Artificial Intelligence (AI) & Machine Learning (ML)

In this article, written for product managers, data scientists, and engineering teams we cover some growth areas and ideas on how to achieve more impact while working on building ML-based products. Read more »

In this Sunday Rewind episode, we look back to when Amber Case, Cyborg Anthropologist spoke at #mtpcon London. In her talk, she discusses the concept of Calm Technology and how it can help us to be more human during a technological age. Read more »

Used properly and in the right place, Machine Learning (ML) is an incredible tool to bring value to your product and your users. But how can you use the five product risks—value, usability, feasibility, viability, and ethics—to know if the opportunity is right for ML? Read more »

In our final AMA of the year, exclusively for Mind the Product members, Kriti Sharma, VP Product at GfK, shares her AI insights. Watch the session again for real-life examples from Kriti’s awesome back catalogue of AI work and learn about AI trends, tackling ethical AI questions as a product manager, the skills needed to Read more »

Currently, there are thousands of products, apps, and services driven by machine learning (ML) that we use every day. As was reported by Crunchbase, in 2019 there were 8,705 companies and startups that rely on this technology. According to PWC’s research, it’s predicted that ML and AI technologies will contribute about $15.7 trillion to global GDP by 2030. It’s obvious Read more »

W. Edwards Deming wrote, “In God we trust, all others must bring data.”  Product people hold on to this as a mantra – how else can we defend ourselves from random requests? – but we’re not always taught how best to use data.  In this week’s episode, Lens.ai founder Mike Bugembe joins us on the Read more »

In this MTP Engage Manchester talk, Mayukh Bhaowal, Director of Product Management at Salesforce Einstein, takes us through how product managers must adjust in the era of artificial intelligence and what they must do to build successful AI products. Mayukh outlines five new skillsets that product managers must acquire. Problem Mapping Data is the New UI Read more »

Machine learning (ML) based products have particular characteristics and challenges, from data quality to counterfactual problems and explainability. What then are the implications of ML products for team structure, focus, and hiring? Data science jobs are increasing at around 30% year on year, and if you don’t already have a data scientist in your ranks Read more »

The hype around artificial intelligence (AI) and machine learning has led to lots of jargon, so that this very powerful technique has become more difficult to understand. The tips below have all helped me, so I hope this article will help product managers to cut through the noise and better understand how AI can fit Read more »

In this #mtpcon London keynote, Kate O’Neill, founder of KO Insights, considers the question: How can humanity prepare for an increasingly machine, tech and data-driven future? Key Points We need to prepare society for an increasingly tech and data-driven future We must build products that create meaningful human experiences now and in the future To Read more »