AI Tech Archives - Page 2 of 5

Introduction to AI Part 1: Basic AI Terminology Cheat Sheet

Posted on November 16, 2023November 16, 2023 by mkoftielieva

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.

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.

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:

Do AI Code Generation Tools Help Developers?

Posted on November 9, 2023November 9, 2023 by mkoftielieva

AI code generation tools became a huge hit fast, just like ChatGPT. Naturally, we wondered whether professional developers are worried about becoming redundant. Will human programmers be replaced by AI sometime soon?

We decided to discuss this matter by talking to some of the most experienced Devtorium developers. It turns out that they aren’t worried at all. In fact, they are actively exploring and using AI code generators in their everyday work.

AI software development is one of Devtorium’s core services. Therefore, our specialists spend a lot of their time researching artificial intelligence tech and what it can do. We asked some of our senior software engineers which tools they are using and what they think about their capabilities. Read their replies below.

Are AI Code Generation Tools Useful for Developers Today?

Head of Development Department: AI code generation is too limited to help developers now.

Andrii Bohatyr: Head of Development Department

Over the last six months, I used:

I would admit these tools are raw right now. In general, they don’t offer a lot of value. However, they are capable of helping with some rudimentary tasks.

The issue is that prompts require tweaks and improvements, even when you have a clear understanding of how to engineer prompts. Therefore, 9 out of 10 tasks could be easily completed faster in the old-fashioned way.

The same results could be achieved by using Google. I mean if the task requires diving deeper into a domain you need to research more.

AI code generation tools have good potential. However, as of now, they have too many issues and are still limited. The biggest problem is that even IDE pluggable tools lack the context of the whole project. Therefore, they are largely unusable. They don’t have an understanding of a particular project’s code style, conventions, issues, etc.

I would say, AI-based solutions indeed may help to automate some routine tasks. However, in coding experience is crucial. At least, if you’re not an LLM expert or a prompt engineering guru.

Senior .NET Developer: AI code generation tools make many mistakes but give good ideas.

Alina Nor: Senior .NET Developer

decided to try GitHub copilot immediately when I heard about it. It gave me a ‘wow’ effect – like magic when you’re writing a sentence about what should be here and it writes some meaningful code or when it completes the writing function for you – this impressed me.

I like to witness this magic. It’s a special kind of pleasure seeing how someone adds code for you. The problem is that you can’t rely on keeping that code as is. You need to review that and make sure it’s correct. For me, mostly it wasn’t. I had to rewrite it or improve it all the time.

However, it might give you a couple of ideas when you’re stuck. Also, I should admit that the code written by this tool is clean and follows the best practices.

Unfortunately, AI code generation tools don’t improve my workflow. I’ve been using Copilot for two months. Most of that time I didn’t accept its suggestions because I didn’t want to spend time reviewing or rewriting the code generated by it.

However, I’m using ChatGPT 4 for ideas or when I need to do some routing work. It’s a really powerful solution and it can save developer’s time. Especially if you need a general functionality, for example, some math functions or time calculations, etc. When there are no custom rules, its code works well. I think this is a powerful assistant for developers, even though you cannot rely on it completely.

Senior Front-End Engineer: AI code generators can be really useful when used right.

Maksym Kharkovenko: Senior Front-End Engineer

I have been using GitРub Copilot for about 3 months. If you’re looking to optimize your code, I would recommend giving Copilot a try. It can help you generate new code and patterns quickly and efficiently. In addition, I use GitHub Copilot Chat, which is also a good helper for improving your code.

Github Copilot is a really powerful tool that can enhance the coding workflow. However, sometimes it requires some examples before it can generate anything that can be integrated into your project. This AI code generation tool provides syntactically correct code but it still has trouble referencing types across different files and packages.

Using Copilot really improved my coding efficiency by providing relevant and accurate code suggestions. I like its intuitive handling of function creation, code completion, and creation of unit tests. It makes the coding process more efficient and enjoyable.

Head of R&D Department: AI code generation can be good but only when your prompts are great.

Oleksii Makarov: Solution Architect/Head of R&D Department

I have been using Copilot for several months. From my experience, AI code generation assistants are not a panacea or a way to completely escape coding. Therefore, you can’t rely on them to do all the work for you. Naturally, you need to match the generated code to your expectations. Sometimes, Сopilot can’t generate something I want. However, in most cases, it defines the context correctly and offers good suggestions.

Most of the time, I write in NodeJS and Python. Copilot helps to save time when writing my code. I’ve been doing more RnD lately, and in this mode, sometimes I must make some major changes in the project generated by AI.

Copilot defines the context of the code and the next block I want to implement quite well. For example, Copilot knows that this particular file implements a layer of database accesses. Therefore, based on the name of the method I introduced, it proposes changes with data that match the method name and my expectations. Even at the moment of the suggested completion, I see whether the Copilot understands what I need, and then I can accept or reject this completion.

Will AI Code Generation Tools Replace Developers Anytime Soon?

As you can clearly see, developers aren’t worried about AI taking their jobs. On the contrary, the leaders of them spearhead the innovation and use AI code generators to improve their workflow. Moreover, they use this opportunity to expand the limits of what AI can do.

As a provider of AI-based solution development services, Devtorium strives to contribute to the development of artificial intelligence technology. We understand that AI is the future and we are happy to use all it can offer and then expound on it for our clients.

If you want to know how AI-powered development can help your project, set up a free consultation with our team!

How to Use Generative AI in Real-Life Business

Posted on September 7, 2023September 7, 2023 by mkoftielieva

Generative AI is the buzzword of today. Everyone must have tried using at least one of the available solutions by now. ChatGPT alone has over 100 million users, and Midjourney isn’t too far behind. However, if you are a business owner looking to boost your services or workflow with tech innovation, your main question should be, ‘how to use generative AI in my business?’

We are here to answer this question today. Devtorium offers a wide range of AI software development services, and our solution engineers have expertise with different types of AI frameworks. According to our experts, the only limitation when implementing AI into business is your creativity. Generative AI uses are indeed quite specific. However, using your imagination, you can make these instruments help with many everyday tasks.

Step 1: Choose the Type of Generative AI Applications You Need

Generative AI is a technology that trains using specific datasets, and, based on that training, it learns to generate various types of other datasets. Simply put, developers input massive databases that the AI processes and learns. Then, it creates different types of content based on the information it consumed.

It’s also important to note that generative AI applications are today’s top tech trend. They’ve been on a steep rise since 2020 and, according to Gartner, reached the ‘Peak of Inflated Expectations’ in 2022. ChatGPT made this technology truly mainstream as it introduced AI to the mass market. Every person with a computer and internet access could use ChatGPT and get some answers from it. This showed people without connections to software development that AI technology is real and that generative AI uses are endless.

The quick popularization of other AI tools pushed this technology forward faster because, as mentioned before, it learns when it works with data. Therefore, AI improves with every task it completes.

When you consider how to use generative AI in your business, you need to start by researching the types of these tools. The most basic way to separate generative AI into types is by the kind of output content they produce. However, remember that some AI solutions are highly complex and can generate various content.

Text generation
Code generation
Video generation
Image generation
Sound generation
Research and explanation

The last type refers to generative AI uses in research where such tools can process, simplify, and summarize complex texts. For example, you can use Elicit or Enago Read for these purposes. Other popular generative AI apps are ChatGPT, DALL-E, Bard, DALL-E, and Midjourney, which create different content based on user-given prompts or dialogue.

Step 2: Generative AI Application in Real-Life Situations

Types of these tools seemingly make it obvious how to use generative AI. However, are you sure you’ve thought of all the ways this can be implemented in business?

Here’s a list of some ideas for you to consider:

Text content creation can be used to make marketing materials, blog posts, advertisements, and even work emails for some situations. For example, if you don’t have a talent for creative writing, generative AI tools like ChatGPT can help you write proposals, congratulatory emails, and even feedback.
You can use generative AI to edit texts as well. The tool can help you improve grammar and style or make some translations. However, it’s admittedly not perfect in translations, so be careful with how you use that output.
When you need to make guides or other helpful documentation for your customers, you can use AI generators to simplify technical texts. So, you’ll create posts and papers that are easy even for non-professionals to understand.
Image and video generators can also be used in marketing. You can create everything from a brand logo to a complete set of marketing materials used in ads, banners, blog posts, and even website designs.
In other generative AI uses, 3D models can be implemented in manufacturing and used to aid business planning.
Architects can use generative AI applications to create and analyze existing designs.
If needed, you can use Midjourney and similar tools to create illustrations for your content.
Sound generation can be applied to create tunes for social media or for your personal artwork, as well as advertisements and presentations.
Text generators, like GPT-4, are part of responsive chatbots and other automated reply solutions. For example, you can program these tools to send automatic email replies.
Sound generation solutions can work with speech in different ways. One of them is speech-to-text, which will allow you to create records of lectures or important meetings.

AI Uses for Onboarding and Customer Service

Currently, the main areas of generative AI applications are employee onboarding and customer service. That’s because a chatbot is the most popular AI instrument used in business today. However, generative AI uses allow you to expand on those implementations, further boosting their efficiency.

Some ideas on how to use generative AI in onboarding and customer service include:

Creating educational courses using video and speech generation technologies.
Personalizing customer journey experiences.
Creating a support service for navigating the company or website.
Creating a service to collect and process complaints, using AI capabilities to allow the tool to resolve some common issues.
Developing a content recommendation system for customers to help boost sales.

How to use generative AI to boost your business.

Bottom Line: Who Needs Generative AI Apps Today?

This is only a short list of the most basic AI uses one can come up with today. If you are serious about improving your business’ productivity with this technology, you should book a free consultation and discuss your case with our AI solution developers.

AI applications are so versatile that they can benefit any business today. All you need is a bit of creativity and a lot of technical expertise. We have both and can help you achieve the best results with custom AI solution development services. These can be tailored to your budget and needs. Most importantly, they can grow with your business, offering continuous improvement and optimization.

What Are Devtorium AI Software Development Services?

Posted on August 14, 2023August 30, 2023 by mkoftielieva

Innovation is our everything, and AI software development services offered by Devtorium reflect that. We aim to ensure that our clients have access to cutting-edge technology. Together with you, we can create tech solutions that will give your business a competitive advantage even in this age of hi-tech races. Our software engineers, who specialize in artificial intelligence solution development, can use a variety of AI tools to ensure your business stays ahead.

In this post we’ll explain exactly what kind of AI solution development services Devtorium offers. Moreover, we’ll give you concrete examples of how these technologies can be implemented to give any business a boost.

Devtorium AI Software Development Services: Technologies We Use

Artificial Intelligence technologies used today can be roughly divided into three groups:

AI/Deep Learning/Machine Learning
Reinforcement Learning
Generative Networks

It’s vital to understand that when creating AI software solutions, developers usually have to use all these technologies. The solution architect working on your project will analyze the requirements and suggest a combination of technologies to fit your needs best. AI software is highly complex and usually takes both software developers and data scientists to create.

Data Science services are an integral part of any AI software development. Artificial intelligence literally runs on data. Therefore, working with different types of databases and optimizing these processes, for example, through vector embeddings, is crucial for producing solutions that can learn and improve as they evolve. Devtorium AI software development services create self-educating systems that will grow with your business and adapt to its changing needs.

AI Software Development Technologies: Areas of Implementation

Computer vision

Object Detection
Segmentation
OCR (Optical Character Recognition)
Human Pose Detection
Face Detection

Computer vision is one of the AI software development services we encounter daily. It’s combined with other types of AI tech to produce solutions that surround us everywhere, for example:

Auto-pilots for vehicles
Security systems (FaceID, fingertip scanners, building security footage analysis, etc.)
AR applications used for eCommerce and gaming
Scanning and transcribing text via a photo (try this feature in GoogleTranslate)
Advanced search through images, videos, and large documents

Large Language Models (LLM)

Text generation
Searching
Analytics
Summarising

The most well-known example of an LLM is ChatGPT. Pretty much everyone who is even a little tech-savvy today used this platform at least once. Its most popular features are text generative and writing code. However, it can do much more than that. For example, an LLM can create text summaries, descriptions of images, and different versions of the same content. It can ‘give advice’. However, the system is pretty straightforward in the sense that its advice is content generated based on your inquiry.

Note that these AI solutions can process literary texts, code, and any other kind of textual data. A part of Devtorium AI software development services is creating and ‘training’ LLMs to automatically deliver the type of service you need.

Big Data

Statistic/Data Analytics
Data Transformation

Our world is data, and AI-powered data science solutions can make it work for you in every way possible. Devtorium’s team of data scientists is experienced in creating solutions that can extract and process data from various sources. Some of their projects included:

Deriving relevant information from digital reports and massive databases. Then, processing this data and visualizing multiple reports for eCommerce businesses to help the client make educated decisions.
Processing data from multiple sources and creating a predictive analytics model to forecast trends and market changes.
Transform data from one format to another for future processing and analysis. For example, one of our projects included collating handwritten notes from medical specialists, adding medical records data, prescriptions, and medical test results. The data was transformed, processed, and analyzed to provide necessary insights for an AI-powered healthcare solution.
Another project Devtorium completed entailed collecting data from fitness trackers, medical records, prescriptions, notes, and trainer comments as a part of an interactive athletes’ training tracking app.

Robotics & Automation Engineering

Implementation of robotics technologies at manufacturing lines is nothing new today. However, recent advancements in artificial intelligence solutions take this type of service to the next level. As a part of Devtorium AI software development services, our engineers can do much more than simply program the machines to do specific actions automatically.

We are now able to ‘teach’ robots the basics of behavior and program them to learn. As a result, you get automation that improves with every iteration. So, instead of a mindless drone, you get a helper that can increase overall business productivity and significantly reduce the risk of errors.

Bottom Line: Should You Invest in AI Software Development Services?

Answering the question of whether your business needs an AI-powered solution is easy. Just decide for yourself if you want your business to stay competitive on the market. Today implementing AI in various business processes is not about getting head through groundbreaking innovation. Already this technology is becoming so popular and widespread that not using it is sure to put you hopelessly behind.

If you want to not only retain a competitive edge but actually move forward, taking over a bigger portion of the market, contact us today. Devtorium’s team of AI software engineers will work with you to develop a strategy that can help your company succeed.

How Vector Databases Can Enhance Custom AI Solutions

Posted on July 26, 2023August 30, 2023 by mkoftielieva

Enhancing your workflow with custom AI solutions is the biggest tech trend today. However, as it’s still a relatively new technology, we face some challenges in handling large amounts of data. Vector databases can solve many of these issues and enable AI to process data faster and more accurately. Today, Devtorium AI specialists will share their knowledge of how vector embeddings and databases work and the best options available now.

Why Custom AI Solutions Need Vector Databases

First of all, we need to understand what exactly vector databases are and how custom AI solutions use them. These databases are designed to provide various AI models, for example, conversational AI, with a more efficient way to use data.

Let’s start with vector embeddings, a type of data representation used by generative AI, Natural Language Models, and semantic search. In very simple words, it works like this:

AI generates vector embeddings infusing them with various attributes and features.
Features of embeddings represent patterns, relationships, and structures of data. They are what enables AI to “understand” content and context.
Traditional scalar-based databases aren’t the best fit for working with these embeddings because they can’t keep up with their complexity.
Vector databases are designed to work with vector embeddings. Therefore, they offer the highest levels of productivity and flexibility.
Using these databases allows AI to develop long-term memory and execute more complex tasks.

The picture below shows a basic representation of how a vector database works with vector embeddings. Notice how the database identifies similar embeddings associated with original content. This allows it to be faster and more productive in handling data.

How Does a Vector Database Work?

To understand why exactly vector databases are better for custom AI solutions, you need to know how they differ from other options. Traditional scalar databases store data in rows and columns. That’s pretty straightforward, secure, and efficient, but these rows and columns can be hard for AI to navigate. Even with immense processing power, identifying and reaching the needed data takes a lot of time.

Meanwhile, vector databases are different in their methods of data optimization and querying. Instead of querying a row with a perfect value match, vector databases use a similarity metric. Therefore, they are searching for a vector most similar to your query. To achieve this, they use a variety of algorithms combined into ANN (Approximate Nearest Neighbor) search. To optimize the search, these algorithms use:

Hashing
Quantization
Graph-based search

The vector database pipeline (shown above) allows searching for information extremely fast. However, due to using ANN, the results you get are approximate. So, when working with this type of database, you need to understand that accuracy and speed are interdependent. It means that to get greater accuracy of results, you must lose speed.

That said, a good vector database, when used by custom AI solutions, should work so well that you get ultra-fast and ultra-accurate results.

Here’s how it goes step-by-step:

Indexing
The database indexes vectors using PQ, HNSW, LSH, and some other algorithms. It’s a mapping step that helps speed up the search.
Querying
The database compares indexed queries to the indexed vectors within the dataset to identify ‘nearest neighbors’.
Post processing
When needed, the database will retrieve the nearest neighbors and process them to achieve the final result with the highest accuracy.

How to Use Vector Databases in AI Solutions for Business

If you feel a little lost in all these technicalities and want to know exactly why you should consider using vector databases in custom AI solutions, see how they apply in real life.

Recommendation systems
Providing personalized suggestions on your website certainly increases sales.
Searching for images and text
Converting text and images into vectors makes finding similar ones easier. That’s especially useful in eCommerce, where customers can search for items using descriptions or photos.
Natural language processing
Representing words and sentences as vectors makes it easier for AI to understand and interpret human language. You can use this in document clustering and semantic search to increase accuracy.
Fraud detection
Vector databases can be applied to find data patterns that point to fraud. For example, a specific set of transactions with similar vector representations might alert your security system.

In the nearest future, a successful business will be one that effectively harnesses the potential of AI. At Devtorioum, we know multiple ways to boost the power of custom AI solutions. If you plan on gaining an advantage over competitors using one of these, contact us for a free consultation!

Introduction to AI Prompt Engineering for Midjourney

Posted on July 7, 2023August 30, 2023 by mkoftielieva

AI tools blew up the internet and changed our world almost overnight. However, one thing that most users learn just as fast is that AI prompt engineering is a challenging task. You can ask ChatGPT almost anything, and it will understand and offer some answers. But if you want to make AI generate a specific type of complicated text, you’re in for a struggle. It’s even harder when generating images, even with an AI platform as advanced and ‘experienced’ as Midjourney.

Devtorium teams are actively exploring the capabilities of artificial intelligence technologies. As part of this project, our UI/UX designers tested Midjourney thoroughly to understand the limits of this AI service and find the best ways to apply it in their work. Today, they share some insights about AI prompt engineering that they managed to get while studying AI-powered image generation platforms.

Before AI Prompt Engineering: How to Start Using Midjourney

Using Midjourney is quite simple, but it’s not as straightforward as simply downloading an app and entering a prompt. First of all, you need to have a Discord account. Once you set that up, you’re going to use Discord to access Midjourney. Start by joining its channel.

Midjourney takes care of its users by offering a very detailed and easy-to-understand set of docs. There you’ll find step-by-step instructions on how to:

Set up and use a Discord account
Join the Midjourney community
Generate images using commands
Learn various commands and parameters
Subscribe and manage your subscription
Upscale generated images

If you are having any issues, the community of Midjourney users is vast and quite friendly. Therefore, you should be able to get answers to any question fast.

At the moment, a monthly subscription to Midjourney costs $10 for a Basic Plan, $30 for Standard, and $60 for Pro. You can cut the cost when buying an annual subscription. Also, remember that the platform subscription will run out faster depending on the number of images you generate. Therefore, mastering the art of AI prompt generation will actually save you money as you’ll get accurate images faster.

AI Prompt Engineering Tips for Midjourney

Now that you’ve set everything up, it’s time to generate images. So, you enter the command /imagine and enter your prompt. That’s where the fun starts because the prompt’s detail level and word order define the image you get. Note that the order of words and modifiers in the description matters quite a bit. So, if you want to be understood by AI, you must learn to speak its language.

That said, regardless of how advanced or well-structured your prompt is, you will always get an image. Moreover, the prompt structure isn’t fixed. It means that even if you switch some parts around, you’ll be able to get a beautiful and accurate image. However, as the Devtorium design team discovered, some structures just work better.

Here’s what they suggest using as a standard reference:

[Scene description]. [Modifiers], [Artist or style inspiration], [Technical specifications]

Now, let’s look at those parts and how to make the most out of this formula.

Scene Description

That one’s relatively easy. Your first step in AI prompt engineering is to make a short description of exactly what you want to see. For example, if you want to generate an image featuring pancakes dusted with sugar and surrounded by blueberries with a white background, that’s your scene description.

The most important thing to remember here is that when generating an image, start with a general description, adding specific details after it.

When generating an image with Midjourney, always start with a general description. Then, add details to make it more specific.

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Modifiers

In essence, modifiers are the specifications that explain the machine what you want to achieve. It means that they are the most crucial element during AI prompt engineering. Your selection of modifiers will define the details of your image, so be precise but not overly descriptive.

Here are some general tips specific to Midjourney modifiers:

Put the words in brackets () if you want the AI to pay more attention to this specific detail. You can also use double brackets (()) to highlight the most important elements that AI must pay attention to.
Use modifiers to describe important image details such as lighting, style, mood, or specific image elements.
You can use a structure modifier:1.4 to draw the attention of AI to a specific element. It means that the machine will process it with a 140% value instead of all other words with a default value of 100%.

Types of Modifiers for AI Prompt Engineering

Type of art: digital painting, watercolor, concept art, portrait, full body, etc.
Style: epic realistic, fantasy, intricate, vivid, harmonious, lifelike, fantastic grotesque, architectural, balanced, cinematic, hyperrealistic, sophisticated, etc.
Lighting: warm lights, cinematic lighting, iridescent gold, dramatic lighting, dark, etc.
Detail complexity: intricate detail, photorealistic, realistic, emphasis on detail, etc.
Background description: complex background, color background, flowering landscape, etc.
Character description: appearance details (hairstyle, coloring, etc.), character action or pose.
Visual effects: art styles (oil painting, charcoal, etc.), photography style (bokeh, Adobe Lightroom, Photolab, etc.), 3D effects (Unreal Engine, Volumetric DTX, etc.), general (medieval, muted colors, pastels, impressionism, Bauhaus cubism, etc.)
Technical specifications: micro-details, 4K UHD image, RAW photo, extremely detailed CG Unity 8K wallpaper, etc.

Artist or Style Inspiration

This is also a type of modifier for AI prompt engineering. However, adding this specific element will affect the image greatly, so we thought it deserves a separate mention. Simply put, you can make Midjourney generate an image that will imitate your chosen artist’s style in great detail.

AI’s database is extensive so it can do equally good similes of classical painters and contemporary digital artists.

When you select an art style for this modifier, AI will use the works of top artists in this niche as a reference.

How to Learn AI Prompt Engineering from AI

As this topic is trending today, you can find an AI prompt engineering course on almost any education platform. However, the best way to learn is to ask AI itself. It’s easy to do when generating images through Midjourney because it has a command /describe.

When you use this command, you upload an image onto the platform, and AI will describe it for you. Studying the word patterns it uses will allow you to understand the way AI thinks. So, you can use such descriptions as a reference when making your own prompts.

However, you need to remember that artificial intelligence technology is only in the early stages of development. Therefore, if you use the same prompt that AI generates through /describe, you might get some very fun results.

AI Prompt Engineering Limitations: Why We Still Need Human Designers

AI tech available to every user today can do some incredible things. However, it doesn’t mean that designers and artists will be out of work soon. In fact, knowing how well they work with AI is now one of the qualities of a reliable UI/UX design company. This technology is developing fast, but it has quite a few limitations. Starting to work on AI prompt engineering makes them very clear.

First of all, there are quite a few things that AI doesn’t understand. Nuances of meanings and personalized speech patterns are beyond it.

Simply put, if you take two people and have them describe the same image, they might give you two very different texts. It’s because we are guided by our perception, which is defined by our own experiences. So, where one person sees a stunning mountain landscape, someone else might see a desolate clump of rocks.

AI takes all prompts quite literally, so sometimes it’s very hard to make it generate exactly the type of image you want.

Moreover, artificial intelligence struggles with the negatives. So, your number one tip for AI prompt engineering is to never use exclusions. For example, avoid saying ‘the man doesn’t have an arm’. Instead, choose ‘a one-armed man’.

This limitation in understanding human speech is also the reason why prompts must be structured. When we describe something, we are mostly driven by emotions. However, AI always uses logic and math. The order of words you choose will affect how important it considers the details you list. It’s truly that simple.

However, as Devtorium designers commented while working on AI prompt engineering for Midjourney, the model learns extremely fast. So, we might all live to see when it becomes advanced enough to understand and translate into code even the most complex human emotions and fantasies.

What Are Data Science Services?

Posted on May 26, 2023August 30, 2023 by mkoftielieva

Data rules the world today, and data science services can be a true game-changer for any business. However, few business owners realize the potential and versatility of data science as a service. It’s regrettable, especially for SMBs and startups, which can benefit tremendously from the insight derived from big data.

Today we’ll try to remedy this situation and expand on the subject of why now is the time for data analytics. We’ll start by explaining how some basic data science consulting services can help businesses thrive in competitive and volatile markets.

Data Science Services Explained

Data Mining, Processing & Visualization

The first among data analytics consulting services is data mining. Simply put, the team will find a way to extract all the information you require from any source. This type of service is highly versatile because the type of data that businesses need to process might vary considerably. For example, in one of our cases, data scientists had to develop a method and solution to extract patient information from multiple sources, including healthcare system records and even doctors’ handwritten notes.

Then, the collected data will be translated into some universal format and processed. Depending on the volume, the team might require substantial resources to complete these tasks. Using the help of NLP (Natural Language Processing) tools is quite common when processing and analyzing data.

Data science visualization services allow the client to get the data presented in a manner they can easily use for business. For example, an eCommerce business can get detailed reports on sales dynamics during a specific period. Based on this report, the client will be able to understand exactly how people choose to spend money on their website. Therefore, they will be able to come up with more efficient offers to boost sales.

Predictive Analytics, Forecasting, Risk Management & Optimization

Big Data analytics is a type of service that can benefit any business because it’s your way to get information about the market. Therefore, it enables you to make well-informed decisions that will provide better results.

Data science services allow you to use the tremendous storage of valuable insights that is the Big Data to make accurate predictions to build your business strategy. Predictive analytics works by building NLP models that analyze specific types of situations on the market, identify patterns, and make forecasts based on them.

Data science services that involve forecasting can provide a highly accurate future development model. For a business, such prediction can help reduce risks and prepare for those that are unavailable. In addition, predictive data analytics models can be used as a strong argument for investors.

Another way to implement data science services is to have the team provide an optimization plan for your business. Then, they will use a combination of data mining, processing, analysis, and forecasting to develop a set of custom-tailored recommendations for your business.

The best thing is that such recommendations are based on actual data and existing patterns. Therefore, your company won’t waste money on changes that won’t have a positive impact in the long run. When you rely on data science services, all results are based on hard evidence, so risks are minimal.

NLP, Computer Vision, AI & Machine Learning

Are you wondering how exactly data scientists do the analytics and prediction part of their work? The answer is they use AI to assist them in these processes. The exact technology they use varies based on the task. For example, a data science team can build a very primitive NLP model to ‘mine’ information matching specific parameters from a particular database.

However, they can also use the same technology on a grander scale to build a more complicated model to analyze specific patterns, like market growth trends. Then, another model will process that data and make a forecast based on those patterns accounting for additional factors.

AI technologies are developing rapidly, and so are data science services. Therefore, the scope of insights you can derive from this service increases, giving you new business opportunities.

Another exciting type of AI-based service a data science team can offer is utilizing computer vision technology. Like NLP models derive information from textual data, computer vision extracts information from the visuals. The most common application for this tech now is the facial recognition feature used by multiple gadgets. However, on a larger scope, this technology can be used to prevent crime, analyze a stream of video footage, or monitor specific areas for emergencies.

What Businesses Need Data Science Services?

Applications of AI and data science services are endless. So, regardless of size, every business can benefit from using these services on any level. Therefore, the main factors to consider are your budget and goals.

First, determine what it is that you’d like to achieve at this stage. Do you want to cut costs? Optimize business processes to increase productivity? Enter a new market? Launch an innovative startup? Expand the capabilities of your existing product? Enhance your business strategy?

Once you’ve chosen one or several objectives, book a meeting to discuss data science consulting services. A team of experts will analyze your business and request and develop a list of services that will help achieve your goals.

AI Speaks About How to Use an AI Chatbot for Business

Posted on April 5, 2023August 30, 2023 by mkoftielieva

Devtorim’s AI-powered platform Marquètte is getting better at generating interesting articles you can use for blogs and even for self-education. Today we used one of the new marketing templates to have AI share ideas on using an AI chatbot for business. Check out what it had to say below.

Ideas on How to Use an AI Chatbot for Business in AI’s Own Words

In today’s digital world, businesses increasingly turn to artificial intelligence (AI) chatbots to automate customer service and enhance customer engagement. AI chatbots have the potential to save time and money while providing a more personalized customer experience. In this blog post, we’ll explore how to use an AI chatbot for business, the benefits of using an AI chatbot, examples of successful AI chatbot implementations, challenges of using an AI chatbot, best practices for deploying an AI chatbot, and how to measure the success of an AI chatbot.

How to Use an AI Chatbot to Automate Customer Service

AI chatbots can be used to automate customer service tasks such as:

Answering frequently asked questions
Providing product information
Helping customers with their orders

They can also be used to respond quickly to customer inquiries and provide personalized recommendations based on customer data. By using an AI chatbot, businesses can save time and money while improving customer satisfaction.

Benefits of Using an AI Chatbot for Customer Engagement

AI chatbots can help businesses to provide personalized service. For example, they can answer customer questions 24/7 and provide automated support.

In addition, AI chatbots can collect customer data and improve customer engagement through targeted messages. AI chatbots can save time and money by automating everyday customer service tasks.

Examples of Successful Uses of an AI Chatbot

AI chatbots can be used for a variety of business needs. For instance, the Bank of America uses an AI chatbot to help customers with account inquiries and transactions.

There is also Sephora, a company that leverages AI to power an in-app virtual assistant that helps shoppers find the right products.

Moreover, Domino’s Pizza has created an AI chatbot to take orders and provide personalized recommendations.

Challenges of Using an AI Chatbot for Business

Implementing an AI chatbot for a business can be a challenging process. Designing, developing, and deploying a chatbot requires a lot of time and resources.

Additionally, businesses must consider the cost of hosting and managing the chatbot and other associated expenses. Finally, it may take some time before companies realize their chatbot’s full potential.

Best Practices for Deploying a Chatbot

Deploying an AI chatbot can be a great way to improve customer service. However, it’s important to ensure that the chatbot is designed to meet the customer base’s needs and that it is properly integrated into the existing customer service infrastructure.

Moreover, providing customer service agents with the necessary training to manage conversations with the AI chatbot is crucial. Finally, you also need ways to monitor customer service interactions for quality assurance purposes.

How to Measure the Success of an AI Chatbot

Measuring the success of an AI chatbot depends on the goals you have set. For example, you can measure the number of interactions, user satisfaction and conversion rates, or time on task. Additionally, you can use surveys and feedback forms to gain insights into the user experience.

Final Thoughts: Should You Start Using an AI Chatbot for Business?

AI chatbots can be invaluable for businesses looking to automate customer service and improve customer engagement. While there are potential challenges in implementing an AI chatbot, with the right strategies and best practices in place, businesses can maximize the success of their chatbot. In addition, deploying an AI chatbot is a great way to increase customer satisfaction while reducing customer service costs.

So, those were thoughts directly from AI. What do you say? Do you agree with these opinions and ideas? Will you start using an AI chatbot for your own business?

If you are ready to take this step, set up a free meeting with our development team!

For more thoughts on AI also see:

How Artificial Intelligence Software Development Will Transform in 2023

Using AI for Sustainability: Technology for a Better Future

Using AI for Sustainability: Technology for a Better Future

Posted on March 22, 2023August 30, 2023 by mkoftielieva

Everyone today should understand that our planet isn’t doing too well health-wise. Reports of natural disasters or some other nasty side-effects of environmental changes come almost daily. With the rise of artificial intelligence, we are wondering how using AI for sustainability could help improve this situation. It turns out that AI tech can become a true life-saver for our planet, but only if it’s used wisely.

Artificial intelligence software development technology is developing rapidly. Some of its trends are already focused on using AI for sustainability, but that’s hardly enough. To truly benefit from this tech, people have to invest in the solutions that can make the most difference. However, governments also need to contribute. It’s not only a matter of money but also regulation. It’s because abusing AI tech could lead to tragedies, especially in countries that aren’t liberal democracies.

Keep reading to learn what AI can do to help heal our planet and why we must keep it under control.

Using AI for Sustainability and the UN SDG Program

A giant blob of seaweed moving to the shores of Florida right now is only one of the many natural disasters in action. All of them are red flags waving at the planet’s populace, stating that we need to do something to improve the health of the planet. One of the main initiatives currently working on those objectives is the SDG Program.

SDG stands for Sustainable Development Goals defined by the UN. Those are the goals humanity is working towards to ensure the long-term survival and well-being of our world as a whole. They go far beyond managing climate change, including dramatically important matters like conquering world hunger, inequality, and increasing education quality.

Using AI for sustainability can help achieve over a hundred of those top goals faster and more efficiently. Applying this technology is currently most beneficial in matters concerning climate change and the overall health of our environment.

In turn, this can benefit businesses and economies because we live in a highly interconnected world. For example, according to PwC research commissioned by Microsoft, using AI for various environmental purposes can contribute about $5.2 trillion to the global economy by 2030. That’s not even counting the long-term benefits the entire planet’s population will experience from having a healthier environment.

Using AI for sustainability worldwide could help reduce greenhouse gas emissions by 4% in 2030, which equals the annual emissions of Australia, Japan, and Canada combined!

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Companies Using AI for Sustainability

Many top companies already use AI for energy management and other practices to reduce their carbon footprint. Top examples of this include:

IBM
IBM is applying AI in many of its processes to boost overall business productivity. Regarding increasing sustainability, one of the most valuable contributions comes from using AI for weather forecasting. Artificial intelligence allows for raising the accuracy of predictions by about 30%. As a result, IBM can help renewable energy companies increase their efficiency, especially in resource management and energy production.
Google
It’s no secret that Google takes sustainability projects very seriously. Running power-hungry data centers, the company must do its best to boost energy efficiency. AI helps Google reduce cooling costs by 40%, and that’s only one of its applications.
Carbon Tracker
Carbon Tracker is a think-tank focused on finding ways to improve the climate situation on the planet. One of its projects is using AI for tracking emissions. They can track CO2 based on satellite imagery and use this data to identify coal plants with the lowest footprint. Then, they use the data to help attract investment to these specific plants.
Xcel Energy
It might be surprising to hear that a company that burns coal and has a high emissions level uses AI for sustainability. However, even businesses that are considered non-eco-friendly work on minimizing their impact. Xcel Energy is using AI to increase its efficiency by about 20%.

However, you don’t have to be a huge enterprise that racks up millions in revenue to start using AI for sustainability. You can use this technology to improve your business practices and processes in many ways. Consider the following ways to implement AI:

Precision agriculture
Energy management within office buildings
Analytics for better resource planning
Minimizing energy and water waste
Environmental monitoring
Sustainable supply chains

Why Using AI Might Hamper Sustainability and Cause Global Problems

Using AI for sustainability can help accomplish great things. Some of the most prominent among them are:

Clean power
Sustainable production and consumption
Clean transport
Smart homes and cities
Early warning about possible disasters
Resilient infrastructure
Pollution prevention
Clean oceans
Disease control
Clean water supply
Resource management
Sustainable resource use

These and many other things can be possible if people work together and wisely manage their resources. AI can be a great help by taking out biases and calculating the best strategies for increasing sustainability and equality.

That said, this power can be used for evil as easily as it can be used for good. Therefore, it’s imperative to develop specialized regulatory bodies and strategies to monitor the applications of AI.

One of the most concerning matters is the use of this technology by authoritarian and totalitarian political regimes. Some non-democratic governments are already using AI for tracking purposes. There is a huge risk that they will abuse this technology further in order to maintain power and control of their territories and promote their corruption schemes.

A lack of transparency and competent regulation when it comes to using AI for sustainability or any other purpose can result in tragedies. It will definitely lead to multiple safety risks, including some with possible global consequences. It will also make it impossible to maintain clear ethical standards.

Therefore, any large-scale use of AI should be preceded by the development of proper regulation. Currently, technology is growing and spreading much faster than appropriate political and regulatory protocols. This should be concerning for all of us.

Bottom Line: The Future of AI and Its Impact on the World

Even the most basic AI chatbot can do incredible things today. Therefore, the future of AI technology is certainly bright. It will continue developing and growing fast because it’s the top investment opportunity right now.

However, that growth can lead to unimaginable consequences without proper regulation. So, the question isn’t how we can use AI for sustainability but how to ensure that sustainability is all it’s used for.

Is Bubble the Best No-Code Development Platform?

Posted on March 15, 2023August 30, 2023 by mkoftielieva

No-code development can’t replace professional developers yet. However, it’s certainly getting better at creating software. Some of the best no-code development platforms can already create efficient and reasonably complex apps. Bubble.io stands out among these services due to its reliability and versatility. We know this because one of the Devtorium teams is using Bubble to develop an ERP solution for our company.

According to forecasts, the global no-code/low-code development platform market will reach $187 billion by 2030. If things continue as they are now, Bubble will be among the top platforms there. So, if you are wondering whether to choose it for your own MVP development, keep reading. Our team compiled a list of the platform’s pros and cons based on their experiences.

Benefits of Using No-Code Development Platforms for Small & Medium Business

No-code and low-code development platforms have been around for a while. Initially, they were very limited and mostly served as aids for developers. In essence, even today, no-code development is often used to cut down the software engineer’s work hours and involvement in a project. However, this tech can’t replace the professional entirely. That’s because an expert must manage it.

That said, no-code development offers small and medium businesses a great opportunity. These platforms enable SMBs to create affordable customized software to boost their business’ productivity and speed up inner processes.

Moreover, using a no-code development platform is a fantastic way to build your MVP. If you have an idea, this type of solution will allow you to make it presentable to attract investors.

However, in order to make the most out of your no-code development project, you must understand the capabilities and limitations of the platform you choose. In this article, we’ll analyze Bubble, one of the leaders among the best no-code development platforms today.

Bubble No-Code Development Platform: Pros & Cons

Advantages of Using Bubble

Every Devtorium developer who used or even researched Bubble says its main advantage is that the platform allows you to write your own logic. Quite often, it’s a massive issue with no-code platforms because they only let you develop solutions based on built-in plans.

However, Bubble allows developers the maximum level of flexibility. So, you can use this platform to create almost any type of solution. For example, this includes marketplaces, SaaS, dashboards, or even your very own social network.

Our devs believe Bubble is an excellent platform to test your ideas. You can use it for developing an app fast and evaluating how all the features you want to include fit together. You can also use it to have a focus group test the app. This way, you’ll be able to assess and improve your app development plans. In addition, you can see the audience’s response to it.

Aside from the flexibility, Bubble offers a wide range of functionalities you can integrate into your app. This no-code development platform is extremely versatile and has a vast plugin marketplace. Therefore, there is hardly a feature you can’t find. Therefore you should be able to create precisely the type of software you want.

Moreover, there is a thriving Bubble community, and the platform’s support service works exceptionally well. So you’ll be able to find advice or discuss any questions you have.

Bubble No-Code Platform Limitations

Even the best no-code development platforms aren’t perfect. So you need to consider their limitations before settling on a no-code platform for your project. In fact, your long-term plans are the foremost thing to consider when using Bubble. For example, if the platform doesn’t have the capability for features you want to add in the future, reconsider using it at all.

Of course, no-code development platforms evolve and release new features and functionalities regularly. However, betting the future of your own project on this chance is probably a mistake.

That said, you must remember that the features you might like at the idea stage aren’t what your target audience needs and wants. So from this point of view, creating a high-quality MVP with Bubble might pay off because you’ll be able to collect relevant feedback. But bear in mind that you might need to move to a different platform or even build your native app from scratch again using different technology.

In addition, we discovered that Bubble is flexible for functional design but not for UI/UX design. It’s a fundamental fact to keep in mind when you try to bring your idea to life using Bubble. You’ll be disappointed if you believe you can create a functional app and upgrade the design later, you’ll be disappointed.

Bubble no-code development platform is somewhat limiting in UI/UX design adjustments. It means you have room to create an attractive UI design as you start developing, but you can’t change it much after creating the app. So the solution is to have your design on hand when you start working with this no-code development platform.

Bottom Line: How to Choose the Best No-Code Development Platform

If you think about no-code development, Bubble is definitely one of the best options today. Our developers tested it extensively and were very impressed. However, you need to remember that all these platforms have limitations. The most important among them is processing power.

Simply put, no-code is suitable for small and medium projects. However, enterprise-level businesses won’t be satisfied with the results.

You need to consider your specific needs and plans to choose the best among no-code platforms. Research what each option can deliver and pick the one that meets your needs best. Don’t forget to consider the price because these solutions won’t be free if you want to have a decent amount of processing power.