With big tech still fighting in the big race for AI supremacy, an AGI race is slowly gaining momentum. Who will succeed? And, how?
Yann LeCun said that though RL is inevitable in machine learning, the purpose behind incorporating it in algorithms should be to eventually minimise its use.
LeCun clearly is at odds with reinforcement learning and believes that for AI with common sense, it is not the way forward
Just like reinforcement learning, self-supervised or unsupervised learning also witnessed various new models in 2022. Here are a few of them
While there are various practical applications of reinforcement learning, the concept as a whole poses some limitations when used in developing autonomous machine intelligence
Giving the ability to machines to learn, reason, and plan without being trained or rewarded for failing or succeeding at a task is the ideal future of AI
The Graph Contrastive Learning aims to learn the graph representation with the help of contrastive learning.
As per Gartner, supervised learning will continue to be the most popular type of machine learning in 2022.
Meta is coming up with their first conference detailing their progress of the metaverse after the major rebranding from Facebook. The virtual event ‘Inside the lab: Building for the metaverse with AI’ will be held on February 23 at 10:30 PM (IST) and have speakers across the AI board. The opening and closing notes will […]
graph structure has much additional information with them like node attributes, and label information of nodes. Using this source of information, we can have unprecedented opportunities to design advanced level self-supervised pretext tasks
in the self supervised learning process we are mainly focused about making the data workable to the downstream algorithms. but when using the self-supervised learning we make the data specifically for classification we can say the process is self-supervised classification.
For instance, Facebook AI Research (FAIR) has been championing self-supervised learning (SSL) for quite some time.