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12 apr 2023 · There is some fallacious reasoning along the lines of "Bigger is always better". Example 1: Eating food makes you satisfied. Person A has eaten more food than Person B, therefore Person A will be "more satisfied" than Person B.
9 apr 2024 · Men often think that "bigger is better" when it comes to their defining appendage. However, a new meta-analysis study from Stanford University says that is not the case. Provided by The Daily...
1.1K votes, 46 comments. 70K subscribers in the bigger community. A subreddit dedicated to celebrating female and femme weight gain. We take pride in…
18 gen 2021 · Bigger Isn’t Always Better. Let’s say you’ve exhausted all your options. You’ve shown grace, humility, and open-mindedness. You’ve apologized for your actions.
- Big, Bigger, Better?
- Scaling Laws
- Reasonable Concerns
- The Problems of Scale
- Smarter and smaller?
- Energy-Efficient LLMs
LLMs such as ChatGPT and Minerva are giant networks of computing units (also called artificial neurons), arranged in layers. An LLM’s size is measured in how many parameters it has — the adjustable values that describe the strength of the connections between neurons. Training such a network involves asking it to predict masked portions of a known s...
That the biggest Minerva model did best was in line with studies that have revealed scaling laws — rules that govern how performance improves with model size. A study in 2020 showed that models did better when given one of three things: more parameters, more training data or more ‘compute’ (the number of computing operations executed during trainin...
François Chollet, an AI researcher at Google in Mountain View, is among the sceptics who argue that no matter how big LLMs become, they will never get near to having the ability to reason (or mimic reasoning) well enough to solve new problems reliably. An LLM only appears to reason by using templates that it has encountered before, whether in the t...
While the debate plays out, there are already pressing concerns over the trend towards larger language models. One is that the data sets, computing power and expense involved in training big LLMs restricts their development — and therefore their research direction — to companies with immense computing resources. OpenAI has not confirmed the costs o...
For many scientists, then, there’s a pressing need to reduce LLM’s energy consumption — to make neural networks smaller and more efficient, as well as, perhaps, smarter. Besides the energy costs of training LLMs (which, although substantial, are a one-off), the energy needed for inference — in which LLMs answer queries — can shoot up as the number ...
Meanwhile, researchers are experimenting with different ways to make existing LLMs more energy efficient, and smarter. In December 2021, DeepMind reported a system called RETRO, which combines an LLM with an external database. The LLM uses relevant text retrieved from this database during inference to help it make predictions. DeepMind’s researcher...
1 ago 2023 · It has always been about 'bigger is better'. This could pave the way for more sustainable, cost-effective, and targeted AI applications. Also, the environmental implications cannot be ignored.
[A house that's] bigger [than your current house] is not always better [than the current size of your house]. The comparison in "bigger is not always better" is between what you said ("I have a big house") and what your friend was saying could be better (a smaller house).
