What AI Cannot Do: AI Limitations and Risks

Looking at some articles right now, one could think that AI is omnipotent. However, it’s essential to remember that AI limitations exist, and there are many. Therefore, you should not expect it to be a universal cure for all problems. Unfortunately, it’s still brand-new technology, and its functionality has to be improved.

There are some incredible things you can achieve using AI. It’s also true that it can help your business save money by automating multiple processes and offering valuable analytics. However, some businesses take risks and apply AI in every situation. Such reckless use of tech can badly damage your business security and income.

According to the AI Incident Database, the number of  AI misuse incidents in 2023 increased by 32.3% compared to the previous year. Nowadays, businesses must be realistic when considering the pros and cons of implementing AI. Devtorium Business Analysis and Information Security departments have the expertise to forecast probable risks caused by AI or other digital systems. In this blog post, our specialists will outline AI limitations and risks of implementing it without a system of fail-safes. 

AI Limitations and Risks by Category

What AI cannot do: AI limitations track back to the quality of data

Data Dependency

Data is the main resource on which any AI system runs. Algorithms train on the provided data. Therefore, AI heavily relies on data quality, bias, and availability, which can impact performance and decision-making.

AI limitations caused by data:

  • Creativity
    While AI is good at generating content based on existing data, it struggles with original or innovative thinking.
  • Flexibility
    AI has limitations in adapting to new or unexpected situations outside its training data.
  • Bias
    Data bias can occur at various stages of the AI lifecycle. However, bias often originates from the data used to train and test the models.

Contextual Misunderstanding

What AI cannot do is understand the context. At least, this isn’t possible with the current level of technology development. AI’s lack of contextual understanding refers to its limits in interpreting information. In other words, AI can fail to realize societal context or grasp the subtleties of nuance. 

AI limitations caused by context:

  • Natural Language Processing (NLP)
    While working on NLP tasks, like text analysis or translation, AI may have difficulty understanding language nuances such as idioms, slang, and dialects.
  • Visual recognition
    AI algorithms can fail to recognize objects within their broader context in computer vision tasks.
  • Social interactions
    AI-driven chatbots may struggle to catch the nuances of human conversation, including tone, sarcasm, or implied meanings. If you want to learn more about the capabilities of an AI-powered voice bot, click here.

Ethical Concerns

AI limitations is ethics are impossible to fathom because this technology doesn’t operate in a context that can be governed by ethics. Therefore, programming AI algorithms that could make ethical decisions is nearly impossible. The machine struggles to replicate feelings and emotions. It cannot make moral judgments in the same way humans can. 

AI limitations caused by ethics:

  • Lack of empathy
    AI lacks emotional intelligence and cannot empathize with human emotions. Therefore, as an example, it cannot prioritize emotional well-being as a factor.
  • Cultural contexts
    AI systems may struggle to understand human cultural diversity. This can lead to biased or culturally insensitive outcomes, like stereotypes. As a result, implementing AI in some areas might work to reinforce existing inequalities.
What is the black box problem and the AI limitations it imposes

The Black Box Problem

The Black Box Problem refers to the opacity of AI decision-making processes. AI algorithms are so sophisticated that it is hard to realize how they arrive at their conclusions. Therefore, a human might not be able to trust them completely. As a result, implementing AI in any position where the machine can make decisions that impact human life becomes a huge risk.

AI limitations caused by transparency:

  • Error correction
    When AI systems make errors or produce unexpected outcomes, understanding why those errors occur is crucial. However, without a clear view of the internal workings of black-box AI models, diagnosing errors becomes much more difficult.
  • Trust
    Users may find it challenging to rely on AI when they cannot understand how systems make decisions. The black box problem can be particularly concerning for critical applications such as healthcare or criminal justice.

Privacy and Security

As AI cannot function without data, concerns arise regarding collecting, storing, and using personal data. AI technologies also introduce new cybersecurity risks. Malicious actors may exploit vulnerabilities in AI systems to launch attacks, which presents new threats to financial systems, critical infrastructure, and national security.

AI limitations in the security field:

  • Tracking
    AI-powered surveillance technologies, such as facial recognition and biometric systems, threaten privacy by enabling constant monitoring and tracking of individuals without their consent.
  • Malicious use
    AI technologies can be leveraged for malicious purposes, including generating convincing deepfake videos, launching sophisticated phishing attacks, and automating cyberattacks.
  • Personal data
    AI systems may analyze and process personal data without adequate safeguards. This could lead to unauthorized access, identity theft, financial fraud, and other cases of data misuse.

Bottom Line: How to Avoid Reckless Risks and AI Limitations?

It’s impossible to avoid risks and AI limitations entirely with the current level of technology. Therefore, it’s imperative to address them responsibly to maximize the benefits of AI implementation. Devtorium professionals are always ready to help you understand risks and develop efficient, safe, and secure AI applications for your business. Contact our team for a free consultation on how to use AI to your best advantage.

To learn more about the Devtorium Team and the multiple capabilities of AI, check out our other articles:

Use of AI in Cybersecurity: Modern Way to Enhance Security Systems

Now is definitely the time to use AI in cybersecurity. In fact, those who don’t do this put their businesses at great risk. While some reports aren’t highly detailed, according to cybercrime statistics, the damage caused by it amounts to $12.5 billion. This number is growing every year because complex systems are inherently vulnerable, and criminals use every tech innovation they can. So, no one can afford not to use AI’s help to build the best possible security system while hackers use this technology to exploit your weaknesses.

Modern threats need up-to-date solutions. Devtorium offers a wide range of cybersecurity services, and our security and R&D specialists are working to identify the best AI applications in this area. At the current level of technology, AI can automate tasks, improve threat detection, predict future attacks, and more.  In this blog, you will learn about the benefits and drawbacks of using AI in cybersecurity systems.

Use of AI in cybersecurity: areas of implementation.

Use of AI in Cybersecurity: Applications in Various Systems

Network Security

This type of security protects a computer network from unauthorized access, misuse, or attacks. It involves developing a secure infrastructure for devices, users, and applications to work safely. The tools used in network security include firewalls, VPNs, and data loss prevention (DLP), as well as intrusion detection and prevention systems.

AI applications in network security:

  • Anomaly detection.
    AI can analyze network traffic patterns to identify unusual activity that might indicate a cyberattack.
  • Automated threat mitigation.
    AI systems can automatically take steps to isolate threats, such as blocking malicious IP addresses or quarantining infected devices.

Information Security

This system protects digital information, such as data stored in databases, files, or other repositories. It includes data encryption, access controls, and data backup and recovery. Devtorium is an ISO/IEC 27001:2013-certified company and our specialists are able to ensure complete security of your systems both with and without AI.

AI use in cybersecurity of information systems:

  • Data Loss Prevention (DLP).
    AI can analyze data content to identify sensitive information and prevent unauthorized data exfiltration.
  • Threat intelligence analytics.
    AI can collect and analyze threat data from various sources to predict future attacks and improve security planning.

Application Security

This security system aims to secure software applications from being stolen or hacked. Application security can reveal weaknesses at the application level, helping to prevent attacks. AI implementation in application security can include secure coding practices, vulnerability scanning, and penetration testing. 

  • Static application security testing (SAST).
    AI can analyze code to find potential vulnerabilities before application deployment.
  • Runtime application self-protection (RASP).
    AI-based RASP systems can monitor applications in real-time and detect suspicious behavior.

Cloud Security

Cloud security protects cloud-based assets and services by keeping data private and safe across online infrastructure, applications, and platforms. It is a shared responsibility between the organization and the service provider. 

Uses of AI in cybersecurity of the cloud:

  • User and entity behavior analytics (UEBA).
    AI can analyze user activity in the cloud to identify potential threats or compromised accounts.
  • Cloud workload protection platforms (CWPP).
    AI can continuously monitor and secure cloud workloads from evolving threats.

Identity and Access Management (IAM)

This security system manages user access to IT resources. IAM systems ensure that only authorized users can access specific resources and that their access is logged and monitored. 

Possible AI applications:

  • Risk-based authentication.
    AI can analyze user behavior and context in order to determine the appropriate level of authentication required for access.
  • User behavior anomaly detection.
    AI can detect unusual user login attempts that might indicate a compromised account.

Internet of Things (IoT) Security

This security system is the practice of securing devices connected to the internet, for example these are smart home hubs, wearables, and industrial control systems. IoT security is a growing concern as there are over 17 billion connected devices, and each of them is vulnerable.

Examples of AI uses for IoT:

  • AI can analyze data from IoT devices to identify suspicious activity that might indicate a cyberattack.
  • Predictive maintenance: AI can predict potential device failures and help prevent security breaches caused by vulnerabilities in IoT devices.
Benefits of using AI in cybersecurity.

Benefits and Drawbacks of Using AI in Cybersecurity

There can be no doubt that AI can enhance security systems in many ways, for example:

  • Detecting threats.
    Analyzing vast amounts of data to identify subtle pattern changes that might indicate malicious activity.
  • Automation.
    Automating repetitive tasks such as log analysis. This will increase overall efficiency by allowing the security personnel to focus on other strategic tasks.
  • Faster incident response.
    By automating threat detection and mitigation, AI responds faster to security incidents.
  • Scalability and adaptability.
    AI systems can accommodate growing networks and data volumes. Additionally, AI can adapt to new threats and security landscapes, ensuring continuous protection.

However, you shouldn’t forget that AI itself is still vulnerable. Moreover, using it can introduce additional weaknesses to your system, such as:

  • False Positives and Negatives.
    AI systems can generate false positives (flagging harmless activity as threats) and false negatives (missing actual threats).
  • Data bias.
    AI algorithms are only as good as their trained data. Biased data can lead to corrupted AI models that miss certain threats or unfairly target specific users.
  • Insufficient transparency.
    AI decision-making processes are quite complex and difficult to understand. Therefore, it might be challenging for us, as users, to debug errors and trust the system’s recommendations.
  • Security risks of AI itself.
    AI systems can be vulnerable to attacks. Malicious actors could potentially exploit vulnerabilities in AI models to launch targeted attacks.
  • The lack of legal regulation.
    The lack of clear regulations around AI use in cybersecurity can create uncertainty regarding liability in case of security breaches or misuse of AI systems.

Bottom Line: How to Implement AI in Cybersecurity?

Eventually, as the technological landscape continues to evolve, new cyber threats will appear, too. However, specialized professionals are always ready to secure your services. Contact our team for a free consultation on how to best implement AI power in your cybersecurity systems!

To learn more about the Devtorium Security Team and the multiple capabilities of AI, check out our other articles:

Prompt Engineering Basics: How to Talk to AI

Being proficient with prompt engineering basics has become an essential skill nowadays. Many of us talk to AI almost daily. Sometimes, it’s even without our knowledge as the number of voice chatbots increases. However, today, we’ll talk specifically about how to talk to generative AI.

Generative AI prompt engineering can be a bit tricky because you aren’t just ‘venting’ to a machine or going through some customer service routines. The goal here is to word your command in such a way that you get the most accurate result. You can use the knowledge of AI prompt engineering to complete a great variety of tasks, from generating an image to developing and programming an AI voice bot.

Moreover, all of these tasks are becoming more relevant with every passing day. According to the CompTIA IT Industry Outlook 2024 report, 22% of companies insist on AI integration in the workflow. The percentage of using AI in daily work by usual employees is even higher. However, only a few know how to interact with AI most efficiently.

Our highly qualified specialists maintain that prompt engineering is the main thing that most GenAI users need to improve. So, with their help, you’ll be able to learn the basics of prompt engineering.

Prompt Engineering Basics: What Is Prompt Engineering?

Prompt engineering is creating inputs as specific instructions for large language models (LLMs, more on that here). 

Generative AI models generate specific outputs based on the quality of provided inputs. We call these inputs prompts, and the practice of writing them is called prompt engineering. 

Prompt engineering helps LLMs better process the incoming tasks to produce desired outputs.

How knowing AI prompt engineering basics benefits you.

Where You Can Apply Prompt Engineering Basics

AI software development

Prompt engineering now plays an active role in software development. You can save a great deal of time time by giving the AI model a clear prompt describing the desired functionality. It suggests code snippets or even complete entire functions. That is very helpful, especially for repetitive tasks. Trained on developer prompts, AI can also analyze existing code and identify potential bugs. If you want to read the opinion of Devtorium`s developers on AI code generation tools, check out this post.

Chatbot development

Prompt engineering allows chatbots to respond more naturally and informatively. You provide them with clear instructions and context for understanding customer inquiries. A better understanding of customer questions leads to improved chatbot responses, which means happier customers and shorter wait times. If you want to create your chatbot, read our blog about Assistant API.

Cybersecurity services

Cybersecurity is another field where understanding prompt engineering basics can help you. Security analysts can leverage prompt engineering to guide AI systems in analyzing network activity and logs. AI can efficiently scan vast amounts of data and flag potential security threats when given prompts with specific indicators of compromise (IOCs) or suspicious behavior patterns. Prompt engineering in cybersecurity empowers security professionals by harnessing AI’s analytical power to identify threats, uncover vulnerabilities, and respond to incidents.

Creative content generation

Prompt engineering allows writers to enhance their efficiency. You can give an AI model a starting point and direction for generating creative text formats like blogs, posts, scripts, or even musical pieces. This frees up the writer to focus on refining and polishing the ideas. The same goes for any kind of content, be it visuals, text, or even music.

AI prompt engineering: basics tips.

Essential Tips on Prompt Engineering Basics

Prompt engineers do not only design and develop prompts. They also operate a wide range of skills and techniques that improve the interaction and development of LLMs. Their work encompasses the following:

  • Zero-shot prompting – instructing LLM without relying on any examples.
  • Few-shot prompting – giving the model a few examples before instructing.
  • Chain-of-thought prompting (CoT) – asking the model to explain its steps every time it performs the instruction.

Here are a few tips that will help you communicate with an AI as a prompt engineer on the basic level:

  • Use clear instructions and ask direct questions. Make the sentences as concise as possible.
  • Provide LLM with context. Use any relevant data for it.
  • Give examples in prompts.
  • Specify the desired output format and length.
  • Align prompt instructions with the task’s end goal.
  • Provide the desired output with styles such as bullet points, tables, numbered lists, inline/block code, quotes, hyperlinks, etc.
  • Let the LLM answer “I don`t know” if needed.
  • Break the complex tasks into subtasks.
  • Use a clear separator like “###” to split the instruction and context.
  • Experiment a lot to see what prompts work best.

Bottom Line: Are Prompt Engineering Basics Enough to Talk to an AI?

So, to sum up, everyone who uses GenAI can learn the easiest prompts to get desired but simple outputs. However, to get more complex results, you will need to have a really good understanding of programming and mathematics. Therefore, if you need to use AI in your project as more than a simple user, contact our team for a free consultation on how to best implement its power for you!

AI Chatbot Development: How Does a Voice Bot Work?

AI chatbot development is in high demand. Only about 20% of businesses use one, but over 60% think about adopting this technology. At Devtorium, we often work with chatbots because they are of interest to our clients. Today, we’d like to share one of our current cases, where we created an AI chatbot for a client that runs an innovative marketing solution.

The product is a CMS that gathers data through quizzes. The AI chatbot capabilities are required to collect and verify the collected data. In addition, we are expanding the product with an AI-powered voice chatbot that can schedule calls and generally substitute call center services.

Devtorium has a dedicated Research and Development Team that works with different types of AI services. In this case, our developers did all the work, creating the CMS, integrating a messenger chatbot, and developing a voice bot for calls.

How we used AI chatbot development for a case of a marketing CMS.

AI Chatbot Development for a Marketing Solution: Base Product Outline

In this project, the product is a quiz-based CMS that creates a variety of questionnaires based on user-set parameters. The quizzes are flexible so every user can custom-tailor the questionnaire for their business. The CMS makes collecting, processing, and visualizing data easy to help users understand their target audience.

The project aims to create an effective solution that will help businesses generate qualified leads. This product’s text chatbot collects data. The next phase, the voice bot, can schedule calls, initiate APIs, and send messages. Most importantly, the AI can use advanced ML models to understand what the customer says and proceed according to the information received in real-time.

During this project, our software development team used a variety of technologies:

Back-end:

  • Node.js

Front-end:

  • React

Databases:

  • MongoDB
  • PostgreSQL

DevOps:

  • AWS
  • Jenkins
  • GitHub

AI (phone service): 

  • Vonage 

ML models:

  • OpenAI GPT-3.5(GPT-4)
  • OpenAI speech-to-text (Whisper)
  • OpenAI text-to-speech

It’s essential to note that Devtorium always uses a combination of technologies and frameworks to achieve the best results. We discuss the client’s ideas and goals in depth to build a product that can achieve them while staying within budget.

Voice AI chatbot development; step-by-step AI tools involved.

Voice AI Chatbot Development Services Overview

In this article, we wanted to focus specifically on the voice bot designed for this project. Below, we will detail exactly how such a solution works and what it can achieve within the current level of technology. However, we’d also like to remind you that this tech is evolving fast. Almost anyone today can build an AI chatbot using LangChain or similar frameworks. You don’t need a coding degree for that. In fact, some platforms are so user-friendly that they can help you build a basic bot with limited capabilities with no tech knowledge.

However, if your business wants to invest in a solution that will give you a competitive advantage, you’ll need a professional-grade tool. The Devtorium’s lead AI specialist, Oleksii Makarov, outlines how to create an AI chatbot that can talk to your clients.

Voice AI chatbot development starts with VoIP

First of all, when creating a voice bot, we need to use a phone service. VoIP technology is quite advanced today, so this won’t be an issue. We chose to use Vonage because it is currently the best option regarding both quality and service versatility.

Speech-to-text: an essential part of AI chatbot development services

Speech-to-text technology is crucial for building an AI voice bot because it enables the machine to process audible information. We use Assebly.ai in our projects because it currently delivers the highest level of accuracy. Most importantly, it’s able to process information effectively in real-time. Therefore, it helps create an illusion that the user is talking to a person instead of a machine.

While working on this project, we noticed that lag is the biggest issue with these chatbots. Simply put, processing data takes time, so the pauses in their responses are too long. Assembly’s processing capabilities help us reduce this time to manageable levels.

Machine Learning models do the powerlifting in data processing

Devtorium uses the GPT engine versions 3.5 and 4 to build the most efficient chatbots for every application. This technology is the leading AI power behind any voice bot because the solution uses it to process data. Basically, it’s your bot’s ‘thinking power’.

We actively use advanced prompt engineering techniques while designing instructions for the bot’s conversations. The main task is creating instructions that enable the bot to learn and grow. The critical task is to make the conversation sound as natural as possible to a human.

Going back through text-to-speech

Now that the data has been processed and the machine has created the response, we use the GPT-provided text-to-speech tool. It works pretty well for the current technology level. However, we are excited because there are some announcements for more advanced features. In addition, we expect to see more voices and ‘emotions’ options quite soon.

It’s great to see this technology developing and including the emotional aspect of conversations. This truly bridges the gap between machines and people. Most importantly, we are sure this will boost the bot’s ability to deliver higher-quality customer services fast.

Back to the phone service

AI chatbot development is a complex process that includes many steps. However, at the final stage, it returns to where it started. In our case, the Vonage phone service is where the bot talks to the customer.

Voice AI Chatbot Development Benefits

The extraordinary thing about using voice bots is that they do not only cut down the cost of outsourcing call center services. Even with the current technology level, we can create a bot that extracts data from spoken conversations in JSON format. In addition, it’s able to send out a call to third-party APIs.

In simple terms, the bot can trigger an application to run in response to your customer’s query. It will also automatically process all data from the conversation and show it to you in the way you choose. This offers limitless opportunities for studying your customers’ preferences, reactions, and interests. Therefore, a voice bot can become your single most valuable tool for interacting with and researching your target audience. It can also initiate various programs or connect the client to a human operator if the machine cannot process the query.

If you want to see how it could work in practice, set up a free consultation with the Devtorium AI team!

More on AI from Devtorium:

Devtorium R&D Department: Meet the People

As you already know, Devtorium has a Research and Development Department that helps our team understand and master cutting-edge technologies. Currently, they are focusing on AI and Machine Learning and have workshops that anyone willing to learn can join.

Today, we’d like to tell you more about the Head of R&D, Oleksii Makarov, and the amazing team the Department has already assembled.

Oleksii Makarov: Head of Research and Development Department in Devtorium.

Oleksii Makarov: Solution Architect/Head of Research and Development

Oleksii is Devtorium’s leading expert in AI and ML technology, who loves to share his knowledge as much as he enjoys learning new technologies. He has 24 years of experience in development, and over this time, Oleksii has worked with multiple projects and technologies, including:

  • Developing desktop applications for Windows (C++)
  • Web development (.NET)
  • Front-end development (HTML, JavaScript, React, NodeJS)
  • Deep Learning and AI (Python)

For the last seven years, Oleksii has been dealing with Machine Learning and Deep Learning technologies. He has experience developing several AI-related projects where he was actively working on the following:

  • Computer Vision: object detection, video processing, data filtering, behavior tracking.
  • LLM + prompt engineering + GPT Engine

Oleksii says that Deep Learning is one of his main interests in technology right now. It enables us to teach computers to perceive the world similarly to humans. This is one of the things involved in the work of the Devtortium R&D Department, and studying this technology offers excellent opportunities for personal growth. In addition, this looks great on your CV😉

Research & Development Team: Not Only for Developers

We’d also like to introduce some of the R&D Team members who have been studying new technologies with Oleksii for the last few months. We were surprised to learn that not all members of the Department are developers. Some Devtorium team members are very active and forward-thinking. Therefore, they want to discover everything they can about cutting-edge tech to stay sharp and expand their professional skills and opportunities.

Olha Romanets: Senior front-end engineer

Olha Romanets: Senior Front-End Engineer

Olha is one of the most active members of the R&D Department. Her drive is contagious, and her interest in the topic has no bounds. Olha has a successful career in front-end development, so we wondered what pushed her to take on this new challenge.

She said joining the R&D Team was essentially an ‘impulse purchase’. For some time, the topic of AI was popping up all around her, so when the opportunity to study it deeper presented itself, Olha took it immediately.

She is a highly dedicated person who enjoys studying things to their full potential. This is exactly how she approaches R&D workshops and never fails to impress her teammates.

Oleksandr Shapran: Business Analyst

Oleksandr Shapran: Strong Junior Business Analyst

As mentioned before, not all members of the Research and Development study group are developers. One of our business analysts, Oleksandr, is a good example of a person who doesn’t allow any perceived limitations to prevent them from reaching their full potential.

We asked him why he decided to join and spent some of his time studying something far removed from his primary specialty. His answers were truly inspiring.

First of all, Oleksandr highlights the importance of knowledge about AI for any professional working within the IT industry. This is the most cutting-edge technology that is already revolutionizing the world. Therefore, everyone must understand at least the basics of how it works and what it’s capable of.

Moreover, as Oleksandr wisely notes, the level of professionalism one has is determined by the depth and versatility of their knowledge. Simply put, any professional can bring more value if they are knowledgeable in many relevant areas. This way, they can be most helpful to the team, and that’s precisely how he wants to be.

That said, studying AI on your own is extremely difficult, especially for someone without a technical background. Joining the Devtorium Research and Development Department allowed him to learn from a great mentor and get support from his colleagues. They face challenges and grow together, each contributing something valuable to the work of this Department.

Also, Oleksandr notes that the knowledge and skills gained while working with the R&D Team help predict future trends. By learning these complex topics as an analyst, he can better understand changes in technology trends. Therefore, he can see what to pay attention to and how to benefit his current and future projects.

Ivan Danyliuk: Senior front-end endgineer

Ivan Danyliuk: Senior Front-End Engineer

Ivan is one of Devtorium’s leading front-end developers. He has shown tremendous talent and interest in working with AI through our Research and Development Department workshops.

According to Ivan himself, joining the R&D Team aligns with his passion to stay up-to-date with any changes in the industry. Due to his participation in this Department, Ivan broadened his horizons significantly. His direct quote:

Being a part of the R&D team has shown me new perspectives, encouraging me to approach everyday matters from a fresh and innovative standpoint.

Excited to be on this journey and eager to explore the limitless possibilities that our AI endeavors bring, I am confident that our collective efforts will shape the potential of our company.

Serhii Bevz: Senior full-stack engineer

Serhii Bevz: Senior Full-Stack Engineer

Serhii Bevz joined the Devtorium team not long ago but has proven to be an exceptionally talented full-stack engineer. However, despite being busy with his project, he makes time to actively participate in the work of the Research and Development Department. When we asked why he chose to join this team, he said that, first of all, it’s a highly promising direction for professional development. His second reason is that it’s a fascinating field for developers. Studying and working with AI enables you to move up to a completely different level of skill and knowledge.

In addition, Serhii mentions how he loved math in school and how working with AI requires a deep knowledge of mathematics. Moreover, studying a new programming language is much easier when you have real-life tasks for practice. Therefore, he enjoys mastering Python with the R&D Team and is progressing quickly in this area.

Serhii notes that expanding your personal tech stack is an outstanding professional opportunity. Also, he very much enjoys the way Oleksii teaches these complex subjects. The subject matter is very complicated, so it’s crucial to have someone who can explain it in a way that’s easy to understand. As a full-stack developer, Serhii knows he must know AI and how it works. This knowledge is vital for any ambitious developer today.

The only thing that Serhii does complain about is that he wishes to have more time for this exciting project. However, he hopes that working with the Research and Development Department on these topics will allow him to be near the start of changes in tech trends. Similar to how it was with NodeJS, AI has launched a revolution now, and those who are proficient with technology have a much better chance of professional success. Serhii also notes how important it is for a development company to have a team of professionals with these trending skills, as Devtorium has.

Oleksandr Kostylenko: strong-middle .NET engineer

Oleksandr Kostylenko: Strong Middle .NET Engineer

Oleksandr is a very experienced and talented developer in his professional life and a highly artistic person outside of work. When we talked to him about his reasons for joining the Research and Development Team, he showed that both parts of his character played into it. 

We can already feel the presence of AI everywhere, and personally, I use ChatGPT and DALL-E in my daily life. The future of the IT industry is inseparable from AI technology, and I’m sure studying it will help me in my work. Moreover, this outstandingly fascinating technology allows you to realize your artistic potential in development, which is crucial for me.

Oleksandr states that he enjoys being a part of the R&D Team and how Oleksii teaches such a complex subject. He especially commends the fact that Oleksii, as a teacher, encourages the team to write their own code and experiment. It’s a very productive and efficient method of teaching such complicated topics.

According to Oleksandr, knowledge is the main benefit he gets from joining Research and Development. Despite being a team member for only a few months, he already understands the core principles of Deep Learning and looks for ways to apply this in practice. He is looking forward to learning more about AI and reaching new heights with the help of this technology.

Devtorium Research and Development Department: Progress Report 2023

Despite being so new, the Devtorium R&D Department has grown fast and attracted many talented people who wish to expand their knowledge of innovative technologies. According to Oleksii, who heads the Department, the main achievements so far were:

  • Building a core team of people who are interested, driven, and have an aptitude for this type of work.
  • Covering the basics of Machine Learning, Deep Learning, Python, and Algebra needed for understanding and further work with AI technology.
  • Bringing the team to approximately the same level of knowledge.
  • Starting to work on simple model training tasks.

Currently, the Research and Development Team will continue to expand their knowledge and learn more about AI programming. We hope the team will soon have new exciting projects to work on.

Introduction to AI Part 2: Next-Level AI Terms Glossary 

2023 was a breakout year for Generative AI, and it has proven beyond all doubt that expanding your vocabulary with common AI terms is necessary now. Artificial intelligence technology is fast becoming an indispensable part of our lives on every level. Therefore, you must understand at least the basics of how it works and what it can do.

Devtorium will continue our series of articles about AI, its capabilities, and developments that are revolutionizing multiple industries. Today, we will expand our AI terms glossary with some more fundamental terminology. Understanding these concepts will help you get a better idea of how artificial intelligence models work with data. You can find the first post on basic AI terminology here.

AI Terms Glossary (Still in Alphabetical Order)

Bias

Bias is a phenomenon in machine learning (ML) that occurs when the outputs of ML algorithms are skewed. This happens due to the prejudiced assumptions made during the algorithm development. Simply put, it’s AI’s reaction to an error in the initial algorithm. This is often a reaction to human error or prejudice that occurs during programming the ML algorithm.

For example, it can happen if the data AI is given to learn from isn’t comprehensive enough or is programmed with cognitive human biases. In this case, the bias starts leading AI toward specific outcomes, affecting the results’ clarity and accuracy. If you are looking for a more technical explanation of how bias works in ML models, check out the relevant page on GeeksforGeeks.

Embedding

Embedding is a technique used to represent data (text, images, and audio) as a mathematical vector. Machine Learning models use data converted through embedding to capture semantic relationships and patterns. That’s the only data format they can directly process at this point of technological development, so embedding is one of the most important AI terms you need to understand right now.

Embeddings allow NLP models to process data, find contextual meanings, and perform tasks like querying, classification, comparison, and recognition. For example, Word2Vec and GloVe are popular embedding methods that are used for word embeddings. In simpler words, they help AI understand texts.

AI terms: Reinforcement Learning

Reinforcement Learning (RL)

Unsurprisingly, Reinforcement Learning is a type of Machine Learning that improves decision-making algorithms over time. It enables an AI-driven system to learn through interacting with its environment using the trial-and-error method. In very basic AI terms, this can be explained as the AI’s ability to learn from all its interactions with the user, both successful and not.

Also, RL uses the exploration-exploitation trade-off. It means that a computer balances the need to discover new, better strategies while exploiting the ones it already knows. The machine does this in order to achieve maximized rewards. This ML model is commonly used in robotics, gaming, and various autonomous systems.

Vector Database 

Vector databases are designed specifically for handling embeddings. The main difference between traditional and vector databases is in their data optimization and querying methods. Basically, instead of querying a row with a perfect value match, vector databases use a similarity metric, searching for a vector most similar to the query.

Devtorium developers used vector databases while working on AI-based recommendation systems, searching for images and text, NLP, and fraud detection software. We’ve also written an extensive article on the topic of vector database applications in AI and their pros and cons.

AI terms glossary: Vector Database

What’s After AI Terms Glossary?

The topic of AI is getting hotter and more relevant in our fast-developing world. Therefore, Devtorium will continue with our effort to explain exactly how this technology works and what it can do. The potential AI tech has is unlimited, and it has already changed human lives in significant ways. We hope that our developers and solution architects will be able to leave their mark on the world as well by creating new AI-powered products.

If you want to read more about AI, check out some of our previous articles:

Build an AI Chatbot with OpenAI Assistant API and LangChain

Technology has progressed so far that you can build an AI chatbot with minimum effort today. The world now really looks like some sci-fi stories come to life. OpenAI is one of the businesses standing at the forefront of this technological revolution.

We talked to Devtorium developers about how OpenAI’s Assistant API is reshaping how we develop digital assistants. Have you ever wondered what the development of chatbots looks like? Devtorium’s developers explore this superb AI-powered tool and share a tutorial on how to build a chatbot with Assistant API and LangChain.

Why Choose Assistant API to Build an AI Chatbot

Developers might have vastly different opinions on whether or not AI code-generation tools are helpful. However, everyone with experience working with OpenAI Assistant API agrees this is a magnificent tool. 

Assistants API is an NLP(natural language process) API currently available as a beta version. You can already use Assistants API for question answering, language translation, and code generation. However, its primary function is to assist developers in building chatbots within their apps. To start using Assistants API, you must have an OpenAI API account. 

Assistants API uses OpenAI-hosted models, access files, persistent threads, and call tools to respond to user queries. According to Devtorium developers, its most prominent and valuable features are:

  • Code interpreter and retrieval: Access and execute code from various sources.
  • Function calling mechanism: Call functions from other APIs.
  • Knowledge base: Store and access information from a variety of sources.
  • Easy conversation management with threads: Keep track of the context of a conversation.
  • Support for different models: Choose the best model for your specific task.
  • Customizable instructions: Control how your assistants respond to user requests and perform tasks.
  • Easy deployment: Deploy to a variety of platforms.

How to Build an AI Chatbot with Assistant API and LangChain

The range of AI software development services offered by Devtorium is vast. Therefore, we explored multiple AI solutions and technologies available today. One of our developers’ favorites is LangChain, a framework built around LLMs (large language models) and designed to simplify the creation of complex apps. This tool connects components: prompt templates, LLMs, agents, and memory to create a chain, hence the name LangChain.

In order for LangChain to work correctly with Assistants API, make sure you download version 0.0.331rc2 or higher. The latest RC version of LangChain can support Assistant API using an experimental package. The only class you need is OpenAIAssistantRunnable, which makes code much cleaner. 

Now, let’s get on with the guide on how to build an AI chatbot using these tools. Using the tips below, you can make an MnA assistant that will answer queries using a retrieval tool. No Chunking, no embeddings, and no vector database are required.

The steps to build an AI chatbot using this approach include:

  1. Create an Assistant API account and get an API key.
  2. Create an Assistant in the API by defining its custom instructions and picking a model.
  3. Install LangChain and create a LangChain project.
  4. Write a script that uses the Assistant API to send and receive messages from users and access and store information from LangChain.
  5. Deploy your chatbot to a web server, messaging platform, or mobile app.

Technical Instructions on Working with Assistant API and LangChain

1. Set up Assistant API 

  • Sign up for an OpenAI account
  • Get API key
import openai

     openai.api_key = "YOUR_API_KEY"

2. Create an Assistant

  • Define instructions for the scope of your chatbot, tools it can access, etc. 
  • Pick a model (code recommendation, text embedding, etc.)
assistant = openai.Assistant("assistant name", model="Davinci")

3. Install LangChain

  • Install LangChain and LangChain-experimental package
  • Import OpenAIAssistantRunnable
from langchain.llms import OpenAIAssistantRunnable

4. Handle user input and get assistant response

  • Take user message as input 
  • Process with OpenAIAssistantRunnable to get assistant response
user_message = input("User: ")

     assistant_response = OpenAIAssistantRunnable(assistant).run(prompt=user_message)["response"]

5. Connect LangChain memory

  •   Store data to use across conversations
  •   Access external APIs through LangChain agents
memory = {"context": {}} 

     agent = ExampleAgent()

     assistant_response = OpenAIAssistantRunnable(assistant, memory=memory, agent=agent).run(prompt=user_message)["response"]

6. Deploy chatbot

  •   Wrap in a web app/API, connect to a messaging platform, etc.

Bottom Line: Who Can Build an AI Chatbot with Assistant API and LangChain

As you can see from the post above, anyone with minimal coding knowledge can build an AI chatbot using tools like LangChain and Assistant API. Of course, an average person with no software development background won’t be able to do this unless they learn extensively.

However, the essential factor is that any small business can now access all the benefits of using a chatbot with minimal investment. All you have to do is contact our team and set up a free consultation. Our experts will discuss your ideas and requirements and come up with a plan that will fit your budget.

No reason for any business to not benefit from a chatbot today exists. So, contact us and take the next step in your tech growth!

If you want to learn more about how Devtorium developers work with AI, check out the following articles:

Devtorium Offshore Software Development Company: Progress Report 2023

As an offshore software development company, Devtorium did not escape the turmoil shaking up the global economy today. However, no matter how trying the times might be, we firmly believe that investing in people is the best way for any business. That’s the core of our values, and we are proud to say that putting our trust in our employees has never failed this company. We can see evidence of it when looking at the 2023 company performance report results.

This year was wrought with conflict, and many businesses struggle with the volatility of markets and economic downturns. Devtorium also faced many challenges during this year. However, we are happy to say that the year’s results are positive despite it all.

What We Achieved as an Offshore Software Development Company in 2023

  • Devtorium started five new projects in 2023, engaging 40 developers and other technical professionals. The projects come from a range of industries, including Health and Education.
  • Over the year, our company welcomed 14 new specialists. Our teams grow, and we actively search for new talent when the projects demand it. You can always find a list of our open vacancies here. If there isn’t a suitable position at this time, leave your CV using the form on the page. Our Recruiting Department will reach out to you when we have an opening.
  • To ensure that Devtorium developers master cutting-edge technologies and can offer top-quality services to our clients, our company launched a Research and Development Department. This department is currently focused on NLP, Deep Learning, Machine Learning, and Neural Networks Training. Developers working within the R&D Department also study Python and contribute to our AI software development services greatly.
  • According to our project statistics, React, NodeJS, .NET, and Angular are the most popular technologies today. However, the interest in no-code development services is also growing. This year, we had some clients interested in developing solutions using Bubble.io.
  • Devtorium launched two in-house solutions in 2023. One is an ERP system that mainly serves our Accounting, HR, and PMO Departments. However, all our employees use it to track working hours on various projects and manage leave requests. The solution is created using the Bubble no-code platform, and it’s constantly evolving. The second in-house project we launched is a CMS system based on Frappe.io. Our Sales Department is using the many capabilities of this platform to manage and grow our leads database.
  • Devtorium completely redesigned our website, devtorium.com, to ensure its design matches our updated brand style and expanded list of services.

Devtorium: Plans for the Future

Our main priority as an offshore software development company is to focus on trending technologies and ensure we can deliver the services our clients need. At this time, this means shifting the focus to the rapidly evolving AI industry. We are proud to see that our developer teams have mastered this technology and learned how to use it to its full potential in the service of our clients.

Next year, we will continue our work to expand the company and introduce new technologies to our tech stack. Moreover, we will continue searching for projects in a variety of industries. However, our primary focus is maintaining the integrity and quality of service that our clients value.

Progress of Devtorium offshore software development company in 2023.

How to Use Photoshop AI Generative Fill

Photoshop AI Generative Fill, recently introduced as a beta version by Adobe, is truly an incredible tool. Even as limited as it is now, it makes the work of designers and photographers much easier. Moreover, Generative Fill has a high potential to change the image editing industry. In this post, Devtorium`s designers will explain the features of Photoshop AI and show off some of its applications in graphic design services.

What is Photoshop AI Generative Fill?

Generative Fill is an AI solution that can generate or remove parts of an image with minimal effort from the user. In simple terms, it works like this, you select an area of an image and AI can produce realistic textures, patterns, and details that perfectly integrate into the rest of the picture.

The step-by-step process goes like this:

  • First, you select an area within your image where you want to add content.
  • Next, you provide text prompts (commands).
  • After this, Photoshop AI analyzes your prompts and the surrounding image content.
  • Following the analysis, Generative Fill generates new pixels that match the style, lighting, texture, and details based on your input.
  • Finally, you receive several updated image preview options and can choose the best one.

Note that Photoshop AI beta is available to all paid Adobe Photoshop subscribers. All you need to do as a registered user is to download and install the solution from the official website.

Our graphic designer, Khrystyna Byelova, has already tried using Photoshop AI Generative Fill when working on photo editing for our social media posts. Take a look at the amazing results it can produce. You can see the prompts she used in the image below.

Photoshop AI Generative Fill use case with prompts

How Generative Fill Benefits Designers

The main benefit of using Photoshop AI is that it saves designers a lot of time, which is usually spent on image editing. AI assistance offers some additional benefits as well:

  • Contextual image generation that matches the original (objects and backgrounds)
  • Multiple style options, including textures, patterns, lighting, and details
  • Layers support for non-destructive editing
  • Customization: you control the adjustment settings, like Color Adaptation, Rotation Adaptation, and Scale
  • Faster iteration by generating dozens of variations

Bottom Line: How to Implement Benefits of Photoshop AI for Your Business

If you are a graphic designer or a photographer, you can greatly speed up your work using Generative Fill. However, it’s not only professionals who work with visuals who can benefit from it. Content generated with Photoshop AI can be used across industries ranging from eCommerce to advertising.

The technology is still relatively new, but it continues to improve at a rapid pace. The potential use cases seem endless, whether filling gaps in product images or generating graphical assets for ad campaign concepts. If you want to know how to boost your own business using this tool, contact us for a free consultation. To learn more about the use of AI in design, check out this article about prompt engineering for Midjourney.

Introduction to AI Part 1: Basic AI Terminology Cheat Sheet

Without a doubt, artificial intelligence technology started a new round of modern progress. Soon, understanding it, at least on a basic level, will become crucial, so you must start learning AI terminology fast. Today, we will begin our series of articles that provide a basic guide to AI by explaining some of the terms essential for understanding this tech. In future articles, we will explain how artificial intelligence works and AI software development services in greater detail.

You can already see how big and small businesses, governments, and ordinary people use AI in everyday activities. When we discover new technology, we try to make it a part of our daily lives. For example, with the invention of near-field communication (NFC), humankind came up with the idea of using it as a way of payment. Now, no one is surprised to see others paying with just a phone or hear NFC in regular conversation. 

The same thing is happening to AI. First of all, AI is a set of techniques that imitates human behavior and completes the tasks that would usually require human intelligence. According to Forbes, the AI market is expected to reach $407 billion by 2027. Thus, providing yourself with AI techs is a win-win investment in your business success.

Introduction to basic AI terminology.

AI Terminology Cheat Sheet (in Alphabetical Order)

Chatbot

A chatbot is an app made to simulate human conversations. It uses NLP to process inputs and generative AI to automate responses. A chatbot can perform multiple functions. For example, it can assist businesses from within or to engage customers.

Computer Vision

Computer vision is a set of AI technologies that allows machines to analyze and interpret visual content. Driven by deep learning models, it can recognize patterns, objects, and even emotions in images. In addition, computer vision can process dynamic content and perform gesture recognition and motion analysis. Today, computer vision can analyze images at extreme speeds and identify objects with 99% accuracy. This technology is applied in various industries, from security to healthcare and self-driving vehicles.

Datasets

Datasets – are large collections of various types of digital data. They are a crucial element of any ML algorithm and are used to train AI systems to complete assigned tasks. The most popular public datasets are Kaggle, UCI, ImageNet, and Quandl.

Deep Learning (DL)

It is a subset of machine learning that relies on neural networks trained on massive amounts of data. In this context, “deep” refers to the use of multiple layers in the network. As AI networks become more complex, the importance of deep learning in the scope of AI terminology increases.

Generative AI

Generative AI is an AI algorithm whose primary function is to generate new output from the training dataset. Whereas traditional AI models mostly follow predefined rules to respond to inputs, generative AI can produce variable content such as images, video, text, and code. Also, it can create outputs either in the same prompted medium, like text-to-text, or in a different one, like text-to-image or image-to-video. A great example of generative AI is Midjourney, with its multiple features.

Large Language Models (LLMs)

LLMs are machine learning models trained on vast amounts of textual data. The most well-known example of an LLM is ChatGPT. A part of Devtorium AI software development services includes creating and training LLMs to deliver the output you need automatically.

Essential AI terminology to know today.

Machine Learning (ML)

ML is a subset of AI development that enables machines to self-learn when dealing with specific tasks without preset coding. It’s one of the most basic terms in AI terminology today. There are many machine learning types and methods. Most of them use conditions (ifs), cycles, and internal variables to train the system`s algorithm. The developer needs to train the model using training datasets but does not change the code itself. Then, the model can be saved, loaded, or used to process new data.

Natural Language Processing (NLP) 

The ability of AI-driven systems to analyze, comprehend, and generate human language is called NLP. The three main parts of any NLP are computer science, human language, and AI. Applications that require NLP include chatbots, text generators, translation tools, and autocorrect solutions. Chatbots, like Amazon’s Alexa and Apple’s Siri, utilize NLP to process user queries and find answers.

Neural Networks

Neural networks are models of teaching machines to recognize underlying patterns. The name refers to neurons in the human brain. It’s because the way this network operates looks pretty similar to them. This kind of structure enables neural networks to handle more complicated challenges than traditional programming.

Prompt Engineering

Prompt engineering is the process of creating specific instructions for LLMs to generate desired outputs. The instruction usually contains information on the way and form of outputting content. It’s a handy skill in the modern world because it allows users to interact with AI more efficiently.

What’s the Next Step After Learning Basic AI Terminology?

This little AI terminology cheat sheet should help you understand essential AI-related content. In order to explain the matter more deeply, we will continue expanding this AI terms glossary and explain how artificial intelligence is built and trained in our upcoming articles.

However, if you want to learn how exactly AI can benefit your business today, contact our experts to set up a free consultation.

For those of you who want to continue learning about artificial intelligence technologies, check out our older posts:

cookie-image
cookie-image-mobile

Our website uses cookies

We use cookies and share information about your use of our site with our social media, advertising and analytics partners who may combine it with other information that you’ve provided them.