Surge AI
O que é Surge AI?
Surge AI é a plataforma de rotulagem de dados mais poderosa do mundo. Ela oferece uma plataforma global de rotulagem de dados e uma força de trabalho, permitindo aos usuários criar conjuntos de dados poderosos para treinar modelos de IA.
Como usar Surge AI?
Para usar o Surge AI, basta fazer login no site e acessar a plataforma. A partir daí, você pode criar projetos de rotulagem, definir instruções de rotulagem e gerenciar a força de trabalho de rotulagem.
Principais recursos da Surge AI
Plataforma global de rotulagem de dados
Equipe de elite em mais de 40 idiomas
Integração com APIs e ferramentas modernas
Casos de uso da Surge AI
Modelos de linguagem
Avaliação de pesquisa
Moderação de conteúdo
Análise de sentimento
Suporte ao cliente
Categorização financeira
Ajuste fino do GPT
Perguntas frequentes de Surge AI
O que é o Surge AI?
Como posso usar o Surge AI?
Quais são os recursos principais do Surge AI?
Quais são alguns casos de uso do Surge AI?
Surge AI E-mail de suporte e contato de atendimento ao cliente e contato de reembolso, etc.
Aqui está o e-mail de suporte da Surge AI para atendimento ao cliente: hello@surgehq.ai . Mais contato, visite a página de contato(https://www.surgehq.ai/contact)
Surge AI Empresa
Surge AI Nome da empresa: Surge AI .
Mais sobre Surge AI, visite a página sobre nós(https://www.surgehq.ai/about) .
Surge AI Login
Link de login de Surge AI: https://app.surgehq.ai/users/sign_in
Surge AI Inscreva-se
Surge AI Link de inscrição: https://www.surgehq.ai/sign-up
Preço de Surge AI
Link de preços de Surge AI: https://www.surgehq.ai/pricing
Linkedin de Surge AI
Link de Linkedin de Surge AI: https://www.linkedin.com/company/surge-ai/
Twitter de Surge AI
Link de Twitter de Surge AI: https://twitter.com/HelloSurgeAI
Instagram de Surge AI
Link de Instagram de Surge AI: https://www.instagram.com/hellosurgeai/
Github de Surge AI
Link de Github de Surge AI: https://github.com/surge-ai/
Avaliações de Surge AI (0)
Análise de Surge AI
Surge AI Análise de tráfego do site
Tráfego geográfico
Top 5 Regiões
Surge AI Iniciar incorporações
Use os emblemas do site para obter o apoio de sua comunidade para o lançamento do Toolify. Eles são fáceis de incorporar em sua página inicial ou rodapé.
Alternativa de Surge AI
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How to Unlock the Power of Zero Volume Keywords & Drive Traffic (3000+ Clicks)
Disclosure: There are affiliate links below which means if you make a purchase after clicking on one, I may earn a commission. Zero volume keywords, as they're called, are very misleading - as many of them are not truly "zero volume" and in fact can bring in hundreds - even thousands - of clicks through Google search! But not all low volume keywords are created equal - in this video, I walk you through what zero and low volume keywords are, why they're important, which types to target, and where to find them. VIDEO RESOURCES: Keyword Research Tools: - Keywords Everywhere Chrome Extension - https://bit.ly/cww-kee - MozBar - https://bit.ly/cww-mbe - LowFruits - https://lowfruits.io/?via=corrie - Exploding Topics - https://explodingtopics.com/ - Surge AI - https://www.surgehq.ai/ AI Tools Mentioned for Writing Blog Posts - ChatGPT - https://chat.openai.com/ - Jasper AI - https://jasper.ai?utm_source=partner&fpr=corriewhowrites - Koala AI - https://koala.sh/writer?via=corrie AI Tools Mentioned for SEO: - Surfer SEO - https://get.surferseo.com/41sfdcrf4g2x Tutorials for Writing Optimized Blog Posts with AI : - Featured Snippet informational blog post - https://youtu.be/Bv8ZdOhhj9A - Writing affiliate marketing blog posts - https://youtu.be/OexEBBui34M - ChatGPT tutorial - https://youtu.be/ZCcYTiqxpww - Amazon roundup review posts with Koala - https://youtu.be/vn3qX5ax7Gs - Writing Blog Posts with Jasper Workflow - https://youtu.be/VsGeE85ZhZg - Writing blog posts with ChatGPT & Optimizing articles with Surfer SEO - https://youtu.be/FURiF_OWUi0 TIMESTAMPS: 0:00 | Introduction 0:52 | What Are "Zero Volume" Keywords & Why Do They Matter? 3:38 | The Three Types of Ideal Zero Volume Keywords 6:21 | How to Find 0-Vol Keywords: Method 1 7:28 | How to Find 0-Vol Keywords: Method 2 8:26 | How to Find Trending Topics: Method 1 10:45 | How to Find Trending Topics: Method 2 11:40 | Writing Content for Keywords *My content is for informational/educational purposes only. I aim to accurately represent the information provided in these videos, but I make no guarantees. Everyone’s journey as an online writer is different and you are solely and personally responsible for your results. You are responsible for your own actions and I assume no responsibility if you use the information provided through my content. Find the full disclaimer here: https://corriewhowrites.com/disclaimer/
Earn $30/Day With UHRS Similar Job | Best Part Time Work for Students
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Podcast with Connor: ChatGPT, LLMs, Form vs Meaning
Topics: 00:00 Intro 01:54 Things Connor learnt in the past year that changed his perception of Vector Search 02:42 Is search becoming conversational? 05:46 Connor asks Dmitry: How Large Language Models will change Search? 08:39 Vector Search Pyramid 09:53 Large models, data, Form vs Meaning and octopus underneath the ocean 13:25 Examples of getting help from ChatGPT and how it compares to web search today 18:32 Classical search engines with URLs for verification vs ChatGPT-style answers 20:15 Hybrid search: keywords + semantic retrieval 23:12 Connor asks Dmitry about his experience with sparse retrieval 28:08 SPLADE vectors 34:10 OOD-DiskANN: handling the out-of-distribution queries, and nuances of sparse vs dense indexing and search 39:54 Ways to debug a query case in dense retrieval (spoiler: it is a challenge!) 44:47 Intricacies of teaching ML models to understand your data and re-vectorization 49:23 Local IDF vs global IDF and how dense search can approach this issue 54:00 Realtime index 59:01 Natural language to SQL 1:04:47 Turning text into a causal DAG 1:10:41 Engineering and Research as two highly intelligent disciplines 1:18:34 Podcast search 1:25:24 Ref2Vec for recommender systems 1:29:48 Announcements Show notes: - CAP theorem: https://en.wikipedia.org/wiki/CAP_theorem - Multilingual and Multimodal Vector Search with Hardware Acceleration: https://blog.muves.io/multilingual-and-multimodal-vector-search-with-hardware-acceleration-2091a825de78 - Google's FLANN model: https://ai.googleblog.com/2021/10/introducing-flan-more-generalizable.html - Meta's OPT model: https://huggingface.co/facebook/opt-350m Few-shot learning: https://huggingface.co/blog/few-shot-learning-gpt-neo-and-inference-api Reinforcement Learning with Human Feedback: https://huggingface.co/blog/rlhf - GPT Index: https://gpt-index.readthedocs.io/en/latest/index.html - Neural Magic: https://neuralmagic.com/ - Climbing towards NLU: On Meaning, Form, and Understanding in the Age of Data: https://aclanthology.org/2020.acl-main.463.pdf - Data labeling with Surge AI: https://www.surgehq.ai/ - Experience Grounds Language: https://arxiv.org/abs/2004.10151 - The ladder of causation explained: http://lgmoneda.github.io/2018/06/01/the-book-of-why.html - Interview with Judea Pearl: https://www.youtube.com/watch?v=G5OlWbVOyCM - Big ANN experiments of team Sisu: https://github.com/DmitryKey/big-ann - InstructGPT: https://openai.com/blog/instruction-following/ - WebGPT: https://openai.com/blog/webgpt/ - You.com - AI Search Engine: https://you.com/ - Binary Independence Model: https://en.wikipedia.org/wiki/Binary_Independence_Model - Dmitry Kan on Neural Search Frameworks - Weaviate Podcast #34: https://www.youtube.com/watch?v=1ebCUCUJraE - Understanding Scoring Through Examples at Haystack LIVE! https://www.youtube.com/watch?v=LA_W7aVVZro - Relevant Search by Doug Turnbull and John Berryman: https://www.manning.com/books/relevant-search - Term frequency saturation: https://opensourceconnections.com/blog/2015/10/16/bm25-the-next-generation-of-lucene-relevation/ - SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking https://arxiv.org/abs/2107.05720 - OOD-DiskANN: Efficient and Scalable Graph ANNS for Out-of-Distribution Queries https://arxiv.org/abs/2211.12850 - ANN Benchmarks http://ann-benchmarks.com/ - Big ANN Benchmarks https://big-ann-benchmarks.com/ - BEIR: A Heterogeneous Benchmark for Information Retrieval https://github.com/beir-cellar/beir - Task-aware retrieval with instructions: https://arxiv.org/abs/2211.09260 - NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval https://www.cl.uni-heidelberg.de/statnlpgroup/nfcorpus/ - Muves presentation at Berlin Buzzwords'22: https://www.youtube.com/watch?v=9OS8cMf2rwY - A complete Tweet index on Apache Lucene by Michael Busch https://www.youtube.com/watch?v=MLG39s9wnk4 - CURL: Contrastive Unsupervised Representations for Reinforcement Learning https://arxiv.org/abs/2004.04136 - Causal Inference using Natural Language Processing https://towardsdatascience.com/causal-inference-using-natural-language-processing-da0e222b84b - Transformer Memory as a Differentiable Search Index https://arxiv.org/abs/2202.06991 - Ask Your Developer: https://www.amazon.com/Ask-Your-Developer-Software-Developers/dp/0063018292 - Data? Model? Interaction: https://nips.cc/virtual/2022/invited-talk/55870 - Snorkel: https://snorkel.ai/ - What is Ref2Vec and why you need it for your recommendation system https://weaviate.io/blog/ref2vec-centroid - BERTopic: https://maartengr.github.io/BERTopic/ - Collection of the BEIR benchmarks uploaded and backed up in Weaviate! https://github.com/weaviate/weaviate-BEIR-benchmarks Podcast design: Saurabh Rai: https://twitter.com/saurabhraidev
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