High level summary
The various sub-fields of AI research are centered around particular goals and the use of particular tools. The traditional goals of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception, and the ability to move and manipulate objects.[a]
General intelligence (the ability to solve an arbitrary problem) is among the field's long-term goals. To solve these problems, AI researchers have adapted and integrated a wide range of problem-solving techniques, including search and mathematical optimization, formal logic, artificial neural networks, and methods based on statistics, probability, and economics. AI also draws upon computer science, psychology, linguistics, philosophy, and many other fields.
The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as "0" and "1", could simulate any conceivable act of mathematical deduction. This insight that digital computers can simulate any process of formal reasoning is known as the Church–Turing thesis.
In a 2017 survey, one in five companies reported they had "incorporated AI in some offerings or processes". The amount of research into AI (measured by total publications) increased by 50% in the years 2015–2019. According to AI Impacts at Stanford, around 2022 about $50 billion annually is invested in artificial intelligence in the US, and about 20% of new US Computer Science PhD graduates have specialized in artificial intelligence; about 800,000 AI-related US job openings existed in 2022.
What does McKinsey say?:
About 75 percent of the value that generative AI use cases could deliver falls across four areas: Customer operations, marketing and sales, software engineering, and R&D
The short concepts:
AI and ML are seen as some level of algorithmic magic. But its not...it follows a level of deductive structure to multi-layer keyword ontology redaction. It's a level of knowledge engineering and then plugged into endless streams of data, that align to a query. The key is a query...there is zero self-actuated deduction and reactive thinking.
The keyword to familiarize oneself to is 'ontology'.
What is Generative AI?:
Understanding the data that supports the model. Low level model grunt work. A set of a well known Python libraries. Multiple data points that represent an object People want to categorize of inputs. What is the most likely categorization of the input.
For example, applying for a bank loan. Collect data from applicant. Create inputs, and determine where you fit into various models. Does the applicant fit inside of a quadrant. Think in data points. Cost, space, constraints, color, multiple different attributes.
Is the data available? Sourcing data sets. And getting them into a system.
How can AI Benefit the AEC Industry:
As far as the design and construction and associated software tools is concerned, I see the biggest opportunities for:
Framework for Big Data AI Center of Excellence
McKinsey - Economic Potential of Generative AI (June 2023)
Oracle - CFO's Guide to AI and Machine Learning
Deloitte - Road to Next (Q2 2023)
Webinar - Generative AI
Procore, Document Crunch, and Jeff Sample
Using ChatGPT to Build and FP&A Tool
Transcript of Document Crunch Webinar:
By Glenn Hopper