Make Money with AI by Driving Uber, ChatGPT Enters Slack, and Meta’s Camera Roll Controversy

FEATURED INSIGHT💡

Uber Drivers Can Now Make Money Training AI

If your Uber driver takes a little longer to show up, they might be busy uploading a Spanish menu… or recording audio in their native accent.

Uber has launched a “digital tasks” pilot program that lets U.S. drivers earn extra money between rides by completing data-labeling jobs like uploading documents, recording speech, or submitting images. The data trains AI systems used across Uber’s expanding AI Solutions division, which recently acquired the data-labeling startup Segments.ai.

Uber insists this isn’t about driverless cars, yet. But the initiative positions the company to compete with major players like Scale AI, which landed a $14 billion investment from Meta earlier this year.

Why it matters: This is part of the growing “data labor” economy, where humans fuel the AI that may someday replace them. For Uber, it’s a clever way to both engage workers and quietly scale its AI ambitions.

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ON THE HORIZON 🌅

ChatGPT Moves Into Slack — Your New AI Teammate

Work just got a little more conversational. OpenAI and Slack have teamed up to bring ChatGPT directly inside Slack, creating a seamless way to brainstorm, summarize threads, or write responses without leaving the app.

The integration offers:

• A dedicated ChatGPT sidebar inside Slack for private brainstorming

• Cross-platform sync, so your chats carry over to ChatGPT later

• Semantic search through company messages and files

Security-wise, ChatGPT only sees what you can already access, and Slack admins retain existing controls. The feature is available to ChatGPT Plus, Pro, Business, and Enterprise users with a paid Slack plan.

Why it matters: Slack is where context lives. By embedding ChatGPT directly inside it, OpenAI is turning your workspace into a smarter, more connected knowledge hub.

LATEST IMPORTANT NEWS 📰

Meta’s Camera Roll AI Sparks Privacy Debate

Facebook’s app can now scan the photos on your phone (even those you haven’t posted) and suggest creative edits like collages or restyled images. The company claims this won’t be used for ads or AI training unless users share the edited photos, but the move raises fresh questions about how much personal data Meta’s AI can “see.” Opt-in controls are available, but critics warn the feature gives Meta deeper behavioral insight into users’ offline lives. Bottom line: Convenient creativity often comes at the cost of privacy.

Pinterest Gives Users a Way to Block “AI Slop”

After complaints that feeds were drowning in AI-generated art, Pinterest is letting users limit how much AI imagery appears in categories like fashion, décor, and art. The company says 57% of online content is now AI-generated, making human curation a growing challenge. This move could help Pinterest preserve its core identity as a source of real human inspiration — not just machine-made mimicry.

Kayak Launches “AI Mode” for Smarter Trip Planning

Kayak.com now includes an AI chatbot that helps you plan trips, compare hotels, and even ask open-ended travel questions (“Where should I go for New Year’s Eve on a budget?”). The feature builds on the company’s earlier Kayak.ai experiment and uses OpenAI tech under the hood. It’s part of the broader shift toward AI-native travel planning, with companies like Expedia and Booking.com launching similar integrations.

FOR THE TECHNICALLY INCLINED 🛠️

Stanford’s “Verbalized Sampling” Fixes Mode Collapse

Large language models often get repetitive or overly cautious. This is a symptom of “mode collapse,” where alignment training squeezes out creativity.

Stanford researchers have introduced a simple fix called Verbalized Sampling (VS). Instead of forcing models to pick one best answer, VS prompts them to assign probabilities to multiple possible outputs (“Generate 5 jokes about coffee and estimate their probabilities”).

The result: up to 2.1× more diverse and creative outputs, without retraining or fine-tuning.

Why it matters: VS shows that prompt design alone can restore the richness lost in over-aligned models, potentially unlocking more expressive AI for storytelling, dialogue, and data generation.

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