I've been looking into this. There seems to be some mostly-repeating 2D pattern in the LSB of the generated images. The magnitude of the noise seems to be larger in the pure black image vs pure white image. My main goal is to doctor a real image to flag as positive for SynthID, but I imagine if you smoothed out the LSB, you might be able to make images (especially very bright images) no longer flag as SynthID? Of course, it's possible there's also noise in here from the image-generation process...
Gemini really doesn't like generating pure-white images but you can ask it to generate a "photograph of a pure-white image with a black border" and then crop it. So far I've just been looking at pure images and gradients, it's possible that more complex images have SynthID embedded in a more complicated way (e.g. a specific pattern in an embedding space).
Later down the line, if you want to have separate behaviour for task deadlines vs payment deadlines, you're going to have to go through your codebase and look at every call to set_deadline and figure out if it's being used to set a task deadline or payment deadline. If you have an inkling that the deadlines might need a different behaviour, the “good example” can save you an annoying refactor in the future.
I remember it being pretty simple (like, run one or two bash commands) to get a source table streamed into a kafka topic, or get a kafka topic streamed into a sink datastore (S3, mysql, cassandra, redshift, etc). Kafka topics can also be filtered/transformed pretty easily.
E.g. in https://engineeringblog.yelp.com/2021/04/powering-messaging-... they run `datapipe datalake add-connection --namespace main --source message_enabledness`, which results in the `message_enabledness` table being streamed into a (daily?) parquet snapshot in S3, registered in AWS Glue.
It is open source but it's more of the "look at how we did this" open source VS the "it would be easy to stick this into your infra and use it" kind of open source :(
It seems like VR is less than half of the investment by RL. In Meta's 2022 annual report, they say "Many of our metaverse investments are directed toward long-term, cutting edge research and development for products that are not on the market today and may only be fully realized in the next decade. This includes exploring new technologies such as neural interfaces using electromyography, which lets people control their devices using neuromuscular signals, as well as innovations in artificial intelligence (AI) and hardware to help build next- generation interfaces. ... *in 2023, we expect to spend approximately 50% of our Reality Labs operating expenses on our augmented reality initiatives, approximately 40% on our virtual reality initiatives, and approximately 10% on social platforms and other initiatives.*"
I'm not sure if Horizon falls into "virtual reality" or "social platforms" but it seems to be the latter: "For example, we have launched Horizon Worlds, a social platform where people can interact with friends, ..."
I feel like this is a boring answer but for me, I had to make a habit of it, and then it didn't feel so hard any more. I started doing a master's degree in my free time: when I started I barely had the energy to do anything outside of work, but now I feel like setting aside time for coursework is pretty natural.
For me I find I usually fall into a 2/2/2 pattern for forming habits*: The first two days are super hard, after about two weeks it starts to feel doable, after two months the habit is pretty set and I don't have to worry as much about falling off the bus.
* This entire pattern is probably a placebo but that's fine by me
Of course, your energy is not infinite. If you are trying to work crazy hours and fit in other taxing activities, you are going to fail at some point.
> Finally, RLHF, or "RL with Human Feedback". This is a fancy way of saying that the model now observes two humans in a conversation, one playing the role of a user, and another playing the role of "the AI", demonstrating how the AI should respond in different situations. This clearly helps the model learn how dialogs work, and how to keep track of information across dialog states (something that is very hard to learn from just "found" data). And the instructions to the humans are also the source of all the "It is not appropriate to..." and other formulaic / templatic responses we observe from the model. It is a way to train to "behave nicely" by demonstration.
I think this misses a big component of RLHF (the reinforcement learning). The approach described above is "just" supervised learning on human demonstrations. RLHF uses a reinforcement learning objective to train the model rather than maximizing likelihood of human demonstrations. In fact, you can then take the utterances your model has generated, collect human feedback on those to improve your reward model, and then train a new (hopefully better) model -- you no longer need a human roleplaying as an AI. This changed objective addresses some of the alignment issues that LMs struggle with: Open AI does a pretty good job of summarizing the motivation in https://arxiv.org/abs/2009.01325:
> While [supervised learning] has led to markedly improved performance, there is still a misalignment between this fine-tuning objective—maximizing the likelihood of human-written text—and what we care about—generating high-quality outputs as determined by humans. This misalignment has several causes: the maximum likelihood objective has no distinction between important errors (e.g. making up facts) and unimportant errors (e.g. selecting the precise word from a set of synonyms); models are incentivized to place probability mass on all human demonstrations, including those that are low-quality; and distributional shift during sampling can degrade performance. Optimizing for quality may be a principled approach to overcoming these problems.
where RLHF is one approach to "optimizing for quality".
"An alien admitted or otherwise provided status in E-1, E-2, E-3, H-1B, H-1B1, L-1, O-1 or TN classification and his or her dependents shall not be considered to have failed to maintain nonimmigrant status solely on the basis of a cessation of the employment on which the alien's classification was based, for up to 60 consecutive days or until the end of the authorized validity period, whichever is shorter, once during each authorized validity period. DHS may eliminate or shorten this 60-day period as a matter of discretion. Unless otherwise authorized under 8 CFR 274a.12, the alien may not work during such a period."
This is also what I've been told by my company's lawyers.
Gemini really doesn't like generating pure-white images but you can ask it to generate a "photograph of a pure-white image with a black border" and then crop it. So far I've just been looking at pure images and gradients, it's possible that more complex images have SynthID embedded in a more complicated way (e.g. a specific pattern in an embedding space).