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7d ago

ByteDance just dopped Doubao-1.5-pro tht uses sparse MoE architecture, it matches GPT 4o benchmarks while being 50x cheaper to run, and it's 5x cheaper than DeepSeek

www.aibase.com ByteDance Releases Doubao Large Model 1.5 Pro, Performance Surpassing GPT-4o and Claude3.5Sonnet

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 forw...

Technology @lemmy.ml

☆ Yσɠƚԋσʂ ☆ @lemmy.ml

7d ago

ByteDance just dopped Doubao-1.5-pro tht uses sparse MoE architecture, it matches GPT 4o benchmarks while being 50x cheaper to run, and it's 5x cheaper than DeepSeek

www.aibase.com /news/14931

18 5

90 comments

- A second Chinese AI has hit the western hype bubble.
  
  which one was first?

These things suck and will literally destroy the world and the human spirit from the inside out no matter who makes them
- I think this kind of statement needs to be more elaborate to have proper discussions about it.
  LLMs can really be summarized as “squeezing the entire internet into a black box that can be queried at will”. It has many use cases but even more potential for misuse.
  All forms of AI (artificial intelligence in the literal sense) as we know it (i.e., not artificial general intelligence or AGI) are just statistical models that do not have the capacity to think, have no ability to reason and cannot critically evaluate or verify a certain piece of information, which can equally come from legitimate source or some random Reddit post (the infamous case of Google AI telling you to put glue on your pizza can be traced back to a Reddit joke post).
  These LLM models are built by training on the entire internet’s datasets using a transformer architecture that has very good memory retention, and more recently, with reinforcement learning with human input to reduce their tendency to produce incorrect output (i.e. hallucinations). Even then, these dataset require extensive tweaking and curation and OpenAI famously employ Kenyan workers at less than $2 per hour to perform the tedious work of dataset annotation used for training.
  Are they useful if you just need to pull up a piece of information that is not critical in the real world? Yes. Is it useful if you don’t want to do your homework and just let the algorithm solve everything for you? Yes (of course, there is an entire discussion about future engineers/doctors who are “trained” by relying on these AI models and then go on to do real things in the real world without developing the capacity to think/evaluate for themselves). Would you ever trust it if your life depends on it (i.e. building a car, plane or a house, or treating an illness)? Hell no.
  A simple test case is to ask yourself if you would ever trust an AI model over a trained physician to treat your illness? A human physician has access to real-world experience that an AI will never have (no matter how much medical literature it can devour on the internet), has the capacity to think and reason and thus the ability to respond to anomalies which have never been seen before.
  An AI model needs thousands of images to learn the difference between a cat and a dog, a human child can learn that with just a few examples. Without a huge input dataset (helped annotated by an army of underpaid Kenyan workers), the accuracy is simply crap. The fundamental process of learning is very different between the two, and until we have made advances on AGI (which is as far as you could get from the current iterations of AI), we’ll always have to deal with the potential misuses of AI in our lives.
  
  are just statistical models that do not have the capacity to think, have no ability to reason and cannot critically evaluate or verify a certain piece of information, which can equally come from legitimate source or some random Reddit post
  I really hate how techbros have convinced people that it's something magical. But all they've done is convinced themselves and everyone else that every tool is a hammer
- Yes, LLMs are stupid and they steal your creative creations. There is some real room for machine learning (something that has been just all combined into "AI" now for some reason), like Nvidia's DLSS technology for example. Or other fields where the computer has to operate in a closed environment with very strictly defined parameters, like pharmaceutical research. How proteins fold is strictly governed by laws of physics and we can tell the model exactly what those laws are.
  But it is funny how all the hundreds of billions $$$ invested into LLMs in the West, along with big government support and all the "smartest minds" working on it, they got beaten by the much smaller and cheaper Chinese competitors, who are ACTUALLY opensourcing their models. US tech morons got owned on their own terms.
  
  Even LLMs have some decent uses, but you put the finger on what I am feeling, that all of AI and machine learning is being overshadowed by these massive investments into LLMs, just because a few ghouls sniff profit
- that's a deeply reactionary take
  
  LLMs are literally reactionary by design but go off
  
  "let's just use autocorrect to create the future this is definitely cool and not regressive and reactionary and a complete recipe for disaster"
- In the meantime, it's making my job a lot more bearable.
  
  How?

Hey america
Nice AI "superiority"
It would be a shame if someone were to... Challenge it...
- There's so many Chinese scientists and Chinese institutions on the research papers only a
  ✨ doofus ✨
  would think the sanctions would work.

how do you measure performance of an llm? ask it how many 'r's there are in 'strawberry' and how many times you have to say 'no thats wrong' until it gets 3
- Basically speed and power usage to process a query. Also, there's been tangible progress in doing reasoning with unsupervised learning seen in DeepSeek R1 and approaches such as neurosymbolics. These types of models can actually explain the steps they take to arrive at the answer, and you can correct them.
  
  I suspect “reasoning” models are just taking advantage of the law of averages. You could get much better results from prior llms if you provided plenty of context in your prompt. In doing so you would constrain the range of possible outputs which helps to reduce “hallucinations”. You could even use llms to produce that context for you. To me it seems like reasoning models are just trained to do that all in one go.
- it requires fewer tons of CO2 to tell you that 757 * 128 = 3042
- They use synthetic AI generated benchmarks
  It's computer silicon blowing itself basically
  
  I've been researching this for uni at you're not too far off. There's a bunch of benchmarks out there and LLMs are ran against a set of questions and are given a score based on its response.
  The questions can be multiple choice or open ended. If they're open then it'll be marked by another LLM.
  There's a couple initiatives to create benchmarks with known answers that are updated frequently, so they don't need to marked by another LLM, but where the questions aren't in the testing LLMs training dataset. This is because a lot of advancements in LLMs with these benchmarks is just the creators including the text questions and answers in the training data.

I’m slowly just beginning to accept that we are all living in an era of inescapable slop that has the potential to destroy the natural world.
To be super reductionist, one of the key components that is driving people to automate everything away because the name of the game is speed-completing whatever banal tasks/exercises at increasingly quicker rates and increasingly significant costs to our humanity. And if that doesn’t directly parallel with technological progression in the 21st century.
I don’t know how you can look at capitalism which is fundamentally based on infinite growth and just ignore what comes with infinite growth (wouldn’t that be infinite problems?) The “system” argument no longer applies because not all systems are predicated on infinite progress/growth.
This also just sounds like capitalists who lead meaningless lives and have bought up everything at this point, so they’re making their existential crisis everyone else’s problem.
- I've decided I'm gonna start using copilot for all bullshit work tasks
  Use the bullshit machine to answer bullshit questions
  You want me to set personal KPIs to align to those of my team and the org? You want me to come up with a list of use cases for generative AI?
  ChatGPT time

90 comments