• Artificial Intelligence is fast becoming the face of technology in all sectors across the globe.
• Trends in deep learning and artificial intelligence are crucial to explore in the AI field.
• Explore five emerging deep learning and artificial intelligence trends: federated learning, GANs, XAI, reinforcement learning, and transfer learning.

Deep learning and Artificial Intelligence (AI) are fast-growing decision-making tools in the technological world. They have been fuelled by the emerging new technologies emerging all the time. Federated learning, GANs, XAI, reinforcement learning, and transfer learning are five of this field’s most promising emerging concepts.

These technologies can transform machine learning applications, from image identification to gameplay. They present exciting new prospects for researchers and developers alike.

Federated Learning

Federated learning is a machine learning technique that allows several devices to work together on a single model. They do this without sharing their data with a central server. This method is especially beneficial in instances where data privacy is an issue.

Google, for example, has employed federated learning. This is to increase the accuracy of its predictive text keyboard while maintaining user privacy.

Federated learning addresses this issue. They do this by training models on data that remains on users’ devices rather than sending it to a central server.

Furthermore, because the training data remained on users’ devices, there was no need to send massive amounts of data to a centralized server. This reduced the system’s computing and storage requirements.

General Adversarial Network in Artificial Intelligence

Generated adversarial networks are a form of neural network that can be used to produce new, realistic data from previously collected data. GANs, for example, have been used to create lifelike representations of humans, animals, and even landscapes. GANs function by pitting two neural networks against each other, with one network creating bogus data and the other attempting to determine whether the data is real or bogus.

Explainable Artificial Intelligence

Explainable AI is an AI method that seeks to improve the transparency and comprehension of machine learning algorithms. XAI is critical because it ensures that AI systems make objective, fair judgements. Here’s an example of how you could utilize XAI:

Consider the following scenario: a financial institution use machine learning algorithms to predict the risk that a loan application will default on their loan. In the case of traditional black-box algorithms, the bank would be unaware of the algorithm’s decision-making process and would be unable to explain it to the loan applicant.

ALSO READ: How Africa can utilize Artificial Intelligence in Cryptocurrency

However, using XAI, the algorithm could explain its decision. This allowed the bank to certify that it was based on rational factors rather than erroneous or discriminatory data. For example, the algorithm may state that it calculated a risk score based on the applicant’s credit score, income, and employment history.

This level of transparency and explainability can contribute to increased trust in AI systems and improved accountability. Ultimately, it can lead to better decision-making.

Reinforcement learning

Reinforcement learning is a sort of machine learning that involves teaching agents to learn through criticism and incentives. This method has been used in a variety of applications, including robotics, gaming, and even banking. DeepMind’s AlphaGo, for example, used this strategy to continuously improve its games and finally defeat top human Go players, demonstrating the efficiency of reinforcement learning in complicated decision-making tasks.

READ: How Artificial Intelligence is reshaping the video gaming industry

Transfer learning

A machine learning strategy called transfer learning involves applying previously trained models to address brand-new issues. When there is little data available for a new problem, this method is especially helpful.

For instance, researchers have used transfer learning to adapt image recognition models developed for a particular type of picture (such as faces). This is done to apply the same model to a different sort of image. For example, animals.

This approach allows for the reuse of the learned features, weights, and biases of the pre-trained model in the new task. This can significantly improve the performance of the model and reduce the amount of data needed for training.