One of the key benefits provided by machine learning is the ability to process unstructured data, such as speech and text, to extract facts and ideas without relying on inaccurate keyword searches or laborious audio recording processing.
At Dynalytix, we provide our clients with custom AI and machine learning solutions to help them unlock the value of their data, automate routine tasks, and make data-driven decisions.
While it is easy for us to read a sentence or a paragraph and figure out whether it’s about ordering pizza, the weather, or the news, doing so on a large scale can be very time-consuming.
AI-based natural language understanding (NLU) solutions allow machines to perform these tasks at a speed that is unmatched by humans. This allows us to classify entire documents, divide them into sections by topic, understand the user’s intent in a conversation, or extract pieces of information from long texts.
These features serve as the foundation for conversational interfaces and allow for use cases like topic-based news article filtering, information extraction from CVs, and spam email detection.
It takes a lot of effort to determine what a text document is truly about, even when it is well-organized and searchable, and the majority of longer texts don’t come with a convenient TL;DR or summary. Fortunately, AI is rapidly improving at understanding the key ideas the author was attempting to convey and at generating a summary of the text itself.
The classification of the text by topic allows us to quickly search through the documents, while summarization allows us to quickly grasp the information contained in the text. Everyone who has to deal with large volumes of text, from analysts to executives, will benefit greatly from this.
Languages are very nuanced, so while some of the information is factual, like a judgement that the restaurant’s food was bad, other parts are emotive, like the man’s outrage about the food’s quality.
Machines are generally not very good at reading emotions, but with the aid of AI, they can pick up cues that indicate the sentiment attached to a segment of text, showing whether the content is happy, sad, or evokes any other sentiment the model is trained to recognize.
This allows our clients to better understand their customer feedback, adjust chatbot responses, and react to prevailing sentiments regarding specific topics reflected in the comments. It has also been used to understand sentiment concerning specific topics in the media and on social media. In this case, NLU and sentiment analysis are used in tandem to recognize topics of interest and subsequently interpret sentiments about them.
We worked closely with the VNG team, to identify key areas where AI could bring the most value and evaluated the business data needed to train relevant machine learning models. We also helped provide an additional framework for data collection and strategy to use AI solutions.
We build an AI web-based and mobile-app solution based on computer vision and natural language processing models. The goal strategy is to use AI solutions to help policymakers be more data-driven, cost-effective and efficient in investigating subversive or criminal behaviors of businesses in the main cities of the Netherlands.
We will also offer advice on data collection and labelling, as well as assist you with source data validation, i.e., determining whether the data is indeed useful for the task at hand.SEND US A REQUEST
Working closely with your company, we will identify the key areas where AI can bring the most value. This involves meetings with all stakeholders and developing a roadmap for action together.
We will evaluate if you have the business data needed to train relevant machine learning models. If required, we identify additional frameworks for data collection in your company.
Based on our meetings and data analysis, we’ll share with you the possible AI use cases for your company. We will work hand-in-hand to agree on the desired outcome.
We will build and apply various machine learning models to your business data, to find the best solution. As a result, we will develop algorithms that accomplish the desired goal.
We integrate the machine learning model with an API or front-end product, making it user-friendly and accessible to the end-user.
Any system might require time to time maintenance, and we are happy to support our customers with that.
It depends on what “understanding” implies, so it’s a tricky question, but the overall response would be no, not really. NLP techniques solve specific problems, such as classifying text into predetermined categories, or converting text into sound or vice versa, but this does not mean that computers actually understand the language itself.
Typically, data scientists, machine learning engineers, or both, are involved. NLP is a rapidly developing field where cutting-edge tools are continually being created. This means that using these new tools and techniques frequently necessitates a solid understanding of both data science and the application of these models to specific problems.
This depends on the problem being solved and the data that NLP is applied to. If the problem is fairly simple and the data is text, language-agnostic methods can be used, and there is no language dependency. For more complex problems, there is some dependency even when working with text, because pre-trained models are necessary, but there are resources available for the majority of languages. Audio is particularly complicated because there aren’t many resources to fall back on, and the language dependency is the strongest.
During the project evaluation, we provide a feasibility assessment, and for problems in which we have experience, we can provide a more precise prediction and discuss the forecasted minimum level of performance.