenAI, the creator of GPT-4, suggests that 80 percent of the US workforce could see ten percent of their tasks performed by generative AI.
For example, it can help when sourcing the right products and materials for any project – a process that is extremely time-consuming for designers and architects. However, fine-tuned NLPs can slash research time, suggesting the best product matches and even answering questions relating to product performance.
It can also reduce risk. AI can recognize when certain products or materials are incompatible with specific systems. Identifying this in the early design stages can then reduce the need for costly rework further down the line. Search terms are also vastly improved through the ability to understand natural language. This helps designers find information at a faster pace and with more accuracy.
Ultimately, tools such as ChatGPT improve the overall decision-making process. Systems can alert designers to a product’s feature or availability, ensuring that they are up to date with the latest product information. At a time of difficulty around supply chains, this can help ensure that products are available when architects or designers need them.
Finally, it could also provide solutions to the sector’s almost limitless thirst for data. Today’s construction sites are hotbeds for tech, and AI that analyses data faster and with more insight is in high demand. Deployed correctly, it could deliver improvements to energy usage, carbon calculations, and on-site safety and security.
However, we must remember that each of these elements relies on one key ingredient: human review. No matter how advanced, construction technology can only take us part of the way. It still takes highly-skilled minds to fact-check, analyze and understand the nuances required for each stage of construction.
We must also be clear about the data feeding the machine. In the case of ChatGPT, it was trained on information available during 2021. If we’re serious about using generative AI in a commercial sense then we must ensure that the data in question is up to date and in line with the latest laws and regulations. Again, it highlights the importance of human input. Whilst potentially transformative, generative AI has no understanding of the outputs they produce. In the end, it will be the human touch that makes all the difference.
It’s no exaggeration to say that generative AI will have a transformative effect on global industry. With the ability to generate new outputs based on algorithms and trained data, it represents the next step in artificial intelligence and a new level of sophistication for machine learning. Predictions are already appearing around its economic value. According to the FT, it’s set to provide a boost of 7% to global GDP, affecting over 300 million jobs.One industry that is on the early stages of the technology adoption cycle is construction – as it is one of the least digitalized industries in the world, with almost stagnant productivity over the past two decades - growing at only 1%. With construction being a whopping 6% of GDP for countries like the US and the UK, gains in productivity through better data and adoption of technology through the design process would not only increase productivity but also help build more sustainably.
Take the design of a building for example. Whilst the true impact of generative AI is yet to be seen, for architects in the design process, it marks a turning point in the sector’s “digital journey”. Increased efficiencies, improved 3D modelling, and enhanced user experiences are just some of the ways in which it will benefit.
AI language tools are sure to play a key role in processes, such as improving helpdesk support and customer service response times. Yet the technology’s potential extends much further. Through improved deep-machine learning, it could be used to draw upon existing data sets and product information for a quicker, more efficient construction design process. It can help automate how architects and designers can visualize plans, alter to build-in more sustainable materials, compliant with regulatory standards, and improve the efficiency of buildings over the lifetime.
So, what’s next? Well for one, we should recognize that “natural language” processing tools (NLP), such as ChatGPT, aren’t putting jobs at risk. Instead, I see these tools being used to support, rather than replace. Research by OpenAI, the creator of GPT-4, suggests that 80 percent of the US workforce could see ten percent of their tasks performed by generative AI.
For example, it can help when sourcing the right products and materials for any project – a process that is extremely time-consuming for designers and architects. However, fine-tuned NLPs can slash research time, suggesting the best product matches and even answering questions relating to product performance.
It can also reduce risk. AI can recognize when certain products or materials are incompatible with specific systems. Identifying this in the early design stages can then reduce the need for costly rework further down the line. Search terms are also vastly improved through the ability to understand natural language. This helps designers find information at a faster pace and with more accuracy.
Ultimately, tools such as ChatGPT improve the overall decision-making process. Systems can alert designers to a product’s feature or availability, ensuring that they are up to date with the latest product information. At a time of difficulty around supply chains, this can help ensure that products are available when architects or designers need them.
Finally, it could also provide solutions to the sector’s almost limitless thirst for data. Today’s construction sites are hotbeds for tech, and AI that analyses data faster and with more insight is in high demand. Deployed correctly, it could deliver improvements to energy usage, carbon calculations, and on-site safety and security.
However, we must remember that each of these elements relies on one key ingredient: human review. No matter how advanced, construction technology can only take us part of the way. It still takes highly-skilled minds to fact-check, analyze and understand the nuances required for each stage of construction.
We must also be clear about the data feeding the machine. In the case of ChatGPT, it was trained on information available during 2021. If we’re serious about using generative AI in a commercial sense then we must ensure that the data in question is up to date and in line with the latest laws and regulations. Again, it highlights the importance of human input. Whilst potentially transformative, generative AI has no understanding of the outputs they produce. In the end, it will be the human touch that makes all the difference.
SOURCE:
https://www.forbes.com/sites/forbesbooksauthors/2023/05/02/generative-ai-in-the-construction-sector-taking-building-technology-to-new-heights/?sh=14bdeceb6ee8
Recent Posts
Popular Posts
