Top 10 AI Terms Startups Need to Know

This article breaks down the top 10 AI terms that every startup product manager, user researcher, engineer, and entrepreneur should know.

Artificial Intelligence (AI) is beginning to revolutionize products across industries but AI terminology is new to most of us, and can be overwhelming.

We’ll define some of the most important terms, explain what they mean, and give practical examples of how the apply in a startup context. By the end, you’ll have a clearer grasp of key AI concepts that are practically important for early-stage product development – from generative AI breakthroughs to the fundamentals of machine learning.

1. Artificial Intelligence (AI)

In simple terms, Artificial Intelligence is the broad field of computer science dedicated to creating systems that can perform tasks normally requiring human intelligence.

AI is about making computers or machines “smart” in ways that mimic human cognitive abilities like learning, reasoning, problem-solving, and understanding language. AI is an umbrella term encompassing many subfields (like machine learning, computer vision, etc.), and it’s become a buzzword as new advances (especially since 2022) have made AI part of everyday products. Importantly, AI doesn’t mean a machine is conscious or infallible – it simply means it can handle specific tasks in a “smart” way that previously only humans could.

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Let’s put it into practice, imagine a startup building an AI-based customer support tool. By incorporating AI, the tool can automatically understand incoming user questions and provide relevant answers or route the query to the right team. Here the AI system might analyze the text of questions (simulating human understanding) and make decisions on how to respond, something that would traditionally require a human support agent. Startups often say they use AI whenever their software performs a task like a human – whether it’s comprehending text, recognizing images, or making decisions faster and at scale.

According to an IBM explanation,

“Any system capable of simulating human intelligence and thought processes is said to have ‘Artificial Intelligence’ (AI).”

In other words, if your product features a capability that lets a machine interpret or decide in a human-like way, it falls under AI.

2. Machine Learning (ML)

Machine Learning is a subset of AI where computers improve at tasks by learning from data rather than explicit programming. In machine learning, developers don’t hand-code every rule. Instead, they feed the system lots of examples and let it find patterns. It’s essentially teaching the computer by example.

A definition by IBM says:

“Machine learning (ML) is a branch of artificial intelligence (AI) focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data.”

This means an ML model gets better as it sees more data – much like a person gets better at a skill with practice. Machine learning powers things like spam filters (learning to recognize junk emails by studying many examples) and recommendation engines (learning your preferences from past behavior). It’s the workhorse of modern AI, providing the techniques (algorithms) to achieve intelligent behavior by learning from datasets.

Real world example: Consider a startup that wants to predict customer churn (which users are likely to leave the service). Using machine learning, the team can train a model on historical user data (sign-in frequency, past purchases, support tickets, etc.) where they know which users eventually canceled. The ML model will learn patterns associated with churning vs. staying. Once trained, it can predict in real-time which current customers are at risk, so the startup can take proactive steps.

Unlike a hard-coded program with fixed rules, the ML system learns what signals matter (perhaps low engagement or specific feedback comments), and its accuracy improves as more data (examples of user behavior) come in. This adaptive learning approach is why machine learning is crucial for startups dealing with dynamic, data-rich problems – it enables smarter, data-driven product features.

3. Neural Networks

A Neural Network is a type of machine learning model inspired by the human brain, composed of layers of interconnected “neurons” that process data and learn to make decisions.

Neural networks consist of virtual neurons organized in layers:

Input layer (taking in data)
Hidden layers (processing the data through weighted connections)
Output layer (producing a result or prediction).

Each neuron takes input, performs a simple calculation, and passes its output to neurons in the next layer.

Through training, the network adjusts the strength (weights) of all these connections, allowing it to learn complex patterns. A clear definition is: “An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain.”

These models are incredibly flexible – with enough data, a neural network can learn to translate languages, recognize faces in photos, or drive a car. Simpler ML models might look at data features one by one, but neural nets learn many layers of abstraction (e.g. in image recognition, early layers might detect edges, later layers detect object parts, final layer identifies the object).

Learn more about: What is a Neural Network from AWS

Example: Suppose a startup is building an app that automatically tags images uploaded by users (e.g., detecting objects or people in photos for an album). The team could use a neural network trained on millions of labeled images. During training, the network’s neurons learn to activate for certain visual patterns – some neurons in early layers react to lines or colors, middle layers might respond to shapes or textures, and final layers to whole objects like “cat” or “car.”

After sufficient training, when a user uploads a new photo, the neural network processes the image through its layers and outputs tags like “outdoor”, “dog”, “smiling person” with confidence scores. This enables a nifty product feature: automated photo organization.

For the startup, the power of neural networks is that they can discover patterns on their own from raw data (pixels), which is far more scalable than trying to hand-code rules for every possible image scenario.

4. Deep Learning

Deep Learning is a subfield of machine learning that uses multi-layered neural networks (deep neural networks) to learn complex patterns from large amounts of data.

The term “deep” in deep learning refers to the many layers in these neural networks. A basic neural network might have one hidden layer, but deep learning models stack dozens or even hundreds of layers of neurons, which allows them to capture extremely intricate structures in data. Deep learning became practical in the last decade due to big data and more powerful computers (especially GPUs).

A helpful definition from IBM states:

“Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain.”

In essence, deep learning can automatically learn features and representations from raw data. For example, given raw audio waveforms, a deep learning model can figure out low-level features (sounds), mid-level (phonetics), and high-level (words or intent) without manual feature engineering.

This ability to learn directly from raw inputs and improve with scale is why deep learning underpins most modern AI breakthroughs – from voice assistants to self-driving car vision. However, deep models often require a lot of training data and computation. The payoff is high accuracy and the ability to tackle tasks that were previously unattainable for machines.

Many startups leverage deep learning for tasks like natural language understanding, image recognition, or recommendation systems. For instance, a streaming video startup might use deep learning to recommend personalized content. They could train a deep neural network on user viewing histories and content attributes: the network’s layers learn abstract notions of user taste.

Early layers might learn simple correlations (e.g., a user watches many comedies), while deeper layers infer complex patterns (perhaps the user likes “light-hearted coming-of-age” stories specifically). When a new show is added, the model can predict which segments of users will love it.

The deep learning model improves as more users and content data are added, enabling the startup to serve increasingly accurate recommendations. This kind of deep recommendation engine is nearly impossible to achieve with manual rules, but a deep learning system can continuously learn nuanced preferences from millions of data points.

5. Natural Language Processing (NLP)

Natural Language Processing enables computers to understand, interpret, and generate human language (text or speech). NLP combines linguistics and machine learning so that software can work with human languages in a smart way. This includes tasks like understanding the meaning of a sentence, translating between languages, recognizing names or dates in text, summarizing documents, or holding a conversation.

Essentially, NLP is what allows AI to go from pure numbers to words and sentences – it bridges human communication and computer processing.

Techniques in NLP range from statistical models to deep learning (today’s best NLP systems often use deep learning, especially with large language models). NLP can be challenging because human language is messy, ambiguous, and full of context. However, progress in NLP has exploded, and modern models can achieve tasks like answering questions or detecting sentiment with impressive accuracy. For a product perspective, if your application involves text or voice from users, NLP is how you make sense of it.

Imagine a startup that provides an AI writing assistant for marketing teams. This product might let users input a short prompt or some bullet points, and the AI will draft a well-written blog post or ad copy. Under the hood, NLP is doing the heavy lifting: the system needs to interpret the user’s prompt (e.g., understand that “social media campaign for a new coffee shop” means the tone should be friendly and the content about coffee), and then generate human-like text for the campaign.

NLP is also crucial for startups doing things like chatbots for customer service (the bot must understand customer questions and produce helpful answers), voice-to-text transcription (converting spoken audio to written text), or analyzing survey responses to gauge customer sentiment.

By leveraging NLP techniques, even a small startup can deploy features like language translation or sentiment analysis that would have seemed sci-fi just a few years ago. In practice, that means startups can build products where the computer actually understands user emails, chats, or voice commands instead of treating them as opaque strings of text.

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6. Computer Vision (CV)

Just as NLP helps AI deal with language, computer vision helps AI make sense of what’s in an image or video. This involves tasks like object detection (e.g., finding a pedestrian in a photo), image classification (recognizing that an image is a cat vs. a dog), face recognition, and image segmentation (outlining objects in an image).

Computer vision combines advanced algorithms and deep learning to achieve what human vision does naturally – identifying patterns and objects in visual data. Modern computer vision often uses convolutional neural networks (CNNs) and other deep learning models specialized for images.

These models can automatically learn to detect visual features (edges, textures, shapes) and build up to recognizing complete objects or scenes. With ample data (millions of labeled images) and training, AI vision systems can sometimes even outperform humans in certain recognition tasks (like spotting microscopic defects or scanning thousands of CCTV feeds simultaneously).

As Micron describes,

“Computer vision is a field of AI that focuses on enabling computers to interpret and understand visual data from the world, such as images and videos.”

For startups, this means your application can analyze and react to images or video – whether it’s verifying if a user uploaded a valid ID, counting inventory from a shelf photo, or powering the “try-on” AR feature in an e-commerce app – all thanks to computer vision techniques.

Real world example: Consider a startup working on an AI-powered quality inspection system for manufacturing. Traditionally, human inspectors look at products (like circuit boards or smartphone screens) to find defects. With computer vision, the startup can train a model on images of both perfect products and defective ones.

The AI vision system learns to spot anomalies – perhaps a scratch, misaligned component, or wrong color. On the assembly line, cameras feed images to the model which flags any defects in real time, allowing the factory to remove faulty items immediately. This dramatically speeds up quality control and reduces labor costs.

Another example: a retail-focused startup might use computer vision in a mobile app that lets users take a photo of an item and search for similar products in an online catalog (visual search). In both cases, computer vision capabilities become a product feature – something that differentiates the startup’s offering by leveraging cameras and images.

The key is that the AI isn’t “seeing” in the conscious way humans do, but it can analyze pixel patterns with such consistency and speed that it approximates a form of vision tailored to the task at hand.

7. Generative AI

Generative AI refers to AI systems that can create new content (text, images, audio, etc.) that is similar to what humans might produce, essentially generating original outputs based on patterns learned from training data.

Unlike traditional discriminative AI (which might classify or detect something in data), generative AI actually generates something new. This could mean writing a paragraph of text that sounds like a human wrote it, creating a new image from a text description, composing music, or even designing synthetic data.

This field has gained huge attention recently because of advances in models like OpenAI’s GPT series (for text) and image generators like DALL-E or Stable Diffusion (for images). These models are trained on vast datasets (e.g., GPT on billions of sentences, DALL-E on millions of images) and learn the statistical patterns of the content. Then, when given a prompt, they produce original content that follows those patterns.

This encapsulates the core idea that a generative AI doesn’t just analyze data – it uses AI smarts to produce new writing, images, or other media, making it an exciting tool for startups in content-heavy arenas. The outputs aren’t just regurgitated examples from training data – they’re newly synthesized, which is why sometimes these models can even surprise us with creative or unexpected results.

Generative AI opens possibilities for automation in content creation and design, but it also comes with challenges (like the tendency of language models to sometimes produce incorrect information, known as “hallucinations”). Still, the practical applications are vast and highly relevant to startups looking to do more with less human effort in content generation.

Example: Many early-stage companies are already leveraging generative AI to punch above their weight. For example, a startup might offer a copywriting assistant that generates marketing content (blog posts, social media captions, product descriptions) with minimal human input. Instead of a human writer crafting each piece from scratch, the generative AI model (like GPT-4 or similar) can produce a draft that the marketing team just edits and approves. This dramatically speeds up content production.

Another startup example: using generative AI for design prototyping, where a model generates dozens of design ideas (for logos, app layouts, or even game characters) from a simple brief. There are also startups using generative models to produce synthetic training data (e.g., generating realistic-but-fake images of people to train a vision model without privacy issues).

These examples show how generative AI can be a force multiplier – it can create on behalf of the team, allowing startups to scale creative and development tasks in a way that was previously impossible. However, product managers need to understand the limitations too: generative models might require oversight, have biases from training data, or produce outputs that need fact-checking (especially in text).

So, while generative AI is powerful, using it effectively in a product means knowing both its capabilities and its quirks.

8. Large Language Models (LLMs)

LLMs are a specific (and wildly popular) instance of generative AI focused on language. They’re called “large” because of their size – often measured in billions of parameters (weights) – which correlates with their ability to capture subtle patterns in language. Models like GPT-3, GPT-4, BERT, or Google’s PaLM are all LLMs.

After training on everything from books to websites, an LLM can carry on a conversation, answer questions, write code, summarize documents, and more, all through a simple text prompt interface. These models use architectures like the Transformer (an innovation that made training such large models feasible by handling long-range dependencies in text effectively).

However, they don’t truly “understand” like a human – they predict likely sequences of words based on probability. This means they can sometimes produce incorrect or nonsensical answers with great confidence (again, the hallucination issue). Despite that, their utility is enormous, and they’re getting better rapidly. For a startup, an LLM can be thought of as a powerful text-processing engine that can be integrated via an API or fine-tuned for specific needs.

Large Language Models are very large neural network models trained on massive amounts of text, enabling them to understand language and generate human-like text. These models, such as GPT, use deep learning techniques to perform tasks like text completion, translation, summarization, and question-answering.

A common way startups use LLMs is by integrating with services like OpenAI’s API to add smart language features. For example, a customer service platform startup might use an LLM to suggest reply drafts to support tickets. When a support request comes in, the LLM analyzes the customer’s message and generates a suggested response for the support agent, saving time.

Another scenario: an analytics startup can offer a natural language query interface to a database – the user types a question in English (“What was our highest-selling product last month in region X?”) and the LLM interprets that and translates it into a database query or directly fetches an answer if it has been connected to the data.

This turns natural language into an actual tool for interacting with software. Startups also fine-tune LLMs on proprietary data to create specialized chatbots (for instance, a medical advice bot fine-tuned on healthcare texts, so it speaks the language of doctors and patients).

LLMs, being generalists, provide a flexible platform; a savvy startup can customize them to serve as content generators, conversational agents, or intelligent parsers of text. The presence of such powerful language understanding “as a service” means even a small team can add fairly advanced AI features without training a huge model from scratch – which is a game changer.

9. Supervised Learning

Supervised Learning is a machine learning approach where a model is trained on labeled examples, meaning each training input comes with the correct output, allowing the model to learn the relationship and make predictions on new, unlabeled data.

Supervised learning is like learning with a teacher. We show the algorithm input-output pairs – for example, an image plus the label of what’s in the image (“cat” or “dog”), or a customer profile plus whether they clicked a promo or not – and the algorithm tunes itself to map inputs to outputs. It’s by far the most common paradigm for training AI models in industry because if you have the right labeled dataset, supervised learning tends to produce highly accurate models for classification or prediction tasks.

A formal description from IBM states:

“Supervised learning is defined by its use of labeled datasets to train algorithms to classify data or predict outcomes accurately.”

Essentially, the model is “supervised” by the labels: during training it makes a prediction and gets corrected by seeing the true label, gradually learning from its mistakes.

Most classic AI use cases are supervised: spam filtering (train on emails labeled spam vs. not spam), fraud detection (transactions labeled fraudulent or legit), image recognition (photos labeled with what’s in them), etc. The downside is it requires obtaining a quality labeled dataset, which can be time-consuming or costly (think of needing thousands of hand-labeled examples). But many startups find creative ways to gather labeled data, or they rely on pre-trained models (which were originally trained in a supervised manner on big generic datasets) and then fine-tune them for their task.

Real world example: Consider a startup offering an AI tool to vet job applications. They want to predict which applicants will perform well if hired. They could approach this with supervised learning: gather historical data of past applicants including their resumes and some outcome measure (e.g., whether they passed interviews, or their job performance rating after one year – that’s the label).

Using this, the startup trains a model to predict performance from a resume. Each training example is a resume (input) with the known outcome (output label). Over time, the model learns which features of a resume (skills, experience, etc.) correlate with success. Once trained, it can score new resumes to help recruiters prioritize candidates.

Another example: a fintech startup might use supervised learning to predict loan default. They train on past loans, each labeled as repaid or defaulted, so the model learns patterns indicating risk. In both cases, the key is the startup has (or acquires) a dataset with ground truth labels.

Supervised learning then provides a powerful predictive tool that can drive product features (like automatic applicant ranking or loan risk scoring). The better the labeled data (quality and quantity), the better the model usually becomes – which is why data is often called the new oil, and why even early-stage companies put effort into data collection and labeling strategies.

10. Fine-Tuning

Fine-tuning has become a go-to strategy in modern AI development, especially for startups. Rather than training a complex model from scratch (which can be like reinventing the wheel, not to mention expensive in data and compute), you start with an existing model that’s already learned a lot from a general dataset, and then train it a bit more on your niche data. This adapts the model’s knowledge to your context.

For example, you might take a large language model that’s learned general English and fine-tune it on legal documents to make a legal assistant AI. Fine-tuning is essentially a form of transfer learning – leveraging knowledge from one task for another. By fine-tuning, the model’s weights get adjusted slightly to better fit the new data, without having to start from random initialization. This typically requires much less data and compute than initial training, because the model already has a lot of useful “general understanding” built-in.

Fine-tuning can be done for various model types (language models, vision models, etc.), and there are even specialized efficient techniques (like Low-Rank Adaptation, a.k.a. LoRA) to fine-tune huge models with minimal resources.

For startups, fine-tuning is great because you can take open-source models or API models and give them your unique spin or proprietary knowledge. It’s how a small company can create a high-performing specialized AI without a billion-dollar budget.

To quote IBM’s definition, “Fine-tuning in machine learning is the process of adapting a pre-trained model for a specific tasks or use cases.” This highlights that fine-tuning is all about starting from something that already works and making it work exactly for your needs. For a startup, fine-tuning can mean the difference between a one-size-fits-all AI and a bespoke solution that truly understands your users or data. It’s how you teach a big-brained AI new tricks without having to build the brain from scratch.

Real world example: Imagine a startup that provides a virtual personal trainer app. They decide to have an AI coach that can analyze user workout videos and give feedback on form. Instead of collecting millions of workout videos and training a brand new computer vision model, the startup could take a pre-trained vision model (say one that’s trained on general human pose estimation from YouTube videos) and fine-tune it on a smaller dataset of fitness-specific videos labeled with “correct” vs “incorrect” form for each exercise.

By fine-tuning, the model adapts to the nuances of, say, a perfect squat or plank. This dramatically lowers the barrier – maybe they only need a few thousand labeled video clips instead of millions, because the base model already understood general human movement.

Conclusion

Embracing AI in your product doesn’t require a PhD in machine learning, but it does help to grasp these fundamental terms and concepts. From understanding that AI is the broad goal, machine learning is the technique, neural networks and deep learning are how we achieve many modern breakthroughs, to leveraging NLP for text, computer vision for images, and generative AI for creating new content – – these concepts empower you to have informed conversations with your team and make strategic product decisions. Knowing about large language models and their quirks, the value of supervised learning with good data, and the shortcut of fine-tuning gives you a toolkit to plan AI features smartly.

The world of AI is evolving fast (today’s hot term might be an industry standard tomorrow), but with the ten terms above, you’ll be well-equipped to navigate the landscape and build innovative products that harness the power of artificial intelligence. As always, when integrating AI, start with a clear problem to solve, use these concepts to choose the right approach, and remember to consider ethics and user experience. Happy building – may your startup’s AI journey be a successful one!

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