This Week's Most Read: Google's Gemini Fumbles Super Bowl Performance

Here are this week's top trending stories:

1. Google’s Gemini Botches Super Bowl Post-Game Analysis

Super Bowl LVIII is over and the Kansas City Chiefs have cemented their status as a sports dynasty with a hard-fought win against the San Francisco 49ers.

Google’s newest and most powerful large multimodal model, Gemini Advanced, correctly predicted the winner. Most human sports pundits said the same, but as of game day Las Vegas bettors were favoring the 49ers. In the end, Gemini Advanced and the sports experts were right: Chiefs won.

But when it comes to analyzing the game after it was over, that is another story. Gemini Advanced hallucinated – big time.

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2. OpenAI or Nvidia: Who Will Lead the Future of AI Chips?

OpenAI CEO Sam Altman reportedly is seeking to raise trillions of dollars to increase the world’s capacity to build AI chips.

The Wall Street Journal’s sources claim that Altman is looking to raise as much as $5 trillion to $7 trillion. He is in talks with the U.A.E government, SoftBank and other investors. After Altman raises the funds, the goal was to have chip foundry giant Taiwan Semiconductor Manufacturing Co. build and run them.

The OpenAI CEO tweeted recently that the world “needs more AI infrastructure, fab capacity, energy, data centers, etc. than people are currently planning to build.”

But Nvidia CEO Jensen Huang cast doubt over the amount Altman believes he would need to raise to build AI chipmaking plants.

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3. OpenAI is Planning to Give ChatGPT Memories

OpenAI announced it is testing a new feature in ChatGPT that lets it remember information across chats.

When users start a new prompt, they often have to repeat information from past prompts. OpenAI said this new feature will lessen the need to do so.

Users can tell ChatGPT to remember something specifically, or tell it to forget certain details. You can also turn off this feature entirely in settings.

OpenAI is testing memories with a few ChatGPT free and paid users initially and will share plans for any broader rollout.

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4. Say Bonjour to CrossiantLLM: The Mini Open Bilingual Model

There is a delectable new open source model for English and French workloads - and it is snackable enough in size to run on mobile devices.

CroissantLLM is designed to run on consumer-grade local hardware while being “full open, and truly bilingual,” according to a blog by Manuel Faysse, a lead researcher on the team who created it.

The goal is to make French on par with English in AI models. “With CroissantLLM, we aim to train a model in which English is not the dominant language and go for a 1:1 ratio of English and French data!” he wrote.

The model is just 1.3 billion parameters in size but was trained on three trillion tokens – more tokens than the Llama 2 models − and included a dataset comprised of high-quality French content including legal documents, business data, cultural content and scientific information. It uses the Llama model architecture.

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5. Case Study: How Alibaba Uses AI Chatbots to Serve a Billion Customers

Alibaba Group, China’s largest e-commerce company and one of the world’s biggest companies, has nearly one billion annual active Chinese consumers who make hundreds of millions of transactions daily using its Taobao e-commerce platform.

Alibaba uses AI chatbots to handle customer engagements for more than two million daily sessions and over 10 million lines of daily conversations on Taobao’s two-sided platform, representing about 75% of Alibaba’s online and 40% of phone hotline consultations.

Not only has the use of AI chatbots raised customer satisfaction by 25%, based on initial results, it has saved the company more than one billion RMB annually (~US $150 million) by employing AI instead of human contact center agents.

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