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.
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.
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.
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.
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.
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.
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.
Several of the mistakes mentioned below are not rare. Each one of them usually reduces the effectiveness of an AI product and AI software you create. Companies must know these common mistakes in order to overcome them. Here are the top faults often encountered during artificial intelligence product development:
One of the biggest evils when developing an AI product is that the business might start without clear goals in mind. Lack of goal definition is ineffective in the development process, and this results in resource wastage. Ultimately, users do not find the artificial intelligence product useful. It is worth making a list of potential issues that the AI software is supposed to solve and establishing clear goals to achieve.
Most organizations fail to realize that building a good artificial intelligence model requires large and high-quality data. With this, poor data collection and use of biased or limited datasets may result in the development of substandard artificial intelligence or such systems that continue to enhance bias. AI software must have access to various high-quality data to achieve the intended objectives.
However, technology remains a significant aspect of any AI system, though it becomes tempting to go for the latest technologies without understanding the unique business needs of the organization. Thus, companies should target the use of the right technology to achieve their stated objectives instead of going for the available or popular technologies.
AI product manufacturing without thinking about its ethical consequences and regulatory issues can result in high legal exposure and loss of brand image. AI software can only be built with fair and readable principles, and its developers must adhere to existing laws and rules of data protection.
Lack of planning on how the new systems incorporating artificial intelligence are to be implemented in the current processes and frameworks is another challenge. This means that the problem of its integration must be solved. Otherwise, enterprises can face operational issues. The idea is to make the introduction of a new artificial intelligence system as smooth as possible, integrating it into the existing solutions.
Developing AI solutions in silos without involving other stakeholders, such as the end users or any other professionals in the related field of practice, will lead to the development of products that lack strengths and efficiency. The interaction between different teams in every stage of development is required to design effective and feasible AI software.
It is worth planning to prevent these issues. The company has to understand aspects of artificial intelligence, both from the technology and the business side. On the other hand, business stakeholders also have to participate actively in the AI product-building process. Then, it is possible to enhance the probability of developing effective AI products with obvious values added to the business operations.
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.
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.
I’ll tell you about the tech stack we use at Linkup Studio.
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 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.
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.
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.
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:
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:
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.
What are the legal regulations in AI, and why do you need to comply with them? Developing great AI solutions means more than just achieving technical brilliance. It involves ensuring that the AI software complies with legal standards.
Especially after 2022, AI has become a major transformative power and is under intense scrutiny worldwide. That’s why AI solutions need to be regulated by government data use, privacy, security, and other areas. As a rule, AI should benefit society, not touch or invade individual rights, and avoid causing even unintended harm to people, communities, and countries.
This should matter to you because non-compliance is not just a slap on the wrist but can result in hefty fines, troublesome legal battles, and a bad reputation for your product. Let’s now describe what you and your development team should consider.
We’ll briefly consider the main ones in the US, Canada, Europe, Australia, and some Middle Eastern countries.
To develop an AI software in the United States, you need to take into account:
For AI products in Canada, you need to consider PIPEDA (The Personal Information Protection and Electronic Documents Act), which has rules on how businesses must handle personal customer information.
In this region, there is also the CASL (Canadian Anti-Spam Legislation), which focuses on spam. It is required for AI products involved in electronic messaging and data collection.
If you aim to develop software for users in Europe, you need to consider the GDPR (General Data Protection Regulation). This famous data protection law applies to all European Union countries. From our experience, these rules are really strict, so make sure the team you want to collaborate with has proven experience working with this regulation. Some issues with compliance may occur right before the launch, and it will delay the launch date.
There is also an upcoming ePrivacy Regulation. Up to the moment of writing this article (April 2024), it has not been finalized and has not come into effect. However, it’s better to keep this regulation in mind. Therefore, I’d advise you to check for updates before you launch a product.
For the United Arab Emirates, there is Federal Law No. 2, also known as the UAE Cybersecurity Law. Also, you would need to consider the Dubai International Financial Centre Data Protection Law No. 5, which is pretty similar to GDPR. Additionally, there are Abu Dhabi Global Market Data Protection Regulations presented in 2021.
A set of documents also regulates AI development in the Saudi Arabian market:
It's not a secret that AI must be fair and unbiased. But here's the problem – AI systems learn from data, which can be inherently biased.
In the video about how AI works, we gave the example of AI facial recognition systems, which are quite popular nowadays for diverse products across many industries. Such tools can perform poorly on certain demographic groups and fail to recognize people's faces. This is because there were not many representatives of that group in the training data for that AI software.
However, no one cares about that development oversight. People will feel insulted, and that's a red flag in AI ethics that can lead to further litigation and problems.
To combat this, you need to know who your customers and users are, where they live, what they do, and make sure your AI includes diverse and inclusive training data. This information can change over time, so I'd advise conducting regular audits for bias.
The core issues here revolve around the ownership of AI-generated content and the protection of AI algorithms.
In many products, AI systems can create works that might traditionally be protected under copyright law, such as articles, music, art, and others. At Linkup Studio, we can offer contracts that involve ownership of the AI-generated content and the AI algorithms. This will prevent potential disputes and protect all parties' interests. In other words, you can own an AI product and its creations.
The next point is protecting AI algorithms. It is a valuable asset for a business. Often, creating them from scratch requires significant investments of time, resources, and expertise. You can patent the AI inventions to save them from unauthorized use or replication.
Another solution is to protect your AI technology, especially if it operates internally, by keeping it as a trade secret. This approach avoids the public disclosure required for patents and can offer protection as long as secrecy is maintained.
AI technologies are applied in diverse industries, and each of these industries has its own regulatory landscape. Therefore, take this into account when developing your AI software.
For instance, in Finance, there is the Sarbanes-Oxley Act, which mandates accurate financial reporting and the implementation of internal controls. Also, you need to consider Anti-Money Laundering (AML) and Know Your Customer (KYC) regulations to ensure the integrity of your financial transactions.
In the US, Healthcare and many beauty-related digital products must comply with HIPAA. Specific safety regulations also apply to developing AI for self-driving cars, and other areas.
Finally, there's the issue of international scale. If your AI software or solution deals with users across the whole world, then it is not enough to consider regulations for such a type of product in the US only, or only in the European Union. Your solution must comply with international laws and frameworks. This is a complex task, but it’s crucial for successful scalability.
The first thing you should do, even before contacting any company or agency, is to clearly identify the problem you want to solve and the business or operational goals you aim to achieve.
The next step is defining the project's scope or what should be done to achieve this. At this stage, it is useful to measure exactly what AI should achieve and understand its constraints and limitations.
Another important point is determining metrics for success early on. This will help guide the development process and evaluate whether the results are good or bad. It regularly happens that after six months of work, clients hear from developers that what they intend to achieve is not viable with the existing technology. It is a lack of communication
Finally, define the project's scope, including what the AI needs to achieve and its constraints. Establish clear objectives and metrics for success early on to guide the development process.
In a nutshell, I would say that the best approach is to consult with an expert. It's good to communicate and give a detailed explanation of your project requirements. Then, AI development experts will take into account the expected task complexity, its scalability capabilities, and compatibility with the existing and now working systems that your business uses.
The AI Development experts will help you evaluate different algorithms, platforms, and tools based on the preferences you explained. The team can then even prototype your digital product using different technologies to find the best fit. But a more experienced team could name you the tools they would use and explain why.
You should consider the task's complexity, the solution's scalability, and compatibility with existing systems. Evaluate different algorithms, platforms, and tools based on their performance, support, and community. This often involves prototyping with different technologies to find the best fit.
Data collection should align with the AI model's goals for your business. Begin by identifying the types of data needed and the sources available: photos, texts, or other media, and check their reliability and authoritativeness.
Then, collect high-quality and diverse data sets. Such preparation involves steps like:
Testing is a complex project, and at Linkup Studio, we use diverse methods and approaches. In particular, we evaluate the AI model against a separate validation dataset if such is available to check its accuracy, precision, recall, and other relevant metrics. Additionally, we ask subject matter experts to validate the results if the AI product operates within a specific niche like Healthcare, just as we did for SkinSpotter.
We also run various tests under many conditions and scenarios that the product or system part will face after deployment.
Additionally, our validation includes checking for bias and ethics to ensure that the model performs well across various demographics and situations, especially if the product is international.
Our team at Linkup Studio stays updated about the latest technologies in various ways. The first thing I do almost every day is read AI research papers and professional chats. I also regularly collaborate with other CTOs and technical guys in the industry, many of whom I have known for about ten years.
Additionally, I grow the culture of learning something new from regular courses to improve developers' qualifications and skills. I know some of our developers read journals and follow influencers in the AI development industry. All these processes work, and we also educate each other during our development department meetings.
In our company, it begins with compliance with legal and diverse industry regulatory requirements. We explained this in the video.
Additionally, we practice encryption for data storage and transfers. We also implement access controls and audit logs to monitor data usage.
To protect user privacy, we anonymize data for some projects. Our AI developers also regularly update security protocols and conduct vulnerability assessments.
Wholesum Food Calculator is a SaaS platform and mobile app designed for outdoor activities providers, rafting groups, B&Bs, ranches, summer camps, and personal chefs. It streamlines group menu planning, manages dietary restrictions, generates shopping lists, and scales recipes to accommodate varying group sizes.
For this product, we integrated an AI system that generates recipes. Users just enter the meal name, and the AI recommends ingredients, quantities, and preparation steps. Before, manually making a recipe took about 20 minutes, but now, it takes 5 seconds.
We harnessed Chat GPT's capabilities and connected its most cost-effective version via a custom-designed API.
Here is how it works.
Linkup Studio's AI Data Recognition White Label Solution is a sophisticated platform that transforms business operations through AI-driven data handling. This solution handles multiple data types from various sources and optimizes processes like extraction, analysis, and interpretation of information. Here’s a quick overview of how it works:
This tool is particularly useful for various sectors, including:
The solution integrates with social media, emails, and cloud storage, enhancing its utility with advanced NLP features and predictive analytics to anticipate trends and user behavior.
Watch this video to see how this solution can be tailored to meet your specific needs and book a demo session with our AI development experts.
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