Artificial Intelligence Interpretation: Key Stages of Training and Inference

Artificial intelligence (AI), machine learning and generative AI have become important parts of the modern enterprise technology toolbox. AI covers a wide range of technologies, including real-time language translation, facial recognition, voice assistants, personalized recommendation systems and fraud detection. The AI ​​training and inference process is critical to understanding the capabilities of the model. Training involves training a model using a data set, enabling the model to learn from the processed information and make predictions or decisions. The inference phase applies the trained model to new data, enabling it to perform tasks such as image recognition, language translation, or decision making.

Artificial intelligence, machine learning and, more recently, generative artificial intelligence are now part of the technology and methodological toolkit of all companies engaged in digital innovation. Artificial intelligence includes a wide range of technologies capable of performing tasks that typically require human intelligence, such as real-time language translation, facial recognition, voice assistants, personalized recommendation systems or fraud detection, as well as computer-aided medical diagnosis to identify diseases from radiological images.

Let’s discuss the AI ​​training and inference process to better understand the capabilities of the model (*). AI training diagram

Note: Terms marked (*) are defined in the Glossary section at the end of this article.

Artificial Intelligence Training

In short, artificial intelligence training is the process of developing a machine learning model based on a large amount of test data.

It involves feeding a model a data set (*) so that it can learn and make predictions (*) or decisions (*) based on the information it processes. This is the stage where the model acquires the knowledge and skills required to perform specific tasks.

Whether interpreting natural language (*) or performing complex calculations, this step is the foundation. In fact, it determines the accuracy, efficiency, and overall performance of the model and therefore the applications that will use it.

The AI ​​model training process involves several steps.

1. Data Preparation

This step involves collecting, cleaning, and organizing data in a format that allows for efficient use. It is important to ensure the quality and reliability of model input data.

2. Algorithm

The second step involves choosing the appropriate algorithm (*) or neural network (*) architecture that is best suited to solve the problem we want to solve.

3. Refinement

Once the model is selected, the third step is iterative refinement. This involves training and testing the model multiple times to adjust its parameters based on performance, improve its accuracy and reduce errors.

AI training class diagram image

Artificial intelligence training: challenges

Training artificial intelligence models faces real challenges, For example:

Data quality

The quality of the model depends on the quality of the training data. Inaccurate, incomplete or biased data sets can lead to poor predictions.

Information Technology Resources

The computing resources required for training require high processing power and large amounts of memory, especially for complex models such as deep learning networks (*). Phenomena such as overfitting (*) can degrade the quality of prediction or classification tasks.

To illustrate the computational resources required to train an AI model, consider that training a complex deep learning network like GPT-3 requires massive amounts of computing power to incorporate its 175 billion parameters.

Artificial Intelligence Inference

In this stage, a trained machine learning (*) model is applied to new data, enabling it to perform predictions, classifications, recommendations, or Tasks such as making decisions in real-world applications.

In other words, inference is the stage that enables an AI model to provide the intended benefit, such as identifying objects in images, translating languages, providing product recommendations, or guiding self-driving vehicles.

Distinguish between training and inference

There are two main criteria for distinguishing the inference process from artificial intelligence training:

The importance of real-time processing of data

The need for efficiency and low latency

In practice, self-driving or real-time fraud detection systems must have models that can quickly interpret new data and take action quickly.

Challenges to Overcome

The inference phase requires a focus on resource efficiency, maintaining consistent performance across various environments, and optimizing the model in terms of speed. AI models must be adaptable without sacrificing accuracy or reliability. This requires techniques such as model pruning (*) or quantization (*) to reduce computational load while avoiding degradation of model performance.

Examples

Specific examples illustrating the practical application of reasoning are as follows:

CYBER SECURITY

Once accepted Trained on a large dataset of email interactions, the application can identify and flag potential spam or phishing attempts in incoming emails, protecting users from cybersecurity threats.

Autonomous Vehicles

Similarly, the field of autonomous vehicles also relies heavily on the reasoning capabilities of artificial intelligence. In this case, models trained from countless hours of driving data are used in real-time to navigate roads, recognize traffic signs and make split-second decisions.

Training and Inference: Comparative Analysis

Training and inference are two critical and complementary stages in artificial intelligence model development, each fulfilling specific needs. The training phase allows the model to acquire knowledge from historical data. This step requires a lot of computing power to adjust the model’s parameters to achieve accurate predictions.

Inference, on the other hand, applies trained models to new data to make predictions or decisions in real time, highlighting the importance of efficiency and low latency.

Points to Remember

Balancing model complexity, comprehensive training, and inference efficiency is critical to developing artificial intelligence systems.

Complex models can provide better understanding and prediction, but require more resources for training and inference.

Developers must produce a model that is complex, accurate, and efficient enough for real-time use.

Techniques such as pruning, quantization, and transfer learning can optimize models in terms of accuracy and efficiency.

Infrastructure Requirements

Infrastructure requirements for the training and inference phases result in a heavy dependence on hardware performance.

Training deep learning models is extremely computationally intensive and requires dedicated resources to provide powerful computing capabilities. This stage often requires high-performance GPUs to manage large datasets, on which the accuracy and efficiency of the model depend.

In contrast, the inference phase requires less computing power but requires low-latency, high-throughput performance. Its infrastructure requires efficiency and responsiveness to enable real-time data processing close to the source of data generation, like self-driving cars or our email servers, but also introducing a new example in healthcare diagnostics.

Conclusion

Understanding the subtleties of AI training and inference reveals the complexities between acquiring knowledge through AI models and deploying that knowledge in concrete applications .

Artificial intelligence needs to be not only powerful, but also adaptable. To achieve this goal, a balance must be struck between the use of extensive training resources and the need for fast, efficient inference. As AI advances in areas such as healthcare, finance, and industry, these training and inference stages are critical because they enable the creation of AI that applies to specific business cases.

One more thing...

What about the carbon footprint?

To advance machine learning and artificial intelligence, there is a clear need to focus on developing more efficient artificial intelligence models, optimizing hardware infrastructure, and wider adoption of innovative strategies. At the same time, perhaps the ecological footprint of AI must also be considered.

“Future artificial intelligence will require energy breakthroughs, and it will consume far more power than people expect.”

-Sam Altman, CEO of OpenAI

DAVOS, Switzerland; January 16, 2024

Indeed, sustainability becomes an important issue as the environmental impact of training artificial intelligence models comes under scrutiny. As businesses and the public adopt it, more electricity and vast amounts of water will be needed to power and cool the tech giant's device platforms. For example, the researchers estimate that manufacturing GPT-3 consumed 1,287 megawatt hours of electricity and produced 552 tons of carbon dioxide equivalent, equivalent to driving 123 gasoline passenger cars for a year.

Striving to achieve a more sustainable future where technological progress and ecological responsibility coexist harmoniously may be the ultimate goal of artificial intelligence evolution.

(*) Glossary

• Algorithm: A defined, step-by-step set of computational procedures or rules designed to perform a specific task or solve a specific problem

• Dataset: A collection of data points or records, usually in tabular form, used to train, test, or validate a machine learning model, including features (independent variables) and labels in supervised learning (dependent variables or result).

• Decision: In machine learning, this refers to the conclusion that a model reaches after analyzing the data, such as a spam filter deciding that an email is spam (and moving it to spam Mail folder) or not spam (leave it in your inbox).

• Deep Learning: A subset of machine learning involving models called multi-layer neural networks that can automatically learn complex patterns and representations from large amounts of data

• Labeled data: This refers to a data set in which each instance is labeled with an outcome or category, providing clear guidance for the machine learning model during the training process.

• Machine Learning: A branch of artificial intelligence that involves training algorithms to recognize patterns and make decisions based on data without being explicitly programmed for each specific task

• Model: A mathematical and computational representation trained on a dataset capable of predicting and classifying new, unseen data by learning patterns and relationships in the training data

• Model pruning: A technique in federated learning that reduces the size of a model by adaptively pruning parameters during training to reduce computational and communication demands on the client device without significantly affecting the model. Accuracy

• Natural language: The way humans communicate with each other, whether spoken or written, containing the complexities, nuances, and rules inherent in human language expression

• Neural Network: A computing model inspired by the structure of the human brain, consisting of interconnected nodes or neurons that process and transmit signals to solve complex tasks by learning from data, e.g. Pattern Recognition and Decision Making

• Overfitting: When a machine learning model learns training data too closely, making it unable to generalize and accurately predict outcomes on unseen data

• Pattern: (in the context of machine learning) a model learns to identify discernible patterns in data that can be used to make predictions or decisions about new, unseen data

• Prediction: (in machine learning) the process of using a trained model to estimate the most likely outcomes or values ​​for new, unseen instances based on patterns learned during the training phase

• Quantization: (in deep learning) the process of reducing the precision of weights and activations in a model to 2, 3, or 4 digits, allowing the model to run more efficiently at inference time while being accurate Sexual losses are minimized.

• Supervised/Unsupervised: The difference between supervised learning and unsupervised learning is that in supervised learning training there is labeled data (*) that guides the model to learn the mapping from input to output, while Unsupervised learning involves finding patterns or structures in data without explicit labels for the results.

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