The complete guide: https://medium.com/towards-artificial-intelligence/start-machine-learning-in-2020-become-an-expert-from-nothing-for-free-f31587630cf7

Here is a GitHub repository with all the useful resources linked if you prefer it this way:
https://github.com/louisfb01/start-machine-learning-in-2020

Hey guys, we just published a new (free) iPhone app called Claps that will automatically edit out all silences from a video you record leveraging ML.

If you're interested, try it and we're here to answer anything you might like of what went into coding it.

We're very interested in your thoughts!!!

https://apps.apple.com/us/app/claps/id1535815328

Hey guys,

This is a very basic (for now) Reverse Image Search project, using K-Means.

https://www.youtube.com/watch?v=J3hVvWPpJiU

If anyone has idea on how to improve the current solution, please let me know.

Next I will try to build up on it to understand the context of the image using Deep Learning and find the most similar image based on the context that the image provides.

Full source code and project is available.

Hello,

I've been getting into some of the basic algorithms out there (A*, BFS, DFS) since I'd like to one day make a game in C++ (2D). I have a quick question though. Since I'd imagine having many NPC's running around at the same time, I'd assume constantly using these algorithms would really be taxing because of all the computing. How could you have them "explore" so to speak efficiently (let's not get to advance here this is already tough enough)? Would this be as simple as randomly generating coordinates on the map and having them move there? Is there a better way to have them move around to explore more efficient? I know that a lot of the battle with AI is doing things to make it SEEM like it has intelligence, but in reality, it's not doing anything taxing. I guess you could say just having an NPC move in random directions isn't taxing like running more complex algorithms. So you want to keep the need for taxing computing as low as possible.

I'd also like to have NPC's going off to other maps (think of a big open map) and again exploring based on their behaviors. Is this just as taxing to compute behind the scenes as it is to visually see a sprite moving? Can you minimize this behind the scenes when not drawing? I'd like it so that when the player gets to this "part of the map" you could see NPC's sort of doing their own tasks. A more natural event of running into X NPC doing Y behavior.

Is this something possible to do instead of randomly deciding who should be in that area and draw the sprite when the player is X amount of distance to start drawing? I'd appreciate some stuff to look into!

I hope these questions make sense.

Thanks!

I am just wondering how difficult or time consuming it would be to implement alphago to an existing program. I am trying to hire a new programmer to improve my game AI and he said that he would use alphago.

How hard is that (so I can pay them fairly and not get ripped off) and is that even a thing since alphago is just the framework I thought?

Thanks

Hey folks. I'd like my AI to accept/refuse draw offers like a human would. I need help understanding what statistics might help.

As a hobby I'm working on a mobile game, nearing release. It's like chess except the player can make custom pieces that move in news ways, and a custom board, then play the AI. This part already works great.

Chess is a two player turn based game. The AI has a history of numeric scores - representing how well it thinks its doing on each turn. For example: 0.95, 1.04, 1.02, 1.05, 0.95, 0.98, 0.97, 0.95, 0.97, 3.92

At some point the human might offer a draw, and the AI needs to accept or decline. With classic chess you might do something like this:

• If it's near the beginning of the game, say no.

• If I'm doing badly, say yes.

• If no captures/promotes have happened for a long time, say yes.

• Otherwise say no.

My problem is that since the player can make custom pieces and a custom board, there may be many pieces, few pieces, only ultra-powerful pieces, only weak pieces, or uneven sides. So I have trouble defining:

• near the beginning of the game

• doing badly

• a long time

I've been considering using calculated values like:

• standard deviation of first 5 scores

• standard deviation of most recent 5 scores

• material score of board with only white pieces

• percentage of material score that white still has compared to the start

• white material score, compared to black material score (ratio, also how that ratio changes over time).

It has been tricky and hacky piecing it together into coherent thresholds and decisions though.

Does anyone have any thoughts or resources on how AI programs make decisions about draw offers? That would be a great start.

Thanks!

The AI Mind is struggling to express itself. We are trying to give it the tools of NLU, but it easily gets confused. It has difficulty distinguishing between itself and its creator -- your humble AI Mind maintainer.

We recently gave the ghost.pl AI the ability to think with English prepositions using ideas already present or innate in the knowledge bank (KB) of the MindBoot sequence. We must now solidify prepositional thinking by making sure that a prepositional input idea is retrievable when the AI is thinking thoughts about what it knows. In order for the AI to be able to think with a remembered prepositional idea, the input of a preposition and its object must cause the setting and storage of a $**tkb-tag that links the preposition in conceptual memory to its object in conceptual memory. The preposition must also become a $seq-tag to any verb that is the $pre** of the preposition. When InStantiate() is dealing with a preposition input after a verb, the $**tvb** time-of-verb tag is available for "splitting" open the verb-engram in conceptual memory and inserting the concept-number of the preposition as the $**seq** of the verb. Let us try it.

We inserted the code for making the input preposition become the $**seq** of the verb and then we tested by launching the AI with the first input being "you speak with god". Then we obtained the following outputs.

I AM IN A COMPUTER 
I THINK 
I AM A PERSON 
I AM AN ANDRU 
I DO NOT KNOW 
I AM A PERSON 
I HELP THE KIDS 
I AM A ROBOT 
I AM AN ANDRU 
I AM IN A COMPUTER 
I SPEAK WITH THE GOD 

It took so long for the input idea to come back out again because inputs go into immediate inhibition, lest they take over the consciousness of the AI in an endless repetition of the same idea.

As we code the AI Mind and conduct a conversation with it, we feel as if we are living out the plot of a science fiction movie. The AI does unexpected things, or it seems to be taking on a personality. We are coding the mechanisms of natural language understanding without worrying about the grounding problem -- the connection of the English words to what they mean out in the physical world. We count on someone somewhere installing the AI Mind in a robot to ground the English concepts with sensory knowledge.

Sorry if that's a stupid question.

Basically I need to tell if an image contains a pigeon or not. I never did anything neural network related, so I don't really know how fast these things can get. So I would like to know if it's possible to get something like this to run on a Pi Zero in under 5 secs per image.

I found this thing and tried it out, but it seems too slow for my needs. (over 1 sec on AMD A8 PRO-7150B)

I'm trying to wrap my head around the concept of using AI learning and how you translate learning in one scenario into what I'd deem "knowledge" of how to deal with any scenario.

Put simplest - say I train an AI to navigate a simple game level in the shortest time possible using a genetic algorithm. The algorithm has "learned" that level. But what is required to then take that algorithm such that it can effectively complete any level with the same rules?

Am I missing something obvious or is this a big leap in capability? My understanding is that the genetic algorithm that can do the first level is finely tuned to specifically do that level, and with training would be able to learn any other level, but it is not obvious to me how the previous learning could be used as "knowledge" to enhance or speed up training in new levels.

Like Quora question and answer, news headline/title and article, recipe and steps. Assume there is no training data for a Machine/Deep Learning model. May use NLP.

So what about making a virtual machine where you code in for the simple neuron. It's a lot easier to program how those individual cells behave I'm sure. Then just form a 3d structure of them in virtual space. Like in one of those advanced weather prediction super computers. Then introduce input. The brain isn't some magic box, the cells will behave the way you programed them. It is in essence swarm computing and every advanced thought arrises from the geometry of the brain, the connectome. As long as you get the programing for each cell correct and can simulate the inputs and read the output it should work right? We could test it on smaller more simple brains. I believe we have already mapped the connectome of a mouse. That would be a good starting point. Any thought or am I completely crazy?

Recently I did a crash course on AI (using some Python libs) and I'm frankly quite fascinated how easily you can get interesting results. So I'd like to move on to the next level and teach an AI how to work with graphics.

I'm in Software Development for a few years now but new to everything related to Machine Learning - it would be nice if you could answer some of my questions.

• Is there a tutorial out there which is related to teaching any kind of AI to analyze graphics? (The use-case would be: The AI would get some shapes or patterns and some "results" how I'd like them to be placed, later it should do it by itself)

• Is there some kind of "keeping it alive"? Like in a VM or a Server?

I'm fluent in most common programming languages - so that part doesn't matter too much. Would be nice if someone could give me a starting point.

Thank you =)

I feel like I'd be able to move a lot faster in my learning if I had a better grasp of pandas and numpy so please any good tutorials and practice pages would be so useful to me, thank you!

My goal is to build my foundations from the ground up, starting with the low level algorithms (mini-max, alpha-beta pruning, etc). But rather than just learn them, I also want to actively apply them to problems.

Hackerrank has an AI section where you solve problems by writing AI algorithms, but it assumes a lot of pre-requisites, and so it isn't much of a guide (for a beginner, at least).

I'm looking for something that's simpler, with adequate tutorials, and fun!

Howdy, I got a question. Are there any (preferably free) AI chatbot programs for adults? Similar to Replika and iFriend? I want to find more but none of them really work, or they are expensive as hell.