The Beginning

So we have already played with different neural networks. Cursed image generation using GANs, deep texts from GPT-2 — we have seen it all.

This time I wanted to create a neural entity that would act like a beauty blogger. This meant it would have to post pictures like Instagram influencers do and generate the same kind of narcissistic texts. \

Initially I planned to post the neural content on Instagram but using the Facebook Graph API which is needed to go beyond read-only was too painful for me. So I reverted to Telegram which is one of my favorite social products overall.

The name of the entity/channel (Aida Enelpi) is a bad neural-oriented pun mostly generated by the bot itself.

Hi All,

I have some good news for you…

Data Science Digest is back! We’ve been “offline” for a while, but no worries — You’ll receive regular digest updates with top news and resources on AI/ML/DS every Wednesday, starting today.

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Regards,
Dmitry Spodarets.

This article is a part of Algorithms in Go series where we discuss common algorithmic problems and their solution patterns.

In this edition, we take a closer look at bit manipulations. Bit operations can be extremely powerful and useful in an entire class of algorithmic problems, including problems that at first glance does not have to do anything with bits.

Let's consider the following problem: six friends meet in the bar and decide who pays for the next round. They would like to select a random person among them for that. How can they do a random selection using only a single coin?

The solution to this problem is not particularly obvious (for me:), so let's simplify a problem for a moment to develop our understanding. How would we do the selection if there were only three friends? In other words, how would we "mimic" a three-sided coin with a two-sided coin?

Author: Sergey Lukyanchikov, Sales Engineer at InterSystems

What is Distributed Artificial Intelligence (DAI)?

Attempts to find a “bullet-proof” definition have not produced result: it seems like the term is slightly “ahead of time”. Still, we can analyze semantically the term itself – deriving that distributed artificial intelligence is the same AI (see our effort to suggest an “applied” definition) though partitioned across several computers that are not clustered together (neither data-wise, nor via applications, not by providing access to particular computers in principle). I.e., ideally, distributed artificial intelligence should be arranged in such a way that none of the computers participating in that “distribution” have direct access to data nor applications of another computer: the only alternative becomes transmission of data samples and executable scripts via “transparent” messaging. Any deviations from that ideal should lead to an advent of “partially distributed artificial intelligence” – an example being distributed data with a central application server. Or its inverse. One way or the other, we obtain as a result a set of “federated” models (i.e., either models trained each on their own data sources, or each trained by their own algorithms, or “both at once”).

Distributed AI scenarios “for the masses”

We will not be discussing edge computations, confidential data operators, scattered mobile searches, or similar fascinating yet not the most consciously and wide-applied (not at this moment) scenarios. We will be much “closer to life” if, for instance, we consider the following scenario (its detailed demo can and should be watched here): a company runs a production-level AI/ML solution, the quality of its functioning is being systematically checked by an external data scientist (i.e., an expert that is not an employee of the company). For a number of reasons, the company cannot grant the data scientist access to the solution but it can send him a sample of records from a required table following a schedule or a particular event (for example, termination of a training session for one or several models by the solution). With that we assume, that the data scientist owns some version of the AI/ML mechanisms already integrated in the production-level solution that the company is running – and it is likely that they are being developed, improved, and adapted to concrete use cases of that concrete company, by the data scientist himself. Deployment of those mechanisms into the running solution, monitoring of their functioning, and other lifecycle aspects are being handled by a data engineer (the company employee).

There is a lot of commotion in text-to-speech now. There is a great variety of toolkits, a plethora of commercial APIs from GAFA companies (based both on new and older technologies). There are also a lot of Silicon Valley startups trying to ship products akin to "deep fakes" in speech.

But despite all this ruckus we have not yet seen open solutions that would fulfill all of these criteria:

• Naturally sounding speech;
• A large library of voices in many languages;
• Support for 16kHz and 8kHz out of the box;
• No GPUs / ML engineering team / training required;
• Unique voices not infringing upon third-party licenses;
• High throughput on slow hardware. Decent performance on one CPU thread;
• Minimalism and lack of dependencies. One-line usage, no builds or coding in C++ required;
• Positioned as a solution, not yet another toolkit / compilation of models developed by other people;
• Not affiliated by any means with ecosystems of Google / Yandex / Sberbank;

We decided to share our open non-commercial solution that fits all of these criteria with the community. Since we have published the whole pipeline we do not focus much on cherry picked examples and we encourage you to visit our project GitHub repo to test our TTS for yourself.

Grind is a process of slowly getting valuable resources (be it experience points or loot) by repetitive and often simple tasks in video games. It has been present from the beginning of gaming but has become more widespread with the popularization of online RPG games because of their leveling systems and competitive elements. 

It is highly criticized by gamers around the world for making games boring and work-like, yet many people specifically choose to play grind-heavy games. The reason might be because they find simple repetitive tasks relaxing and distracting from real-life problems, as a form of escapism.

However, there is also a gameplay reason for grinding: getting valuable resources early can make a game easier later. Some popular games like Diablo are even centered around grind. Thus, everyone will have to grind at some point to prevent gameplay from getting too difficult, which quickly becomes an inescapable habit. Later, gamers might apply grinding even to games that do not require it. Interestingly enough, grinding early on can also make the late-game boring because it is going to be too easy if the game was not designed for grinding. For example, in Subnautica, getting a lot of resources early on will make some of the late-game tools useless because all the resources that could have been gained with them have already been gained.

From the example above, we can see that grind does not always improve the gameplay. We can also see that it is not always a necessary process and can be either minimized or avoided entirely: sometimes, the need for it exists only in our mind, forcing us to diminish the fun of actually playing the game. Is it just a question of habit or is there any other reason for us to grind?

Author: Chris Punches (@cmpunches, Silo group). License: "Please feel free to share unmodified".

The following text is an unmodified copy of now removed issue #2250 on rms-open-letter.github.io repository. The text claims multiple violations of different policies, codes of conduct and other documents in creation, content and support of the "Open letter to remove Richard M. Stallman from all leadership positions". The issue has not been addressed.

Gyrators are impedance converters usually used to simulate inductance in circuits. Though they are rarely used in discrete electronics, they are interesting circuits looking like pole dancers in pictures. There are studies on gyrators, but still something is missing, so it is interesting to do another one.

Most solutions to algorithmic problems can be grouped into a rather small number of patterns. When we start to solve some problem, we need to think about how we would classify them. For example, can we apply fast and slow аlgorithmic pattern or do we need to use cyclic sortpattern? Some of the problems have several solutions based on different patterns. In this series, we discuss the most popular algorithmic patterns that cover more than 90% of the usual problems.

It is different from High-School Algorithms 101 Course, as it is not intended to cover things like Karatsuba algorithm (fast multiplication algorithm) or prove different methods of sorting. Instead, Algorithmic Patterns focused on practical skills needed for the solution of common problems. For example, when we set up a Prometheus alert for high request latency we are dealing with Sliding Window Pattern. Or let say, we organize a team event and need to find an available time slot for every participant. At the first glance, it is not obvious that in this case, we are actually solving an algorithmic problem. Actually, during our day we usually solve a bunch of algorithmic problems without realizing that we dealing with algorithms.

The knowledge about Algorithmic Patterns helps one to classify a problem and then apply the appropriate method.

But probably most importantly learning algorithmic patterns boost general programming skills. It is especially helpful when you are debugging some production code, as it trains you to understand the execution flow.

Patterns covered so far:

Sliding Window I

Sliding Window II

Merge Intervals

Dutch National Flag

Matrix Spiral

Iterative Postorder Traversal

Bit Manipulation

Stay tuned :)

<Promo> If you interested to work as a backend engineer, there is an open position in my squad. Prior knowledge of Golang is not required. I am NOT an HR and DO NOT represent the company in any capacity. However, I can share my personal experience as a backend engineer working in the company. </Promo>

In this article, we discuss the postorder traversal of a binary tree. What does postorder traversal mean? It means that at first, we process the left subtree of the node, then the right subtree of the node, and only after that we process the node itself.

Why would we need to do it in this order? This approach solves an entire class of algorithmic problems related to the binary trees. For example, to find the longest path between two nodes we need to traverse the tree in a postorder manner. In general, postorder traversal is needed when we cannot process the node without processing its children first. In this manner, for example, we can calculate the height of the tree. To know the height of a node, we need to calculate the height of its children and increment it by one.

Let's start with a recursive approach. We need to process the left child, then the right child and finally we can process the node itself. For simplicity, let's just save the values into slice out.

Having only programming background, it is impossible to develop software in some areas. Take the difficulties of medical software development as an example. The same is with music software, which will be discussed in this article. Here you need an advice of subject matter experts. However, it's more expensive for software development. That is why developers sometimes save on code quality. The example of the MuseScore project check, described in the article, will show the importance of code quality expertise. Hopefully, programming and musical humor will brighten up the technical text.

Techstars Startup Digest was designed as a discovery tool for entrepreneurs looking for tech events in their area. It was founded in 2009 by Chris McCann who just moved to the Valley. He created an old-school newsletter, featuring promising events in the Bay Area. There was no website, all the events were hand-picked by Chris himself, and the newsletter had 22 subscribers. People liked the idea and that number quickly grew. In 2012 it was acquired by Startup Weekend. Three years later, Startup Weekend’s parent company UP Global was acquired by Techstars — and that’s how the project got its name.

Startup Digest can be a useful tool for startups and event coordinators. If you can successfully leverage it, your event, blog post and/or tech product can reach thousands of people at no cost.

Technology is as adaptable and compatible as mankind; it finds its way through problems and situations. 2020 was one such package of uncertain events that forced businesses to adapt to digital transformation, even to an extent where many companies started to consider the remote work culture to be a beneficiary long-term model. Technological advancements like Hyper automation, AI Security, and Distributed cloud showed how any people-centric idea could rule the digital era. The past year clearly showed the boundless possibilities through which technology can survive or reinvent itself. With all those learnings let's deep-dive and focus on some of the top technology trends to watch out for in 2021.

Common approach to build a third order low-pass filter is to use two circuit stages and two Op Amps. Making good One Op Amp design is not always easy, but it is possible.

SVG-File (actual draft)

Lyrics

Ancient times are known to everyone not with immortal works from Homer's only, but also with the Pythagorean multiplication table, Euclidean geometry and the Archimedes screw and the Pi, which we learned to use only relatively recently. In antiquity the art was not only to be able to write poetry and prose, but to design catapults or battering tools also, now there are rigid frameworks, when the discovering the new another beautiful formula is a formal words play only.
Mathematics rules the modern world completely, cynically intertwining with the world of art, intruding with calculations in all spheres of our recreation and everyday life, when the colors of masterpieces turning into poisonous colours.

Hey everyone! I represent a game studio without a name, and the project we’re working on goes by the technical name of "CGDrone". I started writing this article earlier today, having tortured myself for ages with sketches, colours, algorithms and correcting bugs in rotations based on quaternions (the last one just about finished me off). You can probably understand I needed a break.

I’ve often come across stories people have posted online about how they made their game, the difficulties they faced, and the result they achieved at the end. Likewise, our team has its own story, and I’d like to share a bit about it.

There is a myriad of articles about where to find remote jobs, particularly in tech. Some of them are outdated and most of them don't provide detailed reviews. So that's why I decided to do my own research. I did a basic search by "React" skill (where possible) and expected to see mostly "Frontend Developer" vacancies.

In 2013 Canonical tried to crowdfund Ubuntu Edge smartphone. Its main feature could be the ability to use the smartphone as a full-fledged PС. Unfortunatly, the crowdfunding campaign did not accumulate enough money, so a dream of having a universal device remained to be the dream.

I've been searching for universality, too, on the software side, not the hardware one. Today I can confidently say I found the necessary combination: Git and JavaScript.

As you know, I have already described the benefits of browser applications (nCKOB static site generator) and the benefits of using Git instead of yet another back-end with API (GitBudget to track personal spendings). Once GitBudget was out, I spent the remaining 2020 to build a system allowing one to create browser applications right inside browsers. GitJS is the name of that system.

AngelList is a social network designed to connect startups with investors and vice versa. The founders of it were dissatisfied with how opaque the VC world was, and found a way to increase the amount of available data. The project began in partnership with just 50 volunteer investors wishing to allocate $80 million in capital, and has grown to be the leading website of its kind. Over the past three years more than 75% of startups that received seed funding from American investors used AngelList to make it happen.

This article is about security. I’ll focus on this in the context of web applications, but I’ll also touch on other types of applications. Before I describe approaches and frameworks, I want to tell you a story.

Background

Throughout my years working in the IT sphere, I’ve had the opportunity to work on projects in a variety of fields. Even though the process of authenticating requirements remained relatively consistent, methods of implementing the authorization mechanism tended to be quite different from project to project. Authorization had to be written practically from scratch for the specific goals of each project; we had to develop an architectural solution, then modify it with changing requirements, test it, etc. All this was considered a common process that developers could not avoid. Every time someone implemented a new architectural approach, we felt more and more that we should come up with a general approach that would cover the main authorization tasks and (most importantly) could be reused on other applications. This article takes a look at a generalized architectural approach to authorization based on an example of a developed framework.

Approaches to Creating a Framework

As usual, before developing something new, we need to decide what problems we’re trying to solve, how the framework will help us solve them, and whether or not there is already a solution to these issues. I’ll walk you through each step, starting with identifying issues and describing our desired solution.

We’re focusing on two styles of coding: imperative and declarative. Imperative style is about how to get a result; declarative is about what you want to get as a result.