Right now I’m in the process of optimizing storage for my large dataset. So I’m converting WAV files to FLAC because FLAC is the lossless, more efficient equivalent.

Now to the question’s context: my initial ML model was trained using WAV files.

What would happen if I use FLAC files for test data? Would the output be the same with the equivalent WAV files? Or is transcoding from FLAC back to WAV inevitable?

Thanks in advance.

Pretty much what is says on the title, if you guys could post the names and maybe some personal experience of research internships in non-Big N companies.

While policy-based reinforcement learning (RL) achieves tremendous successes in practice, it is significantly less understood in theory, especially compared with value-based RL. In particular, it remains elusive how to design a provably efficient policy optimization algorithm that incorporates exploration. To bridge such a gap, this paper proposes an Optimistic variant of the Proximal Policy Optimization algorithm (OPPO), which follows an “optimistic version” of the policy gradient direction. This paper proves that, in the problem of episodic Markov decision process with linear function approximation, unknown transition, and adversarial reward with full-information feedback, OPPO achieves Õ(sqrt{d^3 H^3 T}) regret. Here d is the feature dimension, H is the episode horizon, and T is the total number of steps. To the best of our knowledge, OPPO is the first provably efficient policy optimization algorithm that explores.

https://arxiv.org/abs/1912.05830

You have an idea. An idea pretty simple, enough that someone must have had the idea before. In fact, given you discovered it independently, perhaps others have as well.

You go searching, and you find a related idea. You find another that is similar to yours. A few months later, you look again, and unfold another. If you keep looking, you’ll probably find even more citations.

I’m stuck on this right now. The works I’m finding have few citations, so the ideas aren’t super explored, but I know I’ll keep finding other people who I should cite! And with all the controversy recently about citations and lesser-known people, I’m worried about acting unethically.

Basically, how do you know once you’ve done your due diligence?

Intel this morning issued a statement noting that it has picked up Israeli AI chipmaker Habana Labs. The deal, valued at around $2 billion, is the latest piece of some hefty investments in artificial intelligence that include names like Nervana Systems and Movidius.

In July, Habana announced its Gaudi AI training processor, which the Tel Aviv startup promised was capable of beating GPU-based systems by 4x. The company has been rumored to be a target for an Intel acquisition for a while now, as Intel looks to get out in front of the AI market. The company clearly doesn’t want to repeat past mistakes like missing the boat on mobile.

So far, the strategy looks like it just may pay off, giving Intel a marked advantage in a category it notes will be worth around $24 billion by 2024. In 2019 alone, Intel notes, the company expects to generate in excess of $3.5 billion in “AI-driven revenue,” a 20% increase over the year prior.

“This acquisition advances our AI strategy, which is to provide customers with solutions to fit every performance need – from the intelligent edge to the data center,” Intel EVP Navin Shenoy said in a release tied to the news. “More specifically, Habana turbo-charges our AI offerings for the data center with a high-performance training processor family and a standards-based programming environment to address evolving AI workloads.”

For now, Intel expects to operate Habana as an independent business unit, keeping its current management team on board, with operations still primarily based in Israel. Habana chairman Avigdor Willenz will stay on to advise the companies.

https://techcrunch.com/2019/12/16/intel-buys-ai-chipmaker-habana-for-2-billion/

I’m currently looking for internships after my MSc, as sort of a gap year before going for a PhD. I thought I have a decent CV with a double Master’s degree, some prior internships and some freelancing experience, but half the “internship” positions I find expect you to have a PhD already, plus preferably a few first author publications at NeurIPS… Is this just wishful thinking, or am I really expected to do that before applying for a (presumably entry level) internship?

I just finished reading “Analyzing and Improving the Image Quality of StyleGAN” and I’ve very impressed by the improvements to an already very impressive model. NVlabs really knocked it out of the park.

The quality of the images was already quite high, but now are much less prone to distinctive artifacts as before. Additionally, it is now much easier to project a real image into the StyleGAN space for manipulation. It isn’t hard to imagine a system whereby social media users have their photos automatically embedded into a FFHQ StyleGAN feature space and moved along a learned “attractiveness” vector before publishing. Or similarly, the creation of tools for creating fraudulent imagery of others in a way that is even more widespread and hard-to-detect than they already are.

While the paper highlights the advantages of detecting GAN images using projection, this approach only works in a white-box setting, and seems to only have been tested against unobfuscated images. I feel that in some ways, this result provides a false sense of security. While it may be possible to more easily find unaltered images from officially released StyleGAN 2 models, the overall impact of higher-quality generative models will likely be an increase in detection difficulty under practical conditions.

All in all, I’m heavily reminded of past work on adversarial attacks done by Nicholas Carlini, where he repeatedly demonstrated that many published “defenses to adversarial examples” only addressed FGSM or could be easily counteracted by learning a simple approximation of most specially-designed models.

New defense mechanisms are often evaluated under unrealistic laboratory settings against weak attacks. Not enough time has passed for projection-based detection to be evaluated substantively, but I’ll be surprised if projection is a solution that ends up finding much success at scale (particularly given that by the time such a system has been adequately tuned for a particular use-case, the state-of-the-art models will likely have moved past it).

None of this should be interpreted as a criticism of the paper — I don’t think the authors can be expected to do an exhaustive evaluation of abuse countermeasures in addition to making major innovations to the state-of-the-art in image generation.

This is a problem common in the generative model space. There are parallels to the OpenAI decision to hold back their 1.5B parameter GPT-2 model on the initial release of their paper. Holding back a model seems antithetical to machine learning research (and arguably only reduces the number of people who can research countermeasures), but without a lengthy head-start, it seems unrealistic to expect to ever be able to detect the current state-of-the-art generative models in the wild.

So what’s the solution? Should researchers working on generative models be developing a closer working relationship with those trying to detect the outputs of those models? Should models always be released to the entire research community at once, or should there be a staggered release cycle?

Or alternatively, should we give up entirely on detecting generative models via their output, and instead focus on simpler systems that make abusing them at scale more difficult (better online verification practices, curated fact-checking resources, etc.)?

In summary:
What are your thoughts on the potential for abuse of generative models, and what [should we]/[can we] do about it as researchers/practitioners/humans?

I’ve been finding it difficult to search and gather papers related to my field (i.e., interpretability) due to papers being constantly published, either in venues or on arxiv.

What recommendation tools do you use to find potentially interesting papers for your field?

These streaming services were always perceived as pioneers. They had introduced streaming and HD content long before others could widely deliver such materials. I think that it’s time to provide such option, especially that features like video stabilization are already there. Don’t expect everything from content producers. This situation is like with digital cameras and smartphones. The second one has already outstandend first thanks to not waiting for better optical lenses but just by harnessing algorithms. The future actually belongs to algorithms in all aspects because it helps out human being. This is monkey job which no one dreams about but alorithms. So waiting for first 4k materials done from SD content done outside the laboratories.

PS. Maybe it should be hardware or software codec run on client’s side.