I’ve been cultivating seeds of contemplation regarding the realms of Machine Learning and Intelligent Design, diligently chronicling my musings within the pages of my diary for some time now. Then, on an ordinary day, while I reached for the Netflix button on my TV remote, the recommendation engine, driven by the power of artificial intelligence, presented me with ‘Human: The World Within,’ boasting a striking 92% match. As I delved into the inaugural episode, aptly named ‘React,’ my mind embarked on an intellectual journey, where the fertile soil of my thoughts began to sprout ideas connecting Machine Learning and Intelligent Design.
In this article, I’m eager to share my high-level design perspectives on Machine Learning (ML) and highlight its profound impact on the world of design. I’ll delve into how this remarkable AI technology process enriches the design landscape and explore how designers can leverage it to craft intelligent systems (iSD), products (iPD), and experiences (iUX).
Given that my thinking predominantly gravitates towards visual expression, I believe that employing visuals can enhance the efficiency of communication. Consequently, I’ve incorporated several visual representations within this article to elucidate my thoughts and visions. These visualizations elucidate the intricacies of ML, its interconnectedness with various facets of AI, and its pivotal role in shaping intelligent design experiences.
As you peruse this article, I urge you to pay close attention to these visualizations. They not only complement the textual content but also convey additional insights that further enrich the discourse.
Machine Learning in Simple Terms
Machine learnings are packs of algorithm models. These models are designed to ‘Collect, Learn and React’.
Collect = Inputs
Learn = Analyse Inputs, Develop logics and rules
React = Output/s based on developed logics and rules
ML system, AI family tree and Intelligent Design
Building upon the current state of the evolving Machine Learning (ML) technology, I’ve crafted visualizations that delineate its functions, relationships, and its position as the State of the Art (SOTA) within the expansive AI family tree. These visual aids serve to demystify ML and render it more accessible and comprehensible in the context of the design world.
Machine Learning (ML) is a one type of learning model in the AI family.
Reinforcement Learning (RL) is a one type of process in the ML family.
Deep Learning (DL) and Deep RL are the types of advanced processes in the ML family.
Neural Network is a one type of intelligent process in the DL and Deep RL family.
The landscape of AI and ML algorithm models, along with their intricate processes and sub-processes, is in a constant state of advancement. Across the globe, dedicated AI researchers and scientists are tirelessly exploring numerous process models, seeking innovative solutions to complex challenges. As these cutting-edge technological models mature into practical problem-solving tools, they are endowed with names that reflect their functions, protected through patents that safeguard their unique approaches and methods, and then unveiled to the world.
Neural Network Simple Terms
Taking inspiration from the intricate workings of the brain’s neural networks, Machine Learning (ML) neural networks facilitate the flow of input information through multiple signal processing layers, akin to the neurons in the brain. These networks seamlessly transmit thousands of input data signals between layers, analogous to the synapses in the brain, ultimately converging into a unified analytical function within the realm of Machine Learning, all in pursuit of generating the desired output.
Deep Learning in Simple Terms
Much like our brain diligently processes input signals received through our sensory organs, enabling us to identify, learn, cultivate muscle memory, and generate intelligent outputs rooted in recognition, past experiences, memories, and acquired knowledge, the process of deep learning is engineered to gather thousands of inputs, subject them to rigorous analysis, construct logical frameworks, undergo learning and training, ultimately culminating in the production of intelligent outputs.
Intelligent Design
If I define smart design based on my first sense of experience then —
“The Smart Design provides a fixed set of intuitive response, assistance, supervision or guided process etc. to user, subject and system through visible elements of experience.”
Which is happening by designing WHAT to REACT by the trainable products, systems and processes.
But if I go a level deep in my thoughts and define intelligent design with extended sense of experience then —
“The Intelligent Design is not only about the intuitive response, assistance, supervision or guided processes but much beyond, which is to provide the ‘MOST NEEDED REACTIONS’ in varying situations and develop trust with users on subjects in systems through visible and invisible elements of experiences.”
Which would be possible by syncing AI models with product design, designing WHAT, HOW and WHEN to REACT by the TRAINABLE, TEACHABLE, ADAPTABLE products, systems and processes.
In the coming years, we are poised to achieve an extraordinary level of intelligent design by harmonizing the advancements in various AI models with the synchronization of product and UX designs. Machine Learning stands out as a pivotal and indispensable branch of AI, propelling us forward on this transformative journey towards intelligent design.
ML for Intelligent Design
Over the past couple of years, the influence of Machine Learning (ML) has become increasingly evident in our daily digital interactions. It has significantly enhanced our experiences across various platforms such as Netflix for watching, Amazon for shopping, and Google for email, maps, and search. These ML-powered recommendation engines represent just the initial stages in the journey toward Intelligent Design — the much-anticipated transformation — along with the evolution of user experiences (IDX).
Current State of ML
Few examples,
Do we need Netflix ‘More like this’ feature shown as a fixed part of design?
Does the Amazon home page need to show us ‘Inspired by your shopping’ or ‘Top picks for you’ as a fixed part of landing page design?
Or let’s say you shopped on Amazon to gift an item of a guest choice, who is visiting you at your home and the recommendation engine (ML) learned it as part of ‘your need or choice’, which you never use or like and shows similar products at home page under various categories.
Are these Intelligent Designs? Are these ‘Most Needed Reactions’ in varying situations? These are the sections of smart designs. These are going through a fixed set of recommendation output design, dev and business strategy under ML rules and remain surfaced as a fixed set of UI designs to users.
How to align ML systems towards Intelligent Design?
Aligning the development of Machine Learning (ML) with product design and user experience (UX) marks a pivotal starting point on the path to Intelligent Design. In practice, I view ML as a sophisticated research and usability tool that seamlessly integrates with UX research and product design strategies. Together, they help pinpoint the crucial element — the “most needed reaction” — within the design process.
To keep design towards the path of 'the most needed reaction', I try to,
Design the product, architecture and UX around ML and other AI developments.
Define and create design and development channels to boost ML and other AI indexes.
Intermittently, enhance the design development strategies and DDT models (deep design thinking).
Apart from training these models to LEARN, tune them with smart input-output analysis channels of user interactions and train them to learn WHAT, HOW and WHEN to REACT.
Please note, all the above should not be looked only through the lens of digital applications UI (mobile, web) but it also applies well on their systems, processes, all AI ML enabled products, robotics, next generation holographic projection interfaces and beyond!
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