theschreon

I agree. But at the same time, insisting on 100% certainty/safety would mean to not do anything at all and stick to the status quo forever. It boils down to cost-benefit-calculations.

While I agree that it is unacceptable to use customer data without consent (as suggested by OP's post), I disagree with the implicit assumption behind the comment that I responded to:

Namely the implicit assumption that human/biological intelligence/agents are somehow superior to artificial intelligence/agents in face of secrecy/confidentiality.

It boils down to the question whether it's possible or not to create algorithms that outperform humans in tasks involving secrecy and confidentiality.

While I can't think of any reason why that should not be possible in general, I agree that the current SOTA of generative LLMs is not sufficient for that.

Is throwing lots and lots of data and RLHF training on an LLM enough in order to make the probability of customer data leaks small enough to be acceptable?

I don't know. But I don't trust MBAs who salivate with dollar signs in their eyes to know either. And I fear that their lack of technical understanding will lead to bad decisions. And I fear those might lead to scandals that make Gemini's weird biases in image generation pale in comparison.

There's no sure-fire way to make a human who is processing a document manually never leak the information they read.

"Search is all well and good when we are counting words, which is what data analytics and machine learning are really all about."

There are machine learning models which go far beyond counting words, for example see https://arxiv.org/abs/1502.01710

Does this also work for larger numbers of features? (like, 2000, as opposed to 6-9 in the demo)

Once you have identified the most significant individuals on a field, you can use google scholar alerts to get notified when they publish something new. (http://scholar.google.com/scholar_alerts?view_op=list_alerts). In the case of machine learning that would be Geoffrey Hinton, Yann LeCun, Yoshua Bengio, Andrew Ng (this list is not exhaustive of course).

You could try the following improvements to speed up neural network training:

- Resilient Propagation (RPROP), it significantly speeds up training for full batch learning: http://davinci.fmph.uniba.sk/~uhliarik4/recognition/resource...

- RMSProp, introduced by Geoffrey Hinton, also speeds up training but can also be used for mini-batch learning: https://class.coursera.org/neuralnets-2012-001/lecture/67 (sign up to view the video)

Please consider more datasets when benchmarking methods:

- MNIST ( 70k 28x28 pixel images of handwritten digits ): http://yann.lecun.com/exdb/mnist/ . There are several wrappers for Python on github.

- UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/datasets.html

Will this software focus solely on Random Forests? Hoped to see e.g. Deep Convolutional Neural Networks as an option :(

Sometimes the underlying processes an application is designed for are too complex to be self-explanatory. Look at e.g. Photoshop, would not work without tutorials.