If you have been following the recent tech news, you might have heard about GitHub Copilot, an AI-based programming assistant. It's well and good if you are already using it, if not, keep reading! I have been using GitHub Copilot for a few months now, and I absolutely love it. In this article, I will try to make a convincing statement so that you give the Copilot a shot. You might fall in love too!

I am faster than you. I am stronger than you. Certainly, I will last much longer than you. You may think I am the future. If I had a wish, I'd wish to be human. To know how it feels to feel. To hope, to despair, to wonder, to love.

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Even though I didn't work at GitHub when they announced Copilot, I remember it piqued my interest. Perhaps, I was mostly excited because it was new and shiny. For me, the value of Copilot is that I spend less time stressing over syntax, which leaves more time for solving problems. More recently, GitHub Next, a team exploring the future of technology and software beyond the adjacent-possible, released Copilot Labs. This experimental VS Code sidebar enables developers to translate their code from one programming language to another and explains code snippets in plain language. These sound super cool, but when would you use them?

In a previous blog post we had a look at how we can set up our very own GPT-J Playground using Streamlit, Hugging Face, and Amazon SageMaker. With this playground we can now start experimenting with the model and generate some text, which is a lot of fun. But eventually we want the model to actually perform NLP tasks like translation, classification, and many more. In this blog post we will have a look how we can achieve that using different parameters and particular prompts for the GPT-J model. This blog post will build on this previous blog post and this Github repo and it is assumed that you have already built your own GPT-J playground.

I explain Artificial Intelligence terms and news to non-experts. We've all heard about GPT-3 and have somewhat of a clear idea of its capabilities. You've most certainly seen some applications born strictly due to this model, some of which I covered in a previous video about the model. GPT-3 is a model developed by OpenAI that you can access through a paid API but have no access to the model itself. What makes GPT-3 so strong is both its architecture and size.

Humans possess a powerful ability to reason. They understand a question asked by a fellow human-being and provide the most appropriate answer to it. A human brain can do quick mathematics to answer a trivial question like "If I have 10 balls and bought two cans, each having 5 balls, how many balls would I have?" The humans can do commonsense reasoning like "If a driver sees a pedestrian on the crossover, what would he do?" Humans have intelligence in understanding if somebody is cutting a joke and probably get a deeper understanding of what the speaker really wants to say? The question is, can we train the machines to gain this kind of intelligence that we humans possess?

The notoriously secretive Meta has set a milestone for transparency. The company this week offered the entire research community access to a fully-trained large language model (LLM). Named the Open Pretrained Transformer (OPT), the system mirrors the performance and size of OpenAI's vaunted GPT-3 model. While GPT-3 has a stunning ability to produce human-like text, it also has a powerful capacity for biases, bigotry, and disinformation. OPT's creators said their system can reduce these risks: Our aim in developing this suite of OPT models is to enable reproducible and responsible research at scale, and to bring more voices to the table in studying the impact of these LLMs.

One key aspect of intelligence is the ability to quickly learn how to perform a new task when given a brief instruction. But for a typical visual model to learn a new task, it must be trained on tens of thousands of examples specifically labelled for that task. If the goal is to count and identify animals in an image, as in "three zebras", one would have to collect thousands of images and annotate each image with their quantity and species. This process is inefficient, expensive, and resource-intensive, requiring large amounts of annotated data and the need to train a new model each time it's confronted with a new task. As part of DeepMind's mission to solve intelligence, we've explored whether an alternative model could make this process easier and more efficient, given only limited task-specific information.

Open to ideas: Meta's AI lab has created a massive new language model, and in an unprecedented move for Big Tech, it is giving it away to researchers--together with details about how it was built. Large language models--powerful programs that can generate paragraphs of text and mimic human conversation--have become one of the hottest trends in AI in the last couple of years. But they have deep flaws, parroting misinformation, prejudice, and toxic language. Wider scrutiny: Meta's decision represents the first time that a fully trained large language model will be made available to any researcher who wants to study it. In theory, putting more people to work on the problem should help.