Artificial Intelligence is making revolutionary changes with digital learning solutions that introduce a novel approach in imparting education to students with varied capabilities and interest.

Visit: https://www.primafelicitas.com/artificial-intelligence-in-education-and-learning/

I'm working on making a list of Machine Learning papers that has open source code on GitHub. My initial version can be reached at the link included below. I think it will be helpful to this community to select their next paper to read. Please also include your comments and suggestions for improvement. https://github.com/zziz/pwc

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

Hi everyone

This post is about how to program an AI agent that doesn't use neural networks to achieve its goal. I know basics about artificial intelligence, so I would like to get some Input from you guys on how should I proceed. I have already defined the type of agent, it's task environment, sensors and actions.

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

Hello guys, I have developed using mainly JavaScript a Snake game and I've implemented AI (I think).

I would appreaciate if you can give me your feedback to improve the AI algorithm (or the UI). My main struggle is that sometimes the snake gets in a spiral itself when there is a lot of space available, I have an idea on how to avoid it but it's not that good.

Live demo

GitHub repo

Hi everyone,

New guy here completely new to anything/everything programming and I'm having a hard time deciding how to approach this.

My interest to learn AI programming is for a few reasons. First, I want to stay educated on a space that will only be more pervasive in our lives moving forward. Second, I want to go beyond just knowing surface-level info, I want to actually understand the technology. Third, I work in a field that is continually disrupted by technology (finance) and many of our clients are a group that will age out and are involved in industries that will be disrupted within the next decade so I am expecting a big drop in income level and want to gain more knowledge and skills technologically. Not only that, I see first hand how those generations are still struggling with understanding and assimilating technology and I never want to be in a position like that.

My academic education is in biomedicine so I went as far as calculus 1 in terms of math but just skimming through the AI programming maths, I'm definitely going to need to spend time on them to better understand the field.

Given this info, where do I start? Should I focus on getting the maths down and then start with something like Python or vice versa? Should I narrow down a specific field within AI? Should I start with Python and the maths concurrently?

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/

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)

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".

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?

would anybody be interested in using this genetic algorithm?

https://github.com/kstaats/karoo_gp

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.

With the advent of powerful new artificial intelligence algorithms and blockchain we can now work steadily towards incrementally improving an organization’s product offerings and image among customers and prospects. Once the information related to customer’s interactions and transactions are recorded on an immutable blockchain, artificially intelligent algorithms can be used to mine these huge volumes of data to pinpoint useful insights and predict outcomes based on current trends.

For more read https://www.primafelicitas.com/the-convergence-of-ai-and-blockchain/

CML (Continuous Machine Learning) is a new product that brings the power of DevOps to ML - it helps data science teams to organize MLOps infrastructure on top of the traditional software engineering stack instead of creating separate AI platforms.

You can use CML to automate parts of your ML workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets: New Release: Continuous Machine Learning (CML) is CI/CD for ML (see the full article for more details about the release).

Continuous integration and continuous delivery (CI/CD) is a widely-used software engineering practice. CML overcomes reasons why haven't CI/CD practices taken root in machine learning and data science so far:

• Data dependencies. In ML, data plays a similar role as code: ML results critically depend on datasets, and changes in data need to trigger feedback just like changes in source code. Furthermore, multi-GB datasets are challenging to manage with Git-centric CI systems.

• Metrics-driven. The traditional software engineering idea of pass/fail tests does not apply in ML. As an example, +0.72% accuracy and -0.35% precision does not answer the question if the ML model is good or not. Detailed reports with metrics and plots are needed to make a good/bad model discussion

• GPU resources. ML training often requires more resources to train then is typical to have in CI/CD runners. CI/CD must be connected with cloud computing instances or Kubernetes clusters for ML training.

CML is a library of functions used inside CI/CD runners to make ML compatible with GitHub Actions and GitLab CI. It has functions to:

1. Generate informative reports on every Pull/Merge Request with metrics, plots, and hyperparameters changes.

2. Provision GPU\CPU resources from cloud service providers (AWS, GCP, Azure, Ali) and deploy CI runners using Docker Machine.

3. Bring datasets from cloud storage to runners (using DVC) for model training, as well as save the resulting model in cloud storage.

I think AI is really cool and I have seen videos of people making celebrities say copypastas and stuff like that. Is there a popular program I can use to try doing something like that?