In the first part of this article, we reviewed two emerging trends in Artificial intelligence. In this part, we will focus on explainable AI models, generative adversarial networks(GANs), and the transformative impact of multimodal AI learning.
Ethical/ Explainable AI (XAI)
Explainable artificial intelligence (XAI) — also known as Interpretable artificial intelligence (AI) — is a subset of artificial intelligence (AI) in which humans can understand the rationale behind the conclusions, assessments, or forecasts the AI makes. It differs from the “black box” algorithm concept used in machine learning, where even the AI’s creators cannot explain how it came to a particular decision.
Artificial intelligence needs data to learn; for many of the most helpful AI use cases, this frequently consists of sensitive and personal data, such as financial or health information. As such, it is principal to create AI models that are more ethical and transparent.
When a bank uses an AI engine to support credit decisions, it is required to give consumers who are turned down for a loan an explanation of why they were turned down. In a world where AI is used to make critical decisions in access, employment, justice, and healthcare, biased data has resulted in prejudice in automated decisions that may cause discrimination and unfair treatment. This is inexcusable!
Challenges of XAI
By making it more explainable, we reveal more of an AI system’s inner workings to the public and adversarial parties. Businesses may face difficulties due to rivals potentially copying the original AI system’s features in their products, reducing competitive advantage.
Another significant challenge to making AI systems explainable is the technical sophistication of such systems. The issue of technical complexity has two suggested solutions: promoting public coding education to make technical explanations more understandable to end users or offering more straightforward explanations.
Generative Adversarial Networks (GANs)
GANs are neural networks that generate new and realistic data based on pre-existing data. They generally make the base for AI generator tools. In GANs, there is a Generator and a Discriminator.
The Generator and the Discriminator are both Neural Networks, and they both run in competition with each other in the training phase. The Generator creates fake data samples (images, audio, etc.) and tries to fool the Discriminator.
The Discriminator, on the other hand, tries to distinguish between the real and fake samples. The steps are repeated several times, and the Generator and Discriminator get progressively better in their respective jobs after each repetition.
GANs are often used for image synthesis and generation tasks. They may create fresh, lifelike pictures that mimic training data by learning the distribution that explains the dataset. Examples are GPT-3, Midjourney, and DALL-E(GPT-3 variant) AI generator tools.
Other use cases of Generative Adversarial Networks are anomaly detection, data augmentation, video synthesis, and music synthesis. They can also be trained without labeled data, making them suitable for unsupervised learning tasks where labeled data is scarce or difficult to obtain.
Challenges of Generative Adversarial Networks (GANs)
GANs can be challenging to train, with the risk of instability, mode collapse, or converging failure. Generative AI requires many computational resources, especially for high-resolution images or large datasets, increasing operation costs.
Overfitting is another challenge that can potentially limit the integration of this AI system. GANs can overfit the training data, producing synthetic data that is too similar to the input data and lacking diversity.
Multimodal AI Learning
Multimodal artificial intelligence is a new field of AI that allows AI to process and relate multimodal data. These modalities could be visual, audio, or textual, and combining them can enhance the accuracy and reliability of AI systems.
One of the many applications of Multimodal AI is Google Translate. When translating, Google Translate employs a multimodal neural network. It is a step toward creating a single network that combines speech, language, and visual understanding.
The advantages of Multimodal AI include improved precision and efficiency and a better understanding of data. It can also help reduce the collected data needed to train AI models.
Challenges in multimodal learning
Regarding representation and fusion, multimodal AI may encounter potential challenges. These challenges arise when combining data from various modalities, including handling noise levels and missing data. Other difficulties multimodal AI faces when converting data from one modality to another are accuracy and translation quality.
Multimodal learning systems
While addressing these challenges, AI researchers have recently made exciting breakthroughs toward multimodal learning. Some of these developments are summarized below:
- The AI model ALIGN was developed by Google using a noisy dataset of numerous image-text pairs. The model has the highest accuracy on several benchmarks for image-text retrieval.
- Another multimodal AI system created by OpenAI, CLIP, can quickly classify an image into one of a set of categories without needing data if given a set of categories described in natural language.
Conclusion
Per Gartner, approximately 37% of all organizations polled are using some form of AI in their operations, and it is predicted that approximately 80% of modern technologies will be based on AI and ML by the start of 2023.
These stats demonstrate that artificial intelligence (AI) is an essential element of the transformation that traditional manufacturing and industrial companies must undergo to maintain business continuity and remain competitive in the age of Industry 4.0.
