Price Wars are Nigh, SensorFM, Harness Matter, & More

TLDR;

Welcome back! This week, we have a lot to discuss. The last few days were probably the most active for top model releases from the US in perhaps a year, with two Labs that had fallen from grace somehow making a comeback.

We also discuss really cool tech from Google in SensorFM, a robotics hand that is the stuff of nightmares, a Databricks research showing that AI is much more than the model itself, and more.

Enjoy!

THEWHITEBOX

A Universal AI Sensor Model

In one of the most heated weeks so far, with multiple incredible model releases, my favorite news has been a new paper from Google and its model SensorFM.

This model has been trained on data from 5 million people and has learned a general-purpose representation of human physiology that transfers across 35 health prediction tasks.

Ok, but what does that even mean? Well, it means I’m excited now because it opens the path to personalized health. Let me explain why.

Currently, billions of wearables track sensory signals from the wrists of millions of people. However, these devices are extremely noisy, and translating that sensor data into meaningful health insights is debatable at best.

Each company measures differently, each device registers the data differently, and, importantly, each human is different. This means that, in reality, the signal-to-noise ratio is very low.

But what if we trained an AI to process the data across millions of humans? That is what Google did, training the AI on one trillion minutes of data, or roughly 2 million years, and the results are pretty incredible.

But first: how do we train a model with that data?

If you read my last newsletter, which I recommend you do, I explained that an AI can only learn what can be measured. And by "measure," I mean models learn by making predictions and comparing them to a ground truth (what they should actually have predicted).

For these one trillion minutes, we don’t have a ground truth (officially known as ‘label’); we don’t know what each of these one trillion minutes is showing. Depression? Anxiety?

Ideally, you would want a dataset that describes the following: if sensor data looks like this, the human has heart problems. If the data looks like this, the human is fine. Eventually, the AI makes the connection and learns to associate certain data patterns with certain outcomes.

But if there’s no way to measure the outcome, how are they supposed to learn? And the answer is unsupervised learning, which I did not include in an already 8,000-long piece, but it’s essentially a rare type of training where the data itself is the signal.

Essentially, we’re telling the AI: “We can’t really associate the patterns you’re going to find with particular outcomes. However, we still want you to find them.”

But why would we want this? For things like clustering. Although the AI doesn’t fully understand the implications of each pattern, it can still classify them.

If we give an AI…

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