ByteDance officially launches its latest Doubao large model 1.5 Pro (Doubao-1.5-pro), which demonstrates outstanding comprehensive capabilities in various fields, successfully surpassing the well-known GPT-4o and Claude3.5Sonnet in the industry. The release of this model marks an important step forward for ByteDance in the field of artificial intelligence. Doubao 1.5 Pro adopts a novel sparse MoE (Mixture of Experts) architecture, utilizing a smaller set of activation parameters for pre-training. This design's innovation...
I think it’s not fair to call DeepSeek open source. They’ve released the weights of their model but that’s all. The code they used to train it and the training data itself is decidedly not open source.
Sure, but that’s now become the accepted definition for open sourcing AI models. I personally find that’s sufficient especially given that they published the research associated with it, which is ultimately what matters the most.
That said, I strongly believe that the architecture of LLMs are fundamentally incapable of intelligent behavior. They’re more like a photograph of intelligence than the real thing.
I think you’d have to provide the definition of intelligence you’re using here. I’ll provide mine here. I would define it as the capacity to construct and refine mental models of specific domains in order to make predictions about future states or outcomes within those contexts. It stems from identifying rules, patterns, and relationships that govern a particular system or environment. It’s a combination of knowledge and pattern recognition that can be measured by predictive accuracy within a specific context.
Given that definition, I do not see why LLMs are fundamentally incapable of intelligent behavior. If a model is able to encode the rules of a particular domain then it is able to create an internal simulation of the system to make predictions about future states. And I think that’s precisely what deep neural networks do, ad how our own brains operate. To be clear, I’m not suggesting that GPT is directly analogous to the way the brain encodes information, rather that they operate in the same fundamental fashion.
However, you don’t need to dump an absurd amount of resources into training an llm to test the viability of any of the incremental improvements that DeepSeek has made. You only do that if your goal is to compete with OpenAI and others for access to capital.
How do you define what’s an absurd amount of resources, that seems kind of arbitrary to me. Furthermore, we also see that there are emergent phenomena that appear at certain scales. So, the exercise of building large models is useful to see what happens at those scales.
I would be much happier if the capital currently directed towards LLMs was redirected towards this type of work. Unfortunately, we’re forced to abide by the dictates of capitalism and so that won’t happen anytime soon.
I do think LLMs get disproportionate amount of attention, but eventually the hype will die down and people will start looking at other methods again. In fact, that’s exactly what’s already happening with stuff like neurosymbolic systems where deep neural networks are combined with symbolic logic. The GPT algorithm proved to be flexible and useful in many different contexts, so I don’t have a problem with people spending the time to find what its limits are.
Sure, but that’s now become the accepted definition for open sourcing AI models. I personally find that’s sufficient especially given that they published the research associated with it, which is ultimately what matters the most.
I think you’d have to provide the definition of intelligence you’re using here. I’ll provide mine here. I would define it as the capacity to construct and refine mental models of specific domains in order to make predictions about future states or outcomes within those contexts. It stems from identifying rules, patterns, and relationships that govern a particular system or environment. It’s a combination of knowledge and pattern recognition that can be measured by predictive accuracy within a specific context.
Given that definition, I do not see why LLMs are fundamentally incapable of intelligent behavior. If a model is able to encode the rules of a particular domain then it is able to create an internal simulation of the system to make predictions about future states. And I think that’s precisely what deep neural networks do, ad how our own brains operate. To be clear, I’m not suggesting that GPT is directly analogous to the way the brain encodes information, rather that they operate in the same fundamental fashion.
How do you define what’s an absurd amount of resources, that seems kind of arbitrary to me. Furthermore, we also see that there are emergent phenomena that appear at certain scales. So, the exercise of building large models is useful to see what happens at those scales.
I do think LLMs get disproportionate amount of attention, but eventually the hype will die down and people will start looking at other methods again. In fact, that’s exactly what’s already happening with stuff like neurosymbolic systems where deep neural networks are combined with symbolic logic. The GPT algorithm proved to be flexible and useful in many different contexts, so I don’t have a problem with people spending the time to find what its limits are.