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In the last week or so, all the range online has been #OpenAI’s new chatbot called #ChatGPT (you can read more details on ChatGPT here ). This also got me thinking, about how can we use #CoPilot more than just code. GitHub CoPilot as you might recall is your #AI powered pair-programmer. And as we can see below, it indeed is possible to use Codex as sort of a more general purpose usage. I start with the prompts on how one might use CoPilot – a function to read a file and return its contents as a string, just to show there isn’t anything different I am doing in using this. And then for general-purpose usage, I used the prompts in VSCode. ...

OpenAI recently released #ChatGPT , a GPT-3 based chatbot that can be used to chat with. ChatGPT is a fine-tuned model of GPT3.5 , using #RL (specifically a PPO algorithm) similar to the Instruct series. This post is my experience in using it. Blog post with ChatGPT What better place to start with, than asking it about itself? 😃 ...

Here is an example of how one can use a text prompt to generate a series of frames, that then are stitched together into a video. The prompt I used was: “a man walking in the parking lot with a miniature poodle”. the final video generated is shown below. There should have been a video here but your browser does not seem to support it. AI-generated video from a text prompt of a man walking in a parking lot with a miniature poodle ...

I have said this before - with the advent of large AI models, Prompt Engineering is critical and is the next challenge for us to master. What is Prompt engineering? Prompt engineering is the process of fine-tuning large models and often is written in natural language, outlining the intention of the user. Prompt engineering is a key element that allows the output to be accurate and reflect the needs of the user. Prompts should not be thought as the explicit one input to the model, instead are multiple tasks for the model. ...

It is 2021. And we have #AI writing #AI code. 🤪 It is quite interesting, but also can be quite boring once you get beyond the initial technology, and just think of it as one of the tools in your arsenal. And getting to that point is a good think. As part of a think at work I recently started playing with GitHub Copilot , which is using GPT3 to be your pair programmer – helping write code. GPT3 has multiple models (called engines), and Copilot uses one of these family of engines called Codex. Codex is a derivative of the base GPT3 engine that is trained on billions of lines of code. ...

Reinforcement Learning is teaching by example – it is how most of us learn. Reinforcement Learning (#RL) is a different approach to ML – it is a set of techniques that allows AI algorithms to experiment and learn from experience. RL falls in between supervised and unsupervised learning – there isn’t any labeled data, but at the same time it isn’t unsupervised either. At its most simple form, RL is a computational approach for automating goal-oriented decision making and learning. ...

I have been kicking the tires with Open AI’s #GPT-3 . Based on the screenshot below, it might be easy to think “oh boy does the model think highly of itself”, but as with most things in life - the devil is in the details.😃 The screenshot below was a forked version of davinci engine and follows the Q&A structure. ...

A #ML algorithm cheat sheet - helping narrow down to a certain set of #algorithm grouping depending on the problem at hand and what we are trying to solve from a business perspective. ML algorithm cheat sheet Figure 2 shows what additional characteristics we need to consider when choosing the right ML algorithm for your situation at hand. This is something that cannot be generic and is very situational. ...

Floating point calculations are slow for computers (specifically CPUs); possibly representing the same struggle for many humans. :) I remember a time when a FPU (floating point unit) was an upgrade and one had to pay extra to get one. Very useful when you needed that extra precision in computing - and in my head, it always seemed like the Turbo button. :) ...

Sometimes one needs a quick snapshot of what are the options to think through and I really like this for that. Machine Learning Algorithms

I have been playing with a #selfdriving car for a while , and that is super exciting. From a #AI and #ML perspective it is small scale but allows one to exploit all aspects of the tech stack and also appreciate the limitations of not only the software but also the hardware. With this, You run a NN on a raspberry pi that uses TensorFlow, and Keras and run inference on the edge. The pi doesn’t have enough power to train, so you need to do that on a beefier machine and then deploy the model back to run this. ...

{Cross posted from my post on Avanade } Containers just got smarter. That’s the news from Microsoft, which announced recently that Azure Cognitive Services now supports containers . The marriage of AI and containers is a technology story, of course, but it’s a potentially even bigger business story, one that affects where and how you can do business and gain competitive advantage. ...

The naming is unfortunate when talking about #AI. There isn’t anything about intelligence - not as we humans know of it. If we can rewind back to the 50’s we can perhaps rename it to something like Computational Intelligence, which is more accurate. And although I have outlined the difference between some of the elements of AI in the past, I wanted to get back to what the intent was and how this area started. ...

Regularization is a fundamental concept in Machine Learning (#ML) and is generally used with activation functions . It is the key technique that help with overfitting. Overfitting is when an algorithm or model ‘fits’ the training data too well - it seems to good to be true. Essentially overfitting is when a model being trained, learns the noise in the data instead of ignoring it. If we allow overfitting, then the network only uses (or is more heavily influenced) by a subset of the input (the larger peaks), and doesn’t factor in all the input. ...

Neural Networks, today, help in a great set of tasks, that until very recently wasn’t possible at all - be it from computer vision, to medical diagnosis, to speech translation and forms a key cornerstone to a lot of ‘magic’ that Machine Learning and AI offers today. I did blog about Neural Network types (and MarI/O) sometime back ; I surely cannot take credit for creating these three cheat sheets but they are awesome and hope you get to use and enjoy them too. ...

Thought of the week: Artificial Intelligence stands no chance against natural Stupidity. #ArtificalIntelligence

Seem like my training data for the car - perhaps a hint of #bias. 😂 #GeekyJokes #ML #AIJokes

Neural networks have a very interesting aspect – they can be viewed as a simple mathematical model that defines a function. For a given function $f(x)$ which can take any input value of $x$, there will be some kind a neural network satisfying that function. This hypothesis was proven almost 20 years ago (“ Approximation by Superpositions of a Sigmoidal Function ” and “ Multilayer feedforward networks are universal approximators ”) and forms the basis of much of #AI and #ML use cases possible . ...

I was looking at something else and happen to stumble across something called Netron , which is a model visualizer for #ML and #DeepLearning models. It is certainly much nicer than for anything else I have seen. The main thing that stood out for me, was that it supports ONNX , and a whole bunch of other formats (Keras, CoreML), TensorFlow (including Lite and JS), Caffe, Caffe2, and MXNet. How awesome is that? ...

Trained a model to create a synthetic sound that sounds like me. This is after training it with about 30 sentences - which isn’t a lot. To create a synthetic voice, you enter some text, using which is then “transcribed” using #AI and your synthetic voice is generated. In my case, at first, I had said AI, which was generated also as “aeey” (you can listen here ). So for the next one, changed the AI to Artificial Intelligence. ...

Over the last few weeks, I built a self-driving car - which essentially is a remote control Rx car that uses a raspberry pi running Python, TensorFlow implementing a end-to-end convolution neural network (CNN) Of course other than being a bit geeky, I do think this is very cool to help understand and get into some of the basic constructs and mechanics around a number of things - web page design, hardware (maker things), and Artificial Intelligence principles. ...

Can #AI make me look (more) presentable? The jury is out I think. This is called style transfer, where the style/technique from a kind of painting (could be a photos too) is applied to an image, to create a new image. I took this using the built-in camera on my machine sitting at my desk and then applying the different kind of ‘styles’ on it. Each of these styles are is a separate #deeplearning model that has learned how to apply the relevant style to a source image. ...

Yesterday it worked Today it is not working #AI is like that #Haiku #GeekyHaiku #GeekyJokes

One of the challenges we have with AI is that there isn’t any universal definition - it is a broad category that means everything to everyone. Debating the rights, and, the wrongs, and the should’s and the shouldn’t s is another post though. DARPA outlines this as the “programmed ability to process information” and across a certain set of criteria that span across perceiving, learning, abstracting, and, reasoning. ...