[asgerhb]

The use of AI and voice recognition seems mostly designed to make the result seem more sensational than it actually is. Does any computation actually happen in the "organoid" part? How would you even train such a cell to perform a task?

From reading the article it seems to me that the answer is no. The actual contribution is feeding the organoid electric signals, and reading its reactions. (Probably the machine learning algorithm used would have had even better accuracy, if the input signal hadn't been fed through a layer of goo. It doesn't say whether this is the case.) The rest is speculation of future applications.

> To test Brainoware’s capabilities, the team used the technique to do voice recognition by training the system on 240 recordings of eight people speaking. The organoid generated a different pattern of neural activity in response to each voice. The AI learned to interpret these responses to identify the speaker, with an accuracy of 78%.

It "generated a different pattern," with no indication that this pattern was optimized to be useful in any way.

I think the key part of a (bio-)"computer" is the possibility of programming/training it, not just reading input from it.

[Avicebron]

I came to a similar conclusion after reading the article, reading an predictable output map from a known input and then implying that computation occurs within the organoid instead of their results being a function of predictable inputs -> predictable outputs seems overally sensationalized.

Having written some papers myself, I tend to be suspicious of any article that has "$HOT_THING needs a $PART_OF_HOT_THING revolution" in the introduction. Although I sympathize with the need for funding motivating its writing.

[Coder1996]

Yeah. I'm no scientist, but I am ML trained and it seems to me that if the tissue really is learning, the tissue output should be about the same for each speaker.

[morsecodist]

You might find this: https://www.cell.com/neuron/fulltext/S0896-6273(22)00806-6 more interesting. Researchers were able to train neurons to control a pong game.

[Thebroser]

There are research groups that are trying to encode genetic neural networks into cells like the example I have attached, but the neuronal approach from the post does seem to be different here. https://www.nature.com/articles/s41467-022-33288-8