Artificial Intelligence

Artificial Intelligence (AI) is here!

Artificial Intelligence (AI) – One of the greatest revolutions in all of human history and time!

We are experiencing the true potential of Artificial Intelligence via enhanced and extended human capabilities that help businesses achieve faster and more, efficiently. AI driven analytics and real time insights are helping industries transform their business, grow revenues and increase market shares faster than their peers. From logistics and healthcare to automotive and manufacturing, AI helps all.

So, what is Artificial Intelligence? It is an extremely exciting technology with brains. AI is used to develop intelligent systems and machines that can act and think like humans and help mankind grow technologically. Thanks to the ever increasing miniaturization of computers, affordable sensors, ubiquitous networking, and the snowballing availability of “smart” devices around the world, an abundance of information can be effortlessly collected and interpreted. Either expert analysts or machine learning is paving way to more accurate and improved algorithms and thus, this data leads to better and more informed decision making.

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What is Artificial Intelligence (AI)?

Artificial Intelligence is an all encompassing concept that incorporates human intelligence into machines. Going a step further into AI comes machine learning (ML), a subset of AI. It uses data and experiences to automatically tune algorithms. By going even further, we find Deep Learning (DL), a subset of ML which utilises deep neural networks to simulate the learning process.

Benefits of AI

Improved Efficiency

Enhanced Accuracy and Precision

Predictive Capabilities

Scalability

Enhancing User Experience

Data Driven Decisions

AI/ML Workflow

AI Applications

AI powered image classification involves data collection, preprocessing, model selection, training, evaluation, and deployment. For tasks like face recognition, Computer Vision (CV), and Machine Vision (MV), a large dataset of images is collected and preprocessed for consistency. A deep learning model architecture, such as Convolutional Neural Networks (CNNs), is selected and trained on the preprocessed dataset. The model learns to identify patterns and features in the images, and its performance is evaluated on a separate test dataset. Once satisfied, the model is deployed into an application, taking new images as input, and classifying them based on the learned patterns.

Applications

Face recognition
Object detection
Traffic control system
Quality check inspection

AI powered audio recognition can help understand human speech and identify environmental sounds. The process involves collecting a diverse dataset of audio recordings, preprocessing it, extracting meaningful features, training the model using a deep learning architecture, evaluating and refining the model, and integrating it into an application. The specific steps and techniques employed can vary depending on the complexity of the task. Transfer learning can accelerate the training process, while continuous monitoring and adaptation are crucial for maintaining the effectiveness of the AI powered audio recognition system in real-world scenarios.

Applications

Voice recognition
Speech recognition
Language processing

flowchart of audio and noise recognition

Gesture recognition is a touchless technology that uses cameras and sensors to capture human movements, which are then processed into digital data for computer analysis. This technology is used in various fields like video games, virtual reality, healthcare, and cars. Advancements in technology have made gesture recognition faster and more accurate, enhancing user experience by allowing device control through movement.

Applications

Hand gesture recognition
Body gestures
Sign Language Interpretation

AI based condition monitoring is a proactive approach to assessing equipment health using artificial intelligence and data analytics techniques. It involves collecting and preprocessing data from sensors, extracting relevant features, building intelligent models, and training them on historical data. Once deployed, these models can identify anomalies, predict potential failures, and trigger alerts for maintenance personnel. Benefits of AI based condition monitoring include early fault detection, reduced downtime, predictive maintenance, and improved operational efficiency. This approach helps prevent catastrophic failures and minimize downtime, ensuring a smoother operation and reduced downtime.

Applications

Environmental condition
Anomaly detection
Predictive Analytics
Diagnostic Analytics

Bundles and Solutions

Image Classification

Cambiar país/región

Sales

Technical

Customer Service

Website Support

Aprobación de órdenes

Artificial Intelligence at the Edge

The Hub for Cutting-Edge AI Solutions