Product Thinking
Breaking Down Today’s Machine Learning Technology with Christina Pawlikowski
March 15, 2023
Melissa Perri is joined by Christina Pawlikowski, a teaching fellow at Harvard and co-founder of Causal, to help demystify machine learning and AI on this episode of Product Thinking. Christina discusses language models, the different types of machine learning, how they can be used to solve problems, and the importance of good data and ethical considerations when using machine learning algorithms. Christina Pawlikowski is a teaching fellow at Harvard University and co-founder of Causal, a company that helps businesses make better decisions with causal inference.  You’ll hear Melissa and Christina talk about: How machine learning is essentially creating an algorithm or a model that can make good predictions based on data. There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Good training data is crucial for machine learning algorithms to be effective. When considering using machine learning, it's important to ask questions about things like how complex the decision that needs to be made is, whether the model has to produce a definitive answer, how high the stakes are, and how quickly the answer needs to come back. Ethical considerations are important when feeding data into a machine-learning model, especially when making decisions with high stakes. GPT-3 and Chat GPT are examples of language models that use neural nets to generate predictions about what word or sentence comes next based on probabilities. The accuracy of a machine learning model is only as good as the quality of the data that is fed into it. When incorporating ML into a product, it's important to plan for scenarios where the model is wrong and to consider ethical considerations such as false positives and false negatives. Data scientists play a crucial role in assembling and cleaning training data, building and testing the model, and deploying it in production. The process may involve collaboration with machine learning engineers or other teams. The cadence of working on machine learning is different from working on traditional UX-focused teams, with more downtime and exploratory time upfront. Slack time is important for data scientists and machine learning engineers to keep up with new techniques, write papers, and attend conferences. Artificial general intelligence is probably further off than we think, and AI alignment is an important field to prevent any negative outcomes. Resources: Christina Pawlikowski on LinkedIn | Twitter  Casual Labs
Melissa Perri is joined by Christina Pawlikowski, a teaching fellow at Harvard and co-founder of Causal, to help demystify machine learning and AI on this episode of Product Thinking. Christina discusses language models, the different types of machine learning, how they can be used to solve problems, and the importance of good data and ethical considerations when using machine learning algorithms. Christina Pawlikowski is a teaching fellow at Harvard University and co-founder of Causal, a company that helps businesses make better decisions with causal inference.  You’ll hear Melissa and Christina talk about: How machine learning is essentially creating an algorithm or a model that can make good predictions based on data. There are three types of machine learning: supervised learning, unsupervised learning, and reinforcement learning. Good training data is crucial for machine learning algorithms to be effective. When considering using machine learning, it's important to ask questions about things like how complex the decision that needs to be made is, whether the model has to produce a definitive answer, how high the stakes are, and how quickly the answer needs to come back. Ethical considerations are important when feeding data into a machine-learning model, especially when making decisions with high stakes. GPT-3 and Chat GPT are examples of language models that use neural nets to generate predictions about what word or sentence comes next based on probabilities. The accuracy of a machine learning model is only as good as the quality of the data that is fed into it. When incorporating ML into a product, it's important to plan for scenarios where the model is wrong and to consider ethical considerations such as false positives and false negatives. Data scientists play a crucial role in assembling and cleaning training data, building and testing the model, and deploying it in production. The process may involve collaboration with machine learning engineers or other teams. The cadence of working on machine learning is different from working on traditional UX-focused teams, with more downtime and exploratory time upfront. Slack time is important for data scientists and machine learning engineers to keep up with new techniques, write papers, and attend conferences. Artificial general intelligence is probably further off than we think, and AI alignment is an important field to prevent any negative outcomes. Resources: Christina Pawlikowski on LinkedIn | Twitter  Casual Labs

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