What is predictive artificial intelligence?

Predictive AI focuses on anticipating future events by analyzing historical data, using mathematical models, and employing advanced algorithms. It forms the basis of systems that forecast demand, detect failures, and predict behavior.

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What is predictive AI?

Predictive AI uses mathematical and statistical models to estimate future outcomes based on various sources, such as historical patterns, correlations, time signals, exogenous variables, etc. In other words, it is neither generative nor traditional AI; instead of creating or deciding, it focuses on anticipating events.

You may have already heard of the term Internet of Behaviors (IoB) . It’s a concept that combines data from connected devices, human behavior, and AI-based analytics to understand how people act. It’s widely used with Big Data and deep learning to predict a multitude of things. Think of it as a fortune teller’s crystal ball—in this case, a “digital” fortune teller.

It’s not magic, but rather the use of current technology and the ability to process enormous amounts of data to predict behavior…

As you may know if you read us, many modern CPUs also use neural networks for prediction , in this case, jump prediction. Performance depends heavily on them…

Fundamental algorithms of predictive AI

Predictive AI relies on a set of classic and modern algorithms . The most relevant are:

• Classical statistical models: these models are interpretable and efficient, ideal for regulated environments.
  • Linear regression: predicts continuous values, such as product prices, for example.
  • Logistic regression:   intended to predict binary abilities, i.e., yes or no.
  • ARIMA / SARIMA models:   specialized in time series, such as the traffic passing through a road at a given time.
  • Survival models (Cox): predict time until an event. This can also be used in digital twins to predict hardware failures, motor failures, etc.
• Machine learning algorithms: in this other case they capture non-linear relationships and work well with tabular data.
  • Decision trees:  models that divide data into hierarchical rules, such as conditions. If this happens, then…
  • Random Forest:  combines multiple decision trees to improve accuracy and reduce overfitting. It is one of the most widely used algorithms for tabular data. Examples of its use include detecting bank fraud or classifying spam emails.
  • Gradient Boosting (XGBoost, LightGBM, CatBoost)
  • SVM (Support Vector Machines)

• Neural networks for prediction:
  • MLP (Multilayer Perceptron):    for general predictions.
  • LSTM / GRU    specialized in sequences and time series.
  • Transformers for forecasting : modern models such as Temporal Fusion Transformers (TFTs). Transformers are replacing LSTMs in complex forecasting due to their ability to capture long-term dependencies.

All of this gives these predictive systems a significant predictive capacity. This doesn’t mean they’ll be 100% accurate in every case , but there will be a higher probability of success than if this type of AI weren’t used. For example, imagine a system in a factory where IoT sensors capture the temperature, vibration, and pressure of a machine and send this data to an edge server running a predictive AI model that anticipates failures and generates automatic alerts. This way, factory technicians can anticipate problems and perform predictive maintenance or schedule a shutdown to minimize the impact on factory productivity.

Does this mean that the machine is guaranteed to break down? No, but it’s very likely that, given these parameters, a failure could occur. This allows for anticipation, whereas without predictive AI, the failure could happen unexpectedly.

Technical behavior of a predictive AI system

A predictive AI system follows a structured pipeline:

1. Data ingestion and preparation:  This step in the pipeline includes cleaning, normalizing, selecting, and processing data variables. It is very important, as the quality of the dataset will determine the quality of the prediction.
2. Model training: the model learns historical patterns through loss function optimization, cross-validation, and hyperparameter tuning.
3. Inference : The model receives new data and produces numerical values, confidence intervals, and probabilities from it. For example, based on the data it has, it could predict the estimated time until the next failure and the probability of failure (%).
4. Monitoring and recalibration : Predictive models degrade over time (concept drift). Therefore, they require periodic retraining, detection of performance anomalies, and hyperparameter adjustments to maintain a good accuracy rate.

Real-world applications of predictive AI

This type of predictive AI has a large number of important applications, and in many sectors:

• Industry : It can be used to improve predictive maintenance programs, anticipating machine or motor failures that could cause problems. For example, by feeding these algorithms data on vibrations, operating temperature, etc., it’s possible to predict when a part might fail.
• Finance: These models are also widely used, such as XGBoost, which can analyze thousands of transactions per second to detect suspicious patterns. They can be useful not only for predicting stock market declines but also for price forecasting, fraud detection, and more.
• Health : through biotelemetry or other types of data, it is possible to predict the likelihood of patient readmissions, analyze clinical risk, detect diseases early, etc.
• Logistics and transportation: They can optimize transport routes to save fuel and be more efficient by taking into account patterns such as traffic, etc., as well as predict demand, estimate delivery times, etc. For example, an LSTM network can predict traffic congestion in real time.
• Energy : It can predict consumption based on schedules, intelligently manage supply networks, optimize energy use and storage, etc. For example, a transformer-based AI can predict solar energy production based on weather and seasonality, which can help engineers anticipate energy production versus demand in order to compensate the grid with other alternative energy sources if needed.

Hardware for predictive AI: CPUs, GPUs, TPUs and edge computing

Predictive AI doesn’t just rely on algorithms; it also requires optimized hardware to process large volumes of data. And this necessitates a whole heterogeneous ecosystem:

• CPUs: They can handle predictive AI workloads for tabular prediction with lightweight models, real-time inference, and workloads with logical branching. Some examples that work perfectly on a CPU include regression or random forest.
• GPUs : The sheer number of compute units in GPUs makes them highly efficient at running AI workloads, acting as accelerators. Furthermore, newer GPUs also include Tensor Cores, which are even more optimized for the type of simple data used by AI. Therefore, GPUs are used for training neural networks, as well as for massively parallel data processing and complex time series analysis. For example, a GPU could train an LSTM prediction model at speeds up to 20 or 50 times faster than a CPU.
• TPU : This is specialized hardware, as you know, similar to an NPU, but while an NPU might be better for running AI models, a TPU can be very efficient for training them. These units are commonly used in data centers, especially for tensor operations, distributed training, and transformer-based forecasting models.

On the other hand, all this infrastructure, which can be local or remote, like most current cloud-based services, can also leverage other layers, such as Edge Computing. Edge Computing can complement predictive AI by running tasks on local devices, from sensor hubs to cameras, routers, autonomous vehicles, and so on. For example, imagine a small computer with an ARM processor running a simple AI model to process data from sensors. Once processed, this data is sent to the cloud to be integrated into more complex workloads and generate predictions.