Artificial intelligence (AI) has changed the world we live in in ways that we could never have imagined. It lets us explore the universe, automate speech and text, and sift through vast reams of data in a tiny fraction of the time it would take humans. AI has benefits in almost every field and industry, from finance to marketing, and from scientific discovery to hate speech detection. As AI continues to evolve, so will the AI use cases in our world. Let’s dig into a few of the AI use cases here.
AI and NLP are pivotal for improving the customer experience. NLP can help companies to sift through customer enquiries and requests, automatically route people to the appropriate department, and understand customer feedback more efficiently. In a world where people are holding companies to high standards of customer service and follow through, this is crucial. To keep those processes working effectively, though, you need to have accurate data. This is where AI labeling tools come in, since they are the foundation to creating accurate, reliable, and quality chatbots and similar to improve that customer experience.
Chatbots are their own beast, too. AI and chatbots go hand in hand. Machine learning (ML) models let engineers train machines or bots to produce natural-sounding speech in different languages and accents via dynamic text-to-speech applications. NLP helps companies accurately label data to feed the algorithms, build context, and perform entity recognition in order to extract entities from text and data via Named Entity Recognition (NER). AI, NLP, and ML are all pivotal in building increasingly accurate, reliable chatbots.
As AI advances, so does cutting-edge science. For example, AI is helping us understand the mysteries of the human genome. Google has a revolutionary tool called “DeepVariant” which can create highly accurate images of a person’s complete genome. Using a deep neural network, the platform creates photographs of someone’s DNA that can also flag genetic variants and anomalies with next-generation DNA sequencing data. It can map out small mutations and random errors, which can help forecast the person’s potential to develop a variety of diseases like cancer.
AI is also pivotal for advancements in environmentalism and combating climate change. For example, scientists have used machine learning to create new mutations to a natural enzyme called PETase. The goal is to allow bacteria to degrade PET plastics to eliminate the billions of tons of landfill waste that we create. The model predicts which mutations in these enzymes could quickly depolymerize waste plastic at lower temperatures. This lets us break down plastics in a matter of mere days or hours when previously it would have taken centuries.
AI is also being used in meteorology and weather forecasting. Weather forecasting models have to handle big data and modeling variables to produce an accurate forecast. The complexity of this is comparable to simulating the human brain. With advancements in AI, forecasting is becoming more reliable as AI tools can analyze the data faster and with higher accuracy, which is particularly important with changing ocean and wind currents globally. AI can also be used to predict the potential impact from weather events, since it can use historical data to understand how infrastructure and topography have responded to weather events in the past and then use that information to predict the impact of incoming weather events.
There are almost too many use cases for AI in healthcare to list. One of the most promising clinical applications of AI is in diagnostic imaging. AI is being used—and fine tuned—to help detect and quantify a wide range of clinical conditions. AI has a growing role to play in diagnostics, and an evolving AI-based triaging system is being developed to immensely decrease the workload of the radiologists in breast cancer detection. Machine learning algorithms can process more information and spot more patterns than their human counterparts, and can also be used to understand risk factors for disease in large populations. NLP can then be used to help with transcription of doctors’ notes, improving hospital discharge notes, upgrading the patient experience, and more. AI—and its offshoots—have a big role to play in developments in the healthcare industry.
It’s no secret that the internet can be an incredible place or an incredibly toxic place. From hateful content and articles online to trolling and harassment in gaming, toxicity can be rife. A new study by Unity found that the majority of online gamers have experienced abusive behavior, ranging from sexual harrassment to doxing to threats of violence and, sadly, much more. What’s more, a staggering 92% of gamers believe there should be better enforcement of in-game rules and codes of conduct around toxic behavior. This is where AI comes in.
AI can understand gamers’ language to identify toxic and hateful communication. The combination of NLP and Machine Learning (ML) can be used to establish a training model that distinguishes negative comments, sexual harrassment, and personal attacks. The algorithms can also tag and flag keywords that fall under hate speech to track abusive content aimed at particular people or groups of people. Algorithms like this are crucial in making the gamer community and online communities safer.
Financial services deal with inherently sensitive data and high security risks. AI can help tighten security in the financial world via advanced fraud detection and tools that can detect anomalous activity. AI can also help corporate financial firms to predict and assess loan risks via machine learning and data analysis. Beyond that, AI can help in the Fintech industry with customer experience, evolving chatbots that can act as virtual assistants, streamlining workflows and administrative work, reducing human error, and much more.
AI helps companies understand their customers, their motivations, and how to target them. If you’ve ever had ads that targeted you (a good portion of the 4,000+ you’ll see today), you’ve probably experienced this form of AI. Thanks to AI and ML, media companies can predict activity more accurately, and can use that to present people with ads at the right time and in the right place. Not only that, AI now knows how to present relevant, personalized offers that are tested and data-informed to drive conversions. Digital ads are just the tip of the iceberg, though. Companies like Netflix and TikTok use machine learning algorithms to analyze your viewing activity, correlate it with millions of other users, and then present you with recommendations that are relevant to you. These recommendations get smarter all the time with new data and ML models.
The use cases for AI in the legal world are vast. Legal work deals with huge swathes of information and data, and AI—combined with offshoots like NLP—are making legal work significantly more efficient. AI can help lawyers with due diligence, research, automating legal processes, billing, and much more. What’s more, advancements in NLP and the growth of labeling tools let lawyers translate, label, and sift through legal contracts and volumes of information in fractions of the time it would otherwise take.
AI has become an integral part of many industries. As industries advance, new uses for AI are uncovered and brought to the forefront. AI is moving at such a rapid pace, and it’s likely that there will be a vast number of new use cases popping up every year that we can’t even fathom yet! To find out more about AI use cases and how AI/NLP could help your company, feel free to schedule a custom demo or reach out to info@datasaur.ai. We'd love to chat.
