Robot they're using is a Unitree A1: https://www.unitree.com/en/a1

The difference being each human has to spend thousands of hours learning that level of control over their bodies, and work hard to maintain that level of physical fitness. And if they fall off the roof, millions of dollars of potential earnings and GDP die with them. Whereas these robots are only $70k, and once one of them can do this, they all can do it. Just like with Chess and Go. It’s not impressive at first, then a couple years later, it’s better than humans could ever be, and it can be cheaply replicated ad infinitum.

The metrics they're using (2x its height for climbing a wall, 2x its length for crossing a gap) are weird and don't really relate to the same achievements for a traceur. 2x its height is really more like slightly over 1x its usable body for that maneuver (0.4m length, 0.51cm height of the climb). I agree, not extreme but still pretty impressive for a robot. We're not going to see them doing cat leaps any time soon ;)

On this topic, does anyone know of a suitable time-series forecaster for multivariate analysis? Eg 8 independent/input variables, and one output variable? I've been using multiple linear regression (which works impressively!) but it doesn't take into account the time series, only the single prior day of inputs. Thanks :)

Two explanations

First: Prophet is not actually "one model", it's closer to a non-parametric approach than just a single model type. This adds a lot of flexibility on the class of problems it can handle. With that said, Prophet is "flexible" not "universal". A time series of entirely random integers selected from range(0,10) will be handled quite poorly, but fortunately nobody cares about modeling this case.

Second: the same reason that only a small handful of possible stats/ML models get used on virtually all problems. Most problems which people solve with stats/ML share a number of common features which makes it appropriate to use the same model on them (the model's "assumptions"). Applications which don't have these features get treated as edge-cases and ignored, or you write a paper introducing a new type of model to handle it. Consider any ARIMA-type time series model. These are used all the time for many different problem spaces, and are going to do reasonably well on "most" "common" stochastic processes you encounter in "nature", because its constructed to resemble many types of natural processes. It's possible (trivial, even) to conceive of a stochastic process which ARIMA can't really handle (any non-stationary process will work), but in practice most things that ARIMA utterly fails for are not very interesting to model or we have models that work better for that case.

This is the HN comment thread on a well-written skeptical article with this zinger:

“You can imagine my disappointment when, out-of-the-box, Prophet was beaten soundly by a ‘take the last value’ forecast.”

I thought the biggest issue wasn't with the models themselves, but how Zillow decided to apply and act on them, which is why it didn't work in practice.

So on average their predictions may have been pretty good, but since each transaction also depends on the other party to accept their offer, and whether they get outbid, most of their predictions where the offer actually goes through would be on the tail end of where they slightly overestimated the price.

This tweet from the article summed it up nicely

> Zillow made the same mistake that every new quant trader makes early on: Mistaking an adversarial environment for a random one. https://twitter.com/0xdoug/status/1456032851477028870

I was lucky to make and learn from that mistake pretty quickly with some algorithmic trading on much smaller amounts. With housing transactions being much larger and slower, you wouldn't learn this lesson until it was too late. Models never perform as well in practice as they do in theory, and you need to remember to account for both known unknowns and unknown unknowns.

As others have pointed out, Prophet is not a particularly good model for forecasting, and has been superseded by a multitude of other models. If you want to do time series forecasting, I'd recommend using Darts: https://github.com/unit8co/darts. Darts implements a wide range of models and is fairly easy to use.

The problem with time series forecasting in general is that they make a lot of assumptions on the shape of your data, and you'll find you're spending a lot of time figuring out mutating your data. For example, they expect that your data comes at a very regular interval. This is fine if it's, say, the data from a weather station. This doesn't work well in clinical settings (imagine a patient admitted into the ER -- there is a burst of data, followed by no data).

That said, there's some interesting stuff out there that I've been experimenting with that seems to be more tolerant of irregular time series and can be quite useful. If you're interested in exchanging ideas, drop me a line (email in my profile).