Amazon announces Rufus, new generative AI-powered conversational shopping experience


  • Staff

 Amazon News release:

90


Rufus is an expert shopping assistant trained on Amazon’s product catalog and information from across the web to answer customer questions on shopping needs, products, and comparisons, make recommendations based on this context, and facilitate product discovery in the same Amazon shopping experience customers use regularly.

Launching today in beta to a small subset of customers in Amazon’s mobile app, Rufus will progressively roll out to additional U.S. customers in the coming weeks.


Amazon has been using AI very expansively for 25+ years to improve customer experiences. The personalized recommendations customers get when they shop Amazon’s store, the pick paths in our fulfillment centers, our drone deliveries, the conversational capabilities of Alexa, and our checkout-free Amazon Go stores are just a few examples of experiences fueled by AI. And we believe generative AI is going to change virtually all customer experiences that we know.

This past year, we’ve introduced a number of new generative AI-powered capabilities in Amazon’s store to make shopping even easier and more convenient. Our AI-generated review highlights provide customers with common themes from dozens, hundreds, or even thousands of reviews at a glance to help them quickly understand customer insights. We also recently introduced our Fit Review Highlights feature, which offers personalized size guidance and insights so customers can determine which size will fit them best. We are also using generative AI to make product listings even more informative for customers by helping our selling partners write more engaging and effective titles and product descriptions, and enrich existing listings.

Building on these AI customer innovations, today we are launching Rufus.

Rufus is a generative AI-powered expert shopping assistant trained on Amazon’s extensive product catalog, customer reviews, community Q&As, and information from across the web to answer customer questions on a variety of shopping needs and products, provide comparisons, and make recommendations based on conversational context.

From broad research at the start of a shopping journey such as “what to consider when buying running shoes?” to comparisons such as “what are the differences between trail and road running shoes?” to more specific questions such as “are these durable?”, Rufus meaningfully improves how easy it is for customers to find and discover the best products to meet their needs, integrated seamlessly into the same Amazon shopping experience they use regularly.

We're launching Rufus in beta and starting to roll it out to customers in waves, beginning with a small subset of customers in the U.S. using our mobile app, and progressively rolling it out to the rest of our U.S. customers in the coming weeks.

Amazon Rufus shopping AI assistant


With Rufus, customers can:
  • Learn what to look for while shopping product categories: Customers can conduct more general product research on Amazon, asking questions such as “what to consider when buying headphones?”, “what to consider when detailing my car at home?”, or “what are clean beauty products?” and receive helpful information to guide their shopping mission.
  • Shop by occasion or purpose: Customers can search for and discover products based on activity, event, purpose, and other specific use cases by asking a range of questions such as “what do I need for cold weather golf?” or “I want to start an indoor garden.” Rufus suggests shoppable product categories—from golf base layers, jackets, and gloves to seed starters, potting mix, and grow lights—and related questions that customers can click on to conduct more specific searches.
  • Get help comparing product categories: Customers can now ask “what’s the difference between lip gloss and lip oil?” or “compare drip to pour-over coffee makers” so they can find the type of product that best suits their needs and make even more confident purchase decisions.
  • Find the best recommendations: Customers can ask for recommendations for exactly what they need, such as “what are good gifts for Valentine’s Day?” or “best dinosaur toys for a 5-year-old.” Rufus generates results tailored to the specific question and makes it quick and easy for customers to browse more refined results.
  • Ask questions about a specific product while on a product detail page: Customers can use Rufus to quickly get answers to specific questions about individual products when they are viewing the product’s detail page, such as “is this pickleball paddle good for beginners?”, or “is this jacket machine washable?”, or “is this cordless drill easy to hold?”. Rufus will generate answers based on listing details, customer reviews, and community Q&As.
Amazon Rufus shopping AI assistant


With Rufus, customers are now able to shop alongside a generative AI-powered expert that knows Amazon’s selection inside and out, and can bring it all together with information from across the web to help them make more informed purchase decisions.

Get started with the Rufus beta​


Rufus is now being made available to select customers when they next update their Amazon Shopping app. To use Rufus, customers in the beta can simply start typing or speaking their questions into the search bar in Amazon’s mobile app and a Rufus chat dialog box will appear at the bottom of their screen. Customers can expand the chat dialog box to see answers to their questions, tap on suggested questions, and ask follow-up questions in the chat dialog box. Customers can dismiss Rufus to return to their traditional search results at any time by swiping down to send the chat dialog box back to the bottom of their screen.


Rufus generates answers using relevant information from across Amazon and the web to help customers make better, more informed shopping decisions. It’s still early days for generative AI, and the technology won’t always get it exactly right. We will keep improving our AI models and fine-tune responses to continuously make Rufus more helpful over time. Customers are encouraged to leave feedback by rating their answers with a thumbs up or thumbs down, and they have the option to provide freeform feedback as well.

We are excited about the potential of generative AI and will continue testing new features to make it even easier to find and discover, research, and buy products in Amazon’s store. We look forward to progressively rolling Rufus out to additional U.S. customers in the coming weeks.


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Nothing to do with Rufus as we know it - I can feel a lawsuit coming on LOL.
 

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Amazon has the worst search system I have ever experienced. You can not do a precise search, it constantly shoves favoured vendors or products in your face and the results are terrible. It is the least configerable search sytem I’ve ever come across. I can’t see how this Ai will change anything, also, it looks as ugly as could be. It also looks time consuming.

Trying to get Ai to understand a simple directive or question is still frustrating, on any platform.
 

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Nothing to do with Rufus as we know it - I can feel a lawsuit coming on LOL.
Is Rufus as we know it a business or company name though? I did a search, so many business and companies named Rufus it was surprising. I didn’t find “Rufus” as we know it.
 

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I mean I couldn’t find the above entity as a registered name. I’m not saying it isn’t though.
Well now it'll be even harder to find. It's still better then Ventoy in some edge cases.
 

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I bet their Artificial Idiot won't beat me in finding the best discounts being a Yorkshireman
 

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I already know how to do a search for what I'm going to buy. I don't need AI to do all of my thinking for me.
 

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Amazon is one of the big five American technology companies (besides Alphabet, Apple, Facebook, Microsoft) Give them some advance credit that they will do the job well.

The announcement is like providing a helpful saleslady in a brick and mortar store that you don't have to engage if you don't want/need to :copilot:

_765d7c5d-56b4-45b1-9056-ed7236c1cb84.jpg
 

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It would be nice if they focused on an AI that can detect fake listings and consolidate duplicate listings
 

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It would be nice if they focused on an AI that can detect fake listings and consolidate duplicate listings
AI can/has been used to help address the two issues, see first and second issue below.

AI will be an all-pervasive helper in all aspects of life!

------------------first issue
Certainly! Artificial Intelligence (AI) has become a powerful tool for detecting fake listings and disinformation across various platforms. Here are some notable techniques and tools used for this purpose:
  1. Sentinel:
    • Sentinel is a leading AI-based protection platform used by democratic governments, defense agencies, and enterprises in Europe.
    • It allows users to upload digital media (such as images or videos) through their website or API.
    • The system automatically analyzes the media for signs of AI-forgery, determining if it’s a deepfake or not.
    • Users receive a detailed report with visualizations showing areas of manipulation1.
  2. Intel’s Real-Time Deepfake Detector (FakeCatcher):
  3. WeVerify:
  4. Fakespot:
  5. Sensity.AI:
These tools and techniques represent the cutting edge in deepfake detection, utilizing advanced AI algorithms to preserve the integrity of online content13. Keep in mind that the battle against fake listings is ongoing, and researchers continue to explore new methods to counteract AI-generated misinformation4. 🛡️🔍

------------------second issue
Certainly! Artificial Intelligence (AI) can be incredibly useful for consolidating duplicate listings on various platforms. Let’s explore how AI can tackle this challenge:
  1. Text Matching and Similarity:
    • AI algorithms can compare listing titles, descriptions, and other textual attributes to identify duplicates.
    • Techniques like Levenshtein distance, cosine similarity, or Jaccard similarity can measure how similar two pieces of text are.
    • By calculating these metrics, AI can group similar listings together.
  2. Entity Resolution:
    • AI can perform entity resolution by identifying common entities (e.g., addresses, phone numbers, product IDs) across listings.
    • When multiple listings share the same entity, they are likely duplicates.
    • Machine learning models can learn patterns from historical data to improve entity resolution.
  3. Clustering:
    • Unsupervised learning methods, such as k-means clustering or hierarchical clustering, can group similar listings.
    • AI identifies clusters based on shared features, making it easier to consolidate them.
  4. User Feedback and Learning:
    • AI systems can learn from user feedback. When users report duplicate listings, the system adapts and improves its consolidation process.
    • Regular updates ensure that the AI model remains effective against evolving tactics used by scammers.
  5. Graph-Based Approaches:
    • AI can represent listings as nodes in a graph, where edges connect similar listings.
    • Algorithms like connected components help identify groups of duplicates.
  6. Image Analysis (if applicable):
    • If listings include images, AI can analyze visual content to detect similarities.
    • Image-based hashing or deep learning models can identify duplicate images associated with different listings.
Remember that while AI can automate much of the consolidation process, human oversight is crucial. Regular audits and manual checks ensure accuracy and prevent false positives. 🤖✨
 

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AI can/has been used to help address the two issues, see first and second issue below.

AI will be an all-pervasive helper in all aspects of life!

------------------first issue
Certainly! Artificial Intelligence (AI) has become a powerful tool for detecting fake listings and disinformation across various platforms. Here are some notable techniques and tools used for this purpose:
  1. Sentinel:
    • Sentinel is a leading AI-based protection platform used by democratic governments, defense agencies, and enterprises in Europe.
    • It allows users to upload digital media (such as images or videos) through their website or API.
    • The system automatically analyzes the media for signs of AI-forgery, determining if it’s a deepfake or not.
    • Users receive a detailed report with visualizations showing areas of manipulation1.
  2. Intel’s Real-Time Deepfake Detector (FakeCatcher):
  3. WeVerify:
  4. Fakespot:
  5. Sensity.AI:
These tools and techniques represent the cutting edge in deepfake detection, utilizing advanced AI algorithms to preserve the integrity of online content13. Keep in mind that the battle against fake listings is ongoing, and researchers continue to explore new methods to counteract AI-generated misinformation4. 🛡️🔍

------------------second issue
Certainly! Artificial Intelligence (AI) can be incredibly useful for consolidating duplicate listings on various platforms. Let’s explore how AI can tackle this challenge:
  1. Text Matching and Similarity:
    • AI algorithms can compare listing titles, descriptions, and other textual attributes to identify duplicates.
    • Techniques like Levenshtein distance, cosine similarity, or Jaccard similarity can measure how similar two pieces of text are.
    • By calculating these metrics, AI can group similar listings together.
  2. Entity Resolution:
    • AI can perform entity resolution by identifying common entities (e.g., addresses, phone numbers, product IDs) across listings.
    • When multiple listings share the same entity, they are likely duplicates.
    • Machine learning models can learn patterns from historical data to improve entity resolution.
  3. Clustering:
    • Unsupervised learning methods, such as k-means clustering or hierarchical clustering, can group similar listings.
    • AI identifies clusters based on shared features, making it easier to consolidate them.
  4. User Feedback and Learning:
    • AI systems can learn from user feedback. When users report duplicate listings, the system adapts and improves its consolidation process.
    • Regular updates ensure that the AI model remains effective against evolving tactics used by scammers.
  5. Graph-Based Approaches:
    • AI can represent listings as nodes in a graph, where edges connect similar listings.
    • Algorithms like connected components help identify groups of duplicates.
  6. Image Analysis(if applicable):
    • If listings include images, AI can analyze visual content to detect similarities.
    • Image-based hashing or deep learning models can identify duplicate images associated with different listings.
Remember that while AI can automate much of the consolidation process, human oversight is crucial. Regular audits and manual checks ensure accuracy and prevent false positives. 🤖✨
That’s a great post, thank you 🙏

But I wonder how long it will take before Govt’s start using it to detect/correct/determine the fate of humans. It kind of is already though. As mentioned before, there are some courts that use AI to help determine the fate of the convicted or pre convicted.
That movie with Tiny Tom Cruise (Thought Crimes?) scared the crap out of me. Not that I expect AI to read minds, but to pull random people out of an Algorithm and say “You’re likely to offend” is not unrealistic. And to be 100% honest, I believe it already does.
 

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    Gigabyte nVidia GeForce GTX 1660 Super OC 6GB
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    4 x LG 23MP75 1 x 24" LG M38H 1 x 32" LF6300 TV Monitor 1 x Wacom Pro 22" Tablet
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    I’m on a horse.
  • Operating System
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    …still on a horse.
That’s a great post, thank you 🙏

But I wonder how long it will take before Govt’s start using it to detect/correct/determine the fate of humans. It kind of is already though. As mentioned before, there are some courts that use AI to help determine the fate of the convicted or pre convicted.
That movie with Tiny Tom Cruise (Thought Crimes?) scared the crap out of me. Not that I expect AI to read minds, but to pull random people out of an Algorithm and say “You’re likely to offend” is not unrealistic. And to be 100% honest, I believe it already does.
Well, AI (like the billions of knives that humanity has) is neither good nor bad per se, it depends on the billions of AI use cases, and the issue is is there a net positive.

If you read about the modern history of AI (past several decades) then there had been ups and downs of support/funding reflecting the believes of those who are in power and/or have deep pockets, whether there is a net positive.

The last up was in 2020 and breakthroughs were made. The up was so great that even those in power/have deep pockets have been repeatedly calling for slow downs, but there was not even the slightest blip.

And I am tempted to say, of course there wasn't the slightest blip. AI will elevate humanity, just like the steam engine did (y)
 

My Computer

System One

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