Is "homicide rate" specific to firearm homicides?

I think this would be better visualized as a graph of price vs. searches (or some smooth of searches) rather than both versus time.

This sample size is meager, and this sampling process is sketchy. People who answer on-line surveys are not a representative sample of parents in the United States.

This presents on-line survey data of parents about the sweet foods their children consume, n = 1,135, with a minimum of n = 20 per state. Ignoring the obvious sampling issues, it'd be interesting to see confidence intervals of the mean for each state. Also, I'm not sure why the data is presented twice: per day, and per week, which is simply 7 times the daily figure.

It would be interesting to contrast this with the proportion of votes cast in ranked choice elections.

Some modeling algorithms (naive bayes and local models, like k-nearest neighbor or kernel regression) can be updated immediately. In some sense, they can be used for recall and training very close in time.

It is interesting to fit a trend to this data. The least-squares fit of a 3-parameter logistic (Time = years since 1966) is:

548328.1273 * logistic(0.078376864 * Time - 4.852042189)

...which is over 5 times over-determined and has an apparent proportionate reduction in variance of 98.9%.

As an extension of this, it'd be interesting to see the cross-correlation for other offsets in which public sentiment precedes the stock market outcome.

It'd be interesting to see "average miles driven" added to this.

Just to be clear, deep learning is the energy consumer. "Shallow" machine learning (logistic regression, multilayer perceptron, tree induction, etc.) and related technologies cost pennies to fit.

>I get that he is one of the godfathers of AI.

What does that even mean? Very many people have contributed to this field.

>This medium article is ...

Let me stop you right there.

Deep learning and "shallow" machine learning are generally appropriate for different types of problems.

To be clear, there are neural networks which are "deep", and others which are "shallow" (few hidden layers). From a practical standpoint, the latter have more in common with other "shallow" learning methods (tree-induction, statistical regressions, k-nearest neighbor, etc.) than they do with deep learning.

You're right that many people (especially in the non-technical press) have erroneously used "machine learning" to mean specifically "deep learning", just as they've used "artificial intelligence" to mean "machine learning". Regardless, there are still non-deep machine learning methods and other branches of A.I. In practice, non-deep machine learning represents the overwhelming majority of applications today.

I haven't followed the research as closely in recent years, but I can tell you that, deep learning aside, people have only begun to scratch the surface of machine learning application.

>I was in the understanding that two contiguous linear layers in a NN would be no better than only one linear layer.

This is correct: In terms of the functions they can represent, two consecutive linear layers are algebraically equivalent to one linear layer.

The biggest technical challenges I see:

1. Having enough reference samples from known people
2. The difference how people write on Reddit and how they write elsewhere (professional articles, e-mail, etc.: presumably used as reference)
3. If too many Reddit users are being considered, it may all dissolve into mush (estimated probabilities would all be low)

>With more compute could it be easy to quickly un Mask all the people on Reddit by using text correlations to non masked publicly available text data?

With labeled samples of text, I think it would be pretty easy to come up with a a likelihood model, giving a reasonable educated guess of the identity of some Reddit members, and I don't think it would take much computing power.

Can you give some examples of problems that an organization would solve with open source models?

Sobel filters detect edges, not corners.

Assuming that you are trying to locate all corners of a rectangle in a raster image, I suggest researching corner detection in image processing.

No, but the idea is pretty straightforward. Assuming that experts can provide domain knowledge that can be coded as conditions or rules (IF engine_temperature > 95 AND coolant_pressure < 12 THEN engine_status = "CRITICAL"), these can be used to generate 0/1 flags based on existing data to augment the training variables.

This can be made much more complex by using actual expert systems or fuzzy logic. There are entire sections of the technical library for those. For fuzzy logic, I would recommend:

"The Fuzzy Systems Handbook"

by Earl Cox

ISBN-13: 978-0121942700

Expert knowledge could be encoded as rules whose output is used as features for a machine learning system. These rules would accept data you already have, and produce new data as conclusions which would be fed as extra variables to a modeling algorithm.

>Which is why it's important to not give access to dangerous things into hands of those who could misuse it with catastrophic consequences.

What does "give access" mean, in this context? Information on construction of learning systems is widely available. Also, who decides which people "could misuse it"? You?

What, specifically, are you suggesting?

So, your suggestion is that countries- like the United States, China and Russia work together to contain technology? This seems like a serious suggestion to you?