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Making AI more helpful for everyone
Retailers can tap into generative AI to enhance support for customers and employees
As the retail industry witnesses a shift towards a more digital, on-demand consumer base, AI is becoming the secret weapon for retailers to better understand and cater to this evolving consumer behavior. With the rise of highly personalized online shopping, direct-to-consumer models, and delivery services, generative AI can help retailers further unlock a host of …
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Mitigate hallucinations through Retrieval Augmented Generation using Pinecone vector database & Llama-2 from Amazon SageMaker JumpStart
Despite the seemingly unstoppable adoption of LLMs across industries, they are one component of a broader technology ecosystem that is powering the new AI wave. Many conversational AI use cases require LLMs like Llama 2, Flan T5, and Bloom to respond to user queries. These models rely on parametric knowledge to answer questions. The model …
Dynamic Workload Scheduler: Optimizing resource access and economics for AI/ML workloads
We are in the midst of an exciting era of AI-driven innovation and transformation. Today we announced AI Hypercomputer, a groundbreaking architecture that employs an integrated system of AI-optimized hardware, software, and consumption models. With AI Hypercomputer, enterprises everywhere can run on the same cutting-edge infrastructure that is already the backbone of Google’s internal AI/ML …
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Visual AI Takes Flight at Canada’s Largest, Busiest Airport
Toronto Pearson International Airport, in Ontario, Canada, is the country’s largest and busiest airport, serving some 50 million passengers each year. To enhance traveler experiences, the airport in June deployed the Zensors AI platform, which uses anonymized footage from existing security cameras to generate spatial data that helps optimize operations in real time. A member …
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How to Scale Your EMA
*=Equal Contributors Preserving training dynamics across batch sizes is an important tool for practical machine learning as it enables the trade-off between batch size and wall-clock time. This trade-off is typically enabled by a scaling rule; for example, in stochastic gradient descent, one should scale the learning rate linearly with the batch size. Another important …
Building with Palantir AIP: Data Tools for RAG / OAG
https://medium.com/media/bdef0ccaad6ac41db586a138ca62db39/href Introduction Welcome to another installment of our Building with AIP series, where Palantir engineers and architects take you through how to build end-to-end workflows using our Artificial Intelligence Platform (AIP). Today we’re covering Ontology Augmented Generation (OAG), which is a more expansive, decision-centric version of Retrieval Augmented Generation (RAG). At a high level, RAG …
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Beyond basics: Six tips for an exceptional customer service strategy
Enhancing the customer experience through customer service is among the most important disciplines for any organization for one simple reason: without customers, organizations would fail overnight. Customer service, sometimes called customer care or customer support, relates to the activities organizations take to ensure their customers’ needs are being met. While every customer interaction is different, …
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Google at EMNLP 2023
Posted by Malaya Jules, Program Manager, Google Google is proud to be a Diamond Sponsor of Empirical Methods in Natural Language Processing (EMNLP 2023), a premier annual conference, which is being held this week in Sentosa, Singapore. Google has a strong presence at this year’s conference with over 65 accepted papers and active involvement in …
Enable faster training with Amazon SageMaker data parallel library
Large language model (LLM) training has become increasingly popular over the last year with the release of several publicly available models such as Llama2, Falcon, and StarCoder. Customers are now training LLMs of unprecedented size ranging from 1 billion to over 175 billion parameters. Training these LLMs requires significant compute resources and time as hundreds …
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