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. …


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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)

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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! …


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.

Still there?

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.

GW-Basic and C were my first shots in computer science during the time in which memory was directly accessible — for the fun of many and the profit of others. …


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The data governance framework you need for your organization

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. …


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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. …


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Listen to the episode

This post has been published as a podcast episode on Data Science at Home. You can listen to the full episode here

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:

  • Understanding and improving how deep network generalise
  • Representation Learning
  • Natural Language Processing
  • Computer Aided Drug Design

What makes deep learning unique?

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. …


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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. …


This post first appeared on amethix.com

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“If intelligence is a cake, the bulk of the cake is unsupervised learning, the icing on the cake is supervised learning, and the cherry on the cake is reinforcement learning”.

This famous quote from Facebook AI Chief Yann LeCun highlights how humans rely on unsupervised learning. Effective community detection with Markov Clustering. They use such an approach to make sense of the world around them. As a matter of fact, children learn to recognize objects and speak without much supervision. In contrast, sophisticated deep learning algorithms need millions of carefully annotated data. …


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The two most widely considered software development models in modern project management are, without any doubt, the Waterfall Methodology and the Agile Methodology.

An overview of the Waterfall model

The Waterfall approach is the way to go in “consolidated” areas of engineering design. In these fields you can assume that progress flows in one direction. In layman terms, once you make up your mind there are no second thoughts. From here the name waterfall.

Software development purists look at the Waterfall methodology as the model to look at for highly structured projects e.g. Operating System design, real-time codecs, scientific software or software for critical environments.

However, this approach can be deleterious for AI and machine learning projects. Its adoption could lead lead to long development cycles and project failures. …

About

Francesco Gadaleta

Managing Director @ amethix.com Chief Software Engineer & Host @datascienceathome.com

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