QuantiWriter - Writing meets AI

After focusing on the AI side of things over the  last few articles , it's time to circle back to the good old craft of writing. Like I mentioned a few articles back , I'm also an author who wrote a book (spoiler alert: it will be published very soon!). How did I decide to take on such a tedious yet fun endeavour and how did I actually pull it off? That's what this article is about. A book, are you crazy? Yepp, looking back it's certainly one of my crazier ideas. It all started with great news - my wife was pregnant with our first kid. As if I didn't knew I'd have a ton of sleepiness nights ahead of me, I decided to use my 14 weeks of paternity leave (thanks, Google!) for a personal project. Out of my usual comfort zone it was supposed to be, something different from my day to day work as a software engineer. I like reading, in particular thrillers, so hey why not try that myself? The idea of writing a novel was born. And like any other project, it needed prepar

This is the second post in a series from the basics of machine learning to state of the art large language models ( ChatGPT ,  Bard  and friends). Here the links to the entire series: The basics of Artificial Intelligence and Machine Learning Deep Learning and Neural Networks Large Language Models  (this post) The Transformer Architecture As explained in my previous post , neural networks are an ML model designed after the blueprint of our brain, capable of representing complex relationships and hence deep knowledge. The structure of such a neural network - how the artificial neurons are connected, or in mathematical terms the layout of the network graph - is what we call its architecture . Over the last decade or so, ML researchers have found better and better architectures for a number of different tasks, such as computer vision or language understanding. The analogy in real life us how the different parts of our own brain are wired to perform specific parts like vision, memory or ot

This is the second post in a series from the basics of machine learning to state of the art large language models ( ChatGPT ,  Bard  and friends). Here the links to the entire series: The basics of Artificial Intelligence and Machine Learning Deep Learning and Neural Networks (this post) Large Language Models Fundamentals of Neural Networks Artificial neural networks are a type of machine learning model that can be trained to encapsulate knowledge and use it to predict attributes of data (other models are rule sets or decision trees, as discussed in the last post ). They are modelled after the neuronal structure of the human brain (or any biological brain for that matter) - maybe that's why they're working so well. Like in our brain, artificial neural networks consist of a interconnected web of (simulated) neurons . Each neuron receives signals from a number of input neurons, and if the signals accumulate to a certain threshold, can become activated. If activated, it sends a

In my first post , I mentioned the emergence of generative AI -  ChatGPT ,  Bard  and friends. But what are these mysterious black boxes that can produce creative texts and are seemingly indistinguishable from human intelligence?  Let's break it down. This is the first in a series of three posts explaining how these models work: The basics of Artificial Intelligence and Machine Learning (this post) Deep Learning and Neural Networks Large Language Models What's Artificial Intelligence and Machine Learning? Both terms are hot buzzwords these days, and there are many definitions for both. One of the founding fathers of computer science, the great Alan Turing , simply defines Artificial Intelligence (AI) as  the science and engineering of making intelligent machines Well, that didn't help! But that's really what it is: Making machines act intelligently. In other words, make them act like humans. AI is umbrella term for different fields and techniques pursuing this goal i

My name is Timo, I'm 38 years old and I currently live in Zurich, Switzerland - previous stops include Australia (Sydney, Melbourne), the United States (Miami), the United Kingdom (Chester), and Germany, where I'm from originally (around Frankfurt). I have a lovely wife and two young kids (4 and 2) - yes, that's relevant, as you'll see in a minute :)  I'm a software engineer by trade, and always had a strong interest in ML - my master thesis was about detecting money launderers with ML, I once co-founded a startup about quant trading with ML, and have been working at Kaggle  and Google's Core ML department for the last few years, where I now lead a few teams around ML data management. I think it's fair to say that, when it comes to AI, I know what I'm talking about, and I'm always looking for ways to make somebody's life better with it. So how did I get into creative writing?   That's where my first kid comes in, well, sort of. When my first

We live in exciting times! This is probably a universal statement many people would claim at any given time throughout the ages, but it certainly feels true and real to me. Lots of things are happening in the world, for better and for worse. Either way, change is happening all around us, and change presents opportunities. I started to see some of these opportunities in two different world - the world of artificial intelligence , and the world of writing .  A few years ago, many would've said these things don't mix. But with recents advancements in machine learning - the emergence of  ChatGPT , Bard  and other generative AI systems has shaken public society probably more than any other discovery in the field -, it's not a stretch to say these two worlds are on a collision course. Whether that's a death sentence for creative writing ( the Writers Guild of America seems to think so ) or a great opportunity will need to stand the test of time, but it's certainly an int