Artificial Intelligence & Machine Learning - practical use in applications

Piotr Walkowski - Senior Software Developer

You may have noticed that the world of tech is developing a mind of its own. Artificial intelligence (AI) and machine learning (ML) have become key aspects of technological growth. They are used in process automation, text, image and speech recognition, chatbots and voicebots, and of course - robotics. The disruption they are likely to cause to industries like fintech, medtech, and ecommerce is worth watching out for, as it can create amazing new business opportunities.

Machine learning in fintech

Quality of service is the first area where ML can bring you closer to your user’s needs. By learning more about them, you’ll be able to tailor your product or service to their habits, lifestyles, and preferences. Thanks to AI-based customer assistance, such as virtual assistants answering questions at exactly the right time, you will also be able to make your offer more convenient to customers. Clarity is another big benefit. Thanks to ML-powered data visualization, educating users becomes easier and their experience is greatly improved.

On the business side, ML-based risk management, particularly regarding loans and insurance, can be a very powerful tool that helps providers become more competitive while bringing better and more affordable products to their customers. Finally, machine learning can improve app security thanks to smarter fraud detection.

Machine learning in medtech

Simply put, more data in medicine means better insights, better diagnoses, better care, and better patient outcomes. It has to be high-quality data, however, interpreted and conveyed in a useful manner - which is where machine learning comes in. ML allows medtech solutions to collect complex data, analyze it with multiple factors in mind, learn based on this data, and provide actionable reports.

Prediction and diagnosis can happen more quickly with ML, while medical professionals are free to interact with patients, not data. Aside from solid treatment recommendations, deep learning can help us learn about previously unknown trends and correlations, boosting medical research. Finally, personalized care is not only better for patients - it also gives care providers a competitive edge.

Machine learning in ecommerce

ML can support ecommerce businesses across a number of areas. Firstly, improved conversion rates arise from good recommendation systems and smarter catalog search results. The former comes with algorithms that analyze the behavior of potential buyers and offer smart, situational suggestions based on data. The latter uses language processing to handle a variety of phrases users might put into a search bar, and applies data from previous searches to help customers find what they are really looking for.

On the magazine side, managing stock can become much easier through ML-powered predictions, based on customer data and current trends. High quality solutions consider multiple factors, such as storage costs, number of sales, taxes, shipping costs, and more. Thus, your overall decision making can improve, limiting losses and setting you up to take advantage of the best opportunities.

Machine learning in marketing

Marketing and sales are fields that thrive on data analytics. An ad campaign will be effective only if it’s relevant to the target audience needs and interests. Better segmentation of your customer base will let you tailor your message and show them the right content. ML-powered retargeting activities become more effective because they target individual users, not personas.

How to empower your business with machine learning and AI

Once you decide that AI and machine learning can help your business, it’s time to start thinking about implementation. The fastest and most cost-efficient option is outsourcing. Today’s IT job market is extremely competitive for employers. Building the right offer for candidates, reaching out to them, and finally going through the hiring process can be expensive. On top of that, without ML expertise already present in your time, you may have a hard time vetting candidates’ technical skill.

Working with a partner like iRonin.IT removes all of these obstacles. You are provided either with a full project team ready to take on any job, or specific developers through team extension if you want to extend the skill set of your existing team. Experts from outsourcing companies have a lot of experience across various fields, and know how to deliver successful commercial projects. They also come with an important added value - deep know-how and effective process which you can apply at your company.

For example, Woospeak, a French portal for learning English, needed a ML-powered chatbot using the RASA library. RASA supports the creation of functional chatbots that converse with human users based on learned scenarios. iRonin.IT’s experts built the chatbot as a React component, equipped with the capacity to learn various written conversation scenarios, embedded into Woospeak’s website.

What’s going to be happening with ML over the next few years?

While future is difficult to predict, we can be certain of one thing: the number of AI and ML based solutions is going to continue growing. Computer vision already rivals human capabilities. Natural language processing gains new applications as it becomes smarter. Automation is on the rise, and businesses that want to remain competitive are going to invest in it - if they aren’t doing so already.

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Author's Bio
Piotr Walkowski

Senior Software Developer

Polyglot software engineer focused mostly on Ruby. Keen on artificial intelligence, especially deep learning methods and knowledge-graphs concept. In free time mixes acid-techno music or beats online opponents in fighting games. If he wouldn't code - would probably work as a chef ;)

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