RT Avi Chawla (@_avichawla)You're in an AI engineer interview at Apple.The interviewer asks:"Siri processes 25B requests/mo.How would you use this data to improve its speech recognition?"You: "Upload all voice notes from devices to iCloud and train a model"Interview over!Here's what you missed:Modern devices (like smartphones) host a ton of data that can be useful for ML models.To get some perspective, consider the number of images you have on your phone right now, the number of keystrokes you press daily, ...

Federated Learning Overview - Federated learning addresses the challenge of training ML models on private data residing on user devices [3] - It dispatches a model to end devices for training on private data and aggregates the trained models on a central server [4] - This approach reduces computation requirements on the server side by distributing most computation to user devices [3] Challenges in Federated Learning - Client devices have limited RAM and battery power, requiring efficient training methods [3] - Aggregating different models received from client devices to create a central model poses a challenge [3] - Privacy-sensitive datasets are often biased with personal likings and beliefs, leading to skewness in client data distribution [5] Data Privacy and Bias - Data on modern devices, such as images, messages, and voice notes, is mostly private [2][4] - The skewness in client data distribution, such as an overrepresentation of pet, car, or travel images, needs to be addressed [5] Application in Speech Recognition - Siri processes 25 billion (25B) requests per month, representing a large dataset for improving speech recognition [1] - The data on user devices can be leveraged to improve ML models [1]