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📌 how AI can influence product & design — the power of data, AI and ML | Natalie Kuhn (Meta, Capital One)
Uncover how to successfully adapt to machine learning as a designer with these amazing tips from design leader, Natalie Kuhn (Meta, Capital One)
Hello 👋, and welcome to this week’s ✨ ADPList’s Weekly Pulse ✨ - a community-only newsletter delivered every Tuesday. We tackle your questions about design, product, working with humans, and anything else that’s stressing you out at the office.
🔥 This Week’s Hot Sessions
✨UX Design VS. Content Design: Choose Your Fighter! - Oct 26, 5:30pm PT (RSVP)
✨Weekly Friday Visual Thinking Strategies (VTS) - Oct 28, 9:00 am PT (RSVP)
✨Masterclass on Design Thinking - Oct 29, 12:30 am PT (RSVP)
✨Managing professional relationships as a designer - Oct 28, 4:00 pm PT (RSVP)
✨Transitioning in UX/Product Design - Job Hunting- Oct 30, 8:00 am PT (RSVP)
🤖 The role of designers in the rise of AI
👉 The rising presence of artificial intelligence and algorithms makes it difficult to be a professional creative in today's world. There are dramatic headlines about how technology will redefine what it means to have a skill every day. Because algorithms can even affect creative skills.
As a designer, you need to become aware of the future trends of technology and how they will disrupt the way we view tools and skills. So what does it look like to bring AI & ML together? 🤔
Explore how you can win as a designer in the age of ML with design leader & ADPList Mentor, Natalie Kuhn.
Q: How to get started as a designer in the Machine Learning space?
Any team that develops ML systems that are truly human-centered needs designers as a key component. As a beginner in the field of machine learning, you should start with:
Building something is the greatest way to learn how machine learning actually works.
Internalising guidelines for creating ML systems that are user-friendly
Extending the understanding of the best design practices for ML
Q: How to tailor the design process and activities for ML?
A reliable path forward is to follow the design process where your team can gain more clarity about what your customers need and what you should build, usually by iterating through a set of activities that move from research, to ideation and sketching, to prototyping and testing. But now that you’re working with ML, how does each stage of the design process have to adapt?
1. User research ← Even ML requires real problems to solve
2. Think through opportunities for ML
3. Ideate and sketch different implementations
4. Prototype the ML systems
5. Testing & Advancing your design
Q: What does practicing Design in a Machine Learning space look like?
Here are two excellent illustrations of machine learning design approaches. One system viewed the user as a crucial component, whilst the other concentrated more on the algorithm.
Algorithm-focused: Netflix is algorithm-centered, which causes items like "My List" or "Continue Watching" to constantly change positions since it treats all of the category rows in the homepage recommendations as variables in its algorithm.
User-focused: Airbnb developed a switch for their hosts that allowed the pricing for hosts' properties to be automatically determined by an algorithm. The design was altered by the product team to include minimum and maximum rent restrictions.
The user is treated as a key component of the experience in this user-centered scenario. It is a wonderful illustration of how to approach a project with machine learning design.
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With ❤️ ADPList Team