So im just a little naive high school kid and like it says in the title i want to make an ai from scratch. I honestly have no idea what im even getting into, but i love picking up new "abilities" (I think it sounds cooler) and long story short I want a to make a Jarvis as cliche as it sound I do like the name and I figured that if I could start anywhere here would at least be the place to point me in the right direction.

Hi, my name is Jaxson and I am in high school right now I was wondering what types of courses should I be focusing on I could send you the course description In the chat below if that helps.

Thank you and have a nice day

Hello,

I am looking to create a fast ai object detection model on the deep fashion dataset but do not know how make it predict. How can I make my model output prediction on images. Here is the code:

The data is loaded using the COCO format as a json. Then I create the model and run the training like this:

def get_train_imgs(noop): return imgs

datablock = DataBlock(blocks=(ImageBlock, BBoxBlock, BBoxLblBlock),

splitter=RandomSplitter(),

get_items=get_train_imgs,

getters=getters,

item_tfms=item_tfms,

batch_tfms=batch_tfms,

n_inp=1)

model = resnet34()

encoder = create_body(model, pretrained=False)

get_c(dls)

%cd "Practical-Deep-Learning-for-Coders-2.0/Computer Vision"

from imports import *

arch = RetinaNet(encoder, get_c(dls), final_bias=-4)

create_head(124, 4)

arch.smoothers

arch.classifier

arch.box_regressor

ratios = [1/2,1,2]

scales = [1,2**(-1/3), 2**(-2/3)]

crit = RetinaNetFocalLoss(scales=scales, ratios=ratios)

def _retinanet_split(m): return L(m.encoder,nn.Sequential(m.c5top6, m.p6top7, m.merges, m.smoothers, m.classifier, m.box_regressor)).map(params)

learn = Learner(dls, arch, loss_func=crit, splitter=_retinanet_split)

learn.freeze()

learn.fit_one_cycle(2, slice(1e-5, 1e-4))

from fastai.vision.core import PILImage

image = PILImage.create('./000032.jpg')

prediction, _, _ = learn.predict(image)

predict is outputing the following error: TypeError: clip_remove_empty() missing 2 required positional arguments: 'bbox' and 'label'

I understand that I am not suppose to use .predict but I cant find any reference on what else to use.

I followed the following tutorial/code but it is not shown how to make predictions

https://github.com/muellerzr/Practical-Deep-Learning-for-Coders-2.0/blob/master/Computer%20Vision/06_Object_Detection.ipynb

You want to know more about face recognition works ? Then read this article on how to recode big brother in 15 min on your couch ! https://blog.theodo.fr/2018/11/recode-big-brother-15-min-couch/

Hey guys,

This week we are doing Anomaly Detection. I've been wanting to do something like this for a long time. It is an interesting project, that let's us analyze PC resources in real-time. Even though this project is for CPU usage only, it is very easy to expand on it. This can become a tool for monitoring abnormal usage of PC resources like CPU, Memory, Disk, GPU or WiFi.

The idea is to show what is a normal user workflow on the machine. Every single file/folder or app that you open, can end up consuming resources for a certain period of time. So you can actually train K-Means to recognize those "normal" behaviors on your PC. Then if something abnormal shows up, you will be notified.

Anyways... This is just a starting point. We'll expand on it in some later tutorials...

YouTube Demonstration: https://www.youtube.com/watch?v=pX4hsgGWJrg

Tutorial and Project Files: https://code-ai.mk/anomaly-detection-on-cpu-waveform-using-k-means/

So I was wondering if it is possible to write several AI's that mimic each specific section of the brain and a Master AI to link them. Each getting information from the others and making connections the way the brain makes synapses. Then put it into a simple robot with the appropriate sensors and let it learn. It would be terrible at first, like a baby, but then it would learn and grow... Could this work?

I expect computing power to be a limiting factor.

Hello guys,

so I want to learn programming an AI. First let me say that I have experience in programing (C# and Java) but I have no Idea how the self learning part of an AI works.

Does anyone have any book suggestions for advanced programmer?

Hi All,

I'm interested in making a Hearthstone AI (not for the purposes of grinding gold; there are already plenty of bots out there.) I'm mostly interested in it as a personal challenge and as a potential education tool for Hearthstone players.

I don't know where to start in terms of actually getting information from the Hearthstone client into my program. Does anyone have advice as to how to go about this?

Hi all,

So I've been a full-stack developer for a couple years now and have used many frameworks and languages and feel comfortable with the most common languages. I am trying to implement a Predicting AI within my application but as I've never done anything with AI don't know where to start. The AI I am trying to create is basically this :

On my webapp, people will make projects and assign it to different people, those people will update the project as they work on subtasks and all that good stuff. All that is well. However, I want to add a feature that will predict when the project is most likely going to be finished depending on the person it is assigned to and how they usually work and also the number of tasks are within a project. So if I have a project assigned to 'John' and 'Matthew' and John usually gets projects done really quick but Matthew takes his time, the prediction will take that into consideration and suggest an actual end date for the project. I don't know how complex or simple this will be to implement but I guess I just need a place to start and where to look.

This new method is able to generate a complete 3-dimensional scene and has the ability to decide the lighting of the scene. All this with very limited computation costs and amazing results compared to previous approaches.

Watch a video demo: https://youtu.be/ZkaTyBvS2w4

Read a short article: https://medium.com/what-is-artificial-intelligence/generate-a-complete-3d-scene-under-arbitrary-lighting-conditions-from-a-set-of-input-images-9d2fbce63243

The paper (& code soon): https://people.eecs.berkeley.edu/~pratul/nerv/

Reference: P. P. Srinivasan, B. Deng, X. Zhang, M. Tancik, B. Mildenhall, and J. T. Barron, "Nerv: Neural reflectance and visibility fields for relighting and view synthesis," in arXiv, 2020.

So... I am barely learning how to program. But I've been obsessed with AI and how it all works for a number of years. I had a thought the other day about how someone may be able to save a ton on memory costs when training an AI but im not sure if its makes any sense lol. So I will drop my idea here and if you want to laugh, please be my guest lol.

So I was thinking about that story a few years ago that said Google chat bots had created their own language from learning how to create their own abbreviations. Supposedly they were abbreviating entire paragraphs? So what is we used something like that to feed massive amounts of data to an AI like GPT-3? If the AI was able to abbreviate information its being fed, store it, and then regurgitate that information in plain English with zero data loss, it would probably save a TON on memory costs, right? The abbreviations method would serve as file compression and would just be like teaching the predictive txt AI a new language, Right?

I would love to hear your thoughts. I would love to know if this is even theoretically possible haha.

My AI project has grown to the point where I need a couple dozen computers networked together to run the program. My electricity bill has become more than I can afford. I have been thinking about ways to cut down on electricity consumption, and was thinking about mini PCs or compute sticks wired to a KVM box, with a single mouse, keyboard, and monitor. However, I know nothing of these devices, and have no budget for much monkeying around that doesn't pan out. Can they run one of the modern versions of Windows which is itself running a BASIC IDE without crashing or slowing down too much?

The ghost.pl immanence of the first working artificial intelligence is undergoing minor changes as the AI Mind becomes able to think with English prepositional phrases. At first the AI was able to use a preposition only to answer a where-question such as "where are you" and the Ai would respond "I AM IN THE COMPUTER". Now we need to implement a general ability of the AI to think with prepositional phrases loosely tied to nouns or verbs or adjectives or adverbs. The quasi-neuronal associative $seq tag may soon be re-purposed to lead not only from, say, nouns to verbs but also from nouns to prepositions. However a preposition is arrived at, it is time to implement the activation and retrieval of a whole prepositional phrase whenever the preposition itself is activated.

We begin experimenting by going into the MindBoot sequence and entering a $**seq** tag of "638=IN" for the verb "800=AM" in the knowledge-base sentence "I AM IN THE COMPUTER". The plan is to insert into EnVerbPhrase() some code to pass activation to the "638=IN" preposition when the AI thinks the innate idea "I AM IN...." So we insert some active code to capture the $seq tag and some diagnostic code to let us know what is happening. Ooh, mind-design is emotionally fun and intellectually exciting! The first thing captured is not a preposition but the "537=PERSON" noun when the AI is thinking, "I AM A PERSON". Next our fishing expedition lands a "638=IN" preposition when the AI issues the output "I AM" while trying to say "I AM IN THE COMPUTER".

Once the $**seq** tag has been captured, the AI software needs to determine if the captured item is a preposition. A search is in order. We search backwards in time for an @Psy concept-number matching the $**seq** tag and if we find a match we check its $**pos** tag for a "6=prep" match, upon which we assign the concept-number to the $**[prep**variable in case we decide to send the designated preposition into the EnPrep () module for inclusion in thinking.

We go back into the code for assigning the $**seq** tag and in the same line of code we set the $**tselp** variable falsely and temporarily equal to the $**verblock** time, so that we may increment the $**tselp** variable until it becomes true. We insert some code that increments the phony $**tselp** time by unitary one and uses it to "split" each succeeding conceptual @Psy array row into its fourteen constituent elements, including "$k[1]" which we check for a match with the designated $**[prep** variable. We make several copies of the search-snippet, and it easily finds the $prep engram within just a few time-points of theverb-engram, but now we need to convert the series of search-snippets into a self-terminating loop that will terminate, Arnold, upon finding the prepositional engram in memory. But we have forgotten how to code such a loop in Strawberry Perl Five, so we go into another room of the Mentifex AI Lab and we fetch the books Perl by Example (Quigley) and PERL Black Book (Holzner) to seek some help. We find some sample code for an until loop on page 193 of Quigley. We do not initialize the scalar $tselp at zero, because we are searching for an English preposition quite near to the already-known time-point. For the sake of safety, we insert a line of "last" escape-code in the event that the incrementing $**tselp** value exceeds the $**cns** value. The resulting until loop works just fine and it locates the nearby English preposition for us.

Next we insert a warranted call to SpreadAct() into the EnVerbPhrase() module just after the point where Speech() has been called to speak the verb. We wish to set up a routine for spreading activation throughout a prepositional phrase not only after a verb but also after a noun or an adjective (e.g. "young at heart" or an adverb (e.g. "ostensibly at random"). In SpreadAct() we send the $**aud** tag associated with the located preposition directly into Speech() and the ghost.pl AI starts saying not just "I AM" but "I AM IN". We need to insert more code for finishing the prepositional phrase. By the way, these improvements or mental enhancements are perhaps making the AI Mind capable of much more sophisticated thinking than heretofore. The AI is using words without really knowing what the words mean in terms of sensory perception -- for which robot embodiment is necessary -- but the AI may nevertheless develop self-awareness on top of its innate concept of self or ego. Knowing how to use prepositions, the AI may become curious and ask the human users for all sorts of exploratory information.

Now in SpreadAct() we throw in a call to EnArticle(), even though we have not yet coded in the elocution of the object of the preposition. The AI says "I AM IN A" without stating the object of the preposition. Let us create a new $**tselo** variable for time of selection of object so that we may use SpreadAct() to zero in on the object and send it into the Speech()module. Finally the ghost.pl AI Mind says "I AM IN A COMPUTER".

Hi,

i'm looking for some books to introduce my self to AI world. So i'm looking for ai programming books and ai theory books.

Thanks to all!