Hvad er en AI-model, og hvordan fungerer den? [2024]
Artificial intelligence (AI) is rapidly becoming a staple in today’s society, with every industry using it to interpret datasets more quickly. But what exactly is an AI model and how does it help you with decision-making?
AI models are everywhere — in fact, 86% of IT leaders expect generative AI, for example, to be vital to their company in the near future. It’s a very useful tool, one that mimics human intelligence to make predictions and find patterns in input data.
But it makes you wonder: what is an AI model?
This is a question we’ll try to answer in this guide. Dive in to discover what an AI model is, how it works, and some of the most popular types of models.
What is an AI Model?
An AI model is a computer program trained on specific algorithms that help it replicate human intelligence to make predictions, find patterns, and make decisions.
Think of all the AI-powered chatbots that have recently appeared. They use various AI models to have conversations with humans and answer questions the user types into a text box.
In a nutshell, while you don’t interact with the AI model directly, it’s actually powering the chatbot and helping it make decisions autonomously using the training data the developers feed into it.
The purpose of artificial intelligence models is to do specific tasks and automate decision-making workflows.
Now that you know what an AI model is, let’s discuss how it differs from machine learning and deep learning.
What’s the Difference Between AI, Machine Learning, and Deep Learning?
Artificial intelligence, machine learning, deep learning — they all sound similar, right? Wrong! ❌
It’s a common misconception that these tools are interchangeable but there’s a slight difference between AI and a machine learning model.
Artificial Intelligence (AI)
Artificial intelligence is a computer science field that focuses on developing software or machines that simulate human intelligence. AI-powered apps can usually do all kinds of tasks, such as translating content into other languages or generating art and images.
Don’t worry — it’s not yet at the human brain’s level but it can analyze huge volumes of data faster than a data scientist can. That’s why it often outclasses humans in the data science field.
Machine Learning (ML)
Machine learning is a branch of AI, possibly one of the biggest. It focuses on helping AI software imitate the way humans learn, through algorithms and datasets.
Generally, ML models can learn from data on their own which helps them make accurate predictions (called unsupervised learning). But you can also train the algorithm with specific data in a process named supervised learning.
A good example is any streaming service’s recommendations. They use ML to analyze what a user often watches and offer similar suggestions.
Deep Learning (DL)
Deep learning is a subset of machine learning that teaches computers to process data by mimicking human neural networks. Basically, DL simulates the brain’s decision-making power to make predictions and recognize data patterns.
This is commonly seen in healthcare, especially in image recognition, as it helps detect diseases in MRIs more easily. Besides, it works to improve its accuracy over time.
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Okay, we’ve established what artificial intelligence, machine learning, and deep learning are.
Let’s return to AI models and see how they work.
How Do AI Models Work?
As we’ve already discussed, AI models use multiple algorithms to make predictions and understand patterns in data. It cannot work without these algorithms.
Basically, developers train the AI model to mimic how a human brain sends information through neurons. But they’re not called neurons, just layers. And we can distinguish between different types of layers:
- Input layer — Here’s where data enters.
- Hidden layer — This hidden layer processes data and moves it to other layers.
- Output layer — The output layer spits out the final result.
In general, AI models learn from thousands of open-source data items to generate an answer. Unless you teach them, they won’t know the answer to your question. That’s why you can also categorize AI models by intelligence. Which means that the more data they learn from, the more complex they’ll be.
With this information in mind, let’s talk about discriminative and generative models.
Discriminative vs. generative models
You can classify machine learning models into two categories: discriminative and generative.
A generative model is a computer vision model that learns data patterns in an attempt to generate similar output. It forecasts the probability of what the next word will be based on what it has seen before.
By making correlations, the generative model can generate highly probable outputs. It can either offer autocomplete suggestions or generate entirely new text. You might think that using generative AI is wrong, but 78% of executive leaders believe that the benefits of generative AI outweigh the risks — you can do more in less time, with less effort.
Examples include transformers, which you can use to identify how different elements in a dataset influence one another. Or diffusion models that apply Gaussian noise to destroy training data and recover it.
Discriminative models, on the other hand, are algorithms that focus on distinguishing between different categories or classes of data. They don’t model each class individually; instead, they learn the boundaries that separate those classes.
What’s the purpose? Well, to predict the probability of data belonging to a certain class.
Think of apps like spam detection. The discriminative model classifies emails as spam based on their content.
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After making the distinction between these models, let’s talk about the different types of AI models.
What are the Different Types of AI Models?
Everyone uses AI models nowadays, no matter the industry.
However, there are various types of AI models with different use cases. In the next paragraphs, let’s explore what each type does and how they optimize your flows.
Foundation models
Foundation models are pre-trained ML models that perform a wide range of tasks, including answering questions, text generation, writing code, and summarization.
People mostly use these trained models for self-driving learning, meaning that anyone can use such tools to learn something new or do homework, for example.
Think of platforms such as OpenAI’s ChatGPT, which uses foundation models for different use cases.
Large language models (LLMs)
LLMs are deep learning models that understand and interpret language to generate text and converse like a human using natural language processing (NLP).
Being trained on huge datasets (hence the ‘large’) LLMs can predict the next word in a sentence or phrase. Hvilket giver dem den fleksibilitet og skalérbarhed, der er nødvendig for at udføre forskellige opgaver, såsom sprogoversættelse, generere menneskelignende svar osv.
Du kan stort set finde LLM'er i kundeservice, da de er i stand til at registrere klienters følelser gennem sentimentanalyse. Ved at analysere sociale mediers aktivitet eller online anmeldelser kan du bedre forstå, hvordan folk opfatter dit brand, så du kan forbedre dine produkter og tjenester.
Neurale netværk
Tænk på neurale netværk som neuronerne i den menneskelige hjerne; det er på hvilket disse ML-modeller er baseret. Kort sagt, de er en bunke af sammenkoblede noder, der behandler inputdata og laver forudsigelser baseret på disse data.
Der er mange typer neurale netværk, herunder:
- Feedforward neurale netværk (FNN'er) — den simpleste form for neural forbindelse.
- Konvolutionale neurale netværk (CNN'er) — egnet til grid data.
- Generative modstridende netværk (GAN'er) — består af generelle og diskriminerende neurale netværk.
- Lang korttids hukommelses netværk (LSTM'er) — behandler det forsvindende gradientproblem.
- Recurrent neurale netværk (RNN'er) — fantastisk til sekventielle data.
Disse modeller er gode til billede, video og talegenkendelse, maskinoversættelse, videospil osv.
Multimodale modeller
Multimodale modeller udtrækker information fra forskellige typer data, såsom billeder, lyd, video og endda tale. De “ser” den visuelle input gennem computer vision og får information fra det.
I dag er de fleste grundmodeller blevet multimodale. For eksempel reagerer ChatGPT ikke kun på tekstprompter, men kan også genkende information fra billeder.
Du kan også overveje nogle tekst-til-billede generationsværktøjer som multimodale AI-modeller.
Hvorfor er denne model nyttig? Fordi den kan generere endnu bedre resultater og hjælpe dig med at få det bedst mulige svar.
Beslutningstræer
Beslutningstræer er flowdiagrammer, der opdeler data i undersæt baseret på svaret på et tidligere spørgsmål. Tænk på dem som et træ. Hver node repræsenterer en beslutning baseret på en funktion, mens en gren repræsenterer resultatet af den beslutning. Så, i slutningen af grenen, har du et blad med det endelige resultat.
For eksempel bruger de fleste spamdetektorer beslutningstræer til at finde ud af, om en e-mail er spam eller ej. De gennemgår e-mailen og, hvis de identificerer flere 'no-no' nøgleord, vil de klassificere den som spam.
Derudover kan du bruge beslutningstræer til at klassificere kunder baseret på deres præferencer, adfærd, købshistorik osv. Dette hjælper marketingfolk med at tilbyde mere personligt indhold, hvilket øger engagement og reducerer frafald.
Random forests
Når du sætter flere beslutningstræer sammen, skaber det en random forest. Det er i bund og grund en læringsmodel, der bringer individuelle resultater og beslutninger fra beslutningstræer ind i en enkelt, mere præcis forudsigelse.
Den største fordel er, at den øger nøjagtigheden af dine forudsigelser. Du kan bruge den til at forudsige kundeadfærd og bruge indsigt til at skabe bedre oplevelser og interaktioner.
Diffusionsmodeller
Vi har nævnt diffusionsmodeller før, men vi har ikke forklaret dem i dybden. Lad os gøre det nu.
Diffusionsmodeller fungerer ved at tilføje “støj” til billeder, bryde dem i små stykker, som modellen omhyggeligt analyserer for at opdage nye mønstre. Derefter, ved at “fjerne støjen” fra billedet (arbejde i omvendt), genererer modellen nye mønsterkombinationer.
For eksempel vil du generere et billede af en kat. Diffusionsmodellen ved, at katte har små kroppe, knurhår og poter. Med denne information kan modellen genskabe disse karakteristika til et helt nyt højkvalitetsbillede.
Lineær regressionsmodeller
Lineær regression er en type ML-model, der ofte bruges til at finde ud af forholdet mellem input- og outputvariable. Kort sagt identificerer den og forudsiger det lineære forhold mellem to variable.
For eksempel er det en god model for risikostyrere, der ønsker at identificere, hvor de måske er sårbare.
Logistisk regressionsmodeller
Logistisk regression er en meget anvendt statistisk model, der fokuserer på at løse binære klassificeringsproblemer baseret på en eller flere prædiktorer. Dette oversættes til at bruge uafhængige variable til at måle og estimere chancerne for, at en bestemt begivenhed opstår.
Du kan ofte finde logistisk regressionsmodeller i den medicinske sektor, hvor forskere bruger dem til at forstå, hvilke faktorer der påvirker en sygdom. Dette fører til udviklingen af mere nøjagtige test.
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Sidste punkt på vores liste er at tilbyde tips til, hvordan man udvikler en brugerdefineret AI-model. Lad os gennemgå trinnene i næste afsnit.
Hvordan man udvikler en brugerdefineret AI-model
Med de seneste fremskridt inden for teknologi er der masser af gode værktøjer, du kan bruge til at bygge en banebrydende AI-model selv, såsom TensorFlow, Vertex AI eller PyTorch. Med en AI-model kan du drive innovation på alle fronter og træffe mere datadrevne beslutninger.
For at komme i gang er her nogle af de trin, du skal følge:
- Identificer dine mål — Hvad forsøger du at opnå med den brugerdefinerede AI-model? Vil du forbedre din kundeservice eller generere tekst hurtigere? Sørg for at sætte klare mål, der møder dine forretningsbehov.
- Indsaml data — En AI-model er kun så god som de data, du giver den. Jo mere du fodrer den, jo bedre bliver den til at besvare spørgsmål. Vælg de relevante algoritmer og vælg datasæt, der afspejler dine brugsscenarier.
- Byg strukturen — De fleste værktøjer har et brugervenligt interface, som du kan bruge til at oprette AI-systemet. De har måske endda tutorialer og vejledninger til at hjælpe dig med at opstille de rigtige konfigurationer.
- Træn modellen — Dette trin kræver, at du træner din model og sikrer, at det, den lærer, er korrekt. Hold et tæt øje med fremskridtene og sæt den på den rigtige vej, hvis den afviger.
- Valider og implementer — Når alt er klar, og du har testet modellen, kan du integrere den i dit forretningsframework. Sørg for altid at overvåge dens ydeevne og opdatere den regelmæssigt, da det er vigtigt for at holde modellen nøjagtig og relevant. Og finjuster den til perfektion.
Tillykke! Du er nået til slutningen af artiklen. Lad os sige vores afskedsord.
Over til dig
Med stigningen af kunstig intelligens kommer der en stor udfordring: at beslutte, hvilket AI-værktøj man skal bruge for at optimere sine operationer og automatisere mange kedelige, manuelle opgaver.
Vi kan gøre det lettere for dig ved at præsentere Guru, en enterprise AI-platform der forbinder alle dine apps, chats og dokumenter ét sted og tilbyder øjeblikkelige svar på alle brugerforespørgsler.
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Key takeaways 🔑🥡🍕
What is meant by AI model?
An AI model is a program or algorithm trained on data to recognize patterns, make decisions, and perform specific tasks without explicit human instructions.
Is ChatGPT an AI model?
Yes, ChatGPT is an AI model developed by OpenAI that uses machine learning techniques to generate human-like text based on the input it receives.
What is the AI model in layman's terms?
In layman's terms, an AI model is like a smart computer program that learns from data to make predictions or decisions, similar to how humans learn from experience.
What are the different types of model AI?
There are various types of AI models, including supervised learning, unsupervised learning, reinforcement learning, and generative models, each designed for specific tasks and data structures.
How do different AI models work?
Different AI models work by using algorithms to process data: supervised models learn from labeled data, unsupervised models find patterns in unlabeled data, reinforcement models learn through trial and error, and generative models create new data similar to the training data.
How does AI work step by step?
AI works through several steps: data collection, data preprocessing, model training on the data, validation and testing of the model, and finally deployment where the model makes predictions or decisions based on new data.
How do generative AI models work?
Generative AI models work by learning the patterns and structures of the training data to generate new, similar data. For example, they can create text, images, or music by predicting and constructing new sequences based on what they’ve learned.
How is an AI model created?
An AI model is created by collecting relevant data, preprocessing the data to ensure quality, selecting and training an appropriate algorithm on this data, and then validating and testing the model to ensure it performs accurately.
How does AI work step by step?
AI works through a series of steps: data collection, data preprocessing, model training, validation and testing, and deployment for real-world use.
Hvordan fungerer AI faktisk?
AI works by using algorithms to process large amounts of data, learn from patterns within that data, and make predictions or decisions based on the learned patterns, often improving over time with more data and experience.
How are AI human models created?
AI human models are created by training algorithms on large datasets of human behavior and characteristics, allowing the AI to mimic human-like responses and actions in various contexts.
What are the 4 steps of the AI process?
The four steps of the AI process are data collection, data preprocessing, model training, and model deployment. These steps ensure the AI system learns accurately from data and can apply this learning to make predictions or decisions.
Is ChatGPT an AI model?
Yes, ChatGPT is an AI model.
What type of AI model does ChatGPT use?
ChatGPT uses generative pre-trained transformer (GPT) models to process and generate text. It also uses large language models to understand natural language and respond in a human-like manner.
Can AI models make mistakes?
Yes. Despite their intelligence and sophistication, AI models are not perfect and can make costly errors. For instance, if the training data has biases, the AI model learns and reproduces these inconsistencies, harming your brand’s reputation.
Can AI models make mistakes?
Yes. Despite their intelligence and sophistication, AI models are not perfect and can make costly errors. For instance, if the training data has biases, the AI model learns and reproduces these inconsistencies, harming your brand’s reputation.
Can AI models make mistakes?
Yes. Despite their intelligence and sophistication, AI models are not perfect and can make costly errors. For instance, if the training data has biases, the AI model learns and reproduces these inconsistencies, harming your brand’s reputation.
Can AI models make mistakes?
Yes. Despite their intelligence and sophistication, AI models are not perfect and can make costly errors. For instance, if the training data has biases, the AI model learns and reproduces these inconsistencies, harming your brand’s reputation.
Can AI models make mistakes?
Yes. Despite their intelligence and sophistication, AI models are not perfect and can make costly errors. For instance, if the training data has biases, the AI model learns and reproduces these inconsistencies, harming your brand’s reputation.
Can AI models make mistakes?
Yes. Despite their intelligence and sophistication, AI models are not perfect and can make costly errors. For instance, if the training data has biases, the AI model learns and reproduces these inconsistencies, harming your brand’s reputation.