Let the data speak louder than words. In this first part, we’ll start with the real facts about AI evolution and its market condition. Here are some of the most prominent artificial intelligence statistics that reveal this technology’s growth rate, acceptance, and influence on diverse AI products.
- The AI market’s revenue forecast for 2030 is projected to reach $1.345.2 billion, according to Marketsandmarkets.
- AI is expected to show an annual growth rate of 37.3% from 2023 to 2030, as Grand View Research reports. This data underlines the potential of AI technologies across many industries worldwide.
- Forbes Advisor writes that 64% of business owners foresee that AI will improve their customer relationships.
- Over 60% of business owners envision that AI will positively impact productivity. As Forbes Advisor also states, 42% of them think it will streamline diverse job processes.
- According to Statista, AI is projected to yield a 21% net increase in the United States GDP by 2030. This forecast is going to be a significant contribution to US economic growth.
In this article, you’ll see the ideas, thoughts, and understandings of Oleksandr Bachynskyi, who has been a chief technology officer (CTO) at Linkup Studio for the past 10 years. Under his technical guidance and direction, the company has created over 100 digital products, including many artificial intelligence solutions. Oleksandr has played a major role in putting the company name into the list of Clutch’s Top 100 Fastest-Growing Companies in 2023 worldwide. In that period, Linkup Studio had a 192.5% growth rate between 2022 and 2023. He’s an expert in, mobile, web, and no-code development, solution architecture, AI implementation, and software testing.
How AI Works: The Technical Aspect
Understanding AI and Its Types
In essence, Artificial Intelligence is about creating algorithms that enable computers to perform tasks that would normally require human intelligence. These are tasks like recognizing speech, interpreting data, making decisions, translating languages, and others.
There are 2 main types of artificial intelligence. General AI and Narrow AI. General AI is not made yet and its concept is now rather theoretical. It is the type of AI that would be equipped to handle any intellectual task that a human can. A hypothetical example would be an imaginary AI health assistant robot that could not only analyze medical data or diagnose diseases only but would also understand the emotional and psychological states of patients and would offer holistic healthcare advice like a highly skilled physician.
Now, we’ll focus on the existing types of Narrow AI. How do AI algorithms work here? Such AI solutions operate within a limited specific context and simulate human intelligence in specific tasks. Think of it like an expert chess player that can’t play poker or other board games. Such AI solutions are designed to perform a single task.
There are voice assistants, like Siri or Google Assistant. These tools are programmed to understand and respond to specific voice commands. They can perform tasks like playing music, setting reminders, or answering factual questions but are limited to the programmed responses and tasks.
Another type of Narrow AI is recommendation systems like those on Netflix or Amazon. These systems analyze users’ past behavior, such as previous purchases or watched shows, and use that data to suggest or recommend new products or movies.
One more example is Email Spam filters which use AI to filter out spam. AI systems learn to identify which emails are spam and which are not based on certain characteristics of the emails.
Another very popular example is AI chatbots. How does an AI chat work? It is programmed to simulate expert conversation and can provide users with answers to their questions. However, chatbots may struggle with understanding and responding to queries that are off-script or complex.
Machine Learning - The Heart of AI
All these complex AI systems work by Machine learning. This technology allows computers to learn from data and make decisions based on it. At the heart of Machine Learning, there are algorithms. Essentially, those are specific sets of rules or processes which a machine follows on problem-solving operations.
AI algorithms work based on the training data. That is the information or dataset from which the learning algorithm learns. The quality, diversity, and volume of data directly impact the performance of a machine-learning model.
At Linkup Studio, we created an AI-based app that helps to identify skin diseases, SkinSpotter. For this, we used the training data from the international skin imaging collaboration with over 200 thousand photos of various skin diseases. This helped the AI system to better identify potential skin problems based on user images of their skin diseases.
Let’s talk about the main types of learning.
Supervised learning. Here, models are trained on labeled datasets. They learn to predict outcomes based on past examples. As in the earlier-discussed example, spam filters in email services are trained on the dataset where some emails are labeled as spam, and others are as “not spam”. This way, incoming emails are classified based on this training.
Unsupervised learning. Models are trained on unlabeled data to discover underlying patterns and group links. For instance, with that type of training healthcare organizations may understand the common characteristics and risk factors of patients within each certain group of patients, and develop preventative strategies for those patients who may be at risk but have not yet developed symptoms.
Reinforcement learning. Such models learn to make decisions when they perform actions and receive rewards or penalties. A good example of it is technology working with autonomous vehicles where the AI algorithms are trained to react according to the various unexpected road situations that occur in simulations. After attaining a certain level of proficiency, the algorithms are then refined, tested, and used in real life.
Neural Networks and Deep Learning
A neural network is a computing system which is also inspired by the human brain. It's made up of special units or neurons that process information. These units are arranged in layers. The complex version of neural networks with many layers is called deep learning. With the deep learning technique, systems can learn from large volumes of data, tackle more complex tasks.
In the above-mentioned example of voice assistants, this technology uses deep learning to understand spoken language. They convert the spoken words into text, interpret the meaning, and then perform actions based on that interpretation.
It works similarly in our SkinSpotter application. The system scans the photo, identifies patterns in customers’ images that may be indicative of diseases or medical conditions based on its training data, and then gives a result that is comparable or maybe even exceeding that of human experts.
Natural Language Processing (NLP)
NLP or Natural Language Processing is a field at the intersection of computer science, artificial intelligence, and linguistics. It deals with enabling computers to understand, interpret, and respond to human language in a meaningful and useful way.
NLP involves a few steps and components:
The first one is text processing. This is the initial step where the system processes raw text. It includes tasks like tokenization (breaking text into words or phrases), tagging some parts of speech, and parsing.
Then, NLP systems understand the context and semantics in order to understand the intended meaning behind words.
The last component of NLP is machine learning, which is used for complex tasks like sentiment analysis or language translation.
We worked with similar technology in the Reverso document translation system part. There, the system analyzes and translates texts from one language to another with the understanding of grammatical structures and the context.
Additionally, NLP is used in AI chatbots which are designed to understand natural human language and provide helpful information or assistance in their responses.
Reach out to our team, we’ll provide you with the research-based hypotheses about how NLP or AI can enhance your business.
Challenges and Ethical Considerations
You might ask, how to work with AI properly? Yes, when implementing AI, you may face some challenges that you need to consider.
The first is problems with data quality. Make sure the training data for your AI system is reliable and trustworthy. Even if the technology performs well, the results still won’t be satisfying in case the data quality is overlooked.
The next issue is data biases. This happens when the data used to teach an AI system isn't diverse enough. For example, some facial recognition systems have been criticized because they were mostly trained with pictures of people from specific ethnic backgrounds. As a result, AI wasn’t as good at recognizing people from groups that weren't well-represented in its training data.
One more possible problem is the technical requirements to maintain the AI system. The cost of hardware and energy definitely varies depending on the scale of your business and the respective AI needs.
Creating an AI: Programming Languages and Technology Stacks
The First Step to Choose the Right Tech Stack
Before choosing the tech stack, you need to consider a few things. The very first of them is the type of your personality as an owner or product manager, or another person who is in the position of a decision-maker. Some people are well versed in technologies while others are better at creativity, and getting on with the tech world is very difficult for the latter.
If you're a person with a technical background, you can properly learn the technologies and their advantages and then decide on your own or with other technical experts. If you're not, then the best way would be to find or outsource a technical development partner who would be an expert in the field. Such a partner has to be able to explain all the essential details to you so you get the general ideas without feeling you are not on your plate.
Considerations for Choosing Tech Stack
When selecting a tech stack for your AI product, you should also consider your exact product requirements. That includes understanding the nature and scope of your AI project. For instance, if your project involves image processing, then you would need tools like OpenCV and TensorFlow. If you also require data analysis, then Python and its libraries are needed as well.
Next is scalability. Decide on the current or future growth of your AI application. Cloud services like AWS, Azure, and Google Cloud offer scalability, which is important as your user base grows and data processing needs increase. These platforms provide the flexibility to scale your resources up or down based on demand which helps to ensure that your solution remains efficient and cost-effective.
The very important point – technical requirements. For complex algorithms and machine learning models, you might need robust computational power and custom servers to let it all work.
Another consideration is integration capabilities. Make sure that your tech stack seamlessly integrates with existing systems and workflows. This involves checking the compatibility of new tools with your current infrastructure and the ease of integrating various data sources and APIs.
Then, there is expertise availability. When choosing a technology, you want to make sure that skilled professionals are familiar with it. At Linkup Studio, we are experts in the technologies we operate with. In each project, we implement them using the best practices and the latest available AI technologies.
The very important thing to keep in mind is budget constraints. Sometimes, enthusiasm and passion for creating something great raise the costs of development and maintenance for all the tools and technologies. Such expenditure can even harm business if not executed wisely. For instance, model training is quite expensive on any cloud, and it’s worth considering this before going into development.
Last but not least, security and compliance. If your AI product deals with users' sensitive data, then your tech stack must adhere to industry standards for data security and privacy. That is particularly important in sectors like healthcare and finance.
Programming Languages & Tools
I’ll tell you about the tech stack we use at Linkup Studio.
- Python. Probably everyone has heard of it. This tool stands at the forefront of AI development due to its simplicity and a vast array of libraries. It's perfect for everything from quick script writing to developing complex AI algorithms. Python's flexibility is crucial for creating tailored solutions across various industries.
- We use PyTorch for research and experimentation with dynamic computation graphs. This tool allows us to make on-the-fly adjustments. TensorFlow offers a stable and scalable framework designed for large-scale and efficient production use. Together, these last 2 technologies help us develop versatile and custom AI programs, from healthcare diagnostics to financial forecasting.
- Keras is built on TensorFlow. It simplifies the process of building deep learning models. I explained about deep learning in detail in this video. Keras fits our needs perfectly well when we’re looking to move from a concept to a functioning model as soon as possible. Also, it is a great tool for prototyping and rapid development.
- Next, OpenCV. It is essential for computer vision tasks. This technology supports real-time image processing and analysis. Its applications range from facial recognition for security to analyzing customer behavior in retail. OpenCV also helps convert visual data into meaningful and actionable insights.
- We also use Scikit-learn, which offers us a wide range of tools for machine learning. It is indispensable for predictive analytics. Pandas assist us in data manipulation and preparation for making machine learning models.
Cloud Services
Azure and AWS stand as comprehensive platforms for AI development, which offer more than just cloud storage and computing. They are useful for creating AI solutions like natural language processing and machine learning systems. Azure Cognitive Services and Amazon Web Services are crucial for developing such apps containing sentiment analysis, automated customer service chatbots, speech recognition, and more. These platforms would be helpful for businesses that aim to improve customer experiences and automate their operations.
Google Cloud AI provides advanced machine learning services and APIs. It's especially effective for using pre-trained models in tasks like image recognition and language translation. This technology significantly speeds up development time.
DevOps Infrastructure Technologies
DevOps technologies streamline software development, testing, and deployment. They are useful for any type of digital solution but are particularly useful for AI and ML applications.
We use Kubernetes to manage and deploy containerized applications (applications that run in isolated packages of code called containers). It provides consistent environments across development, testing, and production, reduces software bugs, and accelerates deployment processes.
We use custom servers to meet computational needs for faster and more efficient training of extensive deep-learning models. Such servers can be optimized for high-performance computing and provide the processing power required for complex calculations and large datasets, which are typical in AI development.
Analytics & Storages
Our team finds it very important to process, analyze, and visualize large datasets to help clients' businesses get strategic advantages.
So, we use BigQuery and Databricks for quick processing and interpretation of big data to identify patterns and make predictions in AI applications.
Grafana & Tableau Software are both focused on data visualization. They provide tools to create clear, understandable dashboards and charts from complex data sets. This makes it easy to analyze and derive insights from data, which is valuable for informed decision-making.
Building a Custom AI vs. Integrating Solutions like ChatGPT
When businesses have a task for digital innovation, they are often perplexed by the decision between building a custom AI solution or integrating third-party solutions like ChatGPT. At Linkup Studio, we’ve delivered projects dealing with these two approaches. I’ll share some considerations for each to help companies decide which one would be more relevant for them.
Considerations for Building a Custom AI
Evidently, custom AI development allows you to get your own AI system from scratch. It would be designed to meet some specific business requirements and solve your challenges. If your business operates within the Healthcare, Finance, Manufacturing, or other industries like Agriculture or Retail, then you might need to create custom AI tailored for specific medical diagnosis, drug discovery, or algorithmic trading, fraud detection, process optimizations, quality control, demand forecasting, and millions of other options.
However, you should also know that there are some challenges coming along with this custom AI:
- Expert Developers. Not all common development teams can create solid AI solutions. Such a company should have relevant technical expertise, specifically in AI, machine learning, and possess knowledge in data science and neural networks. Therefore, my advice is to ask the team to show their case studies in AI and ask about project outcomes.
- Time and Cost. Developing an AI solution of average difficulty takes roughly from a couple of months to one year of active work. These processes involve not just programming but also collecting, processing, and analyzing datasets, model training, and continuous testing and refinement. They also cost a lot. If you want to know the approximate figures for your AI product idea, contact our team for a free estimation.
- Ongoing Maintenance. Custom AI solutions also require ongoing maintenance and regular updates so they continue to stay effective and secure. It involves additional costs for work and diverse tools for maintenance.
- Lack of Scalability and Flexibility. It is correct that custom AI is totally tailored for a specific business, but due to its specificity and complexity, it can be difficult to adapt or scale this solution as business requirements change.
Considerations for Integrating Solutions like ChatGPT
On the other hand, integrating pre-built AI Solutions like ChatGPT is quicker and way more cost-effective. It empowers businesses with its natural language processing capabilities and can also be integrated into diverse apps to provide conversational interfaces, to automate customer services, and more.
Here are some advantages for choosing integrating solutions like ChatGPT:
- Quicker Deployment. As there is no need to develop and train an AI model from scratch, integration of the ready-made one is quicker and allows businesses to feel the benefits of AI much faster. The point here is to make sure that the AI tool that you’re going to use is able to satisfy your needs.
- Lower Costs. This point follows the previous one and is its consequence. The upfront costs are lower. This makes it a good option for businesses with tight budget constraints or those who want to test the AI field without committing to a significant investment. However, it all depends on the exact cost of the integrated third-party AI solutions.
- Proven & Expected Effectiveness. Solutions like ChatGPT and similar are tested, refined, and mostly debugged — overall, they can ensure that integration will be reliable and that you’ll get the expected level of performance. Also, you get support and updates from its developers, who work to keep this AI updated with advancements and even higher security protocols.
Despite the clear pros, there are cons that should also be considered. First, you get more or less generic solutions, which means that this particular AI tool may not be perfectly aligned with your specific business needs or may not handle unique use cases or scenarios effectively. Also, you’ll be dependent on this third-party provider, which means that there’s a risk in case the provider faces issues or discontinues providing the service.
Concluding the part about considerations for the choice between creating custom AI or integrating the ready AI, it's better to focus on your business needs, available resources, budget limitations, and your general long-term business strategy. There are no better or worse options. It’s actually up to your exact case.
