I know that OpenAI’s retro allows for custom imports if ROMs that aren’t included I was wondering if Unity allows the same. Say if a friend gave you access to his game project, could you use Unity ML tools to create agents to play that game?

We thank the reviewers for the detailed comments, of which some were even based on our paper.

To the reviewer that said our paper was “underdeveloped” because we didn’t use a different methodology Y from field Z, we’d like to point out that a) this is in field A, b) we provided a framework for how to extend this to other methodologies in field A, and c) methodology Y has no obvious way to extend to the problem we’re addressing (and doing so would be a whole paper in its own right). Do you often read papers and get frustrated that they aren’t the papers you’ve written?

To the same reviewer, who asked why we didn’t cite papers Z1 and Z2, we would again point out that this isn’t field Z and those papers have no relevance to the topic at hand except that you’d have written a paper on a different topic, which we didn’t.

To the reviewer that asked why we didn’t cite X, we’d like to point out that we did cite X, and had a whole paragraph discussing the relationship of this work to that one.

To the reviewer that proposed an example dataset to evaluate our model on, we point out that we already evaluate the model on that data set; see our Experiments section.

To the reviewer that pointed out that our method won’t work when assumption 3 isn’t met, yes, you’re correct. That’s why we stated it as an assumption. Congratulations on your reading comprehension.

To the reviewer that directly copy/pasted our introduction into the “what 3 things does this paper contribute” box, we’ll be sure to include in future revisions a copy/paste-able review justifying “score 10, confidence 5” to make your review easier. That you also confused our main claim with a work we were citing, and otherwise completely missed the discussion on relationship to prior work or what makes this paper novel, makes your review particularly useful to development of the work.

To the reviewer that wrote that, while THEY were familiar with the definitions in a reference, we should explain it for readers that might be confused, we understand entirely. We’ll gladly explain it for “a friend of yours”, err “readers”, and not you, because you get it and you’re smart and it’s just the readers who don’t.

To the reviewer who commented that our results were “contradictory” because we said that our modification “in general performed slightly worse” on this metric, when in fact our plots show it sometimes performed better, we’ll gladly fix our claim to be clear that “in general” doesn’t mean “always” and also our results are even better than the previous wording indicated.

To the reviewer that said our comparison method’s results were worse than reported in the original paper, we’ve carefully compared their bar charts to ours and found that the results are the same to the precision of the graphical printout in the previous paper. If you could lend us your image sharpening function so we can get more significant digits out of their plot, we’d be glad to redo the comparison.

To the reviewer who used half of their review to argue that our entire subfield is dumb and wrong, we thank them for reaching across academic lines to provide commentary in an area that pains you deeply.

And finally, to the reviewers who called our paper (all actual quotes) “original, well-motivated, and worthy of study”, “important in its own right”, that said you “greatly enjoyed reading this paper” and that “this is an interesting problem and certainly worth studying” and that “this paper identifies an important problem … [and the authors] then present a simple” solution, thank you for also marking this a reject. Since all of you gave us scores between 5 and 3, neither the AC nor any of you will ever have to read this response or reconsider your scores before we are inevitably rejected, but we hope that your original, well-motivated, worth-studying, important, interesting, clear papers receive reviews of equal quality in the future!

/salt

I have recently published this on meta-learning algorithms for classifying images with few examples:

https://blog.sicara.com/meta-learning-for-few-shot-computer-vision-1a2655ac0f3a

So far I’ve only used meta-learning to solve few-shot tasks (few-shot classification, few-shot regression, etc …). What am I missing ?

The last couple weeks I’ve started experimenting with ML. As an electronic engineer I’m focus on the embedded domain and the last years on the embedded Linux domain. The last few months the semiconductor industry has turned to ML (they like to call it AI) and from now on almost all the new CPUs and MCUs are coming out with some kind of AI accelerator. The software support for that HW is still quite bad though, so there is plenty of HW and no SW, but it will get better in the future, I guess.

That said, I though that it was the right time to get involved and I wanted to experiment with ML in the low embedded and the Linux embedded domain, providing some real-working examples and source code for everything. The result, was a series of 5 blog posts which I’ll list here with a brief description for each one.

1. [ML on embedded part 1]: In this post there’s an implementation of a naive implementation of 3-input, 1-output neuron that is benchmarked in various MCUs (stm32f103, stm32f746, arduino nano, arduino leonardo, arduino due, teensy 3.2, teensy 3.5 and the esp8266.
2. [ML on embedded part 2]: In this post I’ve implemented another naive NN with 3-input, 32-hidden, 1-output. Again the same MCUs where tested.
3. [ML on embedded part 3]: Here I’ve ported tensorflow lite for microcontrollers to build with cmake for the stm32f746 and I’ve also ported a MNIST keras model I’ve found from a book to tflite. I’ve also created a jupyter notebook that you can hand-draw a digit and then from within the notebook run the inference on the stm32.
4. [ML on embedded part 4]: After the results I got from part 3, I thought it would be interesting to benchmark ST’s x-cube-ai framework to do 1-to-1 comparisons with tflite-micro on the same model and MCU.
5. [ML on embedded part 5]: As all the previous posts were about edge ML, I’ve implemented a cloud acceleration server using a Jetson nano and I developed a simple TCP server with python that also runs inferences in the same tflite model that I’ve used also in part 3 & 4. Then I’ve written a simple firmware for the ESP8266 to send random input arrays serialized with flatbuffers to the “AI cloud server” via TCP and then get the result. I’ve run some benchmarks and did some comparisons with the edge implementation.

Although these are more interesting for embedded engineers, I think it also fits in here.

Hello All.

I am looking for some intro in E2E of Machine Learning pipelines. I will be glad if you can share some stories or links of existing case studies that describe ways of E2E implementation of machine learning processes (something like kubeflow or tensorflow extended).

I am trying to find out the golden middle or best-used practices from SRE/DevOps point of view. Thank you.

I originally posted this on /r/learnmachinelearning since it’s technically a question, but I think it goes beyond the scope of questions like “how do I set up tensorflow” and is worth putting here well.

I love tabletop RPGs, and I’m specifically curious if there are any areas of current ML technology that might allow the nerd fantasy of an independent AI dungeon master to come true, whether fully or even partially.

For those unfamiliar with tabletop RPGs, I’ll run over the basic gameplay loop, using Dungeons & Dragons as my example.

At the center of it all you have a system of core rules (easily programmable rules, in fact) which tracks character stats, specifics of movement, items, abilities, success/failure checks, and other details down to a very granular level. This part is easy for computers, evidenced by the fact that RPG video games derived from these rulesets.

However, with a tabletop RPG, the game is ran by a person known as the Dungeon Master or Game Master, who acts as a kind of referee for the rules. The Dungeon Master can take whatever request a player makes, then translate that request into something compatible with the rules.

This process of “compiling” a request into a rule check can be done for something simple, such as “I attack the orc” (damage based on a dice roll + weapon type + skill modifiers + proficiency bonus, etc.), or for something crazy and convoluted, such as “I fashion the rope in my inventory into a lasso and toss it so that it loops around the damaged stone pillar at the other side of the chasm” (dice roll based on the player’s dexterity skill), followed by “I pull the rope as hard as I can” (another roll based on their strength skill, success resulting in the pillar toppling and creating a bridge of rubble, and the player losing the rope item) followed by “I walk across the rubble to cross” (dice roll based on the player’s acrobatics ability to update the player’s position and allow them to continue the loop by interacting with things on the other side of the chasm).

So there is consistency mixed with inconsistency. A player’s input can be wild and unpredictable, but it always resolves into rule checks that are consistent, mechanical, and easily understandable by a machine. The Dungeon Master is the missing link. The adjudication of a human brain allows players to interact with the rules in a way that’s deeper and freer than clicking pre-programmed buttons to perform actions, which is why video games have never been able to replicate the feeling of a real tabletop RPG.

But now we have machine learning. What kind of AI, if any, could be used to taking ambiguous statements and convert them into standard game interactions? And to what extent could it be pushed? If it’s impossible to account for everything (which I’m guessing it is, at least for now), then what kind of sacrifices could be made on the player’s side (e.g. typing only short, simple requests in a specific format, or entering nouns and verbs into text boxes) to make it possible?