Introduction
The term ‘Artificial Intelligence was first coined in 1956 by prominent computer and cognitive scientist John McCarthy, then a young Assistant Professor of Mathematics at Dartmouth College, when he invited a group of academics from various disciplines including, but not limited to, language simulation, neuron nets, and complexity theory, to a conference entitled the ‘Dartmouth Summer Research Project on Artificial Intelligence’ which is widely considered to be the founding event of artificial intelligence as a field. At that time, the researchers came together to clarify and develop the concepts around “thinking machines” which up to this point had been quite divergent. McCarthy is said to have picked the name artificial intelligence for its neutrality; to avoid highlighting one of the tracks being pursued at the time for the field of “thinking machines” that included cybernetics, automata theory and complex information processing. The proposal for the conference stated, “The study is to proceed on the basis of the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”
Today, modern dictionary definitions credit Artificial Intelligence as a sub-field of computer science focussing on how machines might imitate human intelligence — being human-like, rather than becoming human. Merriam-Webster provides the following definition: “a branch of computer science dealing with the simulation of intelligent behaviour in computers.”
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The term ‘Aritifical Intelligence’ has been overused in recent years to denote artificial general intelligence (AGI) which refers to self-aware computer programs, capable of real cognition. Nevertheless, most AI systems, for the foreseeable future, will be what computer scientists call “Narrow AI,” meaning that they will be designed to perform one cognition task well, rather than “think for themselves”.
While most of the major technology companies haven’t published a strict dictionary-type definition for Artificial Intelligence, one can extrapolate how they define the importance of AI by reviewing their key areas of research. Machine learning and deep learning are a priority for Google AI and it’s tools to “create smarter, more useful technology and help as many people as possible;” from translations and healthcare, to making smartphones even smarter. Facebook AI Research is committed to “bringing the world closer together by advancing artificial intelligence” whose fields of research include Computer Vision, Conversational AI, Natural Language Processing, and, Human & Machine Intelligence.
IBM’s three main areas of focus include AI Engineering, building scalable AI models and tools; AI Tech, where the core capabilities of AI such as natural language processing, speech and image recognition and reasoning are explored and AI Science, where expanding the frontiers of AI is the focus.
In 2016, several industry leaders in Artificial Intelligence including Amazon, Apple, DeepMind, Google, IBM and Microsoft joined together to create Partnership on AI to Benefit People and Society to study and formulate best practices on AI technologies, to advance the public’s understanding of AI, and to serve as an open platform for discussion and engagement about AI and its influences on people and society. Those working with AI today make it a priority to define the field for the problems it will solve and the benefits the technology can have for society. It’s no longer a primary objective for most to create AI techniques that operates like a human brains, but to use its unique capabilities to enhance our world.
Algorithms use a large amount of data to adjust their internal structure such that, when new data is presented, it gets categorised in accordance with the previous data given. This is called “learning” from the data, rather than operating according to the categorisation instructions written strictly in the code.Imagine that we want to write a program which can tell cars apart from trucks. In the traditional programming approach, we would try and write a program which looks for specific, indicative features, like bigger wheels or a longer body. We would have to write code specifically defining how these features look and where they should be found in a photo. To write such a program and make it work reliably is very difficult, likely yielding both false positives and false negatives, to a point where it may not be usable in the end at all.
This is where Artificial Intelligence become very useful; once an AI algorithm is trained, it can be shown many images of cars and trucks, clearly labeled as such, and will adjust its internal structure to detect features relevant to the successful classification of the pictures instead of relying on static, prescribed feature definitions.
A core concept regarding AI systems is that their decisions are only as good as their data. Humans are not great at dealing with large volumes of data, and the sheer volume of data available to us sometimes prevents us from using it directly. For example, an algorithm with a million data inputs will outperform the same algorithm with only 10,000 data inputs. With this knowledge in tow, preparing and cleaning data is something that will become more prevalent in the process of applying artificial intelligence techniques.
This step is often the most labour-intensive part of building an AI system, as most companies do not have the data ready in the correct format(s). It can take much longer to build the right data infrastructure and prepare the data to be used than actually constructing the model to run the data. Machine learning will soon allow software applications to synthesise vast amounts of engineering knowledge in seconds. Architects and engineering professionals, by contrast, take years acquiring the skills and experience needed to design buildings, leaving them unable to compete.
Then again architects, regulators, and engineers have a way of increasing the amount of work delivered/energy it takes to produce documents. AI likely will be specialised at first to automate menial tasks, coordinate, and perform quality control. Many tools are starting to display potential in these areas, as AI improves these areas of the field and others will loose billable hours per project.
AEC software is highly monopolised and Revit, for example, has allowed you to run a team with less staff than you might’ve needed 20 years ago, but you pay upwards of £2,200 per individual in software subscription fees per year, so instead of labour cost you have very high software cost paid to companies with market capture.
I think that alongside maybe rendering software is the best example of automation currently, and it hasn't delivered much savings in the end just higher quantity or quality and a transfer of cost to software. Any construction professional that realises what a regulatory quagmire the industry operates in knows that AI will never be able to fully integrate this context, it is a shifting mosaic that would first require complete incorporation – even building codes.
More broadly, computational design is in practice at every large and medium, as well as some small, architecture firms around the world. We use it to do heavy lifting of analysing and optimising our work. And today, combined with BIM, we have the ability to do more with less people. This trend is not going away. We should all get more savvy with technology as it will be the best assistant to our work. Those who can’t will be forced to retire, or leave the profession like those who still wanted to use pencil on drawing boards after CAD was well established.
In the data-driven future of project management, construction project managers will be augmented by artificial intelligence that can highlight project risks, determine the optimal allocation of resources and automate project management tasks. According to Gartner, by 2020, AI will generate 2.3 million jobs, exceeding the 1.8 million that it will remove—generating $2.9 trillion in business value by 2021. Google’s CEO goes so far as to say that “AI is one of the most important things humanity is working on. It is more profound than […] electricity or fire.” With applications of artificial intelligence already disrupting industries ranging from finance to healthcare, construction project managers who can grasp this opportunity must understand how AI project management is distinct and how they can best prepare for the changing landscape.
Human coorperation with intelligent machnies will define the next era of history; using a machine which is connected through the Internet, that can work as a collaborative, creative partner.
Pattern Recognition, Reinforcement Learning, and Machine Learning
Artificial intelligence (AI) is ubiquitous. Whether we are consciously aware of it or unknowingly using it, AI is present at work, at home and in our everyday transactions. From our productivity in the office to the route we take home to the products we purchase and even the music we listen to, AI is influencing many of our decisions. Those decisions are still ours to make, but soon enough the decisions will be made by AI-enabled systems without waiting for the final approval from us.
Machine Learning (ML) is a subset field of artificial intelligence that uses statistical techniques to give computers the ability to learn from data without being explicitly programmed.. Humans learn from experience, so ML is basically learning from experience, where experience is data; taking input from the world (e.g. text in books, camera images from a car, or a complex mathematical function), and then has an output - a decision. ML is transforming many industries and applciations, especially in areas where there’s a lot of data, and predicting outcomes can have a big payoff: finance, sports, and medicine come to mind. AI and ML have been used interchangeably by many companies in recent years due to the success of some machine learning methods in the field of AI. To be clear, machine learning denotes a program’s ability to learn, while artificial intelligence encompasses learning along with other functions.
Deep Learning and Neural Networks
Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to classical task-specific algorithms. Most modern deep learning models are based on an artificial neural network, although they can use various other methods. A neural network is a virtual, much simpler, version of the human brain. The brain is the most complex system in the human body; with 85 billion neurons, each of which fire non-stop, receiving, processing, and sending information. Neural Networks are nowhere near as complex, but that’s the goal. Instead of neurons, we have nodes. The more the nodes are exposed to, the more they learn. Neural networks are biologically inspired, connected mathematical structures which enable AI systems to learn from data presented to them.
There are multiple types of neural networks, each accompanied by its own specific use cases and level of complexity. You might see terms like CNN (convolutional neural network) or RNN (recurrent neural network) used to describe different types of neural network architecture. To better understand how they look and function, here is a great 3D visualization of how neural networks “look” while they are active.
Artificial General Intelligence and Conclusion
Gene Roddenberry would argue Karl Marx was a fool. Money isn’t needed if society has a machine that can not only provide all that is needed, it can build its own replacement parts. And we already have the beginnings of other machines that enable fast travel and communication of all forms including video across great distances, cultural barriers and language. We’re going to realise something like what was imagined in the Star Trek universe, and it will likely look a lot different. The effect is the same. AI will transform society. Or destroy it. It’s a tool, and the choice is collectively ours.
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