Understanding how machine learning, natural language processing, large language models, and deep learning work and how they differ from one another isn’t that easy though. If you don’t understand those subfields, it might be hard to figure out how to use them for your benefit.
So, in this article, we’ll try to answer some of the questions you may have about how and for what you can use AI in your business.
What is Artificial Intelligence?
Firstly, let’s clear up a widespread misconception that natural language processing, machine learning, deep learning, and other subsets of AI are all the same. And for this, we first need to explain what artificial intelligence actually is.
Artificial intelligence is a branch of computer science in which scientists aim to give machines the ability to “think” and “learn from experience” so that they can perform tasks that previously had to be done by humans. To do this, AI algorithms are fed large amounts of training data so that their underlying models can process the data and then spot patterns or trends contained within.
In this way, AI algorithms can be taught how to carry out thousands of tasks, even complex ones – from recognizing product defects on a factory line to spotting trends in financial data.
When it comes to repetitive and detail-oriented tasks, AI can actually perform much better than humans because it won’t get distracted, tired, or overwhelmed even when working 24/7. By handing over all such mundane tasks to AI, we humans will then have far more time to focus on creative or complex activities with which AI algorithms still encounter plenty of problems.
How much exactly? Salesforce and YouGov found that marketing teams could save around five hours each week, and that’s just for starters.
What regularly makes people confused is how many different technologies are a part of artificial intelligence nowadays. Machine learning, deep learning, natural language processing, and large language models are all closely related to AI, but these technologies are actually quite different from one another when it comes to how they work.
Let’s examine each of them in more detail now, starting with machine learning.
What is Machine Learning?
Machine learning (ML) is a subfield of AI that focuses on building models that can learn and improve themselves over time with minimal human input.
The main goal here is to train the algorithms to identify data patterns and relationships so that the models can later use this knowledge to make predictions or execute tasks themselves based on historical data. You could say that ML algorithms are exactly what makes AI-powered tools so smart.
First, though, the ML models need to be trained. Scientists use three main methods for the training process.
Supervised Learning
In supervised learning, the model is trained using a labeled dataset with successful examples of how a given task should be performed. The algorithm is then asked to classify the new data it gets or predict the outcome of a given scenario.
Example: Teaching a healthcare ML model how to recognize heart condition symptoms by showing it patient records with labels indicating whether the person has a heart condition or not. Once trained, the model can predict if a new patient has a heart condition based on the symptoms list.
Unsupervised Learning
Here, the algorithm gets raw or unlabelled data and is then trained to recognize data structure, patterns, or trends contained within. It doesn’t get any specific instructions or information on how to complete a certain task; the scientists expect the ML model to learn it on its own.
Example: Showing the algorithm historical data of all patients so the model can learn and group patients based on symptoms, such as high cholesterol.
Reinforcement Learning
The third method is reinforcement learning, where the ML model interacts with an environment and tries to devise a solution to a given problem via trial and error. To push it in the right direction, scientists reward the model when it performs an action that brings it closer to its goal or punishes it for making a mistake.
Example: Teaching the model what would be the best treatment combination for different groups of patients based on the simulated treatment results.
How can businesses use machine learning?
➡️ Personalized recommendations
For us, analyzing a customer’s entire browsing history, past purchases, and buying preferences then matching the right products or services to them would be impossible. Machine learning can do it with ease though, and for millions of customers at once. So, for example, it can suggest products or services tailored to each customer’s tastes and then keep updating its recommendations as the user’s behavior changes.
A Twilio Segment study found that 92% of businesses already use ML this way to provide fully personalized experiences for their customers.
➡️ Data prediction and analytics
Machine learning can also help brands use the vast amounts of data they have stored because, for the models, processing even terabytes of data and turning them into insights isn’t the slightest problem. What’s more, the models can also immediately update their insights upon gathering more and newer data.
That’s why, according to one piece of research, 48% of businesses are already using machine learning (as well as deep learning and natural language processing) to analyze and effectively use their large data sets.
➡️ Process automation and optimization for streamlining business operations
Through machine learning, businesses can also optimize various processes that were previously manual and time consuming. Manufacturing companies, for example, can implement machine learning algorithms to automate quality checks or detect machine malfunctions. The latter can be especially useful for helping businesses to schedule maintenance or repairs early, thus preventing unexpected downtime and costly repairs.
DHL, for example, implemented machine learning-based predictive maintenance system to monitor their fleet of 60,000 vehicles. As a result, the company has reported a 10% reduction in maintenance costs and a 15% reduction in vehicle downtime.
Deep Learning
Another AI industry term regularly confused with machine learning or simply called “AI” is deep learning (DL), a specialized subfield of ML that uses neural networks modelled after the human brain.
However, deep learning stands out as a subfield of machine learning due to a few distinctive characteristics:
- DL can automatically learn key information or features from raw data or recognize inaccurate information, which means it doesn’t need as much supervision as ML.
- Thanks to complex neuron networks, DL can process and understand intricate patterns and relationships in unstructured data.
- DL is made of several structured network layers (hence the name), which makes it much more complex than ML.
How can businesses use deep learning?
➡️ Anomaly detection
Various financial organizations already use deep learning models for fraud prevention, as DL algorithms can quickly spot unusual or suspicious financial activities. Plus, since the models are self learning, they can quickly adapt to any new fraudulent patterns that appear.
➡️ Image and video recognition for quality control
Manually inspecting products for defects is both time consuming and quite tedious for workers, often leading them to make mistakes during quality checks. DL-powered image and video recognition systems could be a solution here as they can evaluate products at various stages of assembly 24/7 and alert quality inspectors to any defects the system notices during scanning. That way, the amount of mistakes during quality checks can be minimized.
➡️ Speech processing for customer service automation
Businesses can also deploy deep learning algorithms to power their voice assistants. Since DL algorithms can easily understand natural speech patterns, they can be used to let automated phone systems interact with customers and handle simple inquiries in a conversational manner. That makes it easier for customers to solve their problems without the need to contact human agents.
➡️ Speaker recognition for secure authentication
And while we are on the topic of automated customer call support, deep learning models can also be used to recognize individual speakers by analyzing the characteristics of their voices. Using this technology, businesses in sensitive industries like banking, healthcare, or customer support can both provide an extra layer of security to their customers and protect themselves from fraudsters.
Natural Language Processing
Natural language processing (NLP) is a subset of artificial intelligence that allows machines to understand and respond to written or spoken words in almost the same way as we do. How does it work?
In simple terms, NLP takes raw, written, or spoken text and interprets it into a form that a computer can understand and analyze. Then NLP translates the answer back into natural language.
That makes it incredibly useful for tasks such as speech recognition, translation, customer support, and sentiment analysis.
How can businesses use NLP?
➡️ Automatic translation to any language
NLP-powered language translation tools can quickly and accurately translate any type of content – websites, product information, or support materials – to a chosen language. But what makes NLP really stand out is that it can also be used to help customers in their preferred languages. Any questions or issues they mention can be swiftly translated into machine language and then the NLP will answer in the customer’s chosen language, boosting their customer experience.
➡️ Customer sentiment analysis
Until recently, gathering insights from customer reviews, social media comments, and other user-generated content was quite tricky as brands had to analyze them manually. NLP algorithms make this task much easier, as they can understand and then turn insights into data from chatbots or social media communications in a matter of seconds.
➡️ Chatbots with voice recognition
For some people, using a regular chatbot might be a bit too hard – for example, for the elderly. NLP-powered chatbots equipped with voice recognition can be easier for elderly people or the non tech-savvy to use, as such chatbots can understand and respond to spoken questions or commands.
➡️ Speech-to-text transcription
The next place where NLP can boost accessibility is by automatically transcribing audio content into written form to help hearing-impaired or elderly users understand it.
Another way in which business owners or employees can use speech-to-text transcription is by asking it to transcribe and summarize recordings such as those made during conferences or meetings.
Large Language Models (LLM)
Large language models (like OpenAI’s GPT-3 and GPT-4 or PaLM 2 and LLaMA) are the newest of the four AI subsets covered here.
LLMs are designed to understand and generate human-like text in various forms, depending on the prompts they receive.
Why large? Because they are trained on massive datasets from various sources, all to teach the models to understand grammar, context, and sentence structure. As such, GPT-3 and GPT-4 can answer questions or write about virtually any topics imaginable as long as they have access to the necessary data.
How can businesses use LLMs?
➡️ Building smart virtual assistants
LLMs can be a great way to enhance a company’s chatbots and virtual assistant capabilities. Since those models can understand and respond in natural language, they can provide personalized and context-aware responses to customer inquiries, elevating customer satisfaction and saving support agents time.
➡️ Document summary and content generation
ChatGPT can quickly understand lengthy and complex information (such as yearly financial reports) and then create a readable summary with the essential information included. You can also ask ChatGPT to create materials on any topic or form you need, including business reports and legal agreements.
➡️ Faster translations
ChatGPT can understand and answer questions in around 95 different languages, which makes it a great help for businesses who need to quickly translate their materials into other languages.
➡️ Enhancing creativity
A clever way to use LLMs is to ask them to generate ideas, slogans, or content for marketing purposes. While most of those will probably need some more tweaking, they can give marketing teams fresh ideas they didn’t think of themselves.
➡️ Employee training and onboarding
An LLM can also help businesses develop interactive and adaptive employee training programs. With them, companies can provide personalized onboarding experiences tailored to an employee’s position and also give them a “chat buddy” which they can ask any questions to at any time and as often as necessary.
AI, ML, NLP, LLM, and Deep Learning for Better Business
Artificial intelligence in business is absolutely not just “yet another fad” that will quickly pass. No, it’s more like a worldwide revolution – especially for businesses.
Just look at how many things it can help you with:
- Real-time data analytics can give you all the data you might need to make better, data-driven decisions.
- Automating time-consuming, tedious, or repetitive tasks can free your employees’ time and let them focus on more complex tasks.
- With insights from AI-powered platforms, it’s easier than ever to tailor products or services to customer preferences and expectations in order to elevate their experience.
- Predictive analytics can help you spot issues and bottlenecks in your business as well as pinpoint untapped opportunities and incoming trends.
Combining all of these benefits, it’s clear that there’s plenty you can gain from implementing AI solutions in your business. And if you need someone with whom to talk more about how exactly you can use artificial intelligence to its fullest potential, feel invited to reach out to us at Inwedo.
Conclusion
“Artificial intelligence will take over our jobs” – that’s one of the most common fears people have about the growth of AI. But let’s be honest, will we mind that much if AI takes over the 24/7 monitoring of equipment and reporting the state of each machine or answering office-hours related questions repeatedly?
Especially since AI can be much better at handling all sorts of necessary but mundane jobs than us, and we can then handle the more important or creative jobs ourselves. Doesn’t that make AI more of an invaluable business helper than a threat? We certainly think so.
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