polakallen

This is highly relevant to my interests. Also, looks to be the philosophy of intelligence, not just computer science.

Highlights from the table of contents:

9.3 John Searle: Anything Is a Computer . . . . . 360

9.3.5 Everything Is a Computer . . . . . . . . . . 367

9.7 What Else Might Be a Computer? . . . . . . . .375

9.7.1 Is a Brain a Computer? . . . . . . . . . . . .376

9.7.2 Is the Universe a Computer? . . . . . . . . . 377

Spoilers: you might be a computer, inside a computer.

This title has at least two valid semantic interpretations. I'm mildly disappointed.

<bad assumption redacted>

I'd argue that Facebook's moves create wealth, and are better for people in the long run. The opt-out policy allows this type of system to exist.

Getting rid of people would (or should) be the last thing on their minds. Reducing cost does not mean increasing growth. If anything, it further damages any chance to recover.

Now firing someone clearly and directly responsible for the reduced growth on the other hand...

No, I wouldn't necessarily expect the inverse situation to be true (hence why boys still maintain high scores).

It's not a matter of a "strange" experience, its a matter of expectations about outcomes. Basically, you could do the same test with men + women, but instead of having them visualize themselves as a stereotype, have them try a new "drug" that will supposedly improve their focus and math skills.

Comparing the results of that study with the originals will likely lead to some very interesting observations, no matter the result.

Here might be a good place to start learning: https://www.coursera.org/learn/machine-learning/

Much of what research is, is understanding what you're working on in the first place. Getting a solid foundation is a must, or you risk being very lost for some time, and will be fundamentally limited in what you can contribute.

What have you done so far in terms of learning machine learning? There's quite a few resources for getting a good foundation, if that's something you might need.

Ironically, looking dennybritz's comment, JS is particularly ill-suited for this, as, as far as I'm aware, there are no libraries for working on the GPU, which is tremendously useful for working with machine learning algorithms which usually are crunching lots of data.

However, I don't know much about karpathy's specific implementation and experiments, so there might be a reason he choose JS. I'm thinking its more likely to make visualizations easier rather than any computational reason though, so you might be stuck in a niche there.

Hmmm, I wonder how well this correlates with the placebo effect.

In other words, how deep do the effects of negative stereotyping run? The titular study shows that with concentrated effort to overcome the limiting stereotype, the effects of the stereotype can be mostly overcome.

What if you ignored the stereotype, and tried the experiment again, using an an arbitrary advantage. For instance, if the groups in the study were performed using a "drug" or other similar placebo.

Here is my humble recommendation:

1) Contribute to (or just read the code of) open source projects in order to improve your coding skills

2) Read up on existing research in topics you're interested in

I understand the desire to be efficient and kill two birds with one stone. However, finding a good, active, community built research project might be a bit difficult. Even if you find it, you may find that opportunities for you to contribute to it are very few.

Contributing to open source projects will give you a good opportunity to understand the open source contributing process (which you'll need to know when you do find that open source project you want to contribute to). It'll also allow you improve you coding skills while you're at it.

Reading research papers gives you much more bang for your buck in terms of developing your research interest, though is not very resume-able. But, who knows, maybe your reading will spark your own open source research project.