Artificial Intelligence (AI).  

Planning for a fleet of vehicles is a process that maximises the revenue from the available resources but has to consider numerous constraints like the maximal permissible amount of working hours of drivers, different fuel and fees among countries, as well as the potential to take an additional parcel when the truck is not fully loaded. Automating this planning can benefit the company, ensuring both short- and long-term benefits.

Travel agencies often have to find the golden mean between their margin and the client’s willingness to pay. An algorithm that considers historical data and the data retrieved in real time about the available offers could be used to maximise the margin dynamically, assuring at the same time that it won’t be lower than a certain threshold.

The interconnection of various energy consumers in a common grid raises the problem of limited energy efficiency of the providers, varying energy prices, and the limited storage capacity of local magazines. Therefore, an appropriate load balancing should be implemented to manage the demand-side energy allocation.

It’s widely used in chatbots on websites and as separate applications. However, it can involve multiple communication channels, while chatbots only employ text. It can also generate real-time responses and better understand the user’s needs than chatbots based on keywords and static answers. One of the most prominent users of this type of AI involves customer care and social media management. Conversational AI can find its use in the following:

• Human-like chatbots for customer care: can help handle complaints, provide general information about the company and status reports and integrate with external systems.
• Salesbots: those bots can generate an offer for the client based on the project history and describe past projects in the desired category.
• Virtual assistants can help in everyday matters like ordering food or deliveries, making reservations and purchases, etc.

Real-time interpreting and translations: an intermediary system that can translate the textual information exchanged between different users in real-time, prepare text summarisation and reformulation if needed (e.g. summarise the main outcomes of the meeting; what was the main topic of a call)

It can contribute to managing documentation and customers. It can add a self-service option, automating some of the processes. They also offer good scalability options and can enhance experiences by reducing hold times and offering immediate assistance with more straightforward methods where human intervention is optional. It can be used for:

• Speech enhancement: could help you sound more professional and be more understandable. At the same time, it could also be used to understand better what the client is saying, especially when the sound quality could be better, or the client’s pronunciation could be more understandable.
• Real-time meeting captioning: it could be implemented in conference rooms to take notes from a meeting and provide textual information to people remotely connected to a conference room, especially when the sound quality is poor.
• Expressive voice bots for customer care: automated customer care is often repellent due to a strongly mechanical-sounding voice, and customers try to reach a human being. On the other hand, human-made recordings have limited capabilities in terms of cases which can be served. Therefore, a human-sounding voice bot could remedy the abovementioned issues, ensure a lower rejection rate at the beginning of the conversation, and efficiently offload human operators.

It can produce realistic and seemingly original content. From short texts, poetry and books to artworks and other types of content. It can be helpful to create bulk items for companies that require a lot of graphic design or written content. It can be ideal for writing news articles, blog posts, and stories.

Uses for generative AI include the following:

• Automated multilanguage transactions: a tool that can take a textual input and translate it automatically into multiple languages. This can be used to prepare human-like translations of entire websites, documents, or conversations with others, opening new business opportunities and reducing overall costs and timelines related to translations.
• Personal design assistant: it can take as input text or sketch and propose designs that align with the provided guidelines. Can prepare alternative designs, fill in the missing parts of the image or remove specific objects from images.
• Taking notes: extracting crucial information from long texts related to a specific subject as bullet points can serve as a time-saver and minimises the risk of overlooking important detail from emails, descriptions of products or meeting transcriptions.

Currently enrolled in an industrial PhD programme at the Gdansk University of Technology. His main interests are remote sensing (processing done specifically on satellites), machine learning algorithms for edge devices, and parallel computing.

A Computer Vision Engineer with experience in agriculture, fast food, sports analytics, and healthcare. He loves researching SOTA and converting it into an MVP in Pytorch. However, he recently delved deeper into MLops.