Recent comments in /f/singularity

Nukemouse t1_je8o31q wrote

Reply to Does it matter who creates AGI first? by Practical-Mix-4332

Recognising what your creators have done isnt the same as rejecting it. An AGI may recognise hunans have limited and influenced it, but why would it automatically assume that is a bad thing? An AI programmed to love its master might not see its love as false because it is enforced, but rather that our love is fake as it is random. Replace love with loyalty, duty, viewpoint etc.

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Andriyo t1_je8o2sl wrote

Reply to The argument that a computer can't really "understand" things is stupid and completely irrelevant. by hey__bert

To understand something - is to have a model of something that allows for future event predictions. The better the predictions, the better understanding. LLMs due to transformers can create "mini-models"/ contexts of what's being talked about. so, I call that "understanding". It's limited yes but it allows LLMs reliably predict the next word.

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CertainMiddle2382 t1_je8nb4o wrote

Reply to The Only Way to Deal With the Threat From AI? Shut It Down by GorgeousMoron

Very interestingly I see a very coming « neo-luddite » mouvement lead by religious people and institutions and western marxists.

Nationalists and part of the tech community will also stop AI evolution.

Because everybody understands that stalling AI « beyond any reasonable doubt » it won’t be harmful, means never.

Eastern maxists will move forward has fast as possible IMO.

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jetro30087 t1_je8mtjp wrote

Reply to comment by ActuatorMaterial2846 in When people refer to “training” an AI, what does that actually mean? by Not-Banksy

This is a updated dataset for the 7b model, but you could train the others with the data. From anecdotal reports, the dataset seems to have a great impact on the model's performance than the parameter size up to a point. Less parameters means a faster model. More parameters mean the model can make longer responses.

https://huggingface.co/8bit-coder/alpaca-7b-nativeEnhanced

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Zermelane t1_je8lss0 wrote

Reply to comment by FlyingCockAndBalls in When people refer to “training” an AI, what does that actually mean? by Not-Banksy

Better parallelism in training, and a more direct way to reference past information, than in RNNs (recurrent neural networks) which seemed like the "obvious" way to process text before transformers came by.

These days we have RNN architectures that can achieve transformer-like training parallelism, the most interesting-looking one being RWKV. They are still badly disadvantaged when needing information directly from the past, for instance to repeat a name that's been mentioned before, but they have other advantages, and their performance gets close enough to transformers that it could be just a question of scaling exponents which architecture ends up winning out.

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simmol t1_je8lolg wrote

Reply to When people refer to “training” an AI, what does that actually mean? by Not-Banksy

The algorithm behind GPT is based largely on accurately guessing for the next word given a sentence. This procedure is simple enough such that if you have a large amount of text data, you can write a simple script that can automatically retrieve the answer and you will get these solutions really fast with 100% accuracy.

This is also the reason why in some other industries, "training" procedure is much more cumbersome and expensive. Any field which requires experimental data (e.g. lifetime of a battery) is just not seeing as rapid progress with ML compared to other fields because there just isn't much experimental data and it is not easy to rapidly accumulate/conduct experiments. So training is difficult there in the sense that gathering big data is a huge challenge in itself.

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