Month: February 2022

A Thousand Brains: A New Theory of Intelligence : Hawkins, Jeff: Amazon.se: Böcker

This is a new book, written by one of the people behind Palm Pilot, who is both an engineer and a neuroscientist. The book proposes a theory on how to describe neocortex and its functions.

Why is neocortex so important, one may ask. It holds our intelligence and our consciousness. Some would say that it is the place which defines us as humans, which allows us to be aware and intelligent.

The interesting part of this book is the fact that it attempts to provide guidelines on the future of machine intelligence and machine learning. It shows different paths to achieve AGI (Artificial General Intelligence): either as developing a lot of specialized models and winding them together, or as making one large model for everything.

These two approaches are already present in the modern AI community. The latter one (large model for everything) can be seen in the work of OpenAI and GPT-3. The scientists behind that model train it on large corpora of text, hoping that it can understand our natural language and execute our commands. Well, for now it is mostly about creating programs.

The first approach is generally the original idea of AI and ML. The original idea is about training models for specific tasks, such as image recognition, classification, text translation. This is where most of the current research lies and where we have observed the latest breakthroughs – AlphaGo, AlphaStar.

However, the thesis in the book is that the approach of one large model is more natural, similar to how our brain works. The theory of how neocortex stores, frames and recalls information is the core of what we need to achieve in order to make it work in practice.

Well, there is more to the book than I can write in a blog post, so I strongly recommend to read it and reflect on how we use ML and AI today. I’m going to try few of these ideas in 2022!

Only for those who love Terry Pratchett….

Every now and then I read what the Nobel prize winners in literature write. This year, I did read this novel by 1998 winner.

The novel is quite interesting and very well written. If you like Terry Pratchett, then you will find it very good. The synopsis is that one day, Death decides to take a break and do not visit England. It means that no one in England dies that day, and for a while onwards.

At first, this situation brings a lot of joy – people do not die in car accidents, from diseases, etc. After a while, however, this situation brings a few challenges.

First, the challenges for the healthcare – there is not enough room in hospitals anymore, as the very sick patients are in the wards for ever.

Then, the economical – the funeral entrepreneurs are getting out of business.

Finally, the mafia comes in and starts smuggling people to neighboring countries, where they instantly die. For a small fee, naturally.

Now, in the middle of the book, the death returns, but changes the way which she works. Instead of using the scythe to take lives, she uses it to send letters. The letters, nicely put in a violet envelope, have the same effect – the recipient dies.

Again, this sounds like a great idea, but depends on the post office (of course) and the timing of sending the letters, as well as the timing for receiving the letters.

The book ends with Death coming down to earth dressed as a woman, as she fell for a cellist. She watched him perform, visit him at his home, keep his pet on her laps.

I’m not going to spoil the book for you, so here is where I stop.

If you like the style of Terry Pratchett, this book is for you!

IEEE Xplore Full-Text PDF:

A lot has been written about defect prediction, and I’m pretty sure that a lot will be written. It’s one of the research areas which is quite cool to work with because it provides researchers with quite quick results and is relatively quantitative in its nature.

One could also say that this is a holy grail in software development – to predict a location of a defect and fix it before it becomes a problem. It’s a good goal, but it is also a goal that is more like quicksand than a gravel road. Well, for one, not all defects are easy to recognize. Some are not even certain to be defects – sometimes it is not clear how to interpret a requirement, so it’s not easy to say if a piece of code is implementing it correctly or not.

In this paper, the authors have done a great job in creating a system to predict defect location on line-level – DeepLineDP. The requirements for the system are partially based on a survey conducted by the authors with developers.

According to the authors: “DeepLineDP is 14%-24% more accurate than other file-level defect prediction approaches; is 50%-250% more cost-effective than other line-level defect prediction approaches; and achieves a reasonable performance when transferred to other software projects. These findings confirm that the surrounding tokens and surrounding lines should be considered to identify the fine-grained locations of defective files (i.e., defective lines). “

I like this work and I recommend everyone interested in how to use deep learning for code tasks to look at this work.

Our team has done some of these investigations ourselves. You can watch them on Youtube here: