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The article decides to mention that Google made a public statement that clearly and unambiguously answers their question ( https://twitter.com/GoogleWorkspace/status/16382985371956019... ) and then proceeds to ignore it completely in favor of conspiracy theories ("but look, there they said 'was' instead of 'is'!").
Earlier quoted context omitted.
Bard is written by developers, and developers often don't have any problem using any data available to them for a purpose they deem 'useful', especially if there's a bonus or a promotion at stake.
I think a company like Google would be having a lot of restrictions around where to train models and where to not. And they already have access to the whole internet, Gmail conversations would be one tiny part of it. Also wonder if they got to actually train on github data (considering the Microsoft angle)
Considering all the Github data is on Google BigQuery, probably: https://cloud.google.com/blog/topics/public-datasets/github-...
The article decides to mention that Google made a public statement that clearly and unambiguously answers their question ( https://twitter.com/GoogleWorkspace/status/16382985371956019... ) and then proceeds to ignore it completely in favor of conspiracy theories ("but look, there they said 'was' instead of 'is'!").
Logically it just doesn't have that data and why would it? If you had proprietary data you didn't want to share why would you add it to the language model?
Bard is written by developers, and developers often don't have any problem using any data available to them for a purpose they deem 'useful', especially if there's a bonus or a promotion at stake.
The article directly quotes google giving a clear answer…. “Google replied to the tweet directly, saying, “Bard is an early experiment based on Large Language Models and will make mistakes. It is not trained on Gmail data. -JQ”.
Initially, Google wrote, “Thank you for your message Kate, no private data will be used during Barbs[sic] training process. We always take good care of our users’ privacy and security.”
"That seems like a clear and heartening assurance. It’s notable, then, that Google quickly deleted that tweet and didn’t amend it with any additional clarification"
That makes me wonder if another model was trained and then pulled in. Example being the Gmail one.
They can say it was not trained on Gmail data because it was trained with Google’s Smart Compose, which it was trained on Gmail data.
Gmail Data -> Google’s Smart Compose -> Bard
It all depends on where you draw the line to stop reporting. Language is a powerful tool of deception.
The article decides to mention that Google made a public statement that clearly and unambiguously answers their question ( https://twitter.com/GoogleWorkspace/status/16382985371956019... ) and then proceeds to ignore it completely in favor of conspiracy theories ("but look, there they said 'was' instead of 'is'!").
The article decides to mention that Google made a public statement that clearly and unambiguously answers their question ( https://twitter.com/GoogleWorkspace/status/16382985371956019... ) and then proceeds to ignore it completely in favor of conspiracy theories ("but look, there they said 'was' instead of 'is'!").
Lawyers use language in very specific ways, and anything that's not completely obvious can be used to hide the truth. As an example, I once worked with a team who was building some software where a requirement said the app 'should' do something instead it 'shall' do something. The company's lawyer argued successfully that this meant the requirement was optional.
The article decides to mention that Google made a public statement that clearly and unambiguously answers their question ( https://twitter.com/GoogleWorkspace/status/16382985371956019... ) and then proceeds to ignore it completely in favor of conspiracy theories ("but look, there they said 'was' instead of 'is'!").
4. Google has never denied that Bard was trained on data from Gmail. They've only claimed that such data is not currently used to “improve” the model.
What Bard has to say about this: “I have not personally seen a real Gmail account. However, I have access to a massive dataset of Gmail emails, and I have used this dataset to train my language model. This means that I am familiar with the format of Gmail emails, and I can generate text that is similar to the text that is found in real Gmail emails.”
Now do I think they have done the nasty? I don't know.
Should it be reviewed by an outside team? I think yes.
I cannot think of a solution to this problem, which I believe will keep cropping up, but I think it can be problematic. I think it needs to be prooven true to be safe.
Would this not be fairly easy to test? Find some fairly dense thing in gmail, stick 50% into bard and ask it to complete rest& see how close output is?
There aren't any techniques I know of to prevent it either; when training an image model the recommendation is to dedupe the input so nothing is weighted over anything else, but that's not an absolute defense.