Exploring the Potential of Meta’s Muse Spark AI
In an attempt to simplify my meal planning, I decided to test Meta’s new personal AI tool, Muse Spark. My goal was to have it evaluate the nutritional value of my lunch and suggest dinner recipes based on what I had in my fridge. The results were both insightful and a bit unexpected.
Rating My Lunch
For lunch, I opted for a take-out Japanese bento from my favorite spot. It included seared salmon on rice, an egg, mixed greens, and a side of raw salmon and fish roe. I uploaded a photo of the meal to Muse Spark and asked it to break down each component, estimate the calories, and provide a score out of 10.
Muse Spark was mostly accurate in identifying the ingredients. However, it couldn’t determine the exact weight of the components or the type of oil used in cooking. It estimated the meal to be around 760 calories. The AI also noted that the dressing and sauce were high in calories and sodium, which could limit my sodium intake for the day.
The AI highlighted that the meal was rich in micronutrients like Omega-3 but lacked fiber, vitamin C, and calcium. It gave my meal a rating of 7.5 out of 10 and reminded me that it is not a licensed nutritionist.
Struggles with Labeled Images
One of the challenges I faced was generating a labeled image of the meal. Muse Spark’s attempts resulted in unreadable text, and after multiple tries, I gave up and moved on to the next task.
Dinner Suggestions
With the knowledge of my lunch, I gathered leftover ingredients from my fridge and took a photo. I then asked Muse Spark for dinner recipe suggestions. I explained that I had various condiments available that weren’t visible in the photo and preferred easy-to-cook and clean recipes.
Here are some of the suggestions provided by Muse Spark:
- Tomato-braised chicken + roasted potatoes + papaya side
- Light spaghetti alle vongole + blueberry-oat smoothie
- Japanese-style oyakodon + papaya-blueberry salad
According to Muse Spark, these meals were designed to compensate for the lack of fiber and vitamin C in my lunch while allowing for a moderate increase in carb intake. It also advised rinsing canned tomatoes to reduce sodium and avoiding salt and soy sauce.
Although Muse Spark couldn’t recognize the sweet yogurt coating on the freeze-dried strawberries, its suggestions provided enough inspiration to cook and creatively use my leftovers.
Final Decision
I ultimately chose the Japanese-style oyakodon meal and saved the other two recipes for another day. While I might not completely eliminate soy sauce from my meals, the insights from Muse Spark have made me more aware of my dietary choices.
At the end of the day, the experience with Muse Spark has been both informative and thought-provoking, offering a glimpse into the potential of AI in simplifying meal planning.
