AI#
2023 - A year in review
On the 30th of November 2022, I created my ChatGPT account and started trying out the capabilities of ChatGPT, which back then represented a major disruption of what we knew as chatbots. I remember trying out in the first days the capabilities of the chatbot, mainly focusing on what we would now call "reasoning" capabilites. I tested out its flexibility, it's understanding of instructions and its ability to accomplish tasks.
Data Science Azure VM
It seems Microsoft is offering a complete ready to go software bundle for any Data Scientist in the form of a Azure Virtual Machine called the “Data Science Virtual Machine“.
Just looking at the list of pre-installed software, you have some of the common modern languages and frameworks already in the VM, so all you need to do is connect to your data source and start working:
Watch out for Julia
I first heard of Julia from Kaggle’s “First Steps With Julia” competition and started looking around some of its features while on julialang.org and at that point I thought that it was maybe a replacement for R or even a more hardcore version of R developed especially for scientists.
But recently I read a blog post from Microsoft’s Cortana Intelligence and Machine Learning Blog on Julia – “Julia – A Fresh Approach to Numerical Computing” and I have to say that I feel like the tide will turn in a year or so and we will see a rapid adoption of Julia in the Machine Learning community.
My year in Big Data, ML and AI
Although I’ve made this blog live a couple of months ago and it has a Big Data section, I didn’t have the time to work with technologies from this field very much, especially in the latter part of the year.
Even though I didn’t manage to test these technologies and implement them in a “homework” project, I’ve tried to keep myself up to date with things happening in this Big Data and Machine Learning ecosystem. And so, a small review of what small steps I managed to take this year: