Artificial intelligence technology is transforming the way we view and handle our daily activities.

We no longer need to actively search for AI tools online; they are everywhere around us to save much of our precious time and energy. Dismissing AI as just another hype means we overlook all the wonderful potential it can bring to a team!

In this article, we’ll try to list some of the instances of AI systems we use in our everyday life that make many lives better.

What are some qualities of AI applications we use every day?

Some harmful use cases of AIs have been contributing to the negative conceptions towards AI. However, it should be noted that there are more AI applications that benefit people.

Good and useful AIs have the following traits:

  • Useful – Some AI systems exist specifically to reduce tedious processes that take up unnecessary time and energy.
  • Safe – For an AI system to be safe, their capacities should be controllable by a person or a user. A bias-mitigated data ensures that it does not contain any information that can potentially render harmful outcome. Data management and is crucial for this to be possible.
  • Non-invasive – A good AI system aligns with both usefulness and safeness. It should not be trained based on private data that invades anyone’s privacy.

What kind of AI systems do we commonly use?

There are many types of AI systems that we use with or without knowing. They help enhance our lives by saving time and offering entertainment.

Generative AI, another powerful buzzword and one of the most important keywords in 2023, is just one branch among many wonderful AI use cases. Different domains of AI serve different functionalities.

Here’s a list of a few notable common AI systems:

  • Optical character recognition
  • Handwriting recognition
  • Machine translation
  • Generative AI
  • Interactive voice response or speech-to-text systems
  • Object recognition
  • Recommendation algorithm

Where can we find these AI systems?

While some domains of AI systems above can have straightforward use cases, some might seem more obscure in terms of where we can observe their impact in our daily lives.

Here are some examples on where we use these applications:

Handwriting recognition on phones and tablets

Advanced gadgets like smartphones and tablets can recognize text when we write on them using our fingertips or a stylus. They can convert our handwriting into typewritten sentences, thanks to the gadget’s handwriting recognition features.

Achieving this technology requires a specifically trained optical character recognition (OCR) engine in the digital (also called online) handwriting domain.

Because everyone’s writing style is unique and there are many languages in the world, it is highly challenging for devices to recognize handwritten texts. A specific dataset that matches the target language written in diverse handwriting styles serves as the key to make an OCR engine work effectively.

Card scanning

Mobile applications, like travel or banking apps, often require users to input their credit card information or passport numbers. In the past, users had to type these out word by word just to get transactions moving forward. Not only was this process time-consuming, but it also increased the risk of human errors.

Today, we frequently encounter mobile applications that allow users to take pictures of cards using their device’s camera. Optical character recognition (OCR) in the printed digital text domain is the tech that cuts down unnecessary labor put into these processes.

If you’re looking to empower your own OCR engine, check our diverse text image datasets.

Virtual assistants and chatbots

Frustrating virtual customer service systems are thing of the past, thanks to generative AI technology.

Previously, chatbots were limited in the answers they could provide. Answers had to be hardcoded into the chatbot system, leading to limited amount of information to provide. This can lead to customer dissatisfaction.

Generative AI technology, backed by an enormous amount of data, has enabled users and customers to navigate information much better. When finetuned properly, gen AI-backed chatbot services can also speak to customers in a tone that aligns with the brand.

Text summarization

Text summarization is a time-saving AI function for many. It may sound simple, but it requires the AI to be highly advanced in natural language processing capabilities as well as logical reasoning.

The recent development of generative AI greatly advanced this AI application. Thanks to text summarization, users are able to skim through large blocks of texts in a shorter amount of time.

Recent applications are incorporating automatic speech recognition (ASR) functionalities onto text summarization to provide efficient audio or video summarization or even voice call summarization.

Real-time captioning

Captions, including closed captions, come in handy when users cannot access audio content, making them essential in keeping viewers engaged regardless of the situation.

Beyond the convenience they provide, captions play a crucial role in providing accessibility. AI technologies like automatic speech recognition (ASR) and speech-to-text (STT) are behind this service.

Previously, captioning required tedious manual labor. While it still requires a human touch, the process has become much more streamlined with advanced speech-to-text technology that can transform spoken words into text.

Flitto Chat Translation is one example of AI application that utilizes cutting-edge STT engine to understand and translate text in real time as people converse.

Ensuring high accuracy and real-time functionality in captioning takes effort and next-level technology. Once achieved, it will undoubtedly enhance the viewing experience for a broader audience.

Summing up…

Artificial intelligence is not just a buzzword. Its applications have been around us for many years, and researchers are continuing to advance them so that they can provide more seamless experiences for users.

As we move on towards a more mature age of AI, we can expect to leverage these AI applications so that we can save our energy to focus on what really matters to us. In doing so, a thoughtful approach toward these developments, like the consideration of data privacy, will be crucial. Flitto DataLab aims to help these AI systems become better and safer so that they can assist more users’ lives.

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