The hype is dead, long live the hype. After deep learning, a new entry is about ready to go on stage. The usual journalists are warming up their keyboards for blogs, news feeds, tweets, in one word, hype. This time it’s all about privacy and data confidentiality. The new words, homomorphic encryption.
For the record, I am not personally against such a technology — quite the opposite I think it is very powerful and clever, rather against the misleading claims that usually make more followers than the technology itself. …
Testing software is one of the most complex tasks in software engineering. While in traditional software engineering there are principles that define in a non-ambiguous way how software should be tested, the same does not hold for machine learning, where testing strategies are not always defined. In this post, I elucidate a testing approach that is not only highly influenced by one of the most recognized testing strategies in software engineering — that is test-driven development. …
TL;DR; GPT-3 will not take your programming job (Unless you are a terrible programmer, in which case you would have lost your job anyway)
Once again the hype of artificial intelligence has broken in the news. This time under the name of GPT-3, the successor of GPT-2 (of course), a model that is so large and so powerful that is making people think we finally made AGI, artificial general intelligence, possible (AGI is the kind of stuff that charlatans like Ben Goertzel keep claiming since a decade already).
For those who are new to the topic, GPT-2 was a model in the NLP (Natural Language Processing) field of research that can generate text from an input sample. Basically, given a bunch of words or a structured sentence in English or another language, it will continue generating text that is consistent with the input.
<sarcasm> Such an impressive result! Such an amazing artificial intelligence! …
It is very common to fork a repo you are interested in and start reading code and learn how things are done. Then after a few days realizing that the forked repo is already behind upstream and need to resync. Here is a standard way of doing just this safely.
We will call the main project to fork
https://github.com/author/cool-project.git and the forked repo will be at
From the forked repo we need to add the remote upstream (the original repo we have forked from)
Then we need to fetch all branches with
Let’s move to master branch on the forked repo
and finally rebase. This means that all commits that are not yet in upstream (and usually they never are) will be applied on top of the other branch
Easy. Happy coding!
Originally published at https://codingossip.github.io on July 22, 2020.
If you are looking for some kind of metal panel business idea, allow me to be clear: the Rust I am referring to is a programming language.
When I started learning programming languages I was 8 years old, the world was in different shapes and computers were more like romantic and magical boxes rather than the tools people TikTok with today.
Let’s start with the definition of Scrum and its peculiarities. Scrum is an agile framework for developing products by breaking down them in small chunks that can be time boxed from 2 to 4 weeks. Such time boxes are called sprints. Progress is tracked in meetings called daily scrums. From this definition it looks like something that seems to work, right? Wrong.
Scrum makes sense in very complex environments in which the product has been defined in its entirety. The time box nature of sprints helps to identify blockers and dealing with requirements or market changes. …
The adoption of artificial intelligence is rapidly spreading across many businesses. This disruptive technology is driving consistent improvements of the operational efficiencies and decision-making processes across a large variety of industries, and it is helping to better understand customer needs, improve service quality, predict and prevent risks, just to mention a few.
In this realm, the implementation of a proper data governance framework becomes fundamental to enable organizations to fully unlock the potential of their data. This post helps defining data governance framework for your organisation.
Generally speaking, data governance consists of the set of procedures to provide the management of the availability, usability, integrity, and security of data used in an enterprise. More specifically to machine learning, data governance procedures ensure that high-quality data are available to all the stakeholders across the enterprise, making sure that the purpose of such accessibility is always available. …
The adoption of artificial intelligence is rapidly spreading across many businesses. This technology is driving constant improvements in the decision-making processes and overall performance across a large variety of industries. It is also helping to better understand customer needs, improve service quality, predict and prevent risks.
The implementation of a proper data governance framework is essential to enable organizations to fully unlock the potential of their data. This post explains what data governance is and why it’s relevant to artificial intelligence.
Data governance consists of the set of procedures designed to properly manage data. Appropriate policies must guarantee the availability, usability, integrity, and security of enterprise data. In machine learning, data governance procedures ensure that all the interested stakeholders across the enterprise have always access to high-quality data. …
Would you train a neural network with random data? Moreover, are massive neural networks just lookup tables or do they truly learn something?
Today’s episode is about memorisation and generalisation in deep learning, with Stanislaw Jastrzębski. Stan works as post-doc at New York University. His research interests include:
I have asked Stan a few questions I was looking answers for a long time. For instance, what is deep learning bringing to the table that other methods don’t or are not capable of?
Above all, Stan believes that the one thing that makes deep learning special is representation learning. It turns out that all the other competing methods, be it kernel machines, or random forests, do not have this capability. …
Let’s face it: after 85 episodes of podcast Data Science at Home I realised that listening and interacting are two different things. That’s why I created a Discord channel you can join any time and discuss the topics presented in the past episodes or to propose new ones.
The community of Data Science at Home has grown to numbers I personally did not expect. I thank all of you for trusting me with your time. …