AI for real-time translation uses artificial intelligence to instantly convert spoken or written language from one language to another. This technology helps people communicate across language barriers quickly and efficiently. It is commonly used in apps, devices, and online services to support conversations between speakers of different languages.
Category: Deep Learning
AI for Voice Biometrics
AI for Voice Biometrics uses artificial intelligence to analyse and recognise an individual’s unique voice patterns. This technology can identify or verify a person by examining specific characteristics in their speech, such as pitch, tone, and accent. It is often used to enhance security and improve the convenience of authentication processes, making it possible to…
RL for Game Playing
RL for Game Playing refers to the use of reinforcement learning, a type of machine learning, to teach computers how to play games. In this approach, an algorithm learns by trying different actions within a game and receiving feedback in the form of rewards or penalties. Over time, the computer improves its strategy to achieve…
RL with Partial Observability
RL with Partial Observability refers to reinforcement learning situations where an agent cannot see or measure the entire state of its environment at any time. Instead, it receives limited or noisy information, making it harder to make the best decisions. This is common in real-world problems where perfect information is rarely available, so agents must…
Transfer Learning in RL Environments
Transfer learning in reinforcement learning (RL) environments is a method where knowledge gained from solving one task is used to help solve a different but related task. This approach can save time and resources, as the agent does not have to learn everything from scratch in each new situation. It enables machines to adapt more…
RL for Continuous Action Spaces
Reinforcement Learning (RL) for Continuous Action Spaces is a branch of machine learning where an agent learns to make decisions in environments where actions can take any value within a range, instead of being limited to a set of discrete choices. This approach is important for problems where actions are naturally measured in real numbers,…
Experience Replay Buffers
Experience replay buffers are a tool used in machine learning, especially in reinforcement learning, to store and reuse past experiences. These experiences are typically the actions an agent took, the state it was in, the reward it received and what happened next. By saving these experiences, the learning process can use them again later, instead…
Model Distillation in Resource-Constrained Environments
Model distillation is a technique where a large, complex machine learning model teaches a smaller, simpler model to make similar predictions. This process copies the knowledge from the big model into a smaller one, making it lighter and faster. In resource-constrained environments, like mobile phones or edge devices, this helps run AI systems efficiently without…
Neural Network Knowledge Sharing
Neural network knowledge sharing refers to the process where one neural network transfers what it has learned to another network. This can help a new network learn faster or improve its performance by building on existing knowledge. It is commonly used to save time and resources, especially when training on similar tasks or datasets.
Efficient Parameter Sharing in Transformers
Efficient parameter sharing in transformers is a technique where different parts of the model use the same set of weights instead of each part having its own. This reduces the total number of parameters, making the model smaller and faster while maintaining good performance. It is especially useful for deploying models on devices with limited…