What is AI ?

The Data Journey for Gaining the Insight and Converting to Value

banner title banner title

Intro

The Difference Between ML and AI.

ML Machine Learning

Machine learning is a field of artificial intelligence that simulates the human brain's ability to learn from data, information, and knowledge. It utilizes computer-collected datasets to train algorithms into models, and then inputs the data to be predicted into the models to obtain prediction values. It can be used to analyze numerical, textual, image, audio, time series, and signal data for applications such as trend analysis, prediction, optimization, classification, clustering, and recognition.

  • Trains algorithms on datasets to generate models and make predictions.
  • Can process numerical, textual, image, audio, time series, and signal data.
  • Requires moderate computing power.

AI Artificial Intelligence

Artificial intelligence is a broad term that encompasses machine learning, deep learning, neural networks, generative AI, and other algorithms. The rise of artificial intelligence began in the 1950s, and with the rise of cloud computing, computing power has exploded, making it feasible to learn and reason from large-scale data. The future goal of artificial intelligence development is no longer just to learn human intelligence, but to further think and create, just like the human brain works.

  • Trains algorithms on datasets to generate models and uses various models to create human-like creations.
  • Can learn and combine models to generate new creations, understand semantics, answer language questions, write articles, and drive autonomously.
  • Requires high computing power.

AI Data Collection Steps

The Steps from Data Collection to Value Creation.

  1. STEP 1.

    Start with the computer's storage function to define and establish a dataset for learning.

  2. STEP 2.

    Leverage the computing power to train and establish a model, just like the knowledge or experience accumulated after completing learning.

  3. STEP 3.

    Leverage the computing power to infer the output result from the input problem data using the model.

VIEW PRODUCTS

The AI Computing Environment

EDGE AI

Edge AI PC places computing power at the application site, close to devices and objects, to directly collect data, perform real-time model inference, or train small models. This results in lower latency, faster response times, and no risk of offline loss of control.

HYBRID

Hybrid computing is a hybrid configuration of computing power architectures that takes advantage of cloud computing power to train large data models, reducing training time, and then downloads the models to Edge AI PCs for real-time model inference applications using an OTA (Over the Air) mechanism.

CLOUD

Cloud computing uses the powerful computing power of the cloud to reduce the time it takes to train large datasets, such as CNN, RNN, DNN, and LSTM neural network algorithms, for applications such as image, sound, video, generative AI, and model inference.

Featured Products

Edge AI - Training

NVIDIA® Jetson SOM-based High Performance Computing(HPC) power platform.
Intel® Core™ i and Intel® Xeon® CPUs High Performance Computing (HPC) platform. The platform supports for GPU cards via high-speed 4+ lane PCIe interfaces.
  • AIEdge-X®310
    AIEdge-X®310

    Industrial AI Computing System at The Edge 14th/13th/12th Gen Intel® Core™ CPU and NVIDIA® Graphics Card

  • ATC 8010
    ATC 8010

    Powerful Intelligent Platform for ADAS and ANPR/AFR Powered by 9/8th Gen Intel® Core™ Processor

  • ATC 8110
    ATC 8110

    9/8th Gen Intel® Core™ Processor + Inference Accelerator AI Powered for Autonomous and Machine Vision

All Products

Edge AI - Inference

NVIDIA® Jetson SOM-based Computing Power Platform.
Intel Atom® CPU High-Performance Computing (HPC) platform. The platform supports inference acceleration card via up to 2 lanes PCIe interfaces.
All Products