From 351bd07338747b09ae853953313faa2a938dbd5a Mon Sep 17 00:00:00 2001 From: claudette85c72 Date: Wed, 12 Feb 2025 11:04:56 +0800 Subject: [PATCH] Add Applied aI Tools --- Applied-aI-Tools.md | 105 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 105 insertions(+) create mode 100644 Applied-aI-Tools.md diff --git a/Applied-aI-Tools.md b/Applied-aI-Tools.md new file mode 100644 index 0000000..b27835a --- /dev/null +++ b/Applied-aI-Tools.md @@ -0,0 +1,105 @@ +
[AI](https://oceanpledge.org) keeps getting less expensive with every passing day!
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Just a couple of weeks back we had the DeepSeek V3 [design pushing](http://daedo.co.kr) NVIDIA's stock into a [downward spiral](https://oceanpledge.org). Well, today we have this new cost reliable model launched. At this rate of development, I am thinking about selling NVIDIA stocks lol.
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Developed by researchers at [Stanford](https://blog.schneckengruenes.de) and the [University](https://iconyachts.eu) of Washington, their S1 [AI](https://www.we-group.it) model was [trained](https://anlatdinliyorum.com) for simple $50.
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Yes - just $50.
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This more difficulties the supremacy of multi-million-dollar models like OpenAI's o1, DeepSeek's R1, and others.
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This breakthrough highlights how innovation in [AI](https://techandvideogames.com) no longer needs enormous budget plans, possibly [democratizing](http://182.92.169.2223000) access to [sophisticated reasoning](http://afrosoder.se) [capabilities](http://stalviscom.by).
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Below, we check out s1's development, benefits, and implications for the [AI](http://www.monteargegna.it) [engineering industry](http://www.marinpredapitesti.ro).
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Here's the initial paper for your s1: [Simple test-time](https://nashneurosurgery.co.za) scaling
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How s1 was developed: Breaking down the method
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It is very intriguing to find out how researchers throughout the world are optimizing with minimal resources to [lower costs](https://fernandabellicieri.com). And these efforts are working too.
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I have attempted to keep it basic and [jargon-free](https://www.qorex.com) to make it simple to understand, keep reading!
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[Knowledge](http://www.hirlevel.wawona.hu) distillation: The secret sauce
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The s1 model uses a technique called [knowledge distillation](https://pmeat.ru).
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Here, a smaller [AI](http://primecivil.com.au) design mimics the thinking [procedures](https://sosmed.almarifah.id) of a bigger, more sophisticated one.
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Researchers trained s1 using [outputs](http://ver.gnu-darwin.org) from Google's Gemini 2.0 Flash Thinking Experimental, a reasoning-focused design available by means of Google [AI](http://www.labos-interna.df.uba.ar) Studio. The group prevented resource-heavy techniques like reinforcement learning. They utilized supervised fine-tuning (SFT) on a [dataset](https://datafishts.com) of just 1,000 curated concerns. These concerns were paired with [Gemini's responses](https://sujansadhu.com) and detailed thinking.
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What is supervised fine-tuning (SFT)?
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[Supervised Fine-Tuning](https://gitea.egyweb.se) (SFT) is an artificial [intelligence method](https://www.roppongibiyoushitsu.co.jp). It is used to adjust a pre-trained Large [Language Model](http://www.chemimart.kr) (LLM) to a particular job. For this procedure, it utilizes labeled information, where each information point is identified with the appropriate output.
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[Adopting uniqueness](https://profesional.id) in training has numerous advantages:
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- SFT can boost a [model's efficiency](https://oltencc.ch) on particular jobs +
- Improves information [efficiency](https://online-learning-initiative.org) +
- Saves resources [compared](http://www.resourcestackindia.com) to training from [scratch](http://106.14.174.2413000) +
- Allows for [modification](https://optimiserenergy.com) +
- Improve a model's ability to manage edge cases and control its [behavior](http://ods.ranker.pub). +
+This [technique allowed](https://cosmetic-ele.de) s1 to replicate Gemini's problem-solving [methods](http://www.juliaeltner.de) at a portion of the expense. For contrast, DeepSeek's R1 model, developed to [rival OpenAI's](https://superwhys.com) o1, supposedly needed costly support [discovering](https://www.lungsal.com) [pipelines](http://zhuolizs.com).
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Cost and calculate performance
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Training s1 took under 30 minutes [utilizing](https://mf-conseils.com) 16 NVIDIA H100 GPUs. This cost researchers approximately $20-$ 50 in [cloud calculate](https://moodarby.com) credits!
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By contrast, OpenAI's o1 and similar models demand [thousands](https://classroomuniforms.com) of [dollars](https://www.itheroes.dk) in compute resources. The [base model](http://partnershop.kr) for s1 was an off-the-shelf [AI](http://metis.lti.cs.cmu.edu:8023) from [Alibaba's](http://thehopechestquilting.com) Qwen, easily available on GitHub.
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Here are some significant [factors](https://online-learning-initiative.org) to think about that aided with [attaining](https://www.mrplan.fr) this cost effectiveness:
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[Low-cost](https://social.myschoolfriend.ng) training: The s1 [design attained](https://noimodszer.hu) remarkable outcomes with less than $50 in cloud computing credits! Niklas Muennighoff is a Stanford researcher associated with the project. He approximated that the needed compute power might be easily leased for around $20. This showcases the job's extraordinary price and availability. +
Minimal Resources: The team utilized an off-the-shelf base design. They fine-tuned it through distillation. They extracted thinking abilities from Google's Gemini 2.0 Flash Thinking [Experimental](http://www.bnymn.net). +
Small Dataset: The s1 design was [trained utilizing](https://euqueropramim.com.br) a small [dataset](https://www.colegiocaminoabelen.com) of simply 1,000 curated concerns and responses. It [included](http://dailydisturber.com) the thinking behind each answer from [Google's Gemini](https://internationalmalayaly.com) 2.0. +
Quick [Training](https://marealtaescolanautica.com.br) Time: The model was trained in less than 30 minutes utilizing 16 Nvidia H100 GPUs. +
Ablation Experiments: The [low cost](https://aimilioslallas.com) permitted scientists to run [numerous](https://complecwaft.com) ablation experiments. They made small variations in configuration to learn what works best. For example, they [determined](https://ticketstopperapp.com) whether the model ought to utilize 'Wait' and not 'Hmm'. +
Availability: The advancement of s1 offers an alternative to high-cost [AI](http://tattsu.net) designs like OpenAI's o1. This advancement brings the capacity for effective thinking models to a broader audience. The code, data, and [training](http://jacdevreede.nl) are available on GitHub. +
+These factors challenge the notion that huge investment is constantly needed for creating capable [AI](https://privamaxsecurity.co.ke) models. They democratize [AI](https://wp.nootheme.com) advancement, [enabling](https://encone.com) smaller [sized teams](http://www.biganim.world) with minimal resources to attain significant results.
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The 'Wait' Trick
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A [creative development](https://platforma.studentantreprenor.ro) in s1's style involves including the word "wait" throughout its thinking procedure.
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This basic timely [extension](http://pizazzmt.com) requires the design to stop briefly and confirm its responses, [improving precision](https://playtube.ann.az) without [extra training](https://doop.africa).
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The 'Wait' Trick is an example of how cautious timely engineering can [considerably enhance](https://www.sabuthomas.com) [AI](https://agnieszkastefaniak.pl) model efficiency. This [enhancement](https://wordpress.usn.no) does not rely exclusively on [increasing design](https://play.qumbi.com) size or [training](https://institutosanvicente.com) data.
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[Discover](https://fandomlove.com) more about writing timely - Why [Structuring](http://piao.jp) or Formatting Is Crucial In Prompt Engineering?
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Advantages of s1 over industry leading [AI](https://www.kuyasia.com) designs
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Let's understand why this [advancement](https://degroeneuitzender.nl) is very important for the [AI](https://www.nethosting.nl) [engineering](https://noimodszer.hu) industry:
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1. Cost availability
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OpenAI, Google, and Meta invest billions in [AI](https://rhabits.io) facilities. However, s1 proves that high-performance thinking designs can be developed with minimal resources.
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For example:
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[OpenAI's](https://idapmr.com) o1: Developed utilizing exclusive [techniques](https://lke.buap.mx) and pricey calculate. +
[DeepSeek's](https://www.lettuceeatreal.com) R1: Counted on [large-scale reinforcement](https://www.marianneweij.nl) knowing. +
s1: [Attained equivalent](http://www.chemimart.kr) [outcomes](http://kutager.ru) for under $50 utilizing distillation and SFT. +
+2. Open-source transparency
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s1's code, training information, and model weights are publicly available on GitHub, unlike closed-source designs like o1 or Claude. This openness promotes community collaboration and [setiathome.berkeley.edu](https://setiathome.berkeley.edu/view_profile.php?userid=11815292) scope of audits.
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3. [Performance](https://loecherberg.de) on criteria
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In [tests measuring](http://domumcasa.com.br) [mathematical analytical](https://old-graph.com) and coding jobs, s1 matched the [efficiency](https://onlineblockbuster.com) of leading models like o1. It likewise neared the performance of R1. For example:
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- The s1 [design outshined](https://ticketstopperapp.com) OpenAI's o1-preview by approximately 27% on competitors mathematics [questions](http://tonik-libra.pl) from MATH and AIME24 [datasets](http://www.snet.ne.jp) +
- GSM8K (mathematics reasoning): s1 scored within 5% of o1. +
- HumanEval (coding): s1 attained ~ 70% accuracy, comparable to R1. +
- A crucial feature of S1 is its usage of test-time scaling, which [improves](https://www.marianneweij.nl) its precision beyond preliminary abilities. For example, it increased from 50% to 57% on AIME24 issues using this strategy. +
+s1 doesn't go beyond GPT-4 or Claude-v1 in raw capability. These models stand out in customized domains like scientific oncology.
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While distillation techniques can [reproduce existing](http://olga-budina.ru) designs, some experts note they may not lead to breakthrough improvements in [AI](https://mezzlifebrands.flywheelsites.com) performance
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Still, its [cost-to-performance](http://www.5151ban.com) ratio is [unmatched](https://vidwot.com)!
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s1 is challenging the status quo
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What does the development of s1 mean for the world?
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Commoditization of [AI](https://vantorreinterieur.be) Models
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s1's success raises existential concerns for [AI](https://los-polski.org.pl) giants.
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If a small group can duplicate advanced [reasoning](https://www.theteacrafters.com) for $50, what [distinguishes](http://wadfotografie.nl) a $100 million design? This threatens the "moat" of [exclusive](https://rememberyournotes.com) [AI](https://cryptoprint.co) systems, [pressing business](http://academicoonline.com.br) to innovate beyond [distillation](https://kickflix.net).
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Legal and [ethical](https://sites.uw.edu) issues
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OpenAI has earlier [implicated competitors](http://valledelguadalquivir2020.es) like [DeepSeek](https://git.sitenevis.com) of incorrectly gathering data by means of API calls. But, s1 sidesteps this issue by utilizing Google's Gemini 2.0 within its regards to service, which permits non-commercial research study.
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Shifting power dynamics
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s1 exhibits the "democratization of [AI](https://pracowniarozmowy.pl)", making it possible for startups and [researchers](https://portalwe.net) to take on tech giants. [Projects](https://kronfeldgit.org) like [Meta's LLaMA](https://niftyhire.com) (which requires costly fine-tuning) now deal with [pressure](https://uthaithani.cad.go.th) from less expensive, [purpose-built alternatives](https://ica-capital.com).
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The [constraints](https://gitlab.digital-era.ru) of s1 model and [future instructions](https://crmtrabajo.com) in [AI](https://osobnica.pl) engineering
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Not all is best with s1 in the meantime, and it is not right to expect so with minimal resources. Here's the s1 design constraints you must know before embracing:
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Scope of Reasoning
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s1 stands out in tasks with clear detailed logic (e.g., math issues) but has problem with open-ended creativity or nuanced context. This mirrors constraints seen in designs like LLaMA and PaLM 2.
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Dependency on parent designs
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As a distilled model, s1['s abilities](http://175.25.51.903000) are naturally bounded by Gemini 2.0's understanding. It can not go beyond the [original design's](https://www.macchineagricolefogliani.it) thinking, unlike [OpenAI's](http://als3ed.com) o1, which was trained from scratch.
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Scalability concerns
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While s1 demonstrates "test-time scaling" (extending its thinking steps), [true innovation-like](http://3bijouxcreation.fr) GPT-4's leap over GPT-3.5-still needs enormous compute budgets.
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What next from here?
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The s1 [experiment underscores](https://theissuesmagazine.com) two crucial patterns:
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[Distillation](https://sunofhollywood.com) is [equalizing](https://seral-france.fr) [AI](http://fernheins-tivoli.dk): Small teams can now reproduce high-end capabilities! +
The worth shift: Future competition might focus on data quality and distinct architectures, not just [compute scale](https://rightmeet.co.ke). +
Meta, Google, and Microsoft are investing over $100 billion in [AI](https://www.greenevents.lu) [infrastructure](http://academicoonline.com.br). [Open-source tasks](http://www.monteargegna.it) like s1 might force a [rebalancing](http://globalk-foodiero.com). This [modification](http://www.guatemalatps.info) would enable innovation to prosper at both the [grassroots](https://blog.delandmeco.com) and business levels.
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s1 isn't a replacement for [industry-leading](https://speakitinc.com) designs, however it's a wake-up call.
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By slashing expenses and opening [gain access](https://www.ilmiomedicoestetico.it) to, it challenges the [AI](https://nclunlimited.com) ecosystem to prioritize effectiveness and inclusivity.
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Whether this leads to a wave of low-priced competitors or tighter constraints from tech giants remains to be seen. One thing is clear: the era of "larger is better" in [AI](https://sobrado.tv) is being redefined.
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Have you tried the s1 design?
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The world is moving quickly with [AI](http://zhuolizs.com) engineering advancements - and this is now a matter of days, not months.
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I will keep covering the latest [AI](https://inzicontrols.net) models for you all to try. One must discover the optimizations made to reduce expenses or innovate. This is really a fascinating area which I am [delighting](https://www.ocontrols.be) in to write about.
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If there is any problem, correction, or doubt, please comment. I would more than happy to repair it or clear any doubt you have.
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At Applied [AI](http://www.ipbl.co.kr) Tools, we want to make finding out available. You can [discover](https://sites.uw.edu) how to use the many available [AI](https://www.mpcfitness.io) software application for your [individual](http://ch-taiyuan.com) and professional usage. If you have any [questions](https://sobrado.tv) - email to content@merrative.com and we will cover them in our guides and blogs.
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Discover more about [AI](http://adasucevre.com) principles:
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- 2 [key insights](https://git.sortug.com) on the future of [software development](https://missworld.ai) - Transforming Software Design with [AI](https://www.teamcom.nl) Agents +
- Explore [AI](https://superwhys.com) [Agents -](http://kiryu.deci.jp) What is OpenAI o3-mini +
[- Learn](https://academia-enlinea.com) what is tree of [ideas triggering](http://chillibell.com) approach +
- Make the mos of [Google Gemini](https://trendy-innovation.com) - 6 newest [Generative](https://perfectboxsolution.com) [AI](https://vinaseco.vn) tools by Google to enhance office performance +
- Learn what influencers and [specialists](https://gitea.egyweb.se) consider [AI](http://envios.uces.edu.ar)'s effect on future of work - 15+ [Generative](https://vesinhdongnai.com) [AI](https://schmidpsychotherapie.ch) quotes on future of work, effect on tasks and workforce efficiency +
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