playground VS simonwillisonblog

Not the parent, but NNs typically work better when you can't linearize your data. For classification, that means a space in which hyperplanes separate classes, and for regression a space in which a linear approximation is good.

For example, take the circle dataset here: https://playground.tensorflow.org

That doesn't look immediately linearly separable, but since it is 2D we have the insight that parameterizing by radius would do the trick. Now try doing that in 1000 dimensions. Sometimes you can, sometimes you can't or do want to bother.

For visualisation and some fun: http://playground.tensorflow.org/

https://seeing-theory.brown.edu/

https://www.3blue1brown.com/

https://playground.tensorflow.org/

There’s an interactive neural network you can train here, which can give some intuition on wider vs larger networks:

https://mlu-explain.github.io/neural-networks/

See also here:

http://playground.tensorflow.org/

This site is worth playing around with to get a feel for neural networks, and somewhat about ML in general. There are lots of strategies for statistical learning, and neural nets are only one of them, but they essentially always boil down into figuring out how to build a “classifier”, to try to classify data points into whatever category they best belong in.

I don’t know much experimenting you’ve done, but many repeated small scale experiments might give you a better intuition at least. I highly recommend this online tool for playing with different environmental variables, even if you’re comfortable coding up your own experiments: http://playground.tensorflow.org

Even if you can’t code, play around with this tool: https://playground.tensorflow.org — you can adjust the shape of the NN and watch how well it classifies the data. Model size obviously matters.

I don't think so. You can easily play around in the browser, using Javascript, or on https://processing.org/, https://playground.tensorflow.org/, https://scratch.mit.edu/, etc.

If anything the problem is that today's kids have too many options. And sure, some are commercial.

Well there is no point of using a multilayer linear neural network, because a cascade of linear transformations can be reduced to a single linear transformation. So you can only approximate linear functions. However if you have prior knowledge about the non linearity of your data lets say you know that it is a linear combination of polynomials up to certain degree, you can expand your input space by explicitly making non linear transformation. For instance a 1D linear regression can be modeled by 2 input neurons and 1 output neuron where the activation of the output is the identity. The input neuron x0 will take a constant input namely 1 and the second input neuron x1 will takes your data x. The output neuron will be y=w_0 * 1+w_1 *x which is equal to y=w_0 +w_1 * x. Let us say that your data follows a polynomial form, the idea is to add input neurons and expand your input to for instance X=[1 x x2] in this case you have 3 input neurons where the third is an explict non linear form of the input so y=w_0 + w_1 x +w_2 x2. The general idea is to find a space where the problem becomes linear. In real life example these spaces are non trivial the power of neural network is that they can find by optimization such space without explicitly encoding these non linearities. Try playing around with https://playground.tensorflow.org/ you can get an intuition about your question.

Simon Willison has pointed out several examples of prompt injection attacks and why it may never be a solved problem:

I want more people to have link blogs.

I have one in the sidebar of https://simonwillison.net/ which I've been running since November 2003. You can search through all 6,836 links here: https://simonwillison.net/search/?type=blogmark

I can post things to it with a bookmarklet. It has an Atom feed.

It's such a low-friction way of publishing. A lot of https://daringfireball.net works like this too. I also like https://waxy.org/ and https://kottke.org/ for this.

I'd love to see more of these.

My approach is to publish to my own blog at https://simonwillison.net and then copy and paste content from that into a Substack newsletter at https://simonw.substack.com a few times a month.

It's been working really well.

Substack don't have an API, but they do support copy and paste - so I built myself a tool that assembles my blog content into rich text I can copy and paste straight into the Substack editor.

I wrote about how that works here: https://simonwillison.net/2023/Apr/4/substack-observable/

Hah, yeah securing something like WordPress can be a challenge, especially if you're running a bunch of plugins.

My blog is a pretty straight-forward Django setup without many other dependencies, so it's a lot less of an attack surface: https://github.com/simonw/simonwillisonblog

That specific prompt is just an example and it's pretty bad, it was the shortest and simplest prompt I could come up with that would be easily understood.

You can set response content-types (text, html, json, etc...). If you use json it will get pretty good results because I have some is some logic to attempt to pick out json or json5 objects from the text output. I dont yet have logic to support json arrays, but I'm hoping to add that soon.

But still client side validation is needed for applications with untrusted input. I dont attempt to solve prompt injection. I saw a lot of interesting posts on this topic from this blog https://simonwillison.net/. I need to find sometime to read more about it.

Try this one instead, it should be better

I've started to consider "commit to writing about it" as the price I have to pay for giving into the lure of another project. It's one of the main reasons I publish so much content on https://simonwillison.net/ and https://til.simonwillison.net

A project with a published write-up unlocks so much more value than one which you complete without giving others a chance of understanding what you built.

I've maintained internal blogs (sometimes just a Slack channel or Confluence area) at previous employers for this purpose too.

I think you are asking specifically about practical LLM engineering and not the underlying science.

Honestly this is all moving so fast you can do well by reading the news, following a few reddits/substacks, and skimming the prompt engineering papers as they come out every week (!).

https://www.latent.space/p/ai-engineer provides an early manifesto for this nascent layer of the stack.

Zvi writes a good roundup (though he is concerned mostly with alignment so skip if you don’t like that angle): https://thezvi.substack.com/p/ai-18-the-great-debate-debates

Simon W has some good writeups too: https://simonwillison.net/

I strongly recommend playing with the OpenAI APIs and working with langchain in a Colab notebook to get a feel for how these all fit together. Also, the tools here are incredibly simple and easy to understand (very new) so looking at, say, https://github.com/minimaxir/simpleaichat/tree/main/simpleai... or https://github.com/smol-ai/developer and digging in to the prompts, what goes in system vs assistant roles, how you gourde the LLM, etc.

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