Generative AI: ChatGPT And Beyond – Council Post

René Morkos, the founder of ALICE Technologies, has an impressive academic portfolio as he serves as an adjunct professor in Stanford University’s construction engineering Ph.D. program.

The emergence of AI tools such as Midjourney and ChatGPT has caught the public’s attention, largely due to its engagement with younger generations who utilize them for content creation and artistic expression. The subsequent interest has generated a buzz around this quickly spreading technology.

The conversation around generative AI has recently progressed to encompass its wider application and how content produced by the technology may generate potential issues. Estimates place the market for AI-driven tools close to $109 billion by 2030, showing ample growth in interest across all industries.

Many IT leaders recognize the potential of AI and machine learning in transforming the global landscape, but some express alarm that generative AI and machine learning tools could lead to a situation similar to Pandora’s box, particularly concerning the information presented in writing, making it possible for it to evade usual safeguards for publication.

Concerns regarding the misuse of generative tools–for generating pseudo-science and misinformation–are quite valid. Such tools could be abused, leading to an output that (while sounding ‘factual’) is wrong.

ChatGPT and similar tools provide considerable information, though it is essential to delineate between those meant for general use and those designed for specialized scenarios or industries.

AI and machine learning technologies have recently witnessed diverse applications in product design, architectural/engineering (A&E) works, and industrial automation. Such generative AI technology has presented many opportunities for use in these sectors.

Generative AI, incorporated into solutions like Autodesk’s Fusion 360 platform, has dramatically impacted the design process by providing iterative digital modeling capabilities. This technology has enabled tremendous innovation.

ChatGPT is easily differentiated from machine-generated value results due to the data libraries they rely upon. Comparable to large collections of books, these datasets make the difference in outcome between inconsistently inaccurate ChatGPT and valuable machine-generated results.

ChatGPT, for example, uses a substantial language library to answer instructional queries. This dataset comprises text and written material similar to years of digital information.

Bigger might not always be better regarding Twitter threads; a quick look at the available content makes it obvious that much of it is inaccurate and of top quality. As computer scientists often say, “You get out of something what you put into it.”

In contrast to blanket AI tools, generative AI and machine learning tools are trained on datasets carefully crafted for specific purposes, thus producing quality results relevant to their use. To make it easier to understand, imagine a high schooler tasked with answering an engineering question; employing this same methodology would likely lead to success.

A student with access to a library of social media content spanning the last ten years is likelier to create a superior response than one with access to their university’s engineering department library. Possessing such high-quality data can even raise the quality of a mediocre student’s project.

Understanding generative AI and machine learning is essential when assessing their value for specific use cases. Industries with great potential to benefit from generative technology are those with complex needs and specialized data access.

AI-driven technology has made a significant impact in many industries. This is especially true in fields like A&E, industrial design, and product development, where its adoption proved to be speedy. These businesses are now reaping the rewards of AI-driven innovation and development.

Generative AI’s evolution is still set to benefit numerous industries, including construction, medicine, and scientific research – albeit with a slower adoption rate for AI-driven tools in medical and scientific fields.

Despite the abundance of high-quality data about healthcare and clinical research, deployment of such data in hospitals and other establishments is likely to slow due to worries about patient privacy, cybersecurity, and applicable regulations and oversight.

Digitizing data may be a challenge for the construction industry. However, they have been receptive to rapidly introducing generative AI and understanding its potential to transform the industry.

AI-driven tools fueled by quality datasets can easily overcome the complexities of construction scheduling and sequencing, as these have unique challenges to optimization. The advantages provided are immediately recognizable and, therefore, worth the effort.

Implementing these advancements hinges on transforming large amounts of data associated with built structures into digital information. Manual entry is required to provide the necessary details for most projects; however, this process is far from ideal.

AI-driven construction optimization tools provide pathways for faster, safer, and more sustainable building projects. With the evolution of these private databases and datasets, builders and developers can access the knowledge from previous projects to rapidly improve results on current and future builds. Consequently, construction is transforming into a more efficient process.

Generative AI, with its potential for uncovering new opportunities across various industries, should not be forsaken due to misguided and misplaced examples of ChatGPT. Understanding the technology behind it and its capacity for creative exploration is crucial to realizing its advantages.

As generative AI evolves and matures, individuals and organizations must stay informed about its capabilities and limitations. This can involve engaging in ongoing research and development, collaborating with experts in the field, and prioritizing transparency and ethical considerations in using this technology.

Ultimately, generative AI has the potential to revolutionize a wide range of industries and fields. Still, it’s up to us to use it responsibly and for the greater good.

Source: Forbes

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