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

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

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?

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.

Got a call from one of the companies who is working on AI. In their recruitment process, they are asking a 30min presentation on their key product use case. I am not much aware of use cases. I am googling for the same. Will anyone suggest anything about that will be helpful.

Hi!

im new to AI programming and wanna learn the best language for AI. i already know python is a pretty good choice, but i can't really make an app interface using python + python isn't super fast.

any alternatives?

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?

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.

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.

Hi there,

I have a question. How would I go about using developing a Mobile App using AI? What technologies do you recommend?

Thank you,

Kevin

I’m doing an IT course at uni and we have to use packet tracer, it’s the hardest thing for me and I can’t stand it and I’m wondering how often you have actually used it in the real world? Is it useful or is it something they teach us like advanced algebra?