Tag: AI-20250707

The premise of the story is fascinating: AI technology makes it easier for call center workers to communicate with Americans. Call center worker Kartikeya Kumar is pleased, “Now the customer doesn’t know where I am located…If it makes the caller happy, it makes me happy, too.”

India firms have leveraged AI tech to improve their offerings, and now the country has more call centers than anywhere else.

“We don’t see AI as taking jobs away,” said MV Prasanth, the chief operating officer for Teleperformance in India. “We see it as easier tasks being moved into self-serve,” allowing Kumar and his colleagues to focus on “more complex tasks.”

But the article isn’t all roses: concerns about “whitewashing” voices and the fear of job losses (particularly entry-level ones).

Even the most hopeful admit that workers who can’t adapt will fall behind. “It’s like the industrial revolution,” said Prithvijit Roy, Accenture’s former lead for its Global AI Hub. “Some will suffer.”

Waymo recently published a study outlining the importance of large data sets for improved autonomous vehicle performance.

The last few years of AI performance have been powered by scale. It has been repeatedly shown that the performance of deep learning models scales predictably as we increase model size, dataset size, and training compute. These scaling laws drive continuous advancements in large language models (LLMs) in particular, as evidenced by the increasingly capable AI systems we see emerging regularly.

The post is hard to read and features inside baseball terminology, but the results clearly suggest that larger data sets are helpful. Specifically, “Closed-loop performance follows a similar scaling trend. This suggests, for the first time, that real-world AV performance can be improved by increasing training data and compute.“ This certainly suggests that Waymo and Tesla have a huge upperhand for the future autonomy battles because of their enormous troves of data.

Ethan Mollick publishes a very helpful guide for using AI tools right now. I think his conclusions are spot-on:

For most people who want to use AI seriously, you should pick one of three systems: Claude from Anthropic, Google’s Gemini, and OpenAI’s ChatGPT. With all of the options, you get access to both advanced and fast models, a voice mode, the ability to see images and documents, the ability to execute code, good mobile apps, the ability to create images and video (Claude lacks here, however), and the ability to do Deep Research. Some of these features are free, but you are generally going to need to pay $20/month to get access to the full set of features you need. I will try to give you some reasons to pick one model or another as we go along, but you can’t go wrong with any of them.

As for getting started, his advice is great:

So now you know where to start. First, pick a system and resign yourself to paying the $20 (the free versions are demos, not tools). Then immediately test three things on real work: First, switch to the powerful model and give it a complex challenge from your actual job with full context and have an interactive back and forth discussion. Ask it for a specific output like a document or program or diagram and ask for changes until you get a result you are happy with. Second, try Deep Research on a question where you need comprehensive information, maybe competitive analysis, gift ideas for someone specific, or a technical deep dive. Third, experiment with voice mode while doing something else — cooking, walking, commuting — and see how it changes your ability to think through problems.