Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are often used interchangeably, but vary in capabilities, complexity, and the amount of human input required. For lab managers, understanding these distinctions can help you assess which tools fit your lab needs. This article explores the hierarchy of these technologies, explains how they work, and highlights the practical applications of AI, machine learning, and deep learning in the lab.
AI is the term umbrella. ML, DL, neural networks, machine vision, rule-based algorithms, and other methods all fall under AI. ML and DL are nested concepts within AI. ML is a subset of AI, and DL is a subset of ML.
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Definition of artificial intelligence
AI is the ability of computers to mimic human intelligence. There is a wide spectrum of what that means. Something as simple as an algorithm that takes input and compares it with a predefined value can be considered AI. Similarly, neural networks that produce unique outputs (rethink ChatGpt) are AI, and there is a crack in refinement between these technologies.
For lab managers, understanding AI at the broadest level is useful in evaluating vendor claims. When a product is labeled “AI-equipped,” it means anything from basic rule-based automation to complex learning systems. Simple AI tools, such as using IF/Then Logic or predefined heuristics, can provide value by automating repetitive decisions or flagging known risks.
The key is to ask vendors what AI supports the product, whether it adapts over time, or operates deterministically. Knowing that may help you measure the improvements the tool brings and the level of monitoring required.
AI Lab Use Case
Here is an example of AI lab specific:
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- CellProfiler: A cell counting program that distinguishes cells and non-cell objects with rule-driven image processing and allows cells to be counted.
- Chemical Storage Safety: Some chemical storage software vendors provide the ability for AI to automatically identify chemical safety hazards and act as additional insurance for human testing.
Machine Learning Definition
The MIT Sloan School of Business defines ML as “a subfield of AI that provides the ability to learn without explicitly programming the computer.” ML Models – Whether it's a neural network or a statistical algorithm, it identifies relearn patterns from data given meaningful labels from people, applies those patterns to new inputs, making them more adaptable than stiff, rule-based AI systems.
Many ML models rely on two important, human-driven procedures: feature extraction and data labeling.
Features extraction simplifies complex raw data into meaningful variables that the model can use. As the Digital Agricultural Institute at the University of California, Davis explains, “Functional Engineering (also known as feature extraction) is a technique for creating new (more meaningful) features from the original feature.” For example, emails may be reduced to the number of links or keyword frequencies, and images to edge density or color histograms. This step removes noise, increases efficiency and improves the learning ability of the model.
Data labeling provides the truth needed for monitored learning. As defined by the University of Arizona, “data labeling refers to the process of manually annotating or tagging data to provide context and meaning.” Labeled datasets, such as emails tagged as “spam” or images tagged as “CAT” – Train models link functionality to the correct results. High quality labeling is important for accuracy and fairness.
Human expertise shapes the data the system sees and how it interprets it, making ML powerful, but does not have autonomous insight.
For Lab Managers, ML tools balance performance and resource demands. When comparing lab machine learning with deep learning, ML solutions are typically more practical in labs with structured data sets and limited infrastructure, due to their low data and computing capabilities. However, their effectiveness still relies on thoughtful feature selection and high quality labeled data. When evaluating ML-driven software, such as inventory predictors and quality management assistants, look for systems trained with data similar to your own lab workflow and consider whether the software will allow customization or retraining according to your needs.
ML Lab Use Cases
- Peakbot: An open source, ML-based chromatographic peak picking program that debuted in 2022. Bioinformatics Paper on it, Peakbot achieves results comparable to existing peak detection solutions such as XCMS, but can be trained with user reference data, increasing accuracy.
- Inventory Tracking: Some lab inventory management software provides forecasts powered by machine learning, which may allow researchers to notify them in advance of supply times based on supplier lead times and recommend reorder times.
- Experimental Design Help: Other programs provide assistance in experimental design by recommending parameters for different types of tests.
Definition of deep learning
Deep learning is a subset of ML that relies on layered neural networks to identify patterns of data. These layers consisted of interconnected “neurons” that loosely mimic the human brain, but allow some DL models to learn more abstract features from raw inputs such as images and text.
This architecture sets DL apart from the traditional ML approach, requiring you to manually define the capabilities that the model focuses. DL models can automatically extract functionality from raw data, making them particularly suitable for tasks that contain unstructured or extremely complex data.
For lab managers, this means that DL tools can provide a more powerful and flexible solution than traditional ML systems, but with trade-offs. DL requires a vastly greater computing power, often utilizing GPUs or specialized hardware. Ultimately, looking at machine learning and deep learning in the lab, DL enables features at scale and accuracy beyond what only ML can achieve.
DL Lab Use Case
- Large-scale language models: Examples of DL flagship, ChatGPT, Google Gemini, Claude's large model-based applications, and Claude of humanity provide a generalized dataset that can be used by people in almost any industry. The lab offers a variety of use cases, including meeting summary, code writing, and more.
- Organoid Analysis: DL has been successfully applied to organoid analysis over the past few years, enabling rapid and accurate automated analysis.
- Protein Folding: Alphafold and its open source counterpart Boltz are examples of using DL to predict biomolecular interactions and protein folding, allowing for faster early-stage drug discovery innovation.
Table: Comparison of AI, machine learning, and deep learning in the lab
| ai | ml | DL | |
| input | Rules or Data | Labeled data | Raw data (images, text, etc.) |
| Learning methods | Pre-program or reactive | Training, Learning patterns through feature extraction | Learn patterns independently through neural networks |
| Human involvement | High (rules must be defined) | Media (features must be extracted manually) | Low (extracts are autonomously characterized) |
| complicated | Wide range of complexity | It's more adaptable than AI | It is most adaptable. Imitation of human learning |
| example | if/then Equipment Scheduling Logic | Email Spam Filters are trained with labeled sets of emails | chatgpt, alphafold, image classifier |
Buzzwords like “AI-equipped” are thrown frequently, but knowing what's under the hood (rule-based logic, traditional ML, or deep learning) can help you assess the true value of the tool.
