A.I. Imitators and A.I. Problem Solvers - TMO Daily Observations 2023-01-11

On a day that Apple trumpets the many successes of its Services segment, ripoff ChatGPT apps are infesting Apple’s App Store. TMO Managing Editor Jeff Butts joins Ken to discuss the issue. Plus – generative A.I. isn’t just for drawing pictures and cheating on term papers anymore. Jeff looks at ways A.I. is building the cures of tomorrow.

'TinyML' Wants to Bring Machine Learning to Microcontroller Chips

TinyML is a joint project between IBM and MIT. It’s a machine learning project capable of running and low-memory and low-power microcontrollers.

[Microcontrollers] have a small CPU, are limited to a few hundred kilobytes of low-power memory (SRAM) and a few megabytes of storage, and don’t have any networking gear. They mostly don’t have a mains electricity source and must run on cell and coin batteries for years. Therefore, fitting deep learning models on MCUs can open the way for many applications.

Leak Shows Crime Prediction Software Targets Black and Latino Neighborhoods

Here’s some news from the beginning of the month that I missed. Gizmodo and The Markup analyzed PredPol, a crime prediction software used in the U.S.

Residents of neighborhoods where PredPol suggested few patrols tended to be Whiter and more middle- to upper-income. Many of these areas went years without a single crime prediction.

By contrast, neighborhoods the software targeted for increased patrols were more likely to be home to Blacks, Latinos, and families that would qualify for the federal free and reduced lunch program.

AWS Launches No-Code ML Service Called Amazon SageMaker Canvas

Amazon SageMaker Canvas is a new machine learning service that doesn’t require any coding. It lets you build ML models and generate predictions.

SageMaker Canvas leverages the same technology as Amazon SageMaker to automatically clean and combine your data, create hundreds of models under the hood, select the best performing one, and generate new individual or batch predictions. It supports multiple problem types such as binary classification, multi-class classification, numerical regression, and time series forecasting. These problem types let you address business-critical use cases, such as fraud detection, churn reduction, and inventory optimization, without writing a single line of code.

Apple ML Study Compares Supervised Versus Self-Supervised Learning

A research team at Apple published a study in October examining supervised and self-supervised algorithms. The title is “Do Self-Supervised and Supervised Methods Learn Similar Visual Representations?” From the abstract:

We find that the methods learn similar intermediate representations through dissimilar means, and that the representations diverge rapidly in the final few layers. We investigate this divergence, finding that it is caused by these layers strongly fitting to the distinct learning objectives. We also find that SimCLR’s objective implicitly fits the supervised objective in intermediate layers, but that the reverse is not true.