Can AI help me plan my honeymoon?

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I'm getting married later this summer and am feverishly planning a honeymoon together with my fiance. It has been at times overwhelming trying to research and decide between what seem like millions of options while juggling busy work schedules and wedding planning.

Thankfully, my colleague Rhiannon Williams has just published a piece about how to use AI to plan your vacation.You can read her story here. The timing could not be better! I decided to put her tips to the test and use AI to plan my honeymoon itinerary.

I asked ChatGPT to suggest a travel plan over three weeks in Japan and the Philippines, our dream destinations. I told the chatbot that in Tokyo I wanted to see art and design and eat good food, and in the Philippines I wanted to go somewhere laid-back and outdoorsy that is not very touristy. I also asked ChatGPT to be specific in its suggestions for hotels and activities to book.

The results were pretty good, and they aligned with the research I had already done. I was delighted to see the AI propose we visit Siargao Island in the Philippines, which is known for its surfing. We were planning on going there anyway, but I haven't had a chance to do much research on what there is to do. ChatGPT came up with some divine-looking day trips involving a stingless-jellyfish sanctuary, cave pools, and other adventures.

The AI produced a decent first draft of the trip itinerary. I reckon this saved me a lot of time doing research on planned destinations I didn't know much about, such as Siargao.

But ... when I asked about places I did know more about, such as Tokyo, I wasn't that impressed. ChatGPT suggested I visit Shibuya Crossing and eat at a sushi restaurant, which, like,c'mon, are some of the most obvious things for tourists to do there. However, I am willing to consider that the problem might have been me and my prompting. Because I found that the more specific I made my prompts, the better the results were.

But here's the thing. Language models work by predicting the next likely word in a sentence. These AI systems don't have an understanding of what it is like to experience these things, or how long they take. For example, ChatGPT suggested spending one whole day taking photos at a scenic spot. That would get boring pretty quickly. The AI systems of today lack the kind of last-mile reasoning and planning skills that would help me with logistics and budgeting. It also suggested accommodations that were way out of our price range.

But this whole process might become much smoother as we build the next generation of AI agents.

Agents are AI algorithms and models that can complete complex tasks in the real world. The idea is that one day they could execute a vast range of tasks, much like a human assistant. Agents are the new hot thing in AI, and I just published an explainer looking at what they are and how they work.You can read it here.

In the future, an AI agent could not only suggest things to do and places to stay on my honeymoon; it would also go a step further than ChatGPT and book flights for me. It would remember my preferences and budget for hotels and only propose accommodation that matched my criteria. It might also remember what I liked to do on past trips, and suggest very specific things to do tailored to those tastes. It might even request bookings for restaurants on my behalf.

Unfortunately for my honeymoon, today's AI systems lack the kind of reasoning, planning, and memory needed. It's still early days for these systems, and there are a lot of unsolved research questions. But who knows-maybe for our 10th anniversary trip?

Now read the rest of The AlgorithmDeeper Learning

A way to let robots learn by listening will make them more useful

Most AI-powered robots today use cameras to understand their surroundings and learn new tasks, but it's becoming easier to train robots with sound too, helping them adapt to tasks and environments where visibility is limited.

Sound on:Researchers at Stanford University tested how much more successful a robot can be if it's capable of listening." They chose four tasks: flipping a bagel in a pan, erasing a whiteboard, putting two Velcro strips together, and pouring dice out of a cup. In each task, sounds provided clues that cameras or tactile sensors struggle with, like knowing if the eraser is properly contacting the whiteboard or whether the cup contains dice. When using vision alone in the last test, the robot could tell 27% of the time whether there were dice in the cup, but that rose to 94% when sound was included.Read more from James O'Donnell.

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