Konstantin Kotelnik, an analyst at Innotech, ponders over making technical documentation easier to understand for developers and helping the clip-thought generation work effectively with large quantities of data. Read the article to find out about the potential emergence of a graphical IT-Esperanto and the standardisation of technical language.
Creating a product startup can be an exciting experience, but it can be a daunting one as well. On average, only 1 out of 10 startups is successful, according to the Global Startup Ecosystem Report. Therefore, to raise your prospects, there are quite a number of important considerations to make in advance.
Bearing in mind everything you need when launching a startup is a challenging task, so it’d be a sound idea to rely on some well-established methodology. That's why we were inspired by the Systems Engineering methodology, presented in such industry standards as ISO 15288 and CFR21. In this article, we’ll make a brief overview of this methodology and highlight how it can help entrepreneurs to encompass and structure the process of creating and developing a startup.
Conference participation is one of the most important practices for professional development. Hence, Innotech is actively sending out both its speakers and listeners for the biggest events. Senior Analyst Anastasia Kochetova shares her impressions from the Analyst Days/14 conference.
Most people fear of artificial intelligence (AI) for the unpredictability of its possible actions and impact [1], [2]. In regard to this technology concerns are voiced also by AI experts themselves - scientists, engineers, among whom are the foremost faces of their professions [3], [4], [5]. And you possibly share these concerns because it's like leaving a child alone at home with a loaded gun on the table - in 2021, AI was first used on the battlefield in completely autonomous way: with an independent determination of a target and a decision to defeat it without operator participation [6]. But let’s be honest, since humanity has taken in the opportunities this new tool could give us, there is already no way back – this is how the law of gengle works [7].
Imagine the feeling of a caveman observing our modern routine world: electricity, Internet, smartphones, robots... etc. In the next two hundred years in large part thankfully to AI humankind will undergo the number of transformations it has since the moment we have learned to control the fire [8]. The effect of this technology will surpass all our previous changes as a civilization. And even as a species, because our destiny is not to create AI, but to literally become it.
A similar article should have appeared earlier, about ten or fifthteen years ago, when Agile was just starting to be implemented in companies. How many mistakes, problems, conflicts could be avoided if managers immediately approached the issue correctly ...
But during this time, the experience of "implementing" Agile in different conditions, in different companies has accumulated, which should be generalized and widely disseminated.
In previous post I've described an example of kogito-based microservice on quarkus in native mode, containing one embedded pmml model with decision tree. While it can be successfully used for prototyping purposes, in the real life microservice might contain several prediction models. From the first view I've got an impression, that inclusion of several models should be a trivial extension of the prototype with one model. We were completely wrong in our assumption, that's the reason, why I've decided to write this post. Another reason, is absence of guides, in which 2 (or more models) are put inside DMN diagrams in kogito framework.
Very often we've heard that some frameworks fit together so good, that they are considered as "match made in heaven". In this article I would like to share our experience regarding integration of those frameworks.
This is the first in series of articles dedicated to detailed overview of processes and components of enterprise identity and access management (IAM). These articles are designed to help technical architects and system designers to better understand what is involved in complex business processes which drive identity management and provide detailed review of all critical components.
Solid understanding of identity management business processes before implementation of a new IAM system very often makes a difference between success and failure and it must be worked out and agreed to by all key participant before implementation can start.
This review does not address specific software configurations and settings required to support these processes and workflow, but any modern identity management software will be able to support them.
Diagram below documents workflow for an employee on-boarding which is one of the core identity management processes to support new employee who is joining the company. The flow is designed to be integrated into existing hiring process and provide logical and physical access to an employee by day one of the employment.
If you regularly use a static code analyzer, you can save time on guessing why the new code doesn't work as planned. Let's look at another interesting error — the function broke during refactoring, and no one noticed that. No one — except for PVS-Studio that can automatically scan the project and email the report to us.
Let's continue with a series of small notes illustrating the PVS-Studio's ability to quickly find new errors in the code. If the analyzer is regularly used, of course :). Today we have another bug in the Blender project.
This is a set of chapters for young engineers. We give practical advice and discuss goals, challenges and approaches used in modern software engineering.
Along with classical foundations this article contains original ideas of conceptualizing engineer's work with emphasis on bringing order to the situation and finding an insight. Engineering is approached as work in uncertainty with other people which requires special skills. Non-obvious complications regarding modern production in big companies are discussed.
This article is based on 15 years of experience in engineering and management in high-tech industries.
When you have an IT, which supports all aspects of your organization automated performance and you have a firm confidence that IT executes well, there is always a chance that something could go wrong in technology or in related IT processes. Depending on complexity of IT environment varieties of risks scenarios could arise. This article as a summary of different faithworthy sources aims to help you in getting high level understanding on what could go wrong and how you can predict it in a more conscious way.
My name is Maxim Tornov and I have been working in various IT areas for a long period of time. Since then, for over 14 years I am working in the area of IT/IS risk management with focus on in IT/IS audits, internal control implementation and assessment.
I am sure that at the present the topic of Information Technologies risk management became more vital. Organization’s efficiency in IT risk management directly affects the achievement of various organization’s goals, goals which have dependency on IT, those goals may include reliability and efficiency of business processes, the organization's compliance with regulatory requirements, the integrity of financial reporting, and many others.
I sincerely hope that this material will be useful to you and may give you some new ideas that you can contribute to the benefit of your personal development and the development of your organization's risk management culture.
When developers do make mistakes, it's often accidental or because the developers are in a hurry. These errors often make their way into small edits to the code. Let's review one of these cases: a developer fixes an error and introduces a new one simultaneously.
Demid Efremov and Ivan Kornienko.
Music box on Verilog HDL for Cyclone IV.
Tools to improve and control code quality can be a key success factor in a complex software project implementation. Static analyzers belong to such tools. Nowadays, you can find various static analyzers: from free open-source to cross-functional commercial solutions. On the one hand, it's great – you can choose from many options. On the other hand – you have to perform advanced research to find the right tool for your team.
It’s a harsh reality that any investment or security that you have is subject to systematic risk, that’s just the nature of the financial world, with mispricing — causing a divergence between the market price of a security and the fundamental value of that security — a guilty party quite often. This is brought about by the world we now live in, information-heavy, with masses of unstructured data sets and with an infinite number of possible outcomes.
Luckily, there are now methods in place that can combat some of these difficulties and, with any luck, bring the investor a healthy return on their investment.
And it comes in the form of Al-driven, quantum-inspired solutions fuelled by state-of-the-art processors that are able to leverage the unique attributes of quantum physics to solve some of the most, up to now, unsolvable problems, with a handful of startups at the forefront of it.
These include Adaptive Finance Technologies, QuantFi and Zapata Computing which, to a greater or lesser degree, have their own unique approach to investment strategies in the global markets in securities pricing, portfolio optimization, equities, derivatives and the like.
We’ll look now, just at a handful of these whose data readers will be able to find on TQD’s very own data platform, The Quantum Insider (TQI), starting off with the three already mentioned before moving on:
Money theft is one of the most important risks for any organization, regardless of its scope of activity. According to our data, 42% of cyberattacks on companies are committed to obtain direct financial benefits. You can detect an attack at various stages—from network penetration to the moment when attackers start withdrawing money. In this article, we will show how to detect an attack at each of its stages and minimize the risk, as well as analyze two common scenarios of such attacks: money theft manually using remote control programs and using special malware—a banking trojan.
