I'm not qualified enough to figure how drastic measures can be enough.

From countries realising they face a huge common crisis that they only survive it if they forget the squabbles and work together.

To using the AI itself to analyse and prevent it's own threats.

To classifying all trained general-purpose models of scale of ChatGPT and above and preventing the possibility of making the new ones (as I see entire-internet-packed models the biggest threat now, if they can be used without the safeguards)

And up to to forcebly reverting all publically avaiable computing and communication technology to the level of 20 of 30 years ago, until we figure how we can use it safely.

That is just voodoo accounting, all that money is from google.

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I'm sort of confused as to what this buys me as a developer. Sure, I can run the model with one click maybe. But that doesn't seem to get me any closer to my typical goal which is to have a module that I can drop right into my code base and use the model to solve one sub problem of a much larger system. I can see using this as sort of a fast way to demo a model maybe, but most repos are clean enough that it takes maybe 30 minutes to reproduce the environment and run the author model myself.

There are already a lot of open source tools that solve the other problem, by the way. One is pytorch-liberator which can extract all the code and dependencies from a module and package in a nice portable format for integration elsewhere.

As a general tip to you and your sales team, when you go to market with something like this you should have some value propositions lined up already instead of asking us whether we think it's valuable. Most folks will tend to assume not unless you can help them see what makes this useful.

Cool thanks, will try it out. This is just a side project i was trying. I just wanted to practically explore more about time series and how to pass multiple of them to a rnn model. My current objective is what I have written in the post's description. The " detecting ad" one was just a feature i thought of at that time of writing the reply. "If in future I wanted to extend the application how do I proceed with it" that's what the motive was behind asking that to you. Either way thanks, and sorry if I came out as "arguing".

>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?

Look at connectionist temporal classification loss, and a CNN.

Thankyouuu.

One option is Stanford NER, which is a named entity recognition tool developed by Stanford University. It uses a CRF (conditional random field) model trained on a large dataset of named entities, and it's relatively fast and accurate. Stanford NER also has pre-trained models available for various languages, so you could use one of these models or train your own model on a custom dataset.
Another option is spaCy, which is a popular natural language processing (NLP) library for Python. spaCy includes a named entity recognition component that uses a convolutional neural network (CNN) to identify named entities in text. It's generally quite accurate and fast, and it has pre-trained models available for various languages. spaCy also provides tools for training custom models on your own dataset, if you have specific named entities that you'd like the model to recognize.
Finally, you might also consider using the Google Cloud Natural Language API, which is a cloud-based NER service provided by Google. The API uses a machine learning model to identify named entities in text, and it's generally quite accurate and fast. It has pre-trained models available for various languages, and it provides tools for training custom models on your own dataset.

I'm not following what you're saying but you can detect all local minima with a single function call, order them and know their summary statistics with a second function call, and come up with a threshold based comparison for the end of the video if that's what you want.

None of this requires a machine learning model. You lost me when you mixed in "only when an ad occurs". Do you have any data that would help you train such a model? Are you just trying to detect ads? You could:

• identify all local minima attention drops
• engineer features such as distance into video, length of drop (time spent below average before and after local minima), magnitude of drop
• perform unsupervised learning, i.e. PCA/t-sne/k-means
• hope the "structural" features identified by unsupervised learning help you organize ads vs non-ads (they might!)

Again, not a complicated system because you don't have complex features as you've described them.

Is this just a novelty project? The way you're asking about it makes me think there's a low chance of follow through and your questions are kind of "arguing" towards a more complicated model. Run whatever code you are capable of then, I guess. I will probably decline to give further advice if that trend of leading questions continues.

I hate it, but see no other alternatives safe enough.

Haha I see your point. According to VentureBeat, DeepMind has been making 60M in Profit this year from 1,06B in revenue. Google bought it for 500M in 2014. Though you are right in saying that GAFAM already pouts significant funding into ML, 1B would probably mean a significant step up.

And you trust the governments of the world to make and impose these decisions on us? Because they have such a good track record so far?

Isnt 6 just google to deepmind every year? Or does that not count?

I think it’s interesting but for a hobbyist the pricing is too high. I would say some kind of tiered access would allow you to casually try it before committing.

I would like to try it, and I can afford $75/m, but it’s too much for something casual which I might forget about. Codec and MidJourney I both signed up to straight away.

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I understand the sentiment, but I think it's irresponsible. Possible bad consequences of AI misuse is worse by far than enything other research before. It's not a reason to stop them, but a reason to treat them with extreme care.

I'd say abosultely yes for about any field, except for AI.
Yes, it's unfair, but I would prefer to depend on goodwill of people, than on goodwill of machines.

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I agree with you, scientists/engineers and the like's goals is to produce tools/discoverys whether or not it can be used for someone's benefit or harm, what someone does with what they found or created isn't there concern it's the person who's using it that is of concern

If spaCy’s NER isn’t picking up what you need, you’ll probably need to look into creating your own annotations and fine tuning a model or training a custom model. It isn’t too hard using BIO/BILOU tags. Things like “raw materials” and particularly niche models and brands are unlikely to be picked up by off the shelf solutions.

First thought: decide for yourself who your target audience is

If you hope to sell this to companies, or even start-ups, be prepared for a lot of questions around data governance, security, ....

Second: do you have an idea how many users you need for break even and how the infrastructure needs to scale to cope with that? Gpu's aren't cheap of course, neither is electricity or cloud providers

Holmes could help with part of this.

nlp.uoregon.edu/fourie