Find an article, guide, or resource.

Reusable memory for AI agents means keeping notes, sources, and references outside the model so future sessions can use them.

Microsoft's MAI-Thinking-1 report matters because it refuses synthetic data and asks what behavior a model really inherited.

Cheap GPT API proxies can hide model swaps, key logging, and code risks. The small token discount may cost more than it saves.

Loop engineering turns repeated agent babysitting into a controlled workflow with goals, checks, tools, and stop conditions.

A plain-English explanation of image generation, how text steers visual models, and why prompts do not directly draw pixels.

AI coding agents changed how software gets built, but vibe coding breaks quickly without reviews, tests, context, and taste.

A look at how Google moved from being dismissed in AI to becoming one of OpenAI's strongest competitors again.

Self-improving AI is becoming practical in small loops, but the same loop can also optimize the wrong thing and fool you.

A practical explanation of AI distillation, the controversy around model outputs, and why ownership is harder than the technique.

A practical look at bias in AI agents, from audits and guardrails to the human review loops that still matter.

Harness engineering is the missing layer around agents: tools, evals, traces, guardrails, and controls that make them usable.

RAG does not train your model. It gives the model temporary context, which changes the answer without changing the weights.

What stands out in AI engineering interviews and take-homes: judgment, clear thinking, debugging, and shipping useful systems.

A push to move past agent demos and build systems that can be evaluated, debugged, deployed, and trusted under constraints.

A 42-day AI terms project that explains why understanding model behavior matters more than collecting prompting tricks.

A practical checklist for choosing AI architecture before implementation, so teams avoid agent rewrites caused by bad scoping.

A clear distinction between workflows, agents, and multi-agent systems, with the main rule: stay as simple as the problem allows.

A practical editing workflow for using LLMs without losing your voice, with checks for slop, repetition, and fake polish.

Why LLMs feel intelligent, why the comparison to human learning gets messy, and what builders should remember about fluency.

An explanation of the Towards AI Partner Program, where referrals can earn books, courses, full access, or an affiliate tier.

Deep research agents go beyond chat by browsing, planning, synthesizing, and producing cited reports, but they still need checks.

A practical read on GPT-5: what changed in reasoning, tools, cost, and when the full model beats smaller options.

Why 2025 became the year of AI agents: longer context, cheaper tools, better models, and workflows that can finally act.

A practical guide to keeping LLMs honest with grounding, verification, evals, and workflows that do not trust fluency alone.

A business-friendly introduction to prompt engineering: clear instructions, useful context, examples, and knowing when prompts are not enough.

A practical session on building with LLMs, from prompts and RAG to evals, workflows, and the parts teams usually skip.

MCP acts like a standard connector layer for LLM tools, making integrations easier to share, inspect, and reuse.

A free developer-focused LLM training on how models work, how to use them, and where their current limitations still show up.

A practical explanation of reasoning models in 2025, why extra thinking time helps, and where the limits still are.

A practical explanation of what LLMs really learn, why they fail, and which weaknesses builders need to design around.

A compact path for learning practical LLM development basics over a weekend, with enough structure to start building.

A beginner-friendly explanation of how LLMs learn from data, prediction, fine-tuning, and feedback without treating them as magic.

A beginner guide to Python and computer science basics: syntax, variables, inputs, outputs, debugging, and how programs think.

Notes from Jensen Huang's GTC quantum panel on why quantum still feels hyped, where QPUs may help, and what has to work first.

A beginner-friendly comparison of Google Colab and Jupyter notebooks, with when to use each for Python and AI work.

A practical walkthrough of Open-Sora 2.0, its video generation architecture, training approach, and why open video models matter.

A beginner-friendly explanation of how ChatGPT turns text into tokens, predicts responses, and generates language that feels coherent.

A plain-English look at FlashMLA, DeepSeek's KV cache memory trick, and why efficient attention matters for large models.

A practical explanation of reinforcement fine-tuning, how it differs from supervised fine-tuning, and when rewards help.

An introduction to programming with ChatGPT support, focused on giving computers clear instructions and understanding the code.

An announcement for a Python primer built to help beginners move from shaky coding basics toward practical generative AI work.

A practical look at how LLMs affect jobs, which tasks are easier to automate, and why adaptation beats panic.

A practical guide to tracking AI in 2025 without chasing every release, by narrowing sources around your work and goals.

A practical guide to choosing LLM developer courses in 2025 based on projects, depth, and the skills companies actually need.

A comparison of LLM developers, software developers, and ML engineers, focused on the skills each role actually needs.

A practical workflow for using ChatGPT or Claude to revise a lesson, add explanations, produce a conclusion, and verify sources.

A practical way to separate real agents from LLM calls and workflows, so teams choose the right amount of autonomy.

A practical comparison of CAG and RAG for LLM context, including when cached context helps and when retrieval still wins.

A practical look at how LLMs change work, which tasks they affect first, and which skills help people stay competitive.

How to use LLMs to write better lessons without losing your ideas, judgment, sources, or personal style.

Use Perplexity, ChatGPT Search, Ai2 OpenScholar, and RAG to research faster, verify sources, and work with local documents.

An introduction to an 8-hour generative AI primer built to help learners understand models, prompting, and practical use cases.

A practical API deployment guide for AI engineers who need models, data, and products to talk reliably in production.

Lessons from Minitron on building smaller language models, where efficiency comes from, and why model size is not the full story.

A practical guide to when LLMs are overkill, when simpler models work better, and how to decide before spending the compute.

Optimizing Large Language Models for Retrieval-Augmented Generation. How do you deploy these models in a way that’s even better?

Building AI-Powered Search and Recommendations. As your data grows, you’ll need a database that can scale with it.

Build a Smarter RAG System. If you need to understand meaning and context, go for embedding search.

Improve a RAG pipeline with fine-tuned embeddings, better retrieval checks, and a clearer view of when the extra work is worth it.

A practical comparison of long-context models and RAG across data volume, retrieval quality, latency, cost, and the workflows where each approach fits.

What Makes AI Truly Useful? This way, since it’s built into the tool you already use, you don’t need to be a master prompt engineer or explain technicalities.

Evaluate RAG systems with retrieval and answer-quality checks that show whether your pipeline is actually improving.

Advanced Vector Indexing Techniques for RAG. As we’ve discussed in our book and course.

I am super excited to finally announce that we ( Towards AI ) released our first independent industry-focussed course: From Beginner to Advanced LLM Developer.

No-code Custom LLM Evaluation Demo. You just create your benchmark with questions and answers the LLM should provide.

No-code Agentic AI Demo. They then allow you to deploy the system and create an API out of it, all without any code knowledge.

Building the Best RAG Stack: A Breakdown of Wang et al.'s Goldmine Study. Fine-tuning the Generator Lastly.

Unfortunately, as with some of us, this process of thinking before answering makes o1 much, much slower than previous models.

Learn how GraphRAG adds knowledge graphs to RAG, when that extra complexity helps, and when a simpler retrieval approach is the better choice.

Why large language models do more than complete text, and how context, attention, and chain-of-thought prompting change their answers.

The Real AI Tools Revolutionizing Modern Marketing Beyond ChatGPT. We also need to be careful with the data collected from our users and use it ethically.

So-called advanced prompting techniques are mostly good communication. Here are the few methods that really matter and how to structure an LLM’s output.

Eleven essential questions about Llama 3.1 covering performance, cost, infrastructure, tooling, and why it matters to businesses.

Training LLMs with Synthetic Data. Okay, so that’s cool and all; we’ve got a way to create better models with little manual data improvement work.

What, why, when and how…. We then check the actual accuracy in each group: For the predictions the model is 70% confident about.

A clear look at Chameleon, the multimodal LLM idea behind GPT-4o-style systems, and what changes for text, image, and video models.

If you are coding with ChatGPT or Copilot, you may be creating some terrible security leaks!

Our first book: Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG.

Understand how LLMs like GPT-4 decide when they have effecrtively answered your question.

How Google's Infini-attention combines local attention with compressive memory to process long sequences without recalculating the full history.

Mixtral MoE Explained. This means that the model tries to predict the next token, or next word, of a sentence you send as the input prompt.

AI's Impact on Traditional Education. This technology will change not only how and what we learn but also the skills we need for the future.

A practical look at where AI already helps healthcare, from medical imaging and monitoring to drug research, plus the privacy, bias, and safety limits.

Will journalists be replaced by AI? You don’t need much other than ChatGPT or another similar language model like Gemini.

Avery Smith shares a practical route into data analytics: build real projects, learn the tools employers use, and prove you can turn data into decisions.

Tina Huang shares practical advice on learning AI, choosing education, building projects, freelancing, and using AI tools without outsourcing judgment.

A conversation with Holo Art CEO Mariam Brian about AI as a creative tool, artistic ownership, ethics, and how artists may work with models.

Understand how RLHF uses human preferences to align language models and how RLAIF automates part of that process with feedback from another AI model.

Jerome Pasquero on Data Annotation: The Building Blocks of AI's Future - The What's AI Podcast (episode 27).

Jérémy Cohen discusses autonomous vehicles, fragile AI startups, hallucinations, bias, and why transparency and human judgment still matter.

Retreival augmented generation (RAG) explained simply. If you need safe information from an AI chatbot like a medical assistant, a tutor, a lawyer.

Lastly, we witnessed controversy surrounding Google’s demonstration video of the Gemini model and more… In 2022, we witnessed the rise of the mighty ChatGPT.

It's just a programmatic way of prompt engineering. if we think RAG will get better than that, we could see that as a way of prompt engineering.

Lessons on building AI startups, finding useful problems, and avoiding the usual hype traps from Greg Coquillo's founder perspective.

Tips on what to do with your language model or API. Do you ever question yourself if you should be training from scratch, fine-tuning.

Stable Video Diffusion Explained. I’m Louis from What’s AI, and let’s explain how this new model works!

It’s a much more complex problem than processing text as you also need to deal with background noises, different tones or pitch, and, obviously.

Data Science, Startups & More with Ken Jee. You need to be passionate about the problem and try to find solutions, whatever it is.

Learn LLMOps now. A complete guide. Free. LLMs are revolutionizing how we interact with technology, from chatbots to advanced data analysis.

This is an interview with Dmitry Shapiro, previously working at Google and CTO of MySpace Music. Now.

From Microsoft GitHub to Google DeepMind: Paige Bailey. And also you should probably be doing this in the context of an IDE like Android Studio.

Dropping out of a PhD for the startup world. Before the Decision… Just to give some context, my journey into the Ph.D. was quite obvious to me.

Discover 5 useful Applications of GPT-4 and Llama-2 - No Coding Required! This might not be super useful in the real world.

How we Built an Open-Source RAG-based ChatGPT Web App. That’s where the marvel of Retrieval Augmented Generation, or RAG, comes into play.

OpenAI’s BEST text-to-image model to date! Or, we could use another model to understand images and generate better captions first!

Last week, we released our LLM course. We are excited to announce another related project: our own AI tutor!

The What's AI Podcast Episode 20 with Auxane Boch: Ethics Expert. And you, you check all those boxes to be sure that there is a problem and that your fix.

Exploring the unique behaviors of different Large Language Models (LLMs) and mastering advanced prompting techniques!

New AI That Generates useful Music! This is used to allow the model to understand how long the song generated should be and understand various lengths as well.

Boost AI Performance with Fine-Tuning. This means re-training parts of the model on the exact task we want it to achieve.

MVDream: A new text-to-3d approach (explained)! We do all that to better understand where in the image the NeRF model should focus its attention to produce.

Deep learning with a simple analogy. They are powerful because they will have excellent results if you have the data to teach them what you want.

LLaVA: Bridging the Gap Between Visual and Language AI with GPT-4. We train the LLaMA model and the image-to-text conversion.

An introduction to explainable AI. You are a research scientist aiming to build a model able to classify images.

What is MetaGPT? LLM Agents Collaborating to Solve Complex Tasks. The goal of MetaGPT is simple: replace each with a fictive human being: a GPT model.

An overview of the first 3D-LLM. Usually, just to match images to text, you need lots of examples of text-image pairs so that the model can understand both.

StyleGANEX: Enhancing Image Manipulation with Dilated Convolutions explains how visual AI systems work, where they break.

Aleksa Gordić - The What's AI Podcast Episode 18. Having said that, like videos are obviously so much more data intensive, like processing wise, you.

An interview with Petar Veličković - The What's AI Podcast Episode 17. Could you give some, not practical but applied types of data that this can work with.

The What's AI podcast episode 16 with Jay Alammar. Break it down into sentences, embed each sentence, and then you can [00:36:00] create.

AI Does magic with UNets, Diffusion, and clothes! It’s also perfectly fine if you are not into podcasts and just want to understand how the TryOnDiffusion.

Active learning explained in 5 minutes. It means we need lots of very good, balanced, and varied data.

What's AI Podcast Episode 15 with Luis Serrano from Cohere. And right now, since they are not perfect.

Great, now that you know about Instant NGP, let’s look at the two key differences in improving the results with Neuralangelo working on this specific hash.

Navigating the Changing Landscape of AI: Felix Tao's Journey from Researcher to CEO.

Move anything from point A to point B! This kind of architecture is a way to train a generative model where you will train a model, called your generator.

Interview with Yotam Azriel, CTO at TensorLeap - What's AI episode 13. 36:30 Can we do anything to understand the results of the model better when we work.

By now, you all know what prompting is. It’s how we talk with ChatGPT and other AIs.

Something we need to be careful when it comes to plagiarism and arises new questions around the attribution and moral rights of such generated pieces.

Logan Kilpatrick's Definitions for all the words to know for OpenAI, GPT and AI in 2023.

Building with LLMs, ChatGPT, and Working at OpenAI With Logan Kilpatrick (Dev Rel @OpenAI) - What's AI episode 11.

The new Perfusion model by NVIDIA Explained. Oh, and how can we do that without having an immense model that only OpenAI can use?

Join us for an engaging podcast episode with Limarc Ambalina, the Vice President of Editing at HackerNoon.

Ph.D., Twitter, Breaking into the AI Field, and more with Brian Burns (AI Pub). Not really, you will also most probably need to make money.

These AI Tools will 10x Your Productivity! I’ve used it since we had it for free in my first data science job in 2020 and have used it ever since.

Insights to become a self-driving car engineer in 2023. Have you ever wondered what it takes to work in the field of self-driving cars?

SAM, promptable segmentation and the largest segmentation dataset to date! This means you can help it segment unknown objects through prompts without.

A conversation with Learn Prompting's creator on prompting, AI literacy, and how people can communicate better with models.

Since this PaLM model is already super powerful to understand and interact with the world in a textual format, the only thing we need to do is to make it see.

Here’s an interview with Adam Grzywaczewski, a senior deep learning architect at NVIDIA.

GPT-4 may be the most hyped language model we’ve had, with tons of rumors and news even prior to its announcement, and it wasn’t for nothing.

Use Kaggle projects to make a stronger data science resume, with practical proof of skills instead of generic claims.

In this interview, we'll hear from Meriem Bendris, a senior solution architect at NVIDIA, who will answer questions such as:.

The company behind one of the most popular image generation models you certainly know, Stable Diffusion, is taking it a step further.

Introducing my podcast on AI! We cover what it is like to be a data scientist in such a big and important company and how to get there.

An interview with the VP of R&D at a Generative AI company (D-ID). More specifically, we cover three main parts; education, the job interview process.

MusicLM explained. This is how Google Research was able to create a model generating music with such great results from simple text descriptions!

This new model called InstructPix2Pix does exactly that; it edits an image following a text-based instruction given by the user.

The VALL-E Model explained. For that, we need to translate it into something our model can understand, which is often matrices and numbers, not words or sounds.

An interview with the Director of Perception at Zoox, Ruijie (RJ) He, with the goal of demystifying what is a good profile to get an ML engineer job and.

A month-by-month review of the AI research and releases that shaped 2022, from image and video generation to language models and 3D systems.

Disney's New Model Explained. This is a big deal for the film industry, allowing you to instantly re-age someone for a whole movie with very little cost.

What is a prompt engineer and how to improve at it…. While this is also only improving.

OpenAI's most recent conversational AI explained. The second step is to add our reinforcement learning magic.

We’ve heard of deepfakes, we’ve heard of NeRFs, and we’ve seen these kinds of applications allowing you to recreate someone’s face and pretty much make him.

Text Embedding Explained. You’ve certainly heard of the most known and powerful language model: GPT-3.

Galactica, Meta AI's most recent model: The AI Scientist. Tokenization basically means the way the model will see the data instead of words, math.

Have you ever imagined being able to take a picture and just magically explain it as if it would be a door to another world?

NVIDIA's new model has better results, more control and more fidelity than DALLE and Stable Diffusion!

Here's every vision application Diffusion models were a game changer in 2022: image, text, video, 3D, and more!

If you think the recent image generation models like DALLE or Stable Diffusion are cool, you just won’t believe how incredible this one is.

How AI generates 3d models from only text! But then, how can you generate a 3D render from text if the NeRF model only works with images?

A good transcription tool that would accurately understand what you say and write it down.

Generates videos from text! You need a model that understands the world in a better way and leverages this level of understanding to generate a coherent.

What does such a model understand when it sees such a picture or, even more complex, a video?

Personalizing Text-to-Image Generation using Textual Inversion. If you haven’t watched my video yet, you should pause this one.

A New Challenging Task for AI: panoptic scene graph generation. Well, you need two things: a dataset and a powerful model.

A High-Resolution Image Synthesis Architecture: Latent Diffusion. This is why you need hundreds of GPUs to train such a model and why you wait a few minutes.

BANMo tackles a task we call articulated 3D shape reconstruction, which means it works with videos and pictures to model deformable objects.

"Make-A-Scene": a fantastic blend between text and sketch-conditioned image generation.

DALL·E 2 Pre-Training Mitigations. You can see how such a model can become dangerous: allowing anyone to generate anything.

They reconstruct sound using cameras and a laser beam on any vibrating surface, allowing them to isolate music instruments, focus on a specific speaker.

The beginning of data-centric AI with data programming. Unfortunately, the more data you have, the better it is.

Translating 200 languages with a single model - Meta AI. These two models are only used to build the 200 paired-languages datasets we need to train the final.

Dalle mini is a free, open-source AI that produces useful images from text inputs. Here's how it works.

An AI that creates photorealistic images from input text better than Dall-e 2! But before using this diffusion model.

How can Uber deliver food and always arrive on time or a few minutes before? How do they match riders to drivers so that you can \ always\ find a Uber? All.

Gato: A single Transformer to RuLe them all! The first generalist RL agent using transformers!

A GAN model that uses simple blobs to manipulate objects in images…. Once we have good results.

An open-source model that is as powerful as GPT-3! You can say that this new model is very similar to GPT-3 but open-source and better for the environment.

This AI can reconstruct, enhance and edit your images! It will basically find you in the encoded representation of all faces and be re-trained to push the.

A new model for automatically generating summaries using machine learning, released in Google Docs that you can already use!

a useful new model by OpenAI capable of generating and inpainting images from a text input with incredible results!

Then, after such a training process, you only feed the images with the camera angles at inference time.

Combine Lidar and regular Cameras for 3D object detection - Waymo & Google Research.

This new and completely free AI model can fix most of your old pictures in a split second!

HackerNoon is the best place for software developers, blockchain experts, data scientists, and tech people like us to read, write, and publish.

So you just have to train this model once on a general dataset and then use it with your own audio tracks as it should, ideally.

From a few images to a 3D model using AI! And what if the lighting conditions vary in the pictures taken and the generated model looks different depending on.

AI Facial Editing of Real Videos! Stitch it in Time Explained explains how visual AI systems work, where they break.

Learn how this algorithm can understand images and automatically remove the undesired object or person and save your future Instagram post!

The top 10 computer vision papers in 2021 with video demos, articles, code, and paper reference.

A month-by-month review of the AI papers that shaped 2021, with short explanations, videos, articles, code, and full references.

The most interesting December's AI breakthroughs with video demos, short articles, code, and paper reference.

Easily generate text descriptions for images using CLIP and GPT models! We could use such a model to describe movies automatically or create better.

Generate city-scale 3D scenes with high-quality details at any scale! I won’t be covering how NeRF works since I have already done this in an article if you.

"Sleep is not a disposable luxury - it's a non-negotiable biological necessity." -Matthew Walker.

The most interesting November's AI breakthroughs with video demos, short articles, code, and paper reference.

Control any feature from quick drafts, and it will only edit what you want keeping the rest of the image the same! SOTA Image Editing from sketches model by.

Transform your small 512-pixel large images into 4k with AI! Now you only need enough data, and you will have results like this (see the video!).

Take a picture of the style you want to copy, enter the text, and the algorithm will generate a new picture out of it!

The most interesting October's AI breakthroughs with video demos, short articles, code, and paper reference.

Let's construct 3D models from a couple of photos…. And thus, should we use this kind of model when humans are involved?

If we take an audio clip with the music way too loud, you can simply turn up the speech and lower the music!

Biologically-inspired Neural Networks for Self-Driving Cars explains how language models behave, fail, improve.

50+ expert meteorologists assessed DeepMind's new model beating current nowcasting methods in 89% of situations for its accuracy and usefulness.

The 3 most interesting AI papers this month with video demos, short articles, code, and paper reference.

This model can do any video manipulation or video generation application you have in mind!

TimeLens turns a few frames into slow-motion video. Here is how the model fills motion gaps and where generated video can break.

AI could generate images, then, using a lot of brainpower and trial and error, researchers could control the results following specific styles. Now.

The 3 most interesting AI papers of August 2021 with video demos, short articles, code, and paper reference.

Andrej Karpathy's talk on Tesla's autopilot explained clearly in under 10 minutes.

Control GANs outputs based on the simplest type of knowledge you could provide it: hand-drawn sketches.

Say goodbye to complex GAN and transformer architectures for image generation. This new method can generate new images from any user-based inputs.

The 3 most interesting AI papers of July 2021 with video demos, short articles, code, and paper reference.

Using multiple machine learning-based algorithms running privately on your device.

Find out how this AI Generates Code From Words. So if the data is randomly sampled from GitHub, how can you be sure it works and is well-written?

There are already many machine learning newsletters, but this one is different and made for you! (contains videos, articles, code, demos…).

Understand how AI creates new images using Generative Adversarial Networks in 2 minutes.

You can see CNNs as compressing the image, focusing on a specific feature of the image at every step.

Discover how machines become super-human in most domains they attack using reinforcement learning.

AI Spots Wildfires Faster Than Humans. Explained Simply. So, starting with this strong baseline they have.

Using a modified GAN architecture, they can move objects in the image without affecting the background or the other objects!

The 3 most interesting AI papers of June 2021 with video demos, short articles, code, and paper reference.

a) Input Image, b) Eulerian field, and c) movement needed in the frames. Holynski, Aleksander, et al., 2021.

A practical guide to reading more AI research papers without getting buried, with habits for choosing, skimming, and revisiting papers.

This new Facebook AI model can translate or edit every text in the image in your own language, following the same style!

This AI can transfer your hair to see how it would look like before committing to the change.

How deepfake detection works, why artifacts matter, and what a US Army research project showed about spotting generated faces.

a curated list of the latest breakthroughs in AI and Data Science in May 2021 with a clear video explanation, link to a more in-depth article.

Apply any style to your 4K image in real-time using this new machine learning-based approach!

This AI can be applied live to the video game and transform every frame to look much more natural.

Generate 3D models of humans or animals moving from only a short video as input.

Properly relight any portrait based on the lighting of the new background you add.

The next step for view synthesis: Perpetual View Generation, where the goal is to take an image to fly into it and explore the landscape!

a curated list of the latest breakthroughs in AI and Data Science in April 2021 with a clear video explanation, link to a more in-depth article.

I will openly share everything about deep nets for vision applications, their successes, and the limitations we have to address.

The 3 most interesting AI papers this month, March 2021, with video demos, short articles, code, and paper reference.

A fun introduction project for computer vision and python on Google Colab. iterative-grabcut: Using a simple rectangle and a brush removing the background of.

This promising model called GANverse3D only needs an image to create a 3D figure that can be customized and animated!

With this AI-powered nerve interface, the amputee can control a neuroprosthetic hand with life-like dexterity and intuitiveness.

In less than 5 minutes, you will know how the transformer architecture can be applied to computer vision with a new paper called the Swin Transformer.

Odei Garcia-Garin et al. from the University of Barcelona have developed a deep learning-based algorithm able to detect and quantify floating garbage from.

OpenAI just released the paper explaining how DALL-E works! It is called “Zero-Shot Text-to-Image Generation”.

What happened in the AI research in January 2021. The 3 most interesting papers!

Drawing inspiration from Human Capabilities Towards a more general and trustworthy AI & 10 Questions for the AI Research Community.

A curated list of the latest breakthroughs in AI in 2020 by release date with a clear video explanation, link to a more in-depth article, and code.

A professional data scientist answers the 12 most asked questions. Clarify and debunks the myths you've built surrounding this field!

With this new training method developed by NVIDIA, you can train a powerful generative model with one-tenth of the images! Making possible many applications.

This new background removal technique can extract a person from a single input image, without the need for a green screen in real-time!

Facial recognition, targeted ads, and image recognition all have one thing in common. They are all done using the best Convolutional Neural Networks.

This method is called DeOldify and works on pretty much any picture. If you don’t believe me, you can even try it yourself for free.

This artificial intelligence model transfers clothes between photos using only one picture of yourself and one picture of the clothing! This is called garment.

This AI can transform any of your pictures into an accurate representation with a Disney animated movie character style!

Understand how deep fakes work and create your own! You should definitely give it a shot if deepfakes interest you!

If you have ever wondered either what's GPT-3 and how can it be useful to you or your company, this is the article you were looking for.

YOLO, Also Known as You Only Look Once is one of the most powerful real-time object detector algorithms. It is called that way because….

Learn who Louis-François Bouchard is, how What's AI started, and how his work connects AI engineering, education, videos, and Towards AI.

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A beginner-friendly path to learn AI from scratch in 2026, with free resources, projects, and a practical order to avoid getting lost.

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