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I peeped. Said it’s only worth it if it can do complex shyt which it can’t.Goldman Sachs: 'Limited Economic Upside From AI'
Goldman Sachs is throwing cold water on the artificial intelligence industry, saying its ability to solve problems doesn't justify its cost.
The report also says replacing the entire US power grid is the only way to run it effectively.
You can read the report here:
Wayback Machine
web.archive.org
only a 25% of these jobs will be cost effective to use AI with in 10 years?
another resource sucking grift like electric cars
Damn, you all in on generative AIonly a 25% of these jobs will be cost effective to use AI with in 10 years?
data entry, bookkeeping, accounting, customer service, telemarketing, transcription, translation, medical diagnosis, image analysis, manufacturing, assembly line work, self-driving cars, trucking, delivery, warehouse management, inventory management, supply chain management, administrative tasks, scheduling, email management, data processing, data analysis, reporting, bookkeeping, accounting, financial analysis, financial planning, tax preparation, auditing, compliance, legal research, document review, contract review, content moderation, social media management, marketing automation, lead generation, sales forecasting, customer segmentation, chatbots, virtual assistants, language translation, content creation, writing, editing, proofreading, research, data scraping, web development, software testing, quality assurance, network administration, cybersecurity, vulnerability assessment, penetration testing, predictive maintenance, quality control, inspection, monitoring, surveillance, healthcare diagnosis, medical imaging, clinical decision support, medical research, pharmaceutical research, material science, genomics, proteomics, robotics, process automation, industrial automation, building automation, home automation, drones, farming, forestry, environmental monitoring, climate modeling, weather forecasting, traffic management, urban planning, architecture, engineering, design, animation, video production, audio production, music composition, art generation, graphic design, fashion design, interior design.
people have to start understanding, Garbage In, Garbage out. his assessment of summarization would help if they provided examples that includes how it Large Language Model was prompted and what LLM was used. I don't doubt they got nonsensical results but I wonder about the input. there are private evaluation tests that companies and other organizations are using to test for the outliers that AI models wouldn't be able to train on to see how wel they can reason.
I don't think this paper will age well.
Playing god will backfire on these fukks just you watch...
Damn, you all on on generative AI
I think the big issue is the cost to operate it. Idk man, this shyt smells like the final grift.
the tech is in it's infancy so I don't know why theres wide spread sentiment that it can't possibly become cheaper to train and operate.
Google claims new AI training tech is 13 times faster and 10 times more power efficient — DeepMind's new JEST optimizes training data for impressive gains
DeepMind researchers have published their findings on the JEST technique, allowing large language models to be trained at significantly improved speed and efficiency. It remains to be seen whether companies will adopt it to keep power draw low, or crank things to the max.www.yahoo.com
Google claims new AI training tech is 13 times faster and 10 times more power efficient — DeepMind's new JEST optimizes training data for impressive gains
Dallin Grimm
Sun, July 7, 2024 at 3:40 PM EDT·3 min read
Credit:
Google DeepMind, Google's AI research lab, has published new research on training AI models that claims to greatly accelerate both training speed and energy efficiency by an order of magnitude, yielding 13 times more performance and ten times higher power efficiency than other methods. The new JEST training method comes in a timely fashion as conversations about the environmental impact of AI data centers are heating up.
DeepMind's method, dubbed JEST or joint example selection, breaks apart from traditional AI model training techniques in a simple fashion. Typical training methods focus on individual data points for training and learning, while JEST trains based on entire batches. The JEST method first creates a smaller AI model that will grade data quality from extremely high-quality sources, ranking the batches by quality. Then it compares that grading to a larger, lower-quality set. The small JEST model determines the batches most fit for training, and a large model is then trained from the findings of the smaller model.
The paper itself, available here, provides a more thorough explanation of the processes used in the study and the future of the research.
DeepMind researchers make it clear in their paper that this "ability to steer the data selection process towards the distribution of smaller, well-curated datasets" is essential to the success of the JEST method. Success is the correct word for this research; DeepMind claims that "our approach surpasses state-of-the-art models with up to 13× fewer iterations and 10× less computation."
Graphs displaying efficiency and speed gains over traditional AI training methods.
Of course, this system relies entirely on the quality of its training data, as the bootstrapping technique falls apart without a human-curated data set of the highest possible quality. Nowhere is the mantra "garbage in, garbage out" truer than this method, which attempts to "skip ahead" in its training process. This makes the JEST method much more difficult for hobbyists or amateur AI developers to match than most others, as expert-level research skills are likely required to curate the initial highest-grade training data.
The JEST research comes not a moment too soon, as the tech industry and world governments are beginning discussions on artificial intelligence's extreme power demands. AI workloads took up about 4.3 GW in 2023, almost matching the annual power consumption of the nation of Cyprus. And things are definitely not slowing down: a single ChatGPT request costs 10x more than a Google search in power, and Arm's CEO estimates that AI will take up a quarter of the United States' power grid by 2030.
If and how JEST methods are adopted by major players in the AI space remains to be seen. GPT-4o reportedly cost $100 million to train, and future larger models may soon hit the billion-dollar mark, so firms are likely hunting for ways to save their wallets in this department. Hopefuls think that JEST methods will be used to keep current training productivity rates at much lower power draws, easing the costs of AI and helping the planet. However, much more likely is that the machine of capital will keep the pedal to the metal, using JEST methods to keep power draw at maximum for hyper-fast training output. Cost savings versus output scale, who will win?
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In April we published a paper on a new training approach for better & faster LLMs using multi-token prediction. To enable further exploration by researchers, we’ve released pre-trained models for code completion using this approach on
@HuggingFace
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Welcome Multi Token Prediction: Get up to 3-5x tokens/ sec from your llamas!
Kudos to Meta for continuing its commitment to open science
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Meta proposed a new approach to build better and faster LLMs by using multi-token prediction.
Using this approach, they trained language models to predict multiple future words at once—instead of the old one-at-a-time approach.
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