AI as a New Design Material

René Descartes (1596–1650) was a pioneering metaphysician, a masterful mathematician, and a significant scientific thinker. René Descartes first contemplated the concept that machines would be capable of thinking and making decisions in 1637 in his book?Discourse on the Method. He was also the first to identify the distinction between what today is called specialized AI, where machines learn how to perform one specific task, and general AI, where machines can adapt to any job.?

In 1927, the sci-fi film?Metropolis?was released and featured an artificially intelligent robot, and in 1950 a visionary collection of short stories by Isacc Asimov was published called?I Robot. Asimov envisioned the Three Laws of Robotics and a computer that could answer questions because it could store human knowledge. In 1943, a collaboration between Warren McCulloch and Walter Pitts introduced the idea that logical functions could be completed through networks of artificial neurons—what today is known as artificial neural networks (ANNs)—in their paper “A Logical Calculus of the Ideas Immanent in Nervous Activity.”

The term artificial intelligence was first used at a summer workshop organized by professor John McCarthy at Dartmouth College Conference, 1956. Before Alexa and Siri were a figment of their developers’ imaginations, there was ELIZA—the world’s first chatbot. As an early implementation of natural language processing, ELIZA was created at MIT by Joseph Weizenbaum. ELIZA couldn’t speak, but she used text to communicate.

“There are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until–in a visible future–the range of problems they can handle will be coextensive with the range to which the human mind has been applied.” This was Dr Herbert Simon and Allen Newell in 1957.

Dr Herbert A. Simon was the American psychologist and sociologist who, in his 1969 article?"Sciences?of the Artificial", described the word?"design"?as the changing existing circumstances into preferred ones. Together with other scientists he laid the foundation for?"Design?Thinking". Herbert A. Simon’s?"The?Sciences of the Artificial" has long been considered a seminal text for?“design?theorists and researchers"?anxious to establish both a scientific status for design and the most inclusive possible definition for a?“designer,”?embodied in?Simon’s oft-cited?“everyone?designs who devises courses of action aimed at changing existing situations into preferred ones.”

Dr Herbert Simon pioneered the foundations of artificial intelligence, redefined the psychology of human cognition, and transformed every field he explored. His primary research interest was decision-making within organizations and he is best known for the theories of?"bounded?rationality" and?"satisfying".?He received the Nobel Memorial Prize in Economic Sciences in 1978 and the Turing Award in computer science in 1975.

“Anything that gives us new knowledge gives us an opportunity to be more rational.” —?Herbert Simon

Natural Language Processing (NLP), Generative Pre-trained Transformer 3 (GPT-3) presents unique human challenges that have yet to be fully understood. E.g. Solving complex health questions and answering problems (ADA Health App?ada.com), cryptocurrency (Stoic.ai, Cryptohopper), et al. What are systems like GPT-3 used for? They don’t learn about the world or humans — they learn about the text; GPT-3 is a neural network machine learning model trained using internet data to generate any type of text — more data makes for a better, more fluent approximation. Still, it does not make for trustworthy intelligence. The?empiricist’s dream is to acquire a rich understanding of the world from sensory data, but GPT-3 never does that. One of the many reasons humans and non-humans co-design methods can assist with further research is because it is based on humanistic design principles.

In opening his 2021?BBC?Reith Lectures, titled “Living with Artificial Intelligence,” Stuart Russell states that “the eventual emergence of general-purpose artificial intelligence [will be] the biggest event in human history.”

As director of the Future of Humanity Institute at the University of Oxford, Nick Bostrom noted back in 2006, “A lot of cutting-edge?AI?has filtered into general applications, often without being called?AI?because once something becomes useful enough and common enough it’s not labeled?AI?anymore.”

Speech recognition and natural language processing (NLP) are transforming how we communicate with devices and technology. These technologies make machines more intuitive and user-friendly by understanding and responding to human voice and natural language. However, these technologies also present new challenges for designers.

One of the main challenges for designers is creating user interfaces that are intuitive and easy to use. With speech recognition and NLP, the user is interacting with the device in a more natural way, so the interface must be designed to accommodate this new form of interaction. This may involve creating new ways of displaying information, such as using visual cues or haptic feedback or animations to indicate that the device is listening or responding.

Another challenge for designers is ensuring that the device can understand and respond to a wide range of accents, dialects, and languages. This requires extensive testing and fine-tuning of the device's speech recognition and NLP algorithms to ensure that they can accurately understand and respond to a wide range of users.

Designers also need to consider the privacy and security implications of speech recognition and NLP. These technologies involve recording and processing sensitive user data, such as voice recordings, which must be protected against unauthorized access and misuse. This requires implementing robust security measures, such as encryption and secure data storage, to protect user data.

Overall, speech recognition and NLP present new challenges for designers, but they also offer the opportunity to create more intuitive and user-friendly devices that can improve the user experience. With careful planning and design, designers can overcome these challenges and create devices that are more responsive, accurate, and secure.

Artificial intelligence can be thought of as a new design material that can be used to create a wide range of products and services. In design, a material is something that is used as the basis for creating a product or service. AI can be used as a material in design in several ways:

1. AI can be used to create intelligent products and services: AI can be used to create products and services that can think and learn on their own, allowing them to adapt to changing circumstances and perform tasks more efficiently.
2. AI can be used to automate processes: AI can be used to automate certain processes within a product or service, such as data analysis or customer service. This can help to increase efficiency and reduce the need for human labor.
3. AI can be used to create personalized experiences: AI can be used to create personalized experiences for users based on their preferences and behaviors. This can help to increase customer satisfaction and loyalty.
4. AI can be used to create new forms of interaction: AI can be used to create new forms of interaction between people and products or services. For example, AI-powered chatbots or virtual assistants can be used to provide customer support or assist with tasks.

AI can be a powerful tool for designers looking to create innovative and intelligent products and services. Intelligence can transform into a service and become a new design material (Holmquist, 2017). It is important for designers to consider the ethical and societal implications of AI when using it as a design material. AI has the potential to bring significant benefits and improvements to a wide range of products and services, but it also has the potential to create negative impacts if not used responsibly.

Designers have a unique role to play in ensuring that AI is used ethically and responsibly, as they are responsible for creating the user interfaces and user experiences that people interact with. Some considerations for designers to keep in mind when designing with AI include:

1. Bias: AI systems can be biased if they are trained on biased data sets. Designers should work with data scientists and other AI experts to ensure that the data sets used to train AI systems are representative and accurate and that any biases are identified and addressed.
2. Privacy: AI systems often rely on the collection and analysis of large amounts of personal data. Designers should ensure that any personal data collected and processed by AI systems is done so in a way that is transparent and respects users' privacy.
3. Explainability: AI systems can be complex and difficult to understand, making it difficult for people to trust and understand how they work. Designers should work to make AI systems more explainable and transparent so that people can understand how they work and how decisions are made.

Designers face additional challenges in understanding AI capabilities, as they must not only understand the technology but also how to apply it in a design context. Some of these challenges include:

1. Lack of design experience with AI: Many designers may have limited experience working with AI, making it difficult for them to understand the technology and how to incorporate it into their designs.
2. Complexity of AI algorithms: As mentioned earlier, many AI algorithms are complex and opaque, making it difficult for designers to understand how the technology works and how to apply it to their designs.
3. Limited access to AI expertise: Many designers may not have access to experts in AI, making it difficult for them to understand the technology and how to apply it to their designs.
4. Difficulty in predicting AI behavior: AI can be unpredictable, making it difficult for designers to anticipate how the technology will behave in different situations.
5. Difficulty in understanding ethical implications: AI raises many ethical concerns, such as privacy and bias, and designers may find it difficult to understand these implications and how to address them in their designs.
6. Difficulty in integrating AI with other technologies: AI can be challenging to integrate with other technologies, such as IoT, which poses a challenge for designers who need to design systems that are cohesive and seamless.
7. Difficulty in understanding user's needs and preferences: Many AI-driven designs are geared towards meeting the user's needs and preferences, so designers must be able to understand these needs and preferences to design a successful AI-driven product.

According to Harvard Business Review, the impact on consumer behaviour produced an unforeseen problem: an information gap, as data collected prior to the crisis, could no longer be used to predict future patterns accurately. The crucial component for retail customer loyalty programs, AI-driven product suggestions, and a wide range of critical business choices had a serious quality problem. Because of this, the ML models were required to be re-evaluated, re-trained, and re-designed.?

Human-Centered AI (HCAI) is a multi-disciplinary field that requires expertise from various fields such as computer science, psychology, sociology, philosophy, and ethics. It is a relatively new field and it's still evolving as the technology and society are changing.

In summary, Human-Centered AI is much broader than the study of interface design and input devices, it encompasses many aspects of AI that are related to the human experience, and its implications on the society. It aims to ensure that AI systems are designed with the human needs and well-being in mind.