Design thinking in the age of artificial intelligence

The origins of design thinking

Brainstorming was invented in the 50s, and in the 60s a design training program was created at Stanford. A landmark book, "Design Thinking", published by MIT Press, was written by Peter Rowe. Design thinking is a project creation method developed at Stanford University in the USA in the 1980s by Rolf Faste. In 1991, Idéo popularized its methods, which focus on the customer experience, and the method was widely adopted.

In addition to technological solutions, the aim is to explore the user experience. Today, the term Ux Design is used to describe user experience design. The approach is based on two key principles.

The first principle is that of an iterative process, i.e. you can return to previous stages if the current one is inconclusive. This distinguishes design thinking from a linear project process.

The second principle lies in the cooperative operation of diverse, multi-disciplinary teams of different ages and experience at each stage. It should also be noted that design thinking is a process that places people at the heart of experimentation with new solutions, with a logic of rapid, methodical prototyping.

The 5 key stages of the design thinking method

The design thinking process is led by a "facilitator" or designer, who remains neutral and does not seek to influence participants.

Step 1 - Use empathy
In the manner of an anthropologist, this involves defining the target audience and obtaining a clear vision of the problems encountered by users and what they need. It's about understanding their context, what they say they think and feel. Surveys and contextual immersion are the best solution, but it's also possible to fill in an "Empathy Map", organize a survey, lead a focus group or conduct a user test, meet users in the street, or create personas that embody users.

Step 2 - Diagnosis
In this stage, the aim is to understand the nature of the challenge, the points of friction and the problems faced by users, for example, by establishing a "user journey" or experience map, or based on critical incidents. The aim is to identify what is minimum, what is normal and what would be a real break in the service provided.

Step 3 - Build the concept
Create the concept that will deliver the solution using a diverse team to generate a wealth of ideas, based on 3 stages

-Ice-breaking games within the group, to express personal feelings.

-Brainstorming to stimulate creativity (here 20 methods). During this stage, it's possible to propose new constraints or change the rules along the way, to get the group out of its routine,

-The selection of the best viable, feasible ideas according to the criteria and constraints set out in the diagnosis.

Step 4 - Prototyping
The prototyping stage consists of moving as quickly as possible towards the materialization of a solution, and expressing it in creative form using a drawing (poster, metaphor, figurative drawing, plan), in the form of a cut-out, a digital model, an assembly for example, or even in the form of a video and its more or less complex storyline ranging from a hero's journey to a novel-like composition.

The prototype can also be a role-playing game or a theatrical stage on which to play out the imagined solutions. Whatever its form, the prototype aims for simplicity and rapidity. It needs to be usable quickly enough to be understood by users and to benefit from their feedback. It doesn't need to be aesthetic or functional at this stage. Its function is to suggest the solution in preparation

Step 5 Testing
The testing stage consists of exposing the prototypes to users to understand what they think, feel and are motivated by. If the design thinking session takes place with several teams, a pre-test crossing the opinions of the teams is possible. It's also possible to address a prototype to a mass of online users and circulate it to receive a variety of opinions in rapid iteration. This is known as crowdtesting. This solution is particularly well-suited to software or any other digital solution.

Finally, in the testing stage, AI can be used to collect and analyze user feedback in an automated way. Chatbots and semantic analysis systems enable a deeper understanding of user reactions, but again, AI cannot always interpret human emotions with the same precision as a human being.

_{The original content of the note was published on Cursus.edu. To read the full note visit here}

How To Accelerate Software Development With Generative AI

In the span of just a few years, generative AI has transformed how organizations build products, create content and resolve problems. The majority of business and technology leaders using GenAI are focusing on efficiency and cost-effectiveness gains, according to a 2024 Deloitte survey, with 91% of respondents reporting that they “expect generative AI to improve their organization’s productivity.”

It’s no replacement for human expertise and execution, but it can take the lead on mundane, repetitive tasks, allowing teams to dedicate more time and energy toward critical thinking, problem-solving and collaboration.

By using GenAI tools on my own development team, I’ve discovered that they are most valuable in two key applications.

-Code suggestion and autocompletion: AI can analyze developers' code as they work, automatically generating recommendations for code snippets or complete functions based on context and input.

-Code analysis and bug detection: Generative AI can quickly review code to detect errors or bugs early in the development process.

Strategies For Implementing AI In Software Development

To maximize the benefits of AI in software development, I recommend the following four strategies.

1-Test And Evaluate Different Tools

2-Create Better Prompts

3-Review Code Carefully

4-Protect Sensitive Data

Set realistic expectations, and use AI tools strategically and thoughtfully—in conjunction with human expertise and oversight—to deliver software solutions more efficiently than ever before.

_{The original content of the note was published on Forbes.com. To read the full note visit here}

The Evolution of Intelligence

The expert consensus is that human-like machine intelligence is still a distant prospect, with only a 50-50 chance that it could emerge by 2059. We’ve partnered with VERSES for the final entry in our AI Revolution Series to explore a potential roadmap to a shared or super intelligence that reduces the time required to as little as 16 years.

Active Inference and the Future of AI

The secret sauce behind this acceleration is something called active inference, a highly efficient model for cognition where beliefs are continuously updated to reduce uncertainty and increase the accuracy of predictions about how the world works. An AI built with this as its foundation would have beliefs about the world and would want to learn more about it; in other words, it would be curious. At the same time, because active inference models cognitive processes, we would be able to “see” the thought processes and rationale for any given AI decision or belief.

The 4 Stages of Artificial Intelligence

Here are the steps through which an active-inference-based intelligence could develop:

Systemic AI responds to prompts based on probabilities established during training: i.e.current state-of-the-art AI.

Sentient AI is quintessentially curious and uses experience to refine beliefs about the world.

Sophisticated AI makes plans and experiments to increase its knowledge of the world.

Sympathetic AI recognizes states of mind in others and ultimately itself.It is self-aware.

Shared or Super AI is a collective intelligence emerging from the interactions of AI and their human partners.

Stage four represents a hypothetical planetary super-intelligence that could emerge from the Spatial Web, the next evolution of the internet that unites people, places, and things.

A Thoughtful AI for the Future?

With AI already upending the way we live and work, and former tech evangelists raising red flags, it may be worth asking what kind of AI future we want? One where AI decisions are a black box, or one where AI is accountable and transparent, by design.

_{The original content of the note was published on Visualcapitalist.com. To read the full note visit here}