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? 

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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. 

但這讓你想知道：什麼是 AI 模型? 

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. 這使得它們擁有靈活性和擴展性，可以執行各種任務，例如語言翻譯、生成類似人類的回應等。 

您大多可以在客戶服務中找到大型語言模型，因為它們能夠通過情感分析來檢測客戶的情緒。 通過分析社交媒體活動或在線評論，您可以更好地了解人們如何看待您的品牌，以便改進您的產品和服務。 

神經網絡

將神經網絡視為人類大腦中的神經元；這正是這些機器學習模型的基礎。 簡而言之，它們是一組互相連接的節點，處理輸入數據並根據該數據做出預測。 

有多種類型的神經網絡，包括： 

• 前饋神經網絡 (FNNs) — 最簡單的神經連接形式。 
• 卷積神經網絡 (CNNs) — 適用於網格數據。 
• 生成對抗網絡 (GANs) — 由生成器和判別器神經網絡組成。 
• 長短期記憶網絡 (LSTMs) — 解決梯度消失問題。 
• 遞迴神經網絡 (RNNs) — 非常適合序列數據。 

這些模型適合用於圖像、視頻和語音識別、機器翻譯、電子遊戲等。 

多模態模型

多模態模型從不同類型的數據中提取信息，如圖像、音頻、視頻，甚至語音。 它們通過計算機視覺“看”到視覺輸入並從中獲取信息。 

如今，大多數基礎模型已經變得多模態化。 例如，ChatGPT不僅能對文本提示作出回應，還能識別圖像中的信息。 

您還可以將一些文本到圖像生成工具視為多模態AI模型。

這個模型有什麼幫助？ 因為它可以生成甚至更好的結果，幫助您獲得最佳的答案。 

決策樹

決策樹是一種流程圖，根據之前問題的回答將數據拆分成子集。 將它們視為一棵樹。 每個節點代表基於某個特徵的決策，而分支則代表該決策的結果。 然後，在分支的末端，您將得到一個最終結果的葉子。 

例如，大多數垃圾郵件檢測器使用決策樹來判斷電子郵件是否為垃圾郵件。 它們會檢查郵件，如果識別出多個‘不許’的關鍵字，則將其分類為垃圾郵件。 

此外，您可以使用決策樹根據客戶的偏好、行為、購買歷史等進行分類。 這有助於營銷人員提供更具個性化的內容，從而提高參與度並降低流失率。 

隨機森林

當您將多個決策樹組合在一起時，就會產生隨機森林。 這基本上是一個學習模型，將決策樹的單個結果和決策匯聚到一起，形成更精確的預測。 

最大的優勢是它提高了預測的準確性。 您可以使用它來預測客戶行為，並利用這些見解來創造更好的體驗和互動。 

擴散模型

我們之前提過擴散模型，但我們並沒有深入解釋。 讓我們現在進行深入了解。 

擴散模型透過為圖像添加“噪音”來運作，將它們分解成微小的片段，模型仔細分析這些片段以發現新模式。 然後，透過對圖像進行“去噪”（反向操作），模型生成新的圖案組合。 

例如，您想生成一幅貓的圖片。 擴散模型知道貓擁有小身體、鬍鬚和爪子。 根據這些信息，模型可以將這些特徵重建成一張全新的高質量圖片。 

線性回歸模型

線性回歸是一種機器學習模型，常用於找出輸入變量和輸出變量之間的關係。 簡而言之，它識別並預測兩個變量之間的線性關係。 

例如，這是一個非常適合風險分析師的模型，他們希望識別自己的脆弱之處。 

邏輯回歸模型

邏輯回歸是一種廣泛使用的統計模型，專注於根據一個或多個預測因子解決二進制分類問題。 這意味著使用獨立變量來衡量和估計特定事件發生的機率。 

您經常可以在醫療領域找到邏輯回歸模型，研究人員使用它們來了解哪些因素影響疾病。 這有助於開發更準確的測試。 

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我們名單的最後一項是提供有關如何開發自定義AI模型的建議。 讓我們在下一部分中逐步介紹這些步驟。 

如何開發自定義AI模型

隨著技術的進步，您可以使用很多良好的工具來自己構建尖端的AI模型，例如TensorFlow、Vertex AI或PyTorch。 通過AI模型，您可以在各個領域推動創新，做出更多基於數據的決策。

要開始，這裡有一些您應該遵循的步驟： 

1. 確定您的目標 — 您想通過自定義AI模型達成什麼？ 您想改善客戶服務還是更快地生成文本？ 務必設置符合您業務需求的清晰目標。 
2. 收集數據 — AI模型的好壞取決於您提供的數據。 您給予它的信息越多，它在回答問題時就會越好。 選擇適合的算法並選擇反映您的使用情況的數據集。 
3. 構建結構 — 大多數工具都有用戶友好的界面，您可以使用它來創建AI系統。 它們甚至可能擁有教程和指南，幫助您設置正確的配置。 
4. 訓練模型 — 此步驟要求您訓練您的模型，以確保它學到的知識是正確的。 密切關注進展，如果偏離正確的路徑，則立即調整方向。 
5. 驗證和部署 — 當一切準備就緒並且您已測試模型後，您可以將其整合到您的業務框架中。 務必隨時監控其性能並定期更新，這對於保持模型的準確性和相關性至關重要。 並將其微調至完美。 

恭喜！ 您已經閱讀完這篇文章的結尾。 讓我們說一聲再見。 

輪到您了

隨著人工智能的崛起，出現了一個挑戰：決定使用哪種AI工具來簡化您的操作並自動化許多繁瑣的手動任務。

我們可以通過介紹Guru來為您提供便利，這是一個企業AI平台，將您所有的應用程序、聊天和文檔連接在一起，並提供對所有用户查詢的即時回答。 

看看人們對Guru的評價： 

“Guru的突出特點是它的集中庫，所有批准的資源材料都可以在一個地方輕鬆訪問。 這種設置提高了使用的便利性，因為我可以快速收藏和關注與我的部門相關的系列。” 

立即註冊並嘗試今天使用。 

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