In recent times, Generative AI has shown promising leads to solving complex AI tasks. Modern AI models like ChatGPT, Bard, LLaMA, DALL-E.3, and SAM have showcased remarkable capabilities in solving multidisciplinary problems like visual query answering, segmentation, reasoning, and content generation.
Furthermore, Multimodal AI techniques have emerged, able to processing multiple data modalities, i.e., text, images, audio, and videos concurrently. With these advancements, it’s natural to wonder: Are we approaching the top of traditional machine learning (ML)?
In this text, we’ll take a look at the state of the normal machine learning landscape concerning modern generative AI innovations.
What’s Traditional Machine Learning? – What are its Limitations?
Traditional machine learning is a broad term that covers a wide range of algorithms primarily driven by statistics. The 2 fundamental forms of traditional ML algorithms are supervised and unsupervised. These algorithms are designed to develop models from structured datasets.
Standard traditional machine learning algorithms include:
- Regression algorithms akin to linear, lasso, and ridge.
- K-means Clustering.
- Principal Component Evaluation (PCA).
- Support Vector Machines (SVM).
- Tree-based algorithms like decision trees and random forest.
- Boosting models akin to gradient boosting and XGBoost.
Limitations of Traditional Machine Learning
Traditional ML has the next limitations:
- Limited Scalability: These models often need assistance to scale with large and diverse datasets.
- Data Preprocessing and Feature Engineering: Traditional ML requires extensive preprocessing to rework datasets as per model requirements. Also, feature engineering might be time-consuming and requires multiple iterations to capture complex relationships between data features.
- High-Dimensional and Unstructured Data: Traditional ML struggles with complex data types like images, audio, videos, and documents.
- Adaptability to Unseen Data: These models may not adapt well to real-world data that wasn’t a part of their training data.
Neural Network: Moving from Machine Learning to Deep Learning & Beyond
Neural network (NN) models are way more complicated than traditional Machine Learning models. The best NN – Multi-layer perceptron (MLP) consists of several neurons connected together to know information and perform tasks, much like how a human brain functions.
Advances in neural network techniques have formed the premise for transitioning from machine learning to deep learning. As an example, NN used for computer vision tasks (object detection and image segmentation) are called convolutional neural networks (CNNs), akin to AlexNet, ResNet, and YOLO.
Today, generative AI technology is taking neural network techniques one step further, allowing it to excel in various AI domains. As an example, neural networks used for natural language processing tasks (like text summarization, query answering, and translation) are referred to as transformers. Outstanding transformer models include BERT, GPT-4, and T5. These models are creating an impact on industries starting from healthcare, retail, marketing, finance, etc.
Do We Still Need Traditional Machine Learning Algorithms?
While neural networks and their modern variants like transformers have received much attention, traditional ML methods remain crucial. Allow us to take a look at why they’re still relevant.
1. Simpler Data Requirements
Neural networks demand large datasets for training, whereas ML models can achieve significant results with smaller and simpler datasets. Thus, ML is favored over deep learning for smaller structured datasets and vice versa.
2. Simplicity and Interpretability
Traditional machine learning models are built on top of simpler statistical and probability models. For instance, a best-fit line in linear regression establishes the input-output relationship using the least squares method, a statistical operation.
Similarly, decision trees make use of probabilistic principles for classifying data. Using such principles offers interpretability and makes it easier for AI practitioners to know the workings of ML algorithms.
Modern NN architectures like transformer and diffusion models (typically used for image generation like Stable Diffusion or Midjourney) have a fancy multi-layered network structure. Understanding such networks requires an understanding of advanced mathematical concepts. That’s why also they are known as ‘Black Boxes.’
3. Resource Efficiency
Modern neural networks like Large Language Models (LLMs) are trained on clusters of pricey GPUs per their computational requirements. For instance, GPT4 was reportedly trained on 25000 Nvidia GPUs for 90 to 100 days.
Nevertheless, expensive hardware and lengthy training time are usually not feasible for each practitioner or AI team. However, the computational efficiency of traditional machine learning algorithms allows practitioners to realize meaningful results even with constrained resources.
4. Not All Problems Need Deep Learning
Deep Learning isn’t absolutely the solution for all problems. Certain scenarios exist where ML outperforms deep learning.
As an example, in medical diagnosis and prognosis with limited data, an ML algorithm for anomaly detection like REMED delivers higher results than deep learning. Similarly, traditional machine learning is important in scenarios with low computational capability as a versatile and efficient solution.
Primarily, the choice of the most effective model for any problem depends upon the needs of the organization or practitioner and the character of the issue at hand.
Machine Learning in 2023
In 2023, traditional machine learning continues to evolve and is competing with deep learning and generative AI. It has several uses within the industry, particularly when coping with structured datasets.
As an example, many Fast-Moving Consumer Goods (FMCG) firms take care of bulks of tabular data counting on ML algorithms for critical tasks like personalized product recommendations, price optimization, inventory management, and provide chain optimization.
Further, many vision and language models are still based on traditional techniques, offering solutions in hybrid approaches and emerging applications. For instance, a recent study titled “Do We Really Need Deep Learning Models for Time Series Forecasting?” has discussed how gradient-boosting regression trees (GBRTs) are more efficient for time series forecasting than deep neural networks.
ML’s interpretability stays highly beneficial with techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations). These techniques explain complex ML models and supply insights about their predictions, thus helping ML practitioners understand their models even higher.
Finally, traditional machine learning stays a strong solution for diverse industries addressing scalability, data complexity, and resource constraints. These algorithms are irreplaceable for data evaluation and predictive modeling and can proceed to be a component of a knowledge scientist’s arsenal.
If topics like this intrigue you, explore Unite AI for further insights.