• Five Methods For Database Obfuscation

      ClickHouse users already know that its biggest advantage is its high-speed processing of analytical queries. But claims like this need to be confirmed with reliable performance testing. That's what we want to talk about today.



      We started running tests in 2013, long before the product was available as open source. Back then, just like now, our main concern was data processing speed in Yandex.Metrica. We had been storing that data in ClickHouse since January of 2009. Part of the data had been written to a database starting in 2012, and part was converted from OLAPServer and Metrage (data structures previously used by Yandex.Metrica). For testing, we took the first subset at random from data for 1 billion pageviews. Yandex.Metrica didn't have any queries at that point, so we came up with queries that interested us, using all the possible ways to filter, aggregate, and sort the data.

      ClickHouse performance was compared with similar systems like Vertica and MonetDB. To avoid bias, testing was performed by an employee who hadn't participated in ClickHouse development, and special cases in the code were not optimized until all the results were obtained. We used the same approach to get a data set for functional testing.

      After ClickHouse was released as open source in 2016, people began questioning these tests.

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    • Machine Learning in Static Analysis of Program Source Code

        Machine Learning in Static Analysis of Program Source Code

        Machine learning has firmly entrenched in a variety of human fields, from speech recognition to medical diagnosing. The popularity of this approach is so great that people try to use it wherever they can. Some attempts to replace classical approaches with neural networks turn up unsuccessful. This time we'll consider machine learning in terms of creating effective static code analyzers for finding bugs and potential vulnerabilities.
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      • Testing Water Melon using Neural Networks: Full Dev. Cycle from prototyping to the App. at Google Play

        • Tutorial

        The beginning


        It all started when I found an app. on Apple market, that supposedly was able to determine the ripeness of a water mellon. A program was… strange. Just think about it: instead of knocking using your knuckles, you were supposed to hit the water mellon with your iPhone! Nevertheless, I have decided to repeate that functionality on an Andtoid platform.
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      • AI Robotization with InterSystems IRIS Data Platform

          Author: Sergey Lukyanchikov, Sales Engineer at InterSystems

          Fixing the terminology


          A robot is not expected to be either huge or humanoid, or even material (in disagreement with Wikipedia, although the latter softens the initial definition in one paragraph and admits virtual form of a robot). A robot is an automate, from an algorithmic viewpoint, an automate for autonomous (algorithmic) execution of concrete tasks. A light detector that triggers street lights at night is a robot. An email software separating e-mails into “external” and “internal” is also a robot.

          Artificial intelligence (in an applied and narrow sense, Wikipedia interpreting it differently again) is algorithms for extracting dependencies from data. It will not execute any tasks on its own, for that one would need to implement it as concrete analytic processes (input data, plus models, plus output data, plus process control). The analytic process acting as an “artificial intelligence carrier” can be launched by a human or by a robot. It can be stopped by either of the two as well. And managed by any of them too.

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        • The Future of Artificial Intelligence in the Education System: Everything One Should Know

          image


          Artificial Intelligence refers to the theory of computer systems or human-made robots programmed with performing tasks as humans, such as learning, generalization, and reasoning. With this ability, AI has become a significant part of human lives. Similarly, AI and the education & tutoring web solutions are inseparable from being observed by the astounding inventions enabling machines to mimic human roles.
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        • AI-assisted IntelliSense for your team’s codebase

            Visual Studio IntelliCode uses machine learning to offer useful, contextually-rich code completion suggestions as you type, allowing you to learn APIs more quickly and code faster. Although IntelliCode’s base model was trained on over 3000 top open source C# GitHub repositories, it does not include all the custom types in your code base. To produce useful, high-fidelity, contextually-rich suggestions, the model needs to be tailored to unique types or domain-specific APIs that aren’t used in open source code. To make IntelliSense recommendations based on the wisdom of your team’s codebase, the model needs to train with your team’s code.

            Earlier this year, we extended our ML model training capabilities beyond our initial Github trained base model to enable you to personalize your IntelliCode completion suggestions by creating team models trained on your own code.

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          • Python for AI: A match made in heaven

              The artificial intelligence global market is expected to reach $190 billion by 2025. The bright future of this technology allures every entrepreneur. In fact, when we think about the technologies that are going to rule in the future, the one name that comes to our minds is ~ Artificial intelligence.

              AI along with its subsets like machine learning and deep learning is making such things possible which were unimaginable by humankind a few years back. It is affecting the realities and sometimes changing reality completely.



              The power of AI is well acknowledged by businesses as 84% of respondents in a study voted that they believe artificial intelligence will allow them to enjoy a competitive edge over competitors.

              Although entrepreneurs have an idea about AI but what most of them lack is proper implementation. The use of optimum programming tools for a complex technology like AI can create wonders for the world of business.

              Every custom web developer knows that a python is an apt tool for building AI-enabled -applications. The language has been used to create 126,424 websites so far. Since its launch in the late 1980s, python has seen remarkable growth not only in users but in applications too.

              Python is the favorite language for software developers to create applications that have artificial intelligence, machine learning, etc features embedded in them. But there are reasons behind everything.

              This blog is written with the intent to unveil these reasons. Let’s explore why python is extensively used in AI-enabled software development services.
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            • AdBlock has stolen the banner, but banners are not teeth — they will be back

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            • Use AI in marketing: Let’s get into the customers' mind

              “Instead of using technology to automate processes, think about using technology to enhance human interaction.” ~ Tony Zambito, Lead authority in Buyer Personas.

              Do you know ~ according to research, 93% of customers make purchase decisions based on visual appearance. Visual elements of your brand are the key deciding factors for a majority of potential customers.

              Your logo, website colors, chatbot texts, etc all have an impact on the psychology of people who come across them. Some colors or features attract them and some make them leave your website instantly.

              In this era, interactive features with the help of technologies like Artificial intelligence are enhancing such effects. AI has the power to add interactive elements to your presentation. This creates a connection between your company and its customers.
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            • ML.NET Model Builder Updates

                ML.NET is a cross-platform, machine learning framework for .NET developers, and Model Builder is the UI tooling in Visual Studio that uses Automated Machine Learning (AutoML) to easily allow you to train and consume custom ML.NET models. With ML.NET and Model Builder, you can create custom machine learning models for scenarios like sentiment analysis, price prediction, and more without any machine learning experience!

                ML.NET Model Builder


                This release of Model Builder comes with bug fixes and two exciting new features:

                • Image classification scenario – locally train image classification models with your own images
                • Try your model – make predictions on sample input data right in the UI

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              • SpaceFusion: Structuring the unstructured latent space for conversational AI

                  A palette makes it easy for painters to arrange and mix paints of different colors as they create art on the canvas before them. Having a similar tool that could allow AI to jointly learn from diverse data sources such as those for conversations, narratives, images, and knowledge could open doors for researchers and scientists to develop AI systems capable of more general intelligence.


                  A palette allows a painter to arrange and mix paints of different colors. SpaceFusion seeks to help AI scientists do similar things for different models trained on different datasets.
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                • The science behind how our brains work best, and how technology and our environment can help



                    You’re utterly focused. You’ve lost track of time. Nothing else in the world exists. You’re living in the moment.

                    While this might sound like meditation, it’s a description that can also be applied to the state of flow – the feeling of being so engaged by your work, that you lose yourself to it completely, while massively increasing your productivity in the process.

                    It’s the holy grail that we all strive for, whether it’s a hobby we’re passionate about, or a project at work. Achieving our best and utilising our maximum potential at all times, can however, be a struggle.
                    We had the pleasure of talking with Dr. Jack Lewis, a neuroscientist with a passion for exploring how our minds work, to see what motivates us to do our best work, and the important roles that workplace environments, culture, and technology can play.
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                  • Machine Learning for your flat hunt. Part 2

                      
                      Have you thought about the influence of the nearest metro to the price of your flat? 
                      What about several kindergartens around your apartment? Are you ready to plunge in the world of geo-spatial data?


                      The world provides so much information…
                      
                      

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                    • Keyword Tree: graph analysis for semantic extraction

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                        This post is a small abstract of full-scaled research focused on keyword recognition. Technique of semantics extraction was initially applied in field of social media research of depressive patterns. Here I focus on NLP and math aspects without psychological interpretation. It is clear that analysis of single word frequencies is not enough. Multiple random mixing of collection does not affect the relative frequency but destroys information totally — bag of words effect. We need more accurate approach for the mining of semantics attractors.

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                      • More than a game: Mastering Mahjong with AI and machine learning



                          Microsoft researchers have developed an artificial intelligence (AI) system that has taught itself the intricacies of Mahjong and can now match the skills of some of the world’s top players.

                          The complex board game of chance, bluff, and strategy was invented in China thousands of years ago and remains a passionate pastime for millions of Asians today, with many dedicated competitors playing online.

                          Computers have learned to play Chess and another ancient Chinese game, Go, amid much fanfare in the past. But scientists at Microsoft Research (MSR) Asia see their achievement as far more than just a case of technology mastering yet another game.

                          The researchers – who named their system Super Phoenix, or Suphx for short – developed a series of AI algorithmic breakthroughs to navigate the uncertain nature of Mahjong. With more work, these could potentially be applied in real situations to solve problems thrown up by unknown factors and random events.
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                        • What's new in ML.NET and Model Builder

                            We are excited to announce updates to Model Builder and improvements in ML.NET. You can learn more in the «What’s new in ML.NET?.» session at .NET Conf.

                            ML.NET is an open-source and cross-platform machine learning framework (Windows, Linux, macOS) for .NET developers.

                            ML.NET offers Model Builder (a simple UI tool) and CLI to make it super easy to build custom ML Models using AutoML.

                            Using ML.NET, developers can leverage their existing tools and skillsets to develop and infuse custom AI into their applications by creating custom machine learning models for common scenarios like Sentiment Analysis, Recommendation, Image Classification and more!..

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                          • A/B test is not enough

                              A/B test is not enough


                              There is a common opinion that A/B test is a universal, half-automatic tool that always helps to increase conversion, loyalty and UX. However misinterpretation of results or wrong sampling leads to the loss of loyal audience and decrease of margin. Why? A/B is based on the basic assumption that this sample is homogeneous and representative, scalability of results. In reality, the audience is heterogeneous — recall the “20/80” distribution for income. Heterogeneity means that sensitivity to A/B varies significantly within the sample.
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                            • How we made landmark recognition in Cloud Mail.ru, and why



                                With the advent of mobile phones with high-quality cameras, we started making more and more pictures and videos of bright and memorable moments in our lives. Many of us have photo archives that extend back over decades and comprise thousands of pictures which makes them increasingly difficult to navigate through. Just remember how long it took to find a picture of interest just a few years ago.

                                One of Mail.ru Cloud’s objectives is to provide the handiest means for accessing and searching your own photo and video archives. For this purpose, we at Mail.ru Computer Vision Team have created and implemented systems for smart image processing: search by object, by scene, by face, etc. Another spectacular technology is landmark recognition. Today, I am going to tell you how we made this a reality using Deep Learning.
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                              • Important Things to Know About Tensorflow 2.0



                                  Deep Learning applications have changed a lot of things. Some which give hope for a brighter future, and some which raise suspicions. However, for developers, the growth of deep learning applications has made them more perplexed about choosing the best among so many deep learning frameworks out there.

                                  TensorFlow is one of the deep learning frameworks that comes in mind. It is arguably the most popular deep learning framework out there. Nothing justifies the statement better than the fact that Tensorflow is used by the likes of Uber, Nvidia, Gmail among other big corporations for developing state-of-the-art deep learning applications.

                                  But right now, I am on a quest to find whether it indeed is the best deep learning framework. Or perhaps find what makes it the best out of all other frameworks it competes against.
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