Learning Centre - Artificial Intelligence

Social Intelligence

Posted by Sumeet Singh on

Social Intelligence

Artificial intelligence (AI) can not be viewed as a single tool or a one size fits all instruction set, rather it is a suite of algorithmic computing capacities that can perform humanlike functions across varying settings. When we think about AI, it usually refers to dynamic machine intelligence, including facial recognition (computer vision), perception (computer vision and speech recognition), whole language processing (chatbots and data mining), and social intelligence (emotive computing and sentiment analysis), and this is just scratching the surface. The actual lines of code powering AI tools are commands that tell machines what to do, which can be...

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Natural Language Processing

Posted by Eyituoyo Ogbemi on

Natural Language Processing

Understanding how humans communicate with AI is a complex idea. Sending information or data to a machine and processing it in a way that helps the system fully understand the message passed and interpret it the right way to produce the right result. It is not an easy task to teach machines to understand how we communicate as humans. This is where Natural Language Processing comes in. Leand Romaf, an experienced software engineer who is passionate about teaching people how artificial intelligence systems work, said: “In recent years, there have been significant breakthroughs in empowering computers to understand language just...

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Federated Learning

Posted by Sumeet Singh on

Federated Learning

When it comes to machine learning data pipelines, we are more conversant with the central server (on-premise or cloud) approach that hosts the trained model to make predictions. This standard approach to building machine learning models today is to gather all the training data in one place, and then to train the model on the data. This basically means that the information is taken from all points in the chain and sent to the central server for processing. Obviously, this means that there is a roundtrip being made between the server and the various local devices. Leading to an inability...

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Machine Perception

Posted by Eyituoyo Ogbemi on

Machine Perception

Research in machine perception tackles the hard problems of understanding images, sounds, music, and video. In recent years, our computers have become much better at such tasks, enabling a variety of new applications such as content-based search in Google Photos and Image Search, natural handwriting interfaces for Android, optical character recognition for Google Drive documents, and recommendation systems that understand music and YouTube videos. Our approach is driven by algorithms that benefit from processing very large, partially-labeled datasets using parallel computing clusters. A good example is our recent work on object recognition using a novel deep convolutional neural network architecture...

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Computer Vision

Posted by Eyituoyo Ogbemi on

Computer Vision

  Definition Consciously or subconsciously, we all experience and use computer vision in our daily lives, most of the time it’s an effortless activity we perform without thinking. Wikipedia defines Computer vision as an interdisciplinary scientific field that deals with how computers can gain a high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do. Computer vision tasks include methods for acquiring, processing, analyzing, and understanding digital images, and extraction of high-dimensional data from the real world in order to produce numerical or symbolic...

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