Search Results

Technologies evolve quickly, allowing better automation, enhanced quality of services, and faster responses to business inquiries. With the ongoing digital transformation, many software systems of large enterprises have become outdated. The usage of these systems in the evolving environment of today raises more questions than answers. Specialists who have both skills and experience with legacy software are retiring, but the legacy hardware and software are still in use in mission-critical EU and US enterprises. On the one scale, there are so many limitations and risks for a big company if it decides to stick to the legacy system tendency. However, on the other scale, the migration is slower than new technologies appear. So, what is the future of legacy software? And what should enterprises really do in this situation: migrate or not? Will systems like IBM AS-400 and Cobol-based ones live forever? Let’s find unambiguous answers with Sencury. Legacy Migration To start with, let’s define a legacy system. Therefore, it is an outdated class of technology, an old software application that is still in use. The reason for its usage is quite simple: it cannot be easily substituted. According to TechTarget, the following systems are considered to be outdated: older systems and versions systems and software with severe security vulnerabilities technology that is not cost-effective for organizations to run and maintain technology that fails to adequately meet the organization's current needs or hinders growth systems and software with no support from the vendor homegrown systems that run on programming languages few developers still know Legacy migration is the process of changing obsolete software (even hardware) for a newer and better technological representative. And it can be done in several ways. Let’s explore them in detail. Migration Approaches There are three main migration approaches: Refactoring/re-architecting The process of legacy system modernization through the means of altering the system’s code to improve capability without affecting external functionality. Replatforming Moving an existing system to the new platform with little altering the code (if it is possible to do so). Rebuilding/Replacing If the system cannot be modernized with the help of code adjustment, the only way out is to replace this system or rebuild using newer technology. Despite the possibility of migration, many large businesses still have old software they work with and are not likely to transition to the newer version in a short time. Some companies even possess systems written in COBOL - a computer language since 1959. The specialists are retiring but the legacy hardware and software is still in use in mission-critical EU and US enterprises (e.g., big banks, insurances, big travel industry providers, etc.) Why does it happen? There are a number of reasons. The system functions perfectly The uncertainty of the new system Service continuity to cause no disruption Challenges updating systems Insufficient funding Lack of maintenance specialists Potential risks That’s why over 2/3 of businesses still use legacy apps for core business operations. In addition, more than 60% of them rely on legacy software to power customer-facing applications, according to Forbes survey. Will systems like IBM AS-400 and Cobol-based systems live forever? AS400 (Application System/400) is a computer system that is highly secure, stable, reliable, and scalable. It was released in 1988 but is very popular even today due to its wide range of functions. AS400 is constantly being developed and updated, so it is highly compatible to work using outdated technologies without modification. Now this system is called IBM Power Systems. A lot of companies around the world still use the AS400 system, so it might live for as long as it is needed. The main reasons for AS400 usage are: High performance and reliability A wide range of options available Reliable, secure, integrated database Usage of modern technologies Availability of a cloud environment With scalability, security, reliability, modernity and compatibility, the IBM AS-400 system is sure to be popular among businesses. COBOL COBOL is a short version, which stands for Common Business-Oriented Language. It’s an enterprise-level programming language. Despite being old, it is still used in various business and financial applications as well as in many industries (e.g., banking, insurance, and others). Unlike most other programming languages, COBOL is considered easy to understand because it uses familiar words. Before COBOL, each organization had their own programming language. However, this required too much effort and skills. SO, when COBOL entered the market, it became greatly used due to its portability and ease of use. COBOL’s main usage is for government entities. But other industries also use it. Lots of businesses rely on COBOL for their daily transactions. That’s why it is a priority to find the right programmers with good skills and expertise. If COBOL programmers are back in demand, it means that the language is still functional and is going to be used in the future. Is Migration Too Slow? The fact is that sooner or later successful businesses grow, and it means drastic changes in the workload of organizations. To cover all the relevant changes, if the organization’s operational system is becoming old, it is better to migrate to a new technology. However, the migration is slower than new technologies appear. For instance, the core-banking system Avaloq (based on Oracle) or other core-banking systems (e.g., Java + Oracle-based) might be outdated before the migration ends. What’s then? A set of considerations prior to migration and in the middle of it might help you address this issue. For instance, Continuous Evaluation and Planning It's important for organizations to be aware of the current state of their operational systems. So, assessment of technologies with regards to them meeting your business needs should be done continuously. Incremental Migration To decrease the risk of technology becoming outdated during a migration, consider breaking down your process of migration into manageable phases. This way, you will update your enterprise technology gradually. Flexibility in Technology Selection Choose new technology for migration only with a proven track record of adaptability. Select technologies that will stay with us for a while with possible updates over those that might easily become obsolete. API and Integration Focus Building robust APIs (Application Programming Interfaces) and integration capabilities is crucial. This allows you to connect and integrate new technologies as they become available without completely replacing the core system. This way, you can extend the life of your existing system. Scalability and Futureproofing Invest in technology that is inherently scalable and designed for futureproofing. This means the system can adapt and grow with your business needs, reducing the urgency of migration. Stay Informed Keep up with industry trends and emerging technologies. By monitoring tech trends, you can predict when technology is likely to become outdated. Data Migration Strategies When migrating, focus on preserving and migrating your data effectively. Historical data is still valuable. So, ensure that the new system can integrate and migrate this data. Contingency Planning Have contingency plans in place in case your migration takes longer than expected. Maybe, there will be a need for short-term fixes to keep the existing system functional until the migration is complete. Sencury is your #1 Legacy Migration Provider Sencury is a software development and consulting company with years of relevant experience in the competitive market. We lead the way with our business-centric approach and dedicated team. Therefore, Sencury’s expert consults with a scientific level of complexity. Choose us as your #1 migration consulting vendor and forget about migration risks. Contact us and make sure your legacy software migration will be as seamless as possible. Become a step ahead of your competitors. Sencury offers quality in everything we do!

With the introduction of ChatGPT, there was confusion about it being an AGI. Artificial general intelligence (AGI) is rather a hypothetical AI, which performs intellectual tasks similar to humans. It encompasses a wide range of cognitive abilities. Large Language Models (LLMs) belong to AI as well. However, they are trained on vast amounts of text data to generate human-like responses to prompts. So, there are two opposite thoughts on whether LLMs can reach AGI and why they can’t do that. Based on this notion, is ChatGPT an AGI? Or rather an LLM? Sencury’s experts are here to make you more tech-savvy! AGI Artificial General Intelligence or AGI is the machine with the highest form of intellect, capable of doing everything humans do. Humanity hasn’t reached AGI yet. But we are heading towards it, with more than half done to make it happen. The most complex thing for AGI is sentiment analysis. To be able to differentiate, when people use sarcasm, irony, or other emotions in texts, AGI needs to be pre-trained on human emotions and lingual expressions. Nowadays, AI performs both basic and advanced sentiment analysis. If the basic analysis only requires the determination of polarity, the advanced type can identify emotional varieties (sarcasm, joy, sadness, etc.). The first one is used for social media monitoring, customer feedback analysis, and brand reputation management. The latter, in turn, is used in market research, opinion mining, and customer sentiment analysis. LLM Large Language Model or LLM for short is a smart computer program that generates language the way humans do. However, it can generate outputs only based on pre-trained input data. Our experts have enclosed more information on LLMs in our recent blog post “Does AI Think?” There is also a drawback, LLMs can be toxic as the ones inputting information are humans. And, the latter can be toxic, biased, discriminatory, inaccurate, based on location and cultural predisposition. Can LLM reach AGI? It Can, But According to recent investigations, LLMs can reach AGI. The path is promising as LLMs become better and more accurate. However, their limitations still restrict LLMs from the true understanding of human cognition, conscious thinking, and self-awareness. This is one of the biggest drawbacks existing so far. What kind of limitations ground LLMs? In the wrong hands, LLMs can be used to: generate text to mislead or deceive people, spread false information, manipulate public opinion, or incite violence create deep fakes that are very realistic and damage someone's reputation or spread misinformation trigger job losses and economic disruption up to a concentration of power by a few companies controlling LLMs LLMs are trained on data from the real world. It is unspeakably biased. So far, biases have not been fully addressed, and, thus, slowly embedded in the LLMs. Also, these complex systems are difficult to understand and secure. This makes them vulnerable to attacks by malicious programs. So, even the assumption that LLMs are the next step to AGI is incomprehensible. The most crucial fact about LLMs is that they cannot retain short-term and long-term memories, which is one of the essential human learning characteristics. So, the approach LLMs use here is autoregressive. Humans do not learn like that, it is impossible. The road to AGI may look the following way: LLM developers should create larger models that are too complex in their parameters and supported by significant computational resources. Another drawback arising is the environmental unfriendliness of such models (black-box models) with a low ability to be scrutinized. Or Can It Not? The other point of view says LLMs are not heading towards AGI. According to Medical Informatician and Translational AI Specialist, Sandeep Reddy, “...the Large Language Models (LLMs), are no closer to Artificial General Intelligence (AGI) than we are closer to humans settling on Mars.” Reddy’s point of view claims that we should understand the process of human learning first. The way humans learn is the basis for AI learning capabilities. The human brain is a complex functional instrument that has various simultaneous processes carried out at once. LLMs carry out only those processes they were trained to do in the first place. LLMs work by breaking data into smaller tokens, which are then converted into numerical representations. The data is tokenized within the model and the latter uses complex mathematical functions and algorithms to analyze and understand the relationships between tokens. That’s how models are being trained. With the input, the model is fed with large amounts of data and adjusts its internal parameters until it can accurately predict the next token in a sequence. Models presented with new data use those trained parameters to generate outputs by predicting the most likely sequence of tokens following the input. Overall, LLMs use a combination of statistical analysis, machine learning, and natural language processing techniques to process data and generate outputs that mimic human language. ChatGPT4 perfectly illustrates this process in its architecture. AGI, in its turn, is a system performing human cognitions at 100%. Language is essential to human intelligence. It is also essential for LLMs. To define AGI the best, you should understand that language models are proficient at language tasks, but they are unable to perform tasks outside their training data. LLMs cannot generalize, they lack common sense, cannot interact with the physical world, etc. AGI should be capable of doing all of these things. Is ChatGPT an AGI or LLM? ChatGPT stands for the Generative Pre-Trained (GPT) model that can provide answers to different requests. However, these answers cannot be taken as the general truth as they are based on the model’s training dataset. As you probably know, if this dataset has bias and other disinformation text, ChatGPT will “hallucinate” and “confabulate”, etc. Such behaviors can be partially fixed by setting an appropriate context and explicit rules to “ground” the LLM to a specific usage/context. Therefore, LLMs are large language models and ChatGPT is a level upper – a conversational model without complex reasoning to extract the “truth” out of ambiguous data. It is also not an AGI, as it still cannot perform reasoning as humans do. Sencury on LLMs and AGI We are known for delivering cutting-edge technology solutions that meet unique business needs. Whether you need ready open-source LLMs, commercial ones or paid APIs (like OpenAI), Sencury can provide high-quality solutions that cater to your specific requirements. Our experienced team has worked on numerous projects in the field of AGI and LLM, and we stay up-to-date with the latest technologies and trends. At Sencury, we understand that every business is different, and our tailored approach ensures that our clients receive bespoke, scalable and cost-effective solutions. We work closely with our clients to understand their needs and requirements. We believe that our expertise in AGI and LLM can help your business achieve great things. If you have any questions or would like to schedule a consultation, please do not hesitate to reach out.

There are many ways businesses can approach DevOps automation. Recently, it has become a goal to reduce human help in favor of automated tooling. DevOps tools ease up feedback loops between operations and development teams. With smooth communication, teams can build applications faster by giving out iterative updates. Yet, the way organizations approach automation depends on their internal needs. They can choose between the two most used options. It is a CLI approach to writing custom scripts. Or a no-code/low-code automation tool that speeds up workflow development. Sencury would like to speak about these two approaches to make them clearer for the readers. So, let’s proceed! What’s Cloud DevOps? DevOps belongs to a software engineering practice intertwined with cloud computing. In DevOps, software engineers collaborate with different teams, i.e., IT operations. DevOps is the top software development approach on a global level. You can read more about the practice itself here: DevOps and Agile Culture. Cloud DevOps works through a web interface. Therefore, any interventions made require using DevOps tools that do not need coding. By making configurations (or pressing buttons) DevOps experts carry out an easy task. The paradox lies in the fact that the job is relatively easy and highly paid, but there is a lack of skilled specialists to do it. In the DevOps world, your knowledge is equal to the years of your experience. The more the better. Read more about Cloud-Specific DevOps here. What is Scripting? The process of automation for DevOps system administrators begins with command-line tools. These are the perfect means of automation and DevOps engineers have to understand them with flying colors. For instance, CLI tools are free of charge, so you can write scripts for tasks right away. If your organization pursues the goal of full-stack automation, you are most probably going to use a command line 100% of the time. Also, scripting presupposes using DevOps programming scripting languages. These are mostly domain specific languages (DSL). For example, query languages (SQL, XPath) template languages (Django, Smarty) Shell scripts command line web browsers (Twill) data storage and exchange languages (XM, YAML), document languages (LaTex, HTML), infrastructure orchestration languages (Terraform). Reasons to use Scripts Write a script to save time and avoid manual work (daily activities) Write a script to do all the work, e.g., install pre-requisite and to build the code with user input to enable/disable some features Write a script to stop or start multiple applications together Use scripts to observe a large database of files, analyze and find some patterns there What is Low-Code and No-Code DevOps? Low-code/no-code is the DevOps method of app design and development. It is also called Rapid Application Development (RAD). Here, the written code is not required as well as developers. Yet, there is a need to use drag-and-drop intuitive tools. No-code/low-code development tools simplify the SDLC. Thus, they use a visual interface. This way, development becomes easier and faster. Mainly, due to pre-built integrations and configurable application components. With the tools that allow you not to code the focus shifts to the logical steps of a workflow. Experienced developers prefer this method as it's easier to link workflow steps together. Low-code tools still need some technical knowledge. Thus, skilled developers writing code can use them to speed up development. Low-code tools can give you the most support and acceleration in the process. No-code tools allow non-tech people to build workflows on their own. This way they can test applications for business purposes faster. These tools break the automation barrier for non-coders. Also, they can make anyone the automation expert within an organization. Low-Code and No-Code Tools Applicability Let’s compare Low-Code and No-Code development with DevOps tools. By 2024, 65% of applications will use some form of low-code or no-code tools – Gartner predicts. Why You Should Choose Low-Code/No-Code Development? Complex Technology Stack Organizations tend to scale, and, thus, their stack becomes more complex. DevOps should be able to adapt to these prompt changes. Its tools help companies with scaling in an efficient and iterative way. Also, across web, mobile, email, and chat platforms. DevOps tools add strength and agility to provide continuous integration. With continuity at stake, you can deliver at any scale. Adjust to Business Needs With innovations, businesses experience new technology stacks. So, it becomes a great need to start migrations or accept tool overlaps. No-code/low-code development platforms can connect many tools together. This way, you can speed up migrations and track integrations quickly. API Ecosystem New tools and APIs are constantly entering the market. No-code workflow tools allow users to plug in new APIs to their automation strategy. APIs are becoming a part of organizational strategy. Enterprises think about API integration as a critical part of their business strategy. Usage, management, and economy due to APIs will reduce clutter. Also, it will give time to complete automated tasks. Security and Service NTraditional security can barely adapt to dynamic multi-cloud infrastructure configurations. Automated runtime security is critical to DevSecOps and problem management. With no-code/low-code tools you gain the right support. Resource-Limited DevOps Teams DevOps and SecOps teams are understaffed these days. To be a skilled professional you have to have years of experience. Therefore, there’s a skill shortage and critical security requirements to maintain. Here, low-code and no-code tools can be of great help as well. With their help, small teams can speed up their automation coverage. Sencury on Scripting, Low-code and No-code DevOps Our team uses DevOps to build, test and deliver software faster and more reliably. Sencury makes sure our clients receive only continuous integration, continuous delivery, and continuous deployment. Our primary goal is to build a culture of shared responsibility, transparency, and faster feedback. DevOps engineers from Sencury implement DevOps practices and approaches to make your business software competitive in the market.

Today, Artificial General Intelligence (AGI) is a theoretical concept that is still being developed. Artificial intelligence (AI), in its turn, has a narrow practice. AGI is believed to be a substitute for human thinking. It should be creative, have sensory perception, and fine motor skills, possess natural language understanding (NLP), and provide great navigation. However, there is a lot of work and many nuances that have to be met to make AGI become a reality. Just now, according to Dr. Alan Thompson, humanity has developed AGI at 55%.. One of the crucial features that AGI has to develop is sentiment analysis. Human decision-making is irrational and mostly based on the emotional aspects of every situation. Therefore, AGI has to be able to read these emotions correctly and think as a normal human, correspondingly. To understand what is hidden under the sentiment analysis, let’s describe it in detail. Learn more about AGI’s sentiment analysis with Sencury! Sentiment Analysis Defined Sentiment analysis is a sub-field of Natural Language Processing (NLP). Also, it is a tool and a part of such an AI subset called machine learning (ML). With the help of NLP and ML, it is possible to analyze texts for polarity (positive, neutral, negative). The more you train the language model with emotional texts, the better it learns how to detect sentiment within these texts. Further, this model will require no human input. Simply speaking, NLP and ML give computers the possibility to learn new tasks without being programmed beforehand. Sentiment analysis models can be trained to read emotions beyond definitions, that are based on the context. To add, they will even find sarcasm there, or misapplied words. Everything depends on the input and the machine’s ability to learn. How does sentiment analysis work The sentiment analysis process has three distinctive steps: data collection feature extraction classification First, you collect data from various sources (e.g., social media, online reviews, news articles). Then this data undergoes feature extraction. You can automate this step with the help of Natural Language Processing (NLP) techniques. Third, the data is classified and used to generate insights about the sentiment of a given text. Despite helping improve public opinion, sentiment analysis still has some accuracy issues. For example, subjectivity of language irony or sarcasm bias by the demographic groups Therefore, the analysis itself will be relevant to a certain group of people, it will be based on their location, way to express sentiment through irony or sarcasm, and specific platform of use. Types of Sentiment Analysis Fine-Grained It is the basic type of sentiment analysis. The current type of sentiment analysis determines the accuracy of polarity. There are the following polarity categories: Extremely positive Positive Neutral Negative Very negative It is best to apply fine-grained sentiment analysis to reviews and ratings. Here, it will be the most helpful. Aspect-Oriented It also belongs to the basic type of sentiment analysis. Aspect-focused analysis determines the overall polarity of your customer evaluations on a deeper level. It helps determine the very components that people are discussing. Emotion Recognition It is an advanced sentiment analysis. This type of analysis is self-explanatory. People possess all kinds of emotions: anger, happiness, sorrow, anxiety, frustration, panic, etc. To detect all of these emotions, systems should be based on lexicons of specific words denoting concrete emotions. To make this analysis easier and more effective, it is best to use the ML methods. Every person expresses emotions differently and just a lexicon might not be enough to recognize what the text is about in an emotional context. Intent Evaluation It is also an advanced type of sentiment analysis. Consumer intent should be accurately detected from the start. This may be beneficial for any business in terms of cost, time, and effort savings. Some consumers have no intention to buy products and with a careful intent analysis, this barrier can be eliminated. Intent analysis aims to determine what the customer’s purpose is to purchase products, and whether the customer has plans to purchase something while browsing. Sentiment Analysis and AGI AGI is the new AI technology, a certain development of machines that will be capable of reasoning and learning equally to humans. Essentially, this type of intelligence can understand abstract concepts and perform tasks with high-level thinking. AGI is prone to surpass humans in some areas. At least, that is the purpose of its development and that is what scientists intend to develop. With the help of sentiment analysis, AGI will be able to: Provide audience insights Measure marketing campaign ROIs Drive proactive business solutions Augment great PR practices Support customer services However, the biggest challenge of sentiment analysis is to overcome human language subjectivity and have a large dataset that covers sentiment expressions on various topics. This way, AI will catch the tone of the text and differentiate it from bias, sarcasm, irony, demographic phenomena, etc. For AGI it appears to be crucial in context understanding. The other way, it will not learn how to think like humans or surpass humanity. So far, models have to be pre-trained with specific datasets of sarcasm and irony. And other emotions, of course. Nowadays, AI performs both basic and advanced sentiment analysis. If the basic analysis only requires the determination of polarity, the advanced type can identify joy, anger, sadness, fear, sarcasm, irony, and humor. The first one is used for social media monitoring, customer feedback analysis, and brand reputation management. The latter, in turn, is used in market research, opinion mining, and customer sentiment analysis. Sencury Offers Sentiment Analysis Services With our extra knowledge and competency in AI and ML consulting as well as engineering solutions, Sencury also offers sentiment analysis. With its help any business will be able to Seamlessly convert raw text data into AI training data Improve accuracy of model and team outputs Enhance end-user experiences Ensure security of data Global workforce of industry-specific SMEs Save valuable time and money Consider Sencury as your top AI and ML expert to turn to! Contact us now and let’s find your perfect business solution together!

With AI's introduction, scientists and users started questioning its ethical intentions. For example, AI turned out to be biased because it could not be neutral. In processing big data, AI search engines prioritize information based on the number of clicks, user preferences, and location. If you had to ask where the output information came from – humanity generated it. So, AI-based decisions were prone to be inaccurate, discriminatory, and certainly embedded with bias. Then, AI tools still lack transparency. Artificial Intelligence cannot be intelligible to humans all the time and it cannot think the way humans do. Also, due to these facts, there are concerns for fairness and risk to fundamental values set by humanity. In addition, AI has safety issues. According to rules and regulations, e.g., GDPR, there should be informational privacy of the data that belongs to users. Thus, people might feel that LLMs are not reliable and can disclose something important and spread it to the masses. Therefore, let's discover whether AI can meet the three basic principles: be Helpful, Honest, and Harmless. Is there a possibility for Artificial General Intelligence (AGI)?  Sencury is up for this challenge! LLM Toxicity through Bias Artificial Intelligence is full of ethical dilemmas. Large Language Models are being trained on human-specific data. Therefore, this data can be rather toxic. At Princeton University, language models are defined as text-powered tools that perfectly capture statistical patterns. They produce a certain effect on people only applied to downstream tasks. Here, there is a broader social context. Let’s talk more about it. What is bias? Bias can occur in the form of performance disparities. The accuracy of the system might be different for various demographic groups. Therefore, when the system predictions are full of associations between target concepts and demographic groups, the effect might lead to bias on demographic groups. If this bias is input as training data into a large language model, it receives new powerful capabilities. With the output achieved, there is an increased adoption of newly received information. And everything new based on bias leads to increased harm. To know more about LLMs and how they work, read our latest article - “Does AI Think?” What is Toxicity? The toxicity of a large language model is based on texts that are generated in a rude, disrespectful, or unreasonable manner. In neural LLMʼs, this phenomenon is known as neural toxic degeneration. In a toxic conversation, one party may discontinue due to the offensive context. Especially, when the audience is young and vulnerable. Also, when the output was unintended but has led to obvious harm. Toxicity of generated information may not intend to lead to disinformation, but it surely leads to such a consequence. Therefore, the data obtained produces harm unintentionally and misleads people or this data has an intentional purpose to mislead crowds. What is Helpfulness, Honesty and Harmlessness of AI? Helpfulness Helpful AI refers to the systems’ ability to provide users with assistance, so that they can achieve their goals and solve problems. AI is being helpful via its subsets such as natural language processing (NLP), computer vision, and machine learning (ML). To be helpful, an AI system should be personalized, i.e., adapt its behavior to the individual needs and preferences of every user. Also, it has to provide contextually relevant information or suggestions, to enhance better decision-making of humans. Honesty Honesty by AI lies in the accuracy of capabilities, limitations, and biases representation. The system has to be truthful. Therefore, users will trust this system up to the fact that they will not expect any harm from it. Especially, when there's decision-making at sake. To promote honesty requires being transparent. Perhaps, through explanations of how AI systems work, make decisions, answer questions, their algorithms, etc. Harmlessness An AI system, by all means, has to be of no harm to humans or the environment. Therefore, harmlessness presupposes no discriminatory decisions against certain groups of people, no physical harm, and no destructive decisions concerning the environment. So, AI systems have to be continuously tested and evaluated to exclude bias and the impact on marginalized groups. What's more, AI systems must comply with relevant laws, and regulations, and be responsible for the consequences of their actions. So, both bias and toxicity contradict the 3 Hs of AI – Helpful, Honest and Harmless. The situation becomes worse with LLMs increasing in size. The bigger the LLM, the greater data it operates on. To avoid possible non-compliance with these three principles, LLMs are being fine-tuned via the reward model. Fine-Tuning Fine-tuning of AI systems allows organizations to adapt existing AI models to unique use cases. This way, LLMs perform better, show better results, and make decisions faster. According to Open AI, the creators of ChatGPT, fine-tuning through API has: Higher quality results than prompting Ability to train on more examples than a prompt can fit Token savings via shorter prompts Lower latency requests How can an AI model be fine-tuned? One of the options to fine-tune an AI model is to use reinforcement learning with human feedback. Reinforcement learning involves finding the optimal action to maximize accumulated rewards in the current state of the language model. When training a language model, this model will be the agent, the input prompt will be the current state, and human feedback - the reward. To avoid losing too many costs evaluating the newly generated texts directly through users, a reward model that has learned human preferences to streamline the process will be the best solution. 3 Hs based AI - for Humans or Instead of Humans (AGI)? An artificial general intelligence (AGI) is a hypothetical type of intelligence close to the one every human possesses. It is thought that AGI can accomplish any intellectual task that humans (animals) can perform. AGI is autonomous and can surpass human capabilities in economically valuable tasks. Creating AGI is a primary goal of some artificial intelligence research companies. To achieve AGI, humanity should comply with the HHHs of AI and create systems that are harmless, honest and helpful. In general, people find AI such as ChatGPT helpful. Concerning honesty, LLMs have difficulty understanding the concept of truth. And to be harmless, AI has to be safe and unbiased. With the introduction of ChatGPT4 there were safety issues. And bias is still present as it is hard to get rid of it at once. Therefore, potentially, AGI can be achieved. Now there is about 55% progress with AGI according to the Thompson scale. How quickly it will be achieved is an open question. But you can always view this scale live here and monitor the situation in general. With AGI on its way, the other questions arise. Is there going to be a post-intellectual-labor utopia? Will people need to work intellectually or physically then? Artificial intelligence aims to automate all the tasks humans carry out. Moreover, with AGI there is a thought that humans should be divided from economic productivity and focus more on their well-being. This tendency is also called Post-Labor Economics. According to Deloitte, Post-labor economics claims that human identity and worth should not depend on jobs. To make it possible, there is a need to: Accelerate automation Provide universal basic goods and services Redistribute technological wealth Democratize technology Set price externalities Enhance democracy Adopt holistic metrics In addition, society, government, and businesses should be prepared to take this leap. Some believe that AGI will make people lose their jobs more, the others say that AI might create new jobs with new responsibilities and requirements. All in all, this will require rebuilding and redefining traditional sectors. Sencury on 3 Hs of AI Sencury’s technology consulting and AI/ML services is the safest and the most reliable way to comply with 3 Hs principle of AI in solutions of your organization!

Embedded development is one of the core fields of technological progress. It is projected that the market size of embedded computing around the world will reach more than $85 million by 2030. As of now, embedded systems play a crucial role in differentindustries. Also, they have become widespread globally. Concerning legacy technologies, these are outdated embedded systems in need of modernization. The only way these systems can be updated is through updatingthe embedded development technological stack.However, the question is whether embedded development is essential only for legacy systems and what’s there to expect shortlySencury is here to find an answer! What is Embedded Development? Embedded development is the process of building software and firmware that is backgrounded on embedded systems. The latter are computer systems that carry out device-specific tasks. These embedded systems focus on a certain function, operate in real-time, and have limited resources. Embedded development revolves around creating software customized for the target embedded hardware. It includes both programming and design. The core features of embedded development are: Hardware Understanding The knowledge of the hardware you are working with is as essential as the knowledge of microcontrollers, peripherals, interfaces, and hardware limitations. Programming Languages Embedded development requires C, C++, and related language programming. With C the code interacts directly with hardware. This code is low-level. Real-time perspectives Embedded systems need strict timely responses to external events and embedded developers have to be aware of interrupt handling, task scheduling, and timing analysis to provide real-time responses. Optimization Processing power, memory, and energy might be the resources that embedded systems have a limit for. Therefore, it is up to embedded software engineers to optimize the code and ensure the limits of these resources are met as well as the resources themselves are utilized to the possible maximum. Testing and Debugging Testing your code is one of the essentials during embedded development. Hence, software engineers might need specific toolsets to carry out this mission (debuggers, emulators, and hardware probes). Tools help find and fix problems existing in the embedded system. Integration Embedded systems usually belong to the larger operating systems. That’s why they need to be configured and smoothly integrated into these systems as any minor event can produce unneeded crashes to the whole system. Here, proper communication and interoperability are the core pillars of OS workflow continuity. In 2021, the European Automotive Infotainment market was valued at $5,347.35 million. Today, it is projected to grow to $7,668.59 million, at a CAGR of 6.07%. Embedded Systems Work and Architecture Some say an embedded system is like a mini circuit board including a processor, supply of power, memory, and ports connected to the other components of the larger system for communication purposes. This processor can be a microprocessor or microcontroller. Also, there is a System on Chips (SoCs) that is often used in embedded systems of high volume. SoCs work positively in real-time operating environments due to being fast and adjustable to basic variations. There are five most commonly used architecture types of embedded systems. These are: Simple control loop Cooperative multitasking Interrupt-controlled system Preemptive multitasking or multi-threading Microkernels and exokernels Applications of Embedded Development Some industries and enterprises require an operating system to support their critical workflow. This means that a system is developed and customized for an enterprise. However, technologies change once in several years and the internal system becomes obsolete when the technological timeframe runs out. Businesses that potentially need embedded development include: Consumer Electronics (smartphones, tablets, smart home devices, wearables, and gaming consoles) Automotive (engine control units (ECUs), infotainment systems, advanced driver-assistance systems (ADAS), telematics, and vehicle networking) Industrial Automation (manufacturing, robotics, process control, and monitoring systems) Medical Devices (patient monitoring systems, imaging devices, implantable devices, and diagnostic equipment) Aerospace and Defense (avionics systems, flight control systems, navigation systems, communication systems, and military equipment) Internet of Things (IoT) (smart devices, sensors, gateways, and IoT platforms) Energy and Utilities (smart grid systems, energy management systems, power monitoring devices, and energy consumption optimizers) Telecommunications (network equipment, communication protocols, embedded software for routers, switches, and telecommunication infrastructure) What is Legacy Technology? Legacy technology is the term denoting a whole scope of technologies that became outdated due to the introduction of newer alternatives. In the past, legacy technology was widely used but not today. These technologies might include hardware, software, protocols, programming languages, or even entire systems. To be sure a technology has become a legacy one, consider its: age limited technical support compatibility issues maintenance challenges loss of important data low performance outdatedness higher costs of maintenance Notwithstanding all of these features, legacy technology is still being used. The main reasons for this exploitation are the cost, stability, reliability, and compatibility with existing systems. That’s why organizations choose to use legacy technologies unless they are replaced by compelling alternatives, or unless they will come to an end of their lifecycle. Is Embedded Development only for Legacy Systems? To answer the question of whether embedded development focuses only on legacy technologies, the answer would be rather no. The main reason is the extreme development of embedded systems that are more likely to meet the demands of newer technologies. Therefore, embedded systems adapt to the realities of today. Let’s shed a little more light on it and get to the details. Embedded development belongs to the development of non-common electronic boards and computer systems (non-regular CPUs). Here, it is also important to mention the GPU chips as they also belong to non-CPU development. GP GPU is a general-purpose graphics processing unit. This unit performs calculations that are non-specialized. These calculations are done by the central processing unit (CPU). Therefore, the main task of GPU is graphics rendering. GP GPU is now used to carry out tasks that were previously performed by powerful CPUs. These were physics calculations, encryption, decryption, scientific computations, and cryptocurrency generation as it was with Bitcoin. Graphic cards are built to produce massive parallelism and carry out many parallel tasks at once. Even the best CPU can’t do it. These are the shader cores that can render multiple pixels as well as process multiple streams with data simultaneously. Shader cores have been of great interest and programming languages have been developed to ease GPU computations. Nvidia, which is a giant in GPU development, approach GP GPU with personal APIs, e.g., CUDA, and OpenCL. As we have triggered GPU-relevant programming languages, the question is whether only C and C++ can be used or the ones that are up to date. Well, it is both, but mostly C and C++ are used. These languages have a history of being used for GPU programming and offer low-level control and efficient access to GPU resources. That’s why C and C++ are chosen for performance-critical applications. Other programming languages appear based on CUDA and OpenCL frameworks. The Future of Embedded Systems: What’s Next? Based on the technologies used for embedded systems it is quite unknown what the future holds for us in that direction. However, we are at the point of extreme business automation. Therefore, IoT, data analytics, and AI technologies are being implemented almost everywhere. The ability of embedded systems is limited to producing raw data. So, with the help of AI, IoT, and data analytics, embedded systems can provide valuable insights for future innovations. Sencury, Embedded Development, and Legacy Systems Sencury is a software development provider with many years of experience and key tech knowledge. Therefore, our experts have great skills in embedded system development. Sencury’s team can provide you with numerous software development and consulting services, including embedded solutions. Sencury can help you with: Embedded system design (both hardware and software), e.g., based on AUTOSAR stack Technological discovery to select a proper stack. Particularly, the right approach to parallel computing: MPI, GP GPU, or FPGA types Software development for embedded and GP GPU systems Testing and test automation of embedded systems Release management and configuration control of release embedded software (including OTA updates) If you need an embedded solution, contact us right away, and let’s work on it together!

Currently, we are experiencing significant advancements in the field of in-vehicle infotainment technology. Who would have thought about a Wi-Fi connection or watching YouTube on the go at least a decade ago? Now, people are increasingly interested in the combination of information and entertainment, leading to a demand for innovative vehicles that are connected to online and streaming services. However, there is not only high demand for infotainment but also frustration of users with the innovative infotainment systems in their cars. Why is there both excitement and confusion about infotainment systems? Are the trends and risks crucial for the German auto industry and infotainment technology? Sencury decided to shed some light on the matter. Read more to be aware of the latest industry challenges. What is Infotainment? Infotainment is instant access to communication and information applications located in your vehicle. These apps usually include entertainment, information, communication, leisure, lifestyle, fashion, connectivity, and gaming activities. Two years ago, the size of the global automotive infotainment market reached around $41.12 billion. If the tendency is to stay, it is projected that the market size will increase at a CAGR of 9.44% between 2022 and 2027. Finally, it will reach $70.22 billion in 2027. In 2021, the European Automotive Infotainment market was valued at $5,347.35 million. Today, it is projected to grow to $7,668.59 million, at a CAGR of 6.07%. Therefore, the trend is growing. New infotainment apps are being introduced. However, everything new in the industry has potential risks. What are these risks and how to avoid them? German In-car Infotainment System Development The automotive industry is one of the key industries to support the German economy. In-car infotainment systems are developed as part of long-term projects that last several years. The financial status of such projects is usually very good. This guarantees the sufficient quality of the product developed. Potentially, long-term development minimizes risks based on old technology in use. Yet, the biggest flaw is that German automobile manufacturers still use outdated software technology stacks. Moreover, if the project is long-term and large-scale, the time required for its completion corresponds to the time the global technological landscape might change drastically. Risks The result of large ongoing projects with outdated technology use is the loss of agility. The situation is rather negative in this context. As the technologies are intentionally based on embedded systems. Here, embedded software is computer-based software for the particular hardware that it runs on. It has time and memory constraints. Whilst the real trend is something else. It requires agility. Even the software on the chip cards of our credit cards is now based on JavaCard. Previously, it was based on native apps (C/C++). A car's infotainment hardware is much more powerful than that of a credit card and shouldn’t use outdated software. Therefore, the industry has a disparity between modern technology and legacy software systems. This nonconformity starts from the search for high-performance and real-time computing for firmware development. The situation also occurred in the mobile industry, which in recent years has witnessed competition between Android, iOS, Symbian (based on C/C++, the most powerful among competitors, but is no longer available), and Windows Phone (is no longer available). The First In-Car Infotainment Systems Are Here The technology giants have already successfully produced their first systems on the market. To go with the trend of agility and keep up with the tech changes, some large German and Scandinavian car manufacturers announced that they are moving towards Android Automotive as a common platform. However, this move raises a note of controversy. It does not really matter how companies will deal with market changes in the future as there always will be successful companies and those that are not up to the challenge. German automobile manufacturers miss a couple of things to optimally implement infotainment technologies. Automakers lack the flexibility to get through the phase, in which a dominant technology has not yet established itself, without falling behind in their model release cycles. Long-term and high-risk large-scale projects do not match the R&D agility that companies need in order not to miss the trend. Skilled Workers to be Replaced? The focus on greater agility has consequences for specialists of giant manufacturing companies. Large internal development teams with C/C++ knowledge as part of the re-alignment trend will become unnecessary up to inevitable replacement. Mainly, it happens due to the hard truth that the companies using C/C++ will lose in the infotainment development game. This tendency presupposes hundreds of C/C++ engineers at all German automobile manufacturer companies as well as in their tier 1s (auto parts hardware and software manufacturers) will have to be replaced by new experts. So, we should also be prepared for an immense change in the job market. In order to counteract this, at least some preparation must take place within the company. For example, coordination of the feasible specialization of existing employees must be planned. Management of Personnel To deal with the complex situation occurring in this transition phase, external service providers for companies are becoming the key players. In situations where there is an imminent dominance of an undetermined technological stack, an external software engineering service provider is crucial. This provider is able to offer one-time projects, proof-of-concept (PoCs), and rapid engineering ramp-up or shutdown. So, external teams become risk-free for the German automotive OEMs. Service providers also enable companies to carry out R&D and PoCs without changing the permanent personnel structure. At least until a clearly applicable technology is available. Future Technologies/Programming Languages to be Adapted Programming languages and technologies that can be used for the automotive industry in the future are C/C++ (with optional CUDA), Qt, Android, Java, and JavaScript technologies. Therefore, external software providers that offer different technological competencies are best suited when there is a challenge of personnel changes because they offer the opportunity to test PoC even during a technological trend that is still unclear. Sencury on Infotainment Trends and Risks To ensure your automotive company is ahead of your competitors, choose a proficient external software development service provider. Sencury’s managed team has great experience in the automotive industry and possesses all the latest competencies in the ever-changing tech stack. The first natural step here is technology consulting. We can adapt to the market conditions and still carry out all your requirements. Our experts have a keen business focus with certain agility. Sencury can save you from spending extra budget costs and losing valuable time and avoid different problems due to perfect flexibility and technical skills. Write to us today to become the top automotive manufacturer tomorrow! Become a leader with the best infotainment system.

In the last year, there has been a rise in the popularity of Artificial Intelligence (AI), Machine Learning (ML), and Data Science (DS). Most companies have even started planning to engage in digital transformation using each of the technologies mentioned. Gartner believes that 91% of businesses are on the verge of implementing a digital initiative, and about 87% of senior business leaders prioritize digitalization as the biggest perspective to grow. AI, ML, and DS are concepts that are closely interconnected, at least at first glance. Therefore, Sencury would like to make these notions a little clearer for you to get the most value from them. What Makes AI, ML, and DS Different? Artificial Intelligence (AI) Artificial Intelligence, or AI for short, is the ability of a machine (computer) to think, learn, and act like a human. Hence, a computer copies human behavior in a way that is smart and intelligent. What is AI Used For? The main use cases of Artificial Intelligence include: Customer experience Supply chain Human resources Fraud detection Knowledge creation Research and development Predictive analytics Real-time operations management Customer services Risk management and analytics Customer insight Pricing and promotion If you choose AI for your business enhancement, you will receive a better customer relationship, cost-effectiveness, increased efficiency with operations, higher security and safety, and focus on new products and services. Read more about AI here: Top Information Technology Trends 2023 Machine Learning (ML) Machine Learning, or ML for short, is the subfield of AI that focuses on giving computers the ability to learn from examples without being previously programmed. What is ML Used For? There are lots of Machine Learning applications. You can train algorithms for: Image recognition Speech recognition Automatic language translation Medical diagnosis Stock market trading Online fraud detection Virtual personal assistant Email spam and malware filtering Self-driving cars Product recommendations Traffic prediction If you choose ML for your business growth, you will receive advancements and continuous improvement, automation of almost everything, identification of trends and patterns per your need, and a wide range of applicability. Data Science (DS) Data Science, or DS for short, is a broad field of disciplines that uses scientific methods, processes, algorithms, and systems to extract all the possible knowledge from data that is structured or unstructured. What is Data Science Used For? Everything that is related to data and data analysis is, in one way or the other, a part of the data science routine. For example, Mathematics Statistical modeling Statistical computing Data technology Data research Data consulting Real-world applications Advanced computing Visualization Hacker mindset Domain expertise Data engineering Applying to DS any business can benefit from the ease of job hunting, product customization, cost and time optimization, and advantages of AI. AI vs ML vs DS: How they Work Together? Data Science uses AI and ML to interpret a type of old data called historical, recognize patterns, and make predictions. Here, AI and ML offer data analysts valuable insights to work with. With the help of ML, Data Science achieves the next level of automation. Moreover, these two cooperate in many ways. For example, Data Science produces statistics. ML is dependent on data as ML algorithms are trained on data to produce better input (predictions). Key Differences in AI, ML, and DS If to visualize AI, ML, and DS in the form of robots, Machine Learning will be the smallest one as it is a subset of AI and a tool for DS. Artificial intelligence will be the middle robot as it includes ML and helps DS analysts with valuable insights. Data Science is the biggest robot, as it leverages AI and ML to produce research, industry expertise, and statistics for better business decisions. Sencury’s Services of AI, ML, and DS Artificial Intelligence and Machine Learning are the future technologies that will shape many industries as well as enhance their business workflows. Sencury's team focuses on AI and ML projects with special attention to customer requirements, reasoning, learning, and goal-oriented outcomes. Together with DS, these technologies allow you to make better decisions and enhance performance optimization. Everything depends on the technology that suits your case best. Each can give you a great headstart for growth. Choose Sencury to become a market leader and essentially grow your business. AI, ML, and DS are the future of technological progress and automation processes.

Blockchain is surrounded by lots of hype today. Since its introduction together with cryptocurrencies, blockchain has become a disruptive technology in the digital market. However, among the numerous benefits this technology has brought(decentralization, transparency, and security), it has also become surrounded by fraud and legal issues. With Web 3.0 coming, where blockchain and decentralized apps play a huge role, the hype became even louder. Therefore, it is crucial to understand whether this hype is justified. Let’s investigate the roots of blockchain hype with Sencury. Let’s Talk About Fraud Blockchain is really heavily surrounded by fraud. Being a secure recording digital ledger for cryptocurrency transactions makes blockchain an intentional place to strike. Due to their decentralized nature, blockchain networks can regulate and monitor activities in a difficult way. This leads to possible scams, Ponzi schemes, and many more fraudulent activities. The result is quite known - individuals and organizations become victims of various forms of blockchain-related fraud. For example, Statista claims that In 2022, there was a crypto theft of $320 million in about 190 crypto heists worldwide. There is an estimate of e-commerce losses to online payment fraud at $41 billion globally in 2022 which is projected to grow up to $48 billion in 2023. Statista’s Cybersecurity Outlook has estimated that the global cost of cybercrime will rise from $8.44 trillion in 2022 to $23.84 trillion by 2027. Therefore, the statistics show there’s a critical need for blockchain to be more secure and become less vulnerable. Legal Issue Outlook Blockchain entered the technology market before any legal regulations were involved. Governments and regulatory authorities around the world understood there was a need to address this innovative technology. That’s why they have been working non-stop on new laws and regulations to provide clarity with regard to blockchain and protect consumers from possible fraud. The main goal here is to provide a balance between fostering innovation and ensuring investor protection. Laws and regulations should, by all means, protect from money laundering, tax evasion, and market manipulation. As the landscape is constantly changing, new organizations and structures emerge. For instance, let’s take Decentralized Autonomous Organizations (DAOs). These are entities operating on blockchain networks and are regulated via smart contracts and decentralized decision-making processes. So, traditional centralized structures should be substituted by new models of organization and governance. What’s The Fuss with Web 3.0? The term "Web 3.0" points to the next generation of the internet, where blockchain technology and decentralized applications (DApps) play a significant role. Web 3.0 will be more user-centric and decentralized. It will form an internet ecosystem, where users will better control their digital data and online interactions. It aims to substitute centralized intermediaries with distributed networks and protocols. Future Business Outlook Overall, blockchain and Web 3.0 are revolutionizing the digital landscape. Though challenges and complexities still exist, regulations continue to evolve for technology to mature. The crypto world should undergo many transformations and adaptations. Only then it will be adopted globally, and every associated risk will be mitigated. To avoid being taken away by the technological hype it would be wise to use Sencury's R&D/Consulting service. With proper consulting decision makers will understand better which innovative technologies might fit (or vice versa) as a solution for their business. It is essential to primarily check the feasibility of technological implementation. This way, businesses "buy insurance policy" so that they are not missing critical innovations that can move them ahead of the market. Sencury about Blockchain Hype and Web 3.0 To start innovating your business, consider thorough business and technology consultation on the matter. Sencury is eager to share that we provide this type of service for your comfort and convenience. Do not underestimate and skip the consulting stage as a technological feasibility check will save you time, costs, and resources involved. Then, if the technology is feasible in your particular case, choose a managed team to implement the project. It is super convenient if the team that consulted you can take on the project. This team is already aware of every bit of detail regarding the innovative technology your business intends to install. Feel free to contact Sencury if you’d like to hire a managed team as well. In addition, to process customer requests faster and in a more efficient manner, there is a specific managed support line service. Sencury possesses two main support lines – L2 and L3 support. Consider our skilled software engineers to provide you with tech support, management, analysis of issues, configurations, automation, troubleshooting, and many more services. Benefit from Sencury’s technical knowledge and quick ability to technically support your new software deployments.

Natural languages arose as the perfect means of communication and mutual understanding. These are English, German, French, Italian, and the other 7,139 languages in the world. However, concerning technologies, we have artificially created languages that help us communicate with and become understandable by computers. These are Java, C, Python, JavaScript, etc., which are programming languages, technical, existing as code. With technological progress, a need to process and understand human language through computers became a huge necessity. The ability to analyze, assess, and comprehend human language becomes possible with the help of Artificial Intelligence (AI). More specifically, with the help of such AI branches as Natural Language Processing (NLP) and Natural Language Understanding (NLU). To understand the specificity of NLP and NLU, let’s discuss each concept separately. Sencury has some expertise to share. Let’s proceed. What is Natural Language Processing (NLP)? Natural Language Processing (NLP) is the branch of computer science and Artificial Intelligence. Its basic aim is to make human speech and text as comprehensible as possible for computers (machines). To process human language, computers utilize computational linguistics and statistical language models. The first one is the rule-based modeling of human language, and the second includes machine learning (ML) and deep learning (DL). NLP gained much popularity during the last few years and is heavily invested in today. Therefore, Statista projects that by 2028, its market value will exceed $127 billion. How Does NLP Work? NLP undergoes two main processes – data preprocessing and the development of algorithms. Here, data preprocessing is the process of “cleaning” data for machines to be able to analyze it. Data preprocessing can be done in several different ways: Tokenization, e.g., the text is broken down into smaller pieces for the machine to work with it. Stop word removal, i.e., removal of the common words in the text so that the remaining unique words in the text could bring the most value. Lemmatization and stemming, e.g., extracting the stems of the words for easier processing by the machine. Part-of-speech tagging, i.e., marking the words according to their corresponding part of speech (verbs, nouns, adjectives, adverbs, etc.). The data preprocessing is followed by algorithm development. The duty of algorithms is to process the data obtained after it is cleaned out. In a variety of algorithms that exist, there are two common ones that need to be mentioned. These are: “Rules-based” system. It is a system based on carefully designed linguistic rules. “Machine learning-based" system. ML algorithms work using statistical methods. Algorithms need training data to be input for them to learn on it. The more data they are trained on the better the output will be. Therefore, this kind of system uses a combination of ML, DL, and Neural networks (a computer system modeled on the human brain and nervous system). Techniques and Methods of Natural Language Processing NLP utilizes techniques such as syntax and semantic analysis. Syntax sets up the grammatical sense in the sentence. It denotes the correct arrangement of words and NLP uses grammatical rules to assess the meaning of a language. Syntax techniques include: Parsing – grammatical analysis of a sentence with breaking it into different parts of speech. Word segmentation – deriving words in specific forms for the algorithm to recognize these words during the analysis of the webpage. Sentence breaking – using dots, commas, semi-colons, and other punctuation to let the algorithm know where the sentence ends. Morphological segmentation – dividing words into morphemes (smaller parts) to make them more comprehensible for speech recognition and machine translation. Stemming – extracting the stem of the words, or its initial root for the algorithm to understand that the words are the same notwithstanding the different conjugations and letters. Concerning semantics, it denotes the use of words' meanings. NLP needs algorithms to understand both the meaning and the structure of sentences. That’s why there are semantic techniques involved: Word sense disambiguation – extracting the meaning of the word in a context. Named entity recognition - determining words that can form groups based on categories. Natural language generation – via a database with the word meaning the machine can understand what the text means and generate the new one. What is Natural Language Processing Used For? Nowadays, there are lots of applications of NLP businesses exploit to derive the most benefit. For example, Classifying text Extracting text Performing translation by a machine Generating natural language All these possibilities are being used to: Analyse social media reviews for customer feedback; Understand voice via speech recognition and automate customer service; Translate any text into any language automatically via Google Translate, etc.; Perform academic research by analyzing piles of academic materials; Analyze and categorize medical records to predict and prevent diseases; Process text (specifically, words) for plagiarism and proofread (e.g., Grammarly, MS Word, etc.); Forecasting stocks and gaining insights into financial trading via analyzing company documentation history; Recruit talents by human resources tools; Automate routine-driven litigation tasks via AI attorney This is just a simple list of common applications. AI can be applied to almost every sphere of life, and it makes this technology unique and usable. Benefits of Natural Language Processing (NLP) The biggest benefit NLP can provide is the ability for “human-computer” interaction. With NLP, machines can understand different language variations more accurately. Among the other benefits you might find: Documentation efficiency and accuracy; Automatic text summary notwithstanding the original text size; Helpful for Alexa, Siri, Bixby, and other personal assistants that interpret voice commands; Chatbot usage for organizations providing customer support; Performing sentiment analysis better; Provision of personal analytics insights on any data volume. What is Natural Language Understanding (NLU)? Natural language understanding (NLU) is also a branch of AI. Its main goal is to understand human input either in the form of a text or speech. So, it makes “human-computer” interaction possible as well. Unlike NLP, NLU does not only process and understand human languages, but can also provide answers in the language it was addressed in. Therefore, the main purpose of NLU is to create interactive chatbots that will help the public with their requests. Amazon, Google, Microsoft, Apple, and other startups work on NLU projects and offer NLU innovations daily. How Does Natural Language Understanding Work? NLU puts human speech into a structured ontology. The latter is a semantics and pragmatics data model. Therefore, the algorithms trained on the current data model can understand natural language and determine its meaning. Also, NLU is based on the following concepts: Intent recognition – identification of human emotional state in the text and understanding of its goal. This way the meaning of the text is being established. Entity recognition – extraction of entities in the message and finding the most important data about those entities. There are two types of entities: named (people, locations, and companies) and numeric (numbers, currencies, and percentages). More of the Artificial General Intelligence (AGI) NLU concepts will be described in our future blogs related to Large Language Models (LLMs). Natural Language Understanding Applications There are a variety of NLU applications. However, the most common today are IVR and message routing Customer support service through personal AI assistants Machine translation Data capture Conversational interfaces Sencury Offers NLP and NLU Services The development of AI capabilities goes further, and new creative possibilities arise. Therefore, Sencury is your top AI and ML services provider. Our AI-savvy team will provide you with quality Natural Language Processing Computer Vision Neural Networks Cognitive Computing Deep Learning ML Model Development Predictive Analytics Chatbots Development Data Engineering Data Analysis Data Mining Marketing Automation Solutions Artificial Intelligence allows businesses to acquire automation and learn about user data. Therefore, we can help you to meet your business goals. Sencury’s expertise includes top industry professionals, the best toolset, and creative approaches among the other set standards. Start your NLP project together with us! Contact our AI engineers for the details!

Technologies are constantly evolving and advancing. Especially, the ones driven by Artificial Intelligence. According to Francesca Rossi, Head of Ethics at IBM Research, “It's the first time that people all over the world can use and interact with an AI system. It's really a game-changer because everybody can experience the capabilities of an AI system.” The AI system Francesca Rossi speaks about is ChatGPT. It is also one of the large language models or LLMs that redefine AI within public consciousness. However, there are also questions related to ethics and following opportunities. According to Forbes, 60% of businesses report increased efficiency, productivity, and better customer relationships due to predictive AI. The others are rather skeptical. Over 75% of consumers are having trouble trusting AI. The main reason here is misinformation and other significant issues related to generative AI usage. Yet, AI is among the Top Information Technology Trends 2023. Its market size increases with the technology’s demand. AI is expected to conquer even more enterprise trust and reach a market value of $407 billion by 2027. Sencury has a great article based on ChatGPT’s business value. In the current article, let’s talk about AI’s ability to think. What is an LLM? Can it perform reasoning? How does it act? What is its thinking process like? Can LLMs have hallucinations and catastrophic forgetting? What is a Large Language Model (LLM)? A World Economic Forum’s head of AI, Data, and Metaverse, Cathy Li, defines a large language model as an unusually smart computer program with the ability to generate a language that is similar to ours. LLM is driven by deep learning, which is a subset under the AI umbrella. Any LLM is trained on text data from books, articles, and websites. For example, ChatGPT is trained on 570GB of various data. The amount of this data is pretty massive and helps AI trace, understand, and learn all the patterns and relationships between words and sentences. While being trained, the focus of LLM is on predicting further words based on the text data it processes before. So, when a user interacts with a large language model such as ChatGPT, it analyses the question or a prompt. And then, it tries to accurately predict words to meet user expectations of an answer. Reasoning, Acting, and Thought Process in LLMs Large language models (LLMs) possess exceptional reasoning capabilities. This remarkable reasoning became possible with the help of the Chain-of-Thought (CoT) technique. However, LLMs still struggle with generating action plans to carry out tasks in a given environment. Or they cannot perform reasoning that is complex and related to math, logic, and commonsense. Chain-of-thought (CoT) stands for the ability to break down a problem into intermediate reasoning steps. This allows LLMs to improve their performance of complex reasoning. CoT prompting becomes helpful while scaling the model. If to take ChatGPT, this LLM also has outstanding reasoning. But to make it perform complex tasks quicker, your prompt has to be broken down into smaller parts. Hallucinations and Catastrophic Forgetting When the AI LLM answers with information that does not directly correspond to the provided input, this phenomenon is called hallucination. It becomes a tricky situation as the model starts giving out “false knowledge”. Why does LLM hallucinate? Mainly, due to the lack of context. This can be corrected by supplying the LLM with additional text, code, or other relevant data. Then, the process will be called context injection. It involves embedding additional information into the prompt to give LLMs the knowledge they require to respond appropriately. Catastrophic forgetting, in its turn, is the deviation of a neural network that learns new tasks and simultaneously forgets previously learned ones. So, the algorithms lose the past knowledge and overwrite it with new data. “Behind the Scenes” of today’s LLM Pascale Fung, Professor at Hong Kong University of Science and Technology claims that AI has gone through many changes these days. Although generative AI is relatively new to us it takes a lot after deep learning and neural networks. Now, generative AI models happen to be more powerful due to relying on a huge amount of training data. And, also, their huge parameter size impacts LLMs capabilities. These generative AI models are the foundation for conversational AI. However, ChatGPT doesn’t fall under the category of a conversational AI system. ChatGPT is rather a foundational model. Or an LLM performing various tasks simultaneously. The innovation here is the chat's interface which helps users interact with it directly. So, it may seem as if ChatGPT can participate in conversations and lead dialogues, but it cannot. On the contrary, ChatGPT can be used to build conversational AI and other systems. Rossi points out the fact that before ChatGPT, people used AI a lot without even realizing they did it. And AI was hidden inside all of the applications we were using online. Now, this awareness is a bit heightened. However, it is still unclear how to deal with LLM concerns and where’s the limit of ChatGPT. Sencury on AI’s Capabilities Sencury is one of the many companies that also provides AI services to our customers. However, our experts possess great experience and solid AI-based knowledge. This allows us to deliver your quality AI-driven solutions faster. With Sencury’s expertise, you can receive: Natural Language Processing Computer Vision Neural Networks Cognitive Computing Deep Learning ML Model Development Data Engineering Data Analysis Predictive Analytics Chatbots Development Data Mining Marketing Automation Solutions Tell us about your business needs and let’s work together to implement AI into your workflows!

Agile has become one of the most used software development methodologies worldwide. In 2022, about 37% of businesses practiced Agile and were quite satisfied with the results. However, there are lots of methodologies that can be applied to the software development lifecycle. Therefore, is Agile the only methodology to bring success in all cases? Or organizations can adapt practically any methodology that suits them best? What are the Agile use cases, then? Let’s find out with Sencury. What Makes Agile a Perfect Methodology? Agile is a popular class of methodologies for software development and project management due to being flexible, collaborative, and providing the possibility for iterative development. Here, software development teams respond to change and deliver value faster. No wonder, organizations seek ways to implement Agile or one of its sub-variations. Agile stands for adaptation rather than prediction, which means the team is adapting the process throughout the whole lifecycle. Moreover, this adaptivity works great if there are sudden changes in requirements or unpredictable events involved. Predicting lifecycle requires setting a goal and following an exact plan. It is possible to predict a product workflow in some cases. However, when this possibility is faint, it is better to use Agile’s adaptivity to circumstances and build a product incrementally. To apply the adaptive lifecycle to your workflow, you have to meet two specific requirements. These steps allow you to receive a valuable feedback loop and be successful with the product afterward: Delivering Incrementally An increment is a new version of the software that is enhanced, for example, by adding a new feature once a week. Therefore, the software is being delivered in bits and pieces rather than delivering the whole product at once. This method is feedback-oriented and can be applied only to software development, where it is possible to break the product into pieces that function even separately. Developing Iteratively To deliver in increments, it is essential to repeat the development process with every software feature deployed. For instance, design, code, integrate, and test each item separately over and over again. In comparison, in Waterfall methodology, you design everything together, code everything together, and integrate everything at once. When is Agile Methodology Suitable for your Project: Agile Use Cases? The suitability of Agile depends on different factors like project size, complexity, team structure, and organizational culture. Agile is a horizontal approach and requires organizations to be ready for incremental delivery and iterative development. It works well in complex and uncertain cases where the ability to iterate and deliver value incrementally is crucial. If you can’t use Agile, use Waterfall. It is a vertical approach. It is grounded on a set plan starting from requirements specifications, following design, building stage, integrating, and testing. The result is a complete product with as many features as the plan included. Well-defined requirements and stable environments are quite rare these days, but they still exist and require an approach that takes on the challenge and succeeds within the given terms and conditions. The waterfall class of methodologies (e.g., V-Model in the automotive industry) is a good choice in highly documented and in-detail specified fixed requirements. Especially, when it comes to functional safety and mission-critical applications. Even in this case, it is possible to combine agile iterations inside waterfall phases (e.g., IBM RUP process) or to use more formalized (documented) Agile methodology (e.g., SAFe). General feedback on SAFe implementation in the automotive manufacturing industry (both OEMs and Tier-1s) remains controversial according to our insights. Therefore, the choice of methodology should be grounded on a careful evaluation of every factor involved. For instance, project characteristics, team dynamics, customer expectations, and organizational culture. It is important to choose a methodology that aligns with the specific needs and constraints of the project to maximize its chances of success. Sencury on Agile Methodology Sencury practices different kinds of methodologies. Agile is not the only methodology that can bring you potential benefits. Not every case has to include Agile methods. Read more about Agile Methodology in our recent article “DevOps and Agile Culture”. As our experience shows, every project revolves around customer requirements and expectations. To meet both of these factors, it is vital to choose a methodology that suits the project the most. Sencury’s team has succeeded using DevOps, Agile, Scrum, SAFe, Waterfall, Kanban, and other methodologies. Choose Sencury to succeed in your software development project. Contact us and let’s select the best approach to deliver quality software within your standard requirements.