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  • Writer's pictureAbhishek Chitranshi

Artificial Intelligence and UX Design

After the topics of user experience, innovation, design thinking and design system — now topics which trending more are — AI, ML, Deep Learning, ‘AI vs Designers’ and similar in AI-Design contexts.

Simplified Understanding of AI in Design Context

First, I will simplify AI understanding to bring its clarity in context of design to the extent where I can talk about it in the context of design.

Human Brain

Brain consists and works by its basic unit cell neuron, which holds, processes, transmit information and commands our actions based on these information. Pack of these billion cells in human brain means billions of information. As these cells keep growing and dying with time, which means information they hold also either known (information which have been in lot use are transmitted from one cell to another), go faded (memory) or completely lost. These information (memories) in human brain are for limited time span as like any other cell neurons also run on energy which is extracted from nutritious substances of food. That’s why memories could be live, faded or dead as neurons go down in energy level with time and brain forgets information (loses access to old memories).

Interesting fact is that human brain has unique power to process problem solving capability to cope with any situation because its neurons can self evolve (extended features) to think out of the box, which could be completely different from past learned experiences. This self evolving capability makes humans — The Humans.

Pixar/Disney movie ‘Inside Out’ is a good concept and example to understand what I talked above.

Artificial Intelligence (AI)

Now how to put the AI in simple words? We know basic that AI consists and runs on algorithms. If I say algorithms are packs of neural codings inspired by human cognitive functions, then it would not be wrong. Which means the AI basic unit could be considered as Neural Algorithm. So I will call them AI Neurons for simplified understanding.

These AI Neurons (cognitive algorithm or neural code packs) can also hold, transfer and process information like human brain does. But the storage and speed to process the information are far superior and beyond in comparison to so far known capabilities of human brain. Since AI Neuron doesn’t run on food energy like human brain does, it makes AI Neurons and their memory immortal :).

AI Neurons could be managed to not go down in energy, thus AI Neuron consisted information is not limited to time span. Those information are always available to be accessed, processed and connected with each piece of their information network to build amazing experiences.

AI can hold billions of information (may advance to hold infinite), process them with super speed (may advance to run with speed of light) and provide billions of solution based on learnings from input-ML-output patterns.

But AI cannot evolve extended features to solve out of the box problems. Means don’t have self evolving neurons (cognitive algorithm or neural code packs) to solve problem if encountered out of learned algorithm experiences. That’s where human brain makes human — The humans.

“AI self-evolving neuron feature and neural network research already taken the baby steps to progress.” “Since human brain is biggest mystery about how much potential it has and how it evolves with time, eventually it will always dominate the machines.”

Now I explain the ‘AI’ and ‘Designer’ in context of AI’ in single line —

Artificial Intelligence (AI) : Super IQ Product for Products.

AI Designers : Design and align super IQ products (AI) for super empathic user experiences.

Since long AI existed in human innovations

Artificial intelligence is not only about codes and algorithm. Code and algorithms are improved form of AI basic unit. Actually it is existing since humans started to design products and machines which can perform their tasks lowering their cognitive efforts.

In simple language AI’s basic process is a reversible sequence of : input><process (+Machine learning)><output.

  1. Input : Currently the information received caused by text, voice, gesture, image, video and user action patterns.

  2. Process (+Machine Learning) : Access to pre-defined functions, pre-seeded information (forward learning, loop learning, self-learning) and user actions.

  3. Output : Currently, the information outcomes in form of text, voice, gesture, image, video and autonomous actions.

All these 3 steps are kept advancing in technology and features with time.

Now if I say — fountain pen, MCB trip switch, calculator and Alexa, Siri — All are the examples of AI — should not be wrong because all of them go through the above 3 steps. Only difference is that advance technology of software industry advances these 3 features in AI.

How? let’s compare their basic process steps and analyse to understand—

2011 to present : Alexa, Siri, Google Assistant and ‘UI’s with IQ’ in context of various organisation business

  1. Inputs : Voice, text, image, video, gesture and advancing.

  2. Process : Access to pre-defined functions, pre-seeded information, forward, loop machine learning by input-output experience

  3. Technology : Combination of advance codings, cloud, server, IoT techs and advancing.

  4. Output : Voice, text, image, video and advancing as learned by user inputs.

  5. Intelligence : Predictions in voice response, text, image, video and advancing in cognitive functions.

1820 to present : Calculator (Abacus too)

  1. Input : Digits, math equations

  2. Process : Calculation

  3. Technology : Computer and electronics

  4. Output : Numeral results of mathematical equations

  5. Intelligence : Mathematic genius

1879 to present : MCB Switch

  1. Input : Voltage

  2. Process : Detects voltage

  3. Technology : Electrical engineering

  4. Output : Prevents electricity hazards

  5. Intelligence : Automatic trip to close the electrical circuit out of set voltage range.

1663 to present : Fountain Pen

  1. Input : Ink, pressure, drag

  2. Process : Capillary action, gravity

  3. Technology : Fluid dynamics, physics

  4. Output : Guided thin stream of ink

  5. Intelligence : Controlled flow of ink to write, when and when not to release ink based on hand pressure input. Level of intelligence, decision making probably low but it is product intelligence.

All four examples above includes some type of cognitive efforts in their process. Intelligence of the cognitive efforts are being improved in sequence of — fountain pen, MCB switch, calculator and then Alexa Siri. That’s how any product designed to share the human cognitive efforts is injected with AI.

We have lot many such examples around us . To example few more — pre-seeded cooking options on microwave control panel, auto-suggest in UI pattern, whistling kettle, automobile automatic gear mechanism, motion sensor lights — all consist AI.

AI impacts on Design

There was a time when architect’s or designer used mostly drafting stationaries to create design. When tools like Auto-Cad, Photoshop, Visio and many other tools for designers and architects came in use, did they replace their task or given them more opportunities towards creativity speed or both. In fact, more designer roles opportunities established around these.

Software revolution has given great leap to AI definition and to perform human tasks for which specific job roles were there. But it sprouts many great opportunities as well for many AI oriented roles in all fields. Positive impacts of AI would be advancement in education patterns and job roles.

The term AI has filtered up to remark it in human computer interaction.

Design Impacts on AI

To shape the data driven AI and to design the meaningful AI-ML experiences — creativity and design thinking would always play vital role. Its endless.

“Its skilled designers capability to think out of the box and bring natural experiences within the box.”

AI can process with super speed and bring billions of solution for a particular problem but it cannot detect the user needs with empathy and compassion (out of machine learned user behaviour) to provide the situational solution out of the box as it runs only on given inputs and learned behaviour patterns (which may not always be original). So it develops the fixed pattern and combinations. It cannot result out of those learnings (no self-evolving neurons), but designer (human) can. Designers are needed to make the AI experience meaningful, empathic and compassionate. AI may keep taking over the steps of design process, which nothing but will push designers to design more user oriented.

Designers will work to shape AI on its processes like on machine learning strategy, on input and output for language, voice, gesture, text, image, sign, sound, vision, touch, thermal, motion, expression and more.

Future of Design in context of AI

This particular part is inspired by various concepts shown in many sci-fi movies. Leveraging those illustrating my vision of design and AI.

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