Podcast Summary
Avoiding the 'Shiny Object Trap' in AI Implementation: A Product Lead's Advice: Implementing AI should first require identifying a specific problem or pain point to solve, instead of doing it just for the sake of using the technology. Stay updated on AI by subscribing to newsletters and learning how it can be integrated into technology.
Marily Nika, a product lead at Meta, warns against falling into the "shiny object trap" when it comes to AI.It's essential to identify a problem or pain point that needs to be solved before implementing AI.Nika emphasizes that AI should not be done for the sake of doing AI.Once a problem has been identified, the next step is to figure out how to implement the solution.PMs can stay up to date with AI by subscribing to newsletters like MIT Ecology Review and learning about how AI is becoming integrated into all types of technology.
AI Product Manager Says Overhyped Benefits Undervalue AI's True Potential: While some concerns about AI taking jobs are unfounded, AI can enhance our work by providing a personalized experience to users. AI is the future of every product for better recommendations and user segments.
Marily Nika, an AI product manager, believes that some aspects of AI are both overhyped and undervalued.She argues that fears about AI taking jobs away from humans are unfounded, as AI enhances our work and does not steal from us.Nika uses ChatGPT, an AI language model, to help her create mission statements and user segments that surpass her original ideas.She sees a future where AI is the default in every product, providing a personalized experience and recommendation system to users, resulting in an overall better product.
Partnering with Research Scientists to Incorporate Smart Models: A Critical Skill for Modern PMs: PMs should consider incorporating AI in their products by partnering with research scientists. They don't need a technical background, but should focus on addressing specific problems and avoiding the "shiny object trap.
AI is becoming increasingly important in every sector, and PMs need to get comfortable with partnering with research scientists to incorporate smart models into their products.PMs do not need to have a technical background to learn how to build AI tooling into their products, and can start by thinking about how to use the data they already have to improve their product.It is important to avoid the "shiny object trap" and ensure that incorporating AI is addressing a specific problem or pain point.
Tips for aspiring AI Product Managers: Understand the problem you're trying to solve and the audience you're targeting. Use existing or adjacent data to leverage the product, and launch only when the quality of the product is good enough.
If you want to be an AI Product Manager (PM), figure out what problem you want to solve and identify the audience for the product.Don't waste the data scientist's time by trying to build a really cool model for an MVP.Use AI where you already have some data or use adjacent data that you can leverage for your own product.The amount of data needed for AI depends on the application being developed.If you are a big tech company, you will need more data and more diverse data to train and retain your model, but if you buy data packages from agencies, your quality will be the same as your competitors.As an AI PM, it is your responsibility to decide when the quality of your product is good enough to launch.
How AI models learn and make predictions: AI models learn and recognize patterns in data, allowing them to make predictions and decisions. While AI has exciting applications, it is important to focus on its current impact and potential for improvement, rather than speculating about future replacements of human workers.
A model is like a child's brain that can recognize and classify input, such as images or text, with a certain level of certainty, just like a child can recognize animals or objects.Training a model involves providing the model with large amounts of data and allowing it to learn and find patterns on its own, which can then be used to make predictions or decisions.AI and machine learning have amazing and mind-blowing applications, such as bridging the borders of communication through real-time translation.While some may speculate about the replacement of product managers by AI in the future, it is important to focus on the current impact and potential of AI to enhance and improve various fields.
Learning to Code: The Key to Unlocking Strategic Product Management in the Age of AI: Product managers should invest in learning how to code to better understand the tools they use, build confidence, and unlock new areas of product management. AI will not replace them but make their jobs easier and more strategic.
Artificial intelligence (AI) will not replace product managers (PMs), but it will make their jobs easier and more strategic.PMs should invest in learning how to code, even if AI can do some tasks for them.Learning to code can help PMs understand the tools they use and build confidence in using them.There are many resources available for learning to code, including Coursera, Career Foundry, General Assembly, and Coding Dojo.Learning to code can unlock new areas of product management and make PMs smarter.
The Challenges and Strategies for Becoming an AI Product Manager: To become a successful AI product manager, aspiring PMs should invest in AI tools, learn to code, shadow research scientists, communicate potential opportunities, and clarify progress metrics. Maintaining support is crucial for success.
Product managers aspiring to become strong AI PMs should understand the differences in managing an AI product compared to a general product.They should also invest in learning to code and start playing with AI tools.Current PMs in companies with AI research scientists should spend time shadowing and talking to them to gain context and identify potential opportunities.Challenges include managing uncertainty, changing actions in managing from a leadership perspective, getting good data, and clarifying career trajectory with hiring managers.To get buy-in for investment in ML, PMs should communicate the potential opportunity and clarify the progress metrics.Maintaining support can be challenging but it is crucial for success.
Tips for Successfully Implementing AI Solutions in Your Team: To successfully implement AI solutions, product managers should use examples of successful adjacent products, seek inspiration from research blogs and academic papers, bridge the gap between research and production, and create meaningful use cases.
When trying to implement AI solutions in a team or company, it can be helpful to use examples of successful adjacent products to convince leadership.Research blogs and academic papers can also provide inspiration and ideas.It is important for product managers to bridge the gap between research and production and to come up with meaningful use cases to monetize AI solutions.Marily Nika's three-week course for aspiring or current PMs covers idea creation, productionizing AI solutions, collaborating with research scientists, and pathing one's career as a PM.
Develop your AI product from scratch with practical workshops and feedback.: Start creating your AI product with this course, regardless of your coding experience. Gather feedback to improve your course and use AutoML for an easier way of building machine learning models.
The course on AI product development consists of nine workshops where participants get to create and develop their own AI product end to end.They can pair up with each other and submit homework assignments.The most exciting part is when all the participants present their projects and receive feedback.One shocking example is a team that used AI to detect ailments from x-rays, a task typically performed by doctors.The course is for everyone, even those without coding experience, as they teach the basics necessary to build AI products.AutoML is a recommended tool for training custom machine learning models without coding.It is essential to treat course creation like a product and gather feedback to develop a better course.
Finding Your Audience, Duration, and Personal Connections: Tips for Creating Engaging Courses and Bonus Resources: When creating courses, focus on the audience's needs, duration, and personal connections. Bonus resources add value, and teaching can be rewarding. Don't be afraid to share your knowledge and consult resources like "Inspired" or "Adventures of Women in Tech Workbook.
When creating a course, it's important to find the right audience and figure out what they want.Courses should have the right duration, with three weeks being ideal for allowing for networking and presentations.Personal relationships with each participant are also crucial for building trust.Bonus sections can be added as needed, and teaching can be a valuable learning experience for the instructor.Don't underestimate your knowledge and consider creating your own courses.Recommended books include "Inspired" by Marty Kagan and "You Look Like a Thing and I Love You" by Janelle Shane.The "Adventures of Women in Tech Workbook" is also a great resource.
Boz's Podcast, "The White Lotus" TV Show, and Simplifying Technical Terms for Non-Technical People with Marily Nika: Simplifying technical terms is important for effective communication, and tools like Lensa allow us to explore our creativity. Follow Marily Nika on Instagram and YouTube for more insights.
In this section, Boz, the CEO of Facebook, is mentioned to have a great podcast.Marily Nika, a guest, recommends a TV show called "The White Lotus" and shares her favorite interview question about explaining a database to a three-year-old, highlighting the importance of being able to simplify technical terms for non-technical people.She also suggests an AI-based tool called Lensa, an app that allows users to see what they would look like as fantastical heroes.Marily can be found on Instagram and has a product channel on YouTube, where she shares her insights.
