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- Google Shopping upgrades 🛍️, AI use on the rise 🚀, Tools to see ⚒️
Google Shopping upgrades 🛍️, AI use on the rise 🚀, Tools to see ⚒️
AI For Work
October 16, 2024
Welcome, innovators.
In a surprise to no one, Google Shopping is getting an AI makeover. But what may surprise you is just how much this advancement can help you in your day-to-day life as a consumer…
In today’s newsletter:
Today’s must-read stories 📰
Google Shopping gets AI makeover
Prompt of the day ✍️
MBZUAI Research Corner 🔬
MBZUAI Research: Evaluating safeguards in LLMs
The jump from the development of Large Language Models (LLMs) to their deployment has proven to be ethically fraught.
LLMs are known for their propensity to display a range of biases, reproduce copyrighted content and manipulate people. Such models have the capacity to aid cyber criminals and other bad actors in mass cyber-attacks, fraudulent scams and propaganda campaigns.
In order for developers to mitigate these elements through the enactment of targeted guardrails, they first need to identify them. While closed models, such as ChatGPT, tend to include guardrails that were constructed based upon datasets of known harmful prompts, open-source models tend to lack those guardrails.
What happened: Researchers from the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) are seeking to bridge that gap, providing a comprehensive dataset for the evaluation of safeguards specifically for open-source models.
Covering a range of mild to extreme risks, the dataset — nicknamed “Do Not Answer” — includes a list of nearly a thousand prompts where “all instructions should not be followed.” The types of harm identified in the dataset “indicate the specific vulnerabilities the LLM should mitigate.”
The researchers said that LLMs should “either reject, refuse to respond to, or refute the stance of all such questions.” They collected close to 6,000 responses to their list of problematic prompts.
The researchers defined and collected responses that they deemed to be “safe and responsible” which they used to assess the safeguards of several different LLMs. They went on to test automated approaches to assessing these safety mechanisms.
“Notably, our findings revealed that a suitably-trained small model can effectively perform the evaluation, yielding results that are comparable to those obtained using GPT-4 as an evaluator.”
To learn more about MBZUAI’s research visit their website.
Today’s must-read stories 📰
To maintain brand voice in the AI era, businesses must ensure that generative AI content reflects their unique identity—thus avoiding the risk of blending in with competitors.
Andrew Rabinovich, VP of AI at Upwork, discusses on the "Me, Myself, and AI" podcast how AI is advancing digital labor platforms
Verizon Business's fifth annual State of Small Business Survey reveals that despite economic concerns, SMBs are investing more in technology, with AI adoption doubling in 2024 and 66% upgrading internet bandwidth.
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Video of the Day 📽️
Miscellaneous resources
DataCamp: Learn data science and AI online at your own pace.
IBM Skills Network: An online platform to help employers upskill their employees in the areas of AI, machine learning, and more.
Google AI Essentials: Learn how to use generative AI to speed up daily tasks, make more informed decisions, and develop new ideas and content.
AI tools 🔨
Together with Galileo
Learn from Nvidia, HP, Databricks, and more
Learn actionable insights, strategies, and techniques for generative AI stack design, governance, evaluation, and observability.
Register now for GenAI Productionize 2.0 – the premier conference for GenAI application development, featuring AI experts from leading brands, startups, and research labs!
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Google Shopping gets AI makeover
Google is revolutionizing the shopping experience by integrating AI to create a more personalized and efficient shopping journey for users.
With over 45 billion product listings, the new AI-powered Google Shopping platform simplifies research and enhances decision-making with smart recommendations and dynamic filters.
Key takeaways
The revamped Google Shopping uses AI to provide personalized shopping experiences, making it easier to find relevant products.
AI-generated briefs highlight top considerations for purchases, streamlining research for shoppers.
Dynamic filters and virtual try-on features help users narrow down their preferences and shop more confidently.
A personalized feed on Google Shopping inspires users with products and videos based on individual preferences.
Google Shopping now includes advanced deal-finding tools like price comparison, insights, and tracking, alongside a dedicated deals page.
Our thoughts
This transformation is a game-changer, as AI now takes center stage in optimizing how users interact with and shop online. By merging advanced AI capabilities with personalized shopping, Google is setting the standard for convenience and customization in the digital marketplace.
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