What is Artificial Intelligence (AI)?

Artificial Intelligence is a branch in science and engineering that aims to develop a technology mimicking human capacity to learn, retain, and present information. When a traditional algorithmic approach seems infeasible (face recognition, text generation, or advanced predictive modeling), AI provides another approach capable of handling complex pattern recognition tasks. Using AI for business growth has become a crucial strategy for modern enterprises.

Short history of AI:

1. In 1943 McCulloch and Pіtts described an artificial neuron.
2. Later in 1957 Rosenblatt described a perceptron, a simple logical group of neurons. This was basically the first primitive AI. It was extremely limited in its capacities to learn and make decisions since it could have only one layer of neurons.
3. 1970s “AI Winter”. Neural networks with a single layer were extremely limited in their pattern recognition capacities. Funds from research were retracted due to unimpressive results. Formal proofs were published describing the inability of such AIs to recognise even such simple patterns as XOR operations.
4. Revolution happened in 1986 when D.E. Rummelhart, G.E. Hіnton, and R.J. Wіllіams proposed the backpropagation algorithm that finally allowed to involve multiple layers of neurons in training, thus giving birth to “Deep Learning”. In the same year, J.J. Hopfіeld described the model of a multi-layered recurrent neural network designed for associative memory.
5. In 2020 Open-AI scaled the resources invested in the AI and came up with GPT-3 that showed unprecedented capabilities in text generation and contextual involvement, further driving AI in business applications.

Types of AI and problems they solve

The ecosystem of AIs does not end on LLMs. There are different types that cater to specific problems and require different amounts of investment:

1. Classification models are trained to answer a simple question and that is to label the input. The model is trained to assign a class from the specified set to the provided data. Does not require significant resources to train and execute (Low to Moderate complexity) and is mostly used for spam detection, medical diagnosis, image classification. Examples of AI in business include spam filters in email services and fraud detection in banking.
2. Regression models are used to predict the next data point. Through the training process the network identifies patterns and is capable of estimating the future outcomes. The training and execution complexity is Low to Moderate. Such models are often used for stock price prediction, energy usage forecast, and real estate valuation, demonstrating the benefits of AI for business decision-making.
3. Clustering models are trained and used to group similar datapoints. Contrary to classification models, they do not know the labels, they only have the data and try to group it together meaningfully. The training and execution complexity is Moderate. Models are usually used for customer segmentation, anomaly detection, and market analysis, making them highly useful in AI help business scenarios.
4. Generative AI are trained on the vast amount of data and are commonly used to imitate chat-like experience. The training process and execution demand Exceptionally High resources as the models themselves are gigantic compared to the previously described ones. AI in business applications like automated content generation and customer support chatbots rely heavily on such models.
5. Models based on reinforcement learning allow to find optimal actions and decisions to maximize the rewards. The training process complexity is Very High as it requires numerous trial and error simulations. These models are used in robotics, advanced gaming systems, and dynamic pricing systems, showcasing the use of AI in their business operations.

We’ve covered here the most popular directions with AI but there are also major directions such as anomaly detection, feature extraction, dimentiality reduction, and recommendation models. The domain is vast and has been rapidly developing for the past few years. With the large interest and investments, we can expect new capabilities emerging in a not-so-distant future.

Benefits of Artificial Intelligence for businesses

1. Increase in efficiency and productivity: AI as a co-pilot solution allows to greatly boost efficiency of the worker. It might not be at the stage where it can replace the human factor but is certainly a useful tool providing autocompletions, suggestions, and contextual insights. AI-enabled assistants further enhance workers productivity with the abilities to analyse and manage schedules, proactively set reminders, and prioritize daily tasks. AI also provides an enhanced way to interact with the data, allowing for dynamic querying and summarization of complex reports. Ai significantly reduces time and effort to work with the large volumes of data and its comprehension overall.
2. Routine tasks automation: advanced generative models are already capable of significantly boosting the completion speed of mundane tasks. Simple coding problems, test generation, custom reports are possible to be done with AI solutions. For example, OCR (Optical Character Recognition) allows the user to automatically transform the scanned paper into editable text document, which saves a lot of time for office workers. It has to be reminded that human supervision in such cases is still necessary, but the cumbersome, repetitive, and non-creative part of the job could be significantly sped up.
3. Enhancing decision-making: as was shown already, AI technology is not limited to text or code generation. Moreover, it started as an effort to predict values in complex systems, clusterize, and classify the data. As such, the AI technology can provide unique insights into data patterns, predict potential disasters and failures, or suggest an adequate response for the occurring events based on the vast knowledge it is trained on.
4. Cost saving: businesses can expect significant cost savings since the routine task automation will free the workers time for more complex and creative problems. Leveraging predictive maintenance, the company can cut expenses by predicting maintenance needs, optimizing supply chains, and boosting inventory management capabilities. AI technology could be leveraged for determining energy consumption patterns and thus reducing both costs and impact on the environment.
5. Personalization of services: Neural networks could be leveraged to classify and clusterize the customers’ attitudes, preferences, and needs, which significantly improves and simplifies the decision-making process during the construction of a targeting strategy. Based on this information, the chat-based generative AIs also could automatically adjust their attitude and interaction process, promptly providing relevant suggestions to the customers be it products, suggestions, articles, or any other relevant utility. The use of AI in their business strategies leads to enhanced customer experiences.
6. Anomaly and fraud detection: the ability of the AIs to recognize patterns and detect anomalies is invaluable for fraud detection. The model could be trained on the vast sets of already known fraud patterns to efficiently detect both known and potentially new malicious activities. The importance of semantic recognition is evident within the context of “Zero-day attacks”. Contrary to the traditional solutions that check interactions and compare them to the database of signatures, AI is capable of detecting suspicious patterns that were not explicitly shown during the training process.
7. Enhancing customer support: AI technology allows for significantly reducing the cost of customer support and its availability. The machine is available 24/7, does not have lunch breaks or need sleep, and is capable of providing responses to the most common questions in an engaging and targeted way. AI enables machines not only with the chat-like responses but also with semantic search of relevant data in contrast with the traditional exact query search approach.

WebbyLab cases and solutions

User-friendly access to the complex API

We’ve developed a complex service for historical pricing data retrieval from blockchain and centralized entities. The service presents an API for data retrieval as do many others. The issue is that most people do not have deep technical knowledge and intuition on how to interact with the API, which is a potential customer loss. On top of that, it is preferable to provide access not only to the developed service but a set of adjacent solutions such as chain indexers, social network feeds, available code solutions from the sharing platforms such as GitHub and GitLab.

The solution is to leverage Generative AI capabilities to tackle the data access layer. Chat-like interaction is extremely intuitive and easy to follow. Asking questions directly about the data is far more intuitive even then a fully-fledged ui, which would take months of time and resources to build for every service we need to present and integrate. This is one of the benefits of AI for business, as it simplifies access to complex data systems and enhances user experience.

It is quite a common knowledge though that Generative AI based on LLMs are prone to hallucination, so asking questions directly from chat would not yield any satisfactory results. Such models, however, maintain relatively accurate responses if the needed context is provided directly by the conversation engine.

Hence the solution is elegant and innovative and involves fine-tuning the model on the standard querying language shared between multiple services such as SQL, SPARQL, Cypher, or CQL. Before responding to the question made by the user, the model would construct the query, retrieve relevant data and respond with the correct data that is significantly less impacted by hallucination. This AI helps business by ensuring that queries return accurate, reliable results, improving decision-making processes.

Chat-based AI teacher or consultant

Consulting or studying are the spheres where AI in business applications could yield significant improvements. For example, we’ve been working on a product that has to provide requested information to the client in a chat-based interaction.

There are quite a few challenges that need to be addressed before such an application could be released to the public:

1. Chat-based interaction:

   1. Problem: since the interaction has to be done in a chat-based manner to simplify the data access and improve engagement quality attributes, the standard technologies with predetermined dialogs are far behind and often result in unsatisfied clients.
   2. Solution: the solution is the integration of Generative AIs based on LLMs that provide requested information in an interactive way, showcasing another use of AI in their business to streamline customer support and knowledge sharing.

2. Content filtration:

   1. Problem: when deploying custom LLM solutions and giving access to the clients, the problem of detecting inappropriate content is extremely important, especially if you’re providing teaching services to the kids. Traditional approaches that rely on keyword search are extremely unreliable, giving both false-postives and missing actual violations.
   2. Solution: in addition to the LLM, another AI model that performs content labeling and classification tasks should be trained and deployed to detect suspicious behavior.

3. Semantic data access:

   1. Problem: LLMs such as GPT-series hallucinate a lot and are unreliable on their own. The solution must provide correct consulting information or studying material, otherwise it is useless and could be harmful.
   2. Solution: leverage semantic search by training yet another AI model capable of extracting features from the text and representing its meaning as a high-dimensional vector. The vector could be stored in the database and later searched by the query from the client. When the client requests information from the chat about products or course contents, the chat engine requests the embedding generation model to create a vector representation of the client’s message. This vector is passed to the Vector DB where the info about products is associated with the vectors of their own and has optimised indexes for fast search.

Semantic search for procedures, devices, and treatments for diseases based on given symptoms

Let’s say we have a database of treatments, diseases, procedures, and devices. They do not have any meaningful strict links between them and it would require tremendous effort of dozens of scientists to build one. We want to have a system that by given symptoms automatically finds related diseases and suggests procedures, treatment, and devices.

The traditional solutions such as search by keywords fail spectacularly, because the terms describing all these linked components tend to differ significantly. The only viable way to solve such a task is to construct a system that performs the search semantically rather than on word-distance.

For that to happen, an AI model should be leveraged to construct a high-dimensional vector representation of all the introduced entities based on their description and associated in the Vector database. The same neural network would be used to extract the semantic features from the given symptoms as a query, resulting in a query-vector. The said vector could be used to retrieve semantically related data from the database accurately and efficiently.

Maksym Lesnikov

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Expectations from Artificial Intelligence

AI is currently in demand and is expected to keep growing in the future. Here are some insights and potential implications:

• Market Size: the market size in Artificial Intelligence is projected to reach US$243.70bn in 2025 and US$826.70bn by 2030. Source: Artificial Intelligence – Global | Statista Market Forecast
• Economic Impact: AI will contribute $19.9 to the Global Economy and will drive 3.5% of the Global GDP in 2030. Source: IDC: Artificial Intelligence Will Contribute $19.9 Trillion to the Global Economy through 2030 and Drive 3.5% of Global GDP in 2030
• Investments: spending on AI is expected to average at 36% up to 2030. Source: Spend On Generative AI Will Grow 36% Annually To 2030
• Working Effort Impact: Around 30% of working hours in the US could be automated by 2030. Source: Generative AI and the future of work in America | McKinsey
• Jobs Impact: AI could create 170 million new jobs globally while eliminating 92 million positions, resulting in a net increase of 78 million jobs by 2030. Source: AI could create 78 million more jobs than it eliminates by 2030—report – Ars Technica

Conclusion

In this article, we’ve covered the most popular directions of AI research and innovation efforts. The application of these technologies grants a significant competitive edge for businesses via enhanced decision-making and automation capabilities. Through a set of real case studies, we’ve shown how AI could be integrated into your products to solve nontrivial computational and business problems, improving the quality characteristics and hence leading to innovation and uncovering valuable insights.

By leveraging AI technologies, businesses can reduce operational costs and improve both workers and customer interactions efficiency in ways that were previously impractical or infeasible. The long track of scientific and engineering efforts led to the development of numerous AI applications to solve contemporary problems. The different types of AI are aimed at specific use cases, and choosing the direction requires competent and deep understanding of the underlying technology. As reports outlined in this article show, organizations that adapt AI technologies are gaining a significant competitive edge in terms of customer experience and automation.

Besides automation, the future of AI in business is also all about augmentation and enhancing human capabilities through data-driven strategies. AI allows for significantly reducing the research effort needed from engineers and scientists by providing intuitive access to information and required tools. Investing in AI today is a prudent decision leading to the company’s growth, efficiency, and long-term success.