The red flag for me was that they started that demo video with a background noise to make it seem like it's a raw video. A subtle manipulation for no reason, it's obviously not a raw video.
The fact that they did not fact check the videos again makes me not particularly confident in the quality of Google's work. The bit where the model misinterpreted music notation (the circled area does not mean "piano"), and the "less dense than water" rubber duck are beyond the pale. The SVG demo where they generate a South Park looking tree looks like a parody.
I suppose this is a great example of how trust in authentic videos, audio, images, company marketing must be questioned and, until verified, assumed to be 'generated'. I am curious, if the voice, email, chat, and shortly video can all be entirely generated in real or near real time, how can we be sure that remote employee is actually not a full or partially generated entity? Shared secrets are great when verifying bu…
Ask for information that only the actual person would know.
That will only work once if the channels are monitored.
Make up a code phrase/word for emergencies, share it with your family, then use it for these types of situations.
Fair, but that also assumes the recipients ("family") are in a mindset of constantly thinking about the threat model in this type of situation and will actually insist on hearing the passphrase.
You think you ask your legal assistant to find some precedents related to your current case and they will come back with an A4 page full of made up cases that sound vaguely related and convincing but are not real ? I don't think you understand the failure case at all.
That example seems a bit hyperbolic. Do you think lawyers who leverage ChatGPT will take the made up cases and present them to a judge without doing some additional research? What I'm saying is that the tolerance for mistakes is strongly correlated to the value ChatGPT creates. I think both will need to be improved but there's probably more opportunity in creating higher value. I don't have a horse in the race.
What would be the point of a lawyer using chatGPT if it had to root through every single reference chatGPT relied upon? I don't have to doublecheck every reference of a junior attorney, because they actually know what they are doing, and when they don't, it's easy to tell and wont come with fraudulently created decisions/pleadings, etc
I’m not an expert but I suspect that this aspect of lack of correctness in these models might be fundamental to how they work. I suppose there’s two possible solutions: one is a new training or inference architecture that somehow understand “facts”. I’m not an expert so I’m not sure how that would work, but from what I understand about how a model generates text, “truth” can’t really be a element in the training or i…
> this aspect of lack of correctness in these models might be fundamental to how they work. Is there some sense in which this isn't obvious to the point of triviality? I keep getting confused because other people seem to keep being surprised that LLMs don't have correctness as a property. Even the most cursory understanding of what they're doing understands that it is, fundamentally, predicting words from other words…
Because it is assumed that it can think or/and reason. In this case, knowing the concepts of density, the density of a material, detecting the material from an image, detecting what object this image is.
And, most importantly, knowing that this object is not solid. Because then it could not float.
I was fooled. The model release announcement said it could accept video and audio multi-modal input. I understood that there was a lot of editing and cutting, but I really believed I was looking at an example of video and audio input. I was completely impressed since it’s quite a leap to go from text and still images to “eyes and ears.” There’s even the segment where instruments are drown and music was generated. I t…
Do you believe everything verbatim that companies tell you in advertising?
No, but most people tend to make a mental note of which companies tend to deliver and which ones work hard to mislead them.
You do understand the concept of reputation, right?
That's not the only thing wrong. Gemini makes a false statement in the video, serving as a great demonstration of how these models still outright lie so frequently, so casually, and so convincingly that you won't notice, even if you have a whole team of researchers and video editors reviewing the output. It's the single biggest problem with LLMs and Gemini isn't solving it. You simply can't rely on them when correctn…
It also says the attribute of squeaking means it'll definitely float