Discover how to learn from your failures and successes in AI and HCI projects. Learn how to set goals, experiment, analyze data, communicate, and reflect.

Most AI and HCI projects don't usually fail because of not accurate enough models, but because of overly complex ones. As in many product launches, iterating on a product's functionalities is key to avoid overdelivering on something that users don't need. The same applies to AI projects. Many data scientists ship as the initial MVP an overly complex (e.g. deep learning model), instead of testing the market's appetite with something simpler. Focusing on simpler AI models, e.g. linear and logistic regression allows to gain initial feedback and quickly iterate on the solution, swapping the ML model with a different, more powerful one as time progresses.

How do you learn from the failures and successes of your AI and HCI projects?

AI and HCI (Human-Computer Interaction) projects can be challenging, rewarding, and frustrating. Whether you are developing a chatbot, a voice assistant, a computer vision system, or any other application that involves interaction between humans and machines, you need to learn from your failures and successes. This article will share some tips and strategies on how to do that effectively.

1 Identify your goals and metrics

Before you start any AI and HCI project, you need to have a clear idea of what you want to achieve and how you will measure it. What is the problem you are trying to solve? Who are your users and what are their needs and preferences? What are the key performance indicators (KPIs) that will show if your solution is working or not? Having these goals and metrics defined upfront will help you to design, test, and evaluate your project.

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Eugenio Zuccarelli

AI Leader for Fortune 100 | Forbes 30 Under 30 | Fortune 40 Under 40 | Author of “Intelligenza Artificiale” (Mondadori) | TEDx and Keynote Speaker | LinkedIn Top Voice | MIT, Harvard, Imperial College
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Most AI and HCI projects don't usually fail because of not accurate enough models, but because of overly complex ones. As in many product launches, iterating on a product's functionalities is key to avoid overdelivering on something that users don't need. The same applies to AI projects. Many data scientists ship as the initial MVP an overly complex (e.g. deep learning model), instead of testing the market's appetite with something simpler. Focusing on simpler AI models, e.g. linear and logistic regression allows to gain initial feedback and quickly iterate on the solution, swapping the ML model with a different, more powerful one as time progresses.

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2 Experiment and iterate

AI and HCI projects are not linear or predictable. You will encounter many uncertainties, challenges, and surprises along the way. That is why you need to adopt an experimental and iterative mindset. Try different approaches, test different hypotheses, collect feedback, and learn from the results. Don't be afraid to fail and learn from it. Don't get too attached to your initial ideas, and be open to change and improvement.

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Aldo Segnini

AI-Powered Digital Transformation Strategist | Empowering Executives with Data-Driven Insights | +25 Years of Proven Success in Implementing Tech Solutions | Startup Advisor | EaaS
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To effectively learn from both failures and successes in AI and HCI projects, adopting an experimental and iterative mindset is crucial. These projects often come with unpredictabilities, uncertainties, and challenges. Hence, it's essential to experiment with various approaches, test hypotheses, gather feedback, and derive insights from the outcomes. Embrace failure as an opportunity for learning, remain adaptable to change, and be open to continuous improvement, even if it means revising your initial ideas. This approach allows you to navigate the complexities of these projects effectively.

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3 Analyze your data

Data is the fuel of AI and HCI projects. You need to collect, process, and analyze data to train your models, understand your users, and evaluate your outcomes. Data can reveal insights, patterns, and trends that can help you to improve your project. However, data can also be noisy, incomplete, or biased. You need to be careful and critical when working with data, using appropriate methods and tools to clean, validate, and visualize it. Check for errors, outliers, and anomalies. Look for correlations, causations, and confounders. Ask questions and seek explanations.

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4 Communicate your findings

AI and HCI projects are not carried out in isolation. You need to communicate your findings to your stakeholders, collaborators, and users. Clear communication is essential to sharing your vision, goals, and results. It is also important to solicit feedback, opinions, and suggestions. Whether you're sharing a report of revealing a prototype, your communication with your audience is key. Use clear, concise, and engaging language. Use visuals, stories, and examples, and highlight the benefits, challenges, and limitations of your project.

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5 Reflect and document

Reflect on and document your process, outcomes, and lessons learned. Reflection is a powerful way to deepen your understanding, identify your strengths and weaknesses, and recognize your achievements and challenges. Documentation is a useful way to record your decisions, actions, and results. It can also help you to share your knowledge, experience, and best practices with others. Reflection and documentation can be done individually or collectively. You can use tools such as journals, blogs, wikis, or portfolios.

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Stephen B.

Deep Learning / Medical AI Researcher at Stephen M Borstelmann MD
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It’s incredibly important to understand the effects of metric selection on project outcome, because, “ what gets measured gets managed.” For upper level system leaders, clear thought must be given to any optimization to avoid detrimental unintended consequences. Middle managers will ruthlessly go after their bonused metrics, even when it might be at the expense of another area in the organization, and cost far more to rectify in the future than the initial savings yielded.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you learn from the failures and successes of your AI and HCI projects?

1

2

3

4

5

6

1 Identify your goals and metrics

2 Experiment and iterate

3 Analyze your data

4 Communicate your findings

5 Reflect and document

6 Here’s what else to consider

Artificial Intelligence

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How do you learn from the failures and successes of your AI and HCI projects?

1

2

3

4

5

6

1 Identify your goals and metrics

2 Experiment and iterate

3 Analyze your data

4 Communicate your findings

5 Reflect and document

6 Here’s what else to consider

Artificial Intelligence

Rate this article

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