All object detection approaches I know are trained with some data sets to detect only specific classes from the respective data set (also e.g. something like YOLO9000 which can detect 9000 classes). I want to have some more general approach, that can detect arbitrary objects (of any class, returning the containing bounding box) in images.

I did not found anything here, can anyone recommend a related paper or website?

Hello all. I want to know whether this seems like a good idea. I actually am looking for ideas for my Business Plan class and this seems to be like something that hasn’t been done yet.

Basically, the idea is that people can upload their data online with instructions on how to label it. The label can be as simple as assigning a label to the whole picture and it can also be as complicated as marking individual pixels or something like that. I don’t really plan on going too deep into this since it is only for academic reasons but feedback on this would be appreciated.

I think the recent boom in the usage of neural networks means there will be a big market for this idea as everyone from independent machine learning enthusiasts to big corporations would take advantage of this. What do you guys think?

According to article in The Register:

A Netflix spokesperson confirmed to The Register it wasn’t working with Raval, and the ESA has cancelled the whole workshop altogether.

“The situation is as it is. The workshop is cancelled, and that’s all,” Guillaume Belanger, an astrophysicist and the INTEGRAL Science Operations Coordinator at the ESA, told The Register on Monday.

Raval isn’t about to quit his work any time soon, however. He promised students who graduated from his course that they would be referred to recruiters at Nvidia, Intel, Google and Amazon for engineering positions, or matched with a startup co-founder or a consulting client.

In an unlisted YouTube video recorded live for his students discussing week eight of his course, and seen by El Reg, he read out a question posed to him: “Will your referrals hold any value now?”

“Um, yeah they’re going to hold value. I don’t see why they wouldn’t. I mean, yes, some people on Twitter were angry but that has nothing to do with… I mean… I’ve also had tons of support, you know. I’ve had tons of support from people, who, uh, you know, support me, who work at these companies.

He continues to justify his actions:

“Public figures called me in private to remind me that this happens. You know, people make mistakes. You just have to keep going. They’re basically just telling me to not to stop. Of course, you make mistakes but you just keep going,” he claimed.

When The Register asked Raval for comment, he responded:

I’ve hardly taken any time off to relax since I first started my YouTube channel almost four years ago. And despite the enormous amount of work it takes to release two high quality videos a week for my audience, I progressively started to take on multiple other projects simultaneously by myself – a book, a docu-series, podcasts, YouTube videos, the course, the school of AI. Basically, these past few weeks, I’ve been experiencing a burnout unlike anything I’ve felt before. As a result, all of my output has been subpar.

I made the [neural qubits] video and paper in one week. I remember wishing I had three to six months to really dive into quantum machine-learning and make something awesome, but telling myself I couldn’t take that long as it would hinder my other projects. I plagiarized large chunks of the paper to meet my self-imposed one-week deadline. The associated video with animations took a lot more work to make. I didn’t expect the paper to be cited as serious research, I considered it an additional reading resource for people who enjoyed the associated video to learn more about quantum machine learning. If I had a second chance, I’d definitely take way more time to write the paper, and in my own words.

I’ve given refunds to every student who’s asked so far, and the majority of students are still enrolled in the course. There are many happy students, they’re just not as vocal on social media. We’re on week 8 of 10 of my course, fully committed to student success.

“And, no, I haven’t plagiarized research for any other paper,” he added.

https://www.theregister.co.uk/2019/10/14/ravel_ai_youtube/

I hated the guy. I fucking despised him, between his “AI for Supply Chain” video, which made a mockery of one the most challenging domains in ML and Analytics, and his “How to predict the stock market with LSTM” video which downright dangerous (what if somebody actually went out and bet their retirement on 7 lines of Keras?!?!?!?) – I found the guy not just annoying, but pathological.

Yet over the last few days, the sheer completeness of his collapse, has me actually feeling sorry for him. Some of the time he was indeed intentionally malicious, but most of the time he came of more like he a D-bag who just way in over his head and who somehow found himself in a spotlight he didn’t expect.

I mean I know people who are at their core, just refined, more nuanced version of Siraj Raval, who managed to make it to Director and VP level positions on pure self promotions and ability to sell themselves. I am sure we all know people like that as well. What’s the difference? They were simply smart enough to not get caught.

Yet we gawk and we laugh at poor Siraj. Its every started chewing when the guy was pulled over for doing 5 m over the speed limit, and they’re all ignoring all the cars doing 85 and 90…..

Hi,

We’ve recently worked on imperfect information games and reinforcement learning, and we would like to share our toolkit to everyone. RLCard supports various popular card games such as UNO, blackjack, Leduc Hold’em and Texas Hold’em. It also has some examples of basic reinforcement learning algorithms, such as Deep Q-learning, Neural Fictitious Self-Play (NFSP) and Counter Factual Regret Minimization (CFR). Also, it has a simple interface to play with the pre-trained agent. Any generous comments will be appreciated. Have fun!

Github: https://github.com/datamllab/rlcard

Hello everyone!

I was wondering if anyone here has gone through the AI resident interview circuit at X? If yes, could you please provide an overview of what the interview structure is and how many rounds are there in total?

Thanks!

I’m a Master’s student at a university of no repute. I’m not stupid. In fact I would say I’m fairly intelligent. I graduated near the top of my class. I’ve always been performed well in academia and have been decent at math. But I feel like I’ve always had to work harder to get it than others. I’m not a prodigy.

When it comes to ML, specifically ML Engineering (which is where I want to be), it feels like there’s a mountain of things you have to know: Software Engineering principles, a variety of languages, algorithms and their complexities, software frameworks, statistics, mathematics, domain specific requirements. And I feel like the field is always changing and I’m never going to be “informed” as it were.

I feel like I’ve spent most of my Master’s degree just checking off the boxes to get my degree (while also paying for it) and I haven’t had enough time to delve into ML and now that I’m 8 months out from being done I don’t have the knowledge I need to actually move into this field.

But when I read this sub I think that I’m never going to be ready to move into the field. I’m always going to be fighting to understand the math well enough but then I’m not going to have enough time to understand the frameworks or the software engineering. It feels like I’m the jack of all trades but the master of none.

How do I navigate this field to feel like I’m learning effectively? Is it even worth it to pursue this field if I’m not a math prodigy? I want feel competant and that I’m not just another surface level ML practitioner.

I am using BERT for sentence embedding, and currently my best solution is a highly optimized rainbow dictionary, but it’s not scaling well. Attempts to do matching on sub-strings has proved unsuccessful.

I am about to try and train a bidirectional RNN, but that is a huge direction. I was wondering if anyone had advice to look in.