Here are some of the most prominent technologies that are impacting client landscapes with respect to AI adoption.
Balakrishna D R
With technology evolving fast, we’re likely to see a lot of challenges being overcome. Over the recent years, AI has garnered a vast amount of global attention and is deemed as one of the technologies that will usher the next biggest technological change.
Today, enterprises consider AI as a tangible, efficient, and scalable method to extract value from information. That said, AI applications have started to closely reflect its potential in the market with ready integration capabilities and features that significantly impacts how businesses operate across industry verticals. For instance, data synthesis methodologies are now available to combat data challenges in AI.
With the emergence of techniques such as meta-learning, AI is becoming far less data-hungry. Additionally, the advent of ‘Explainable AI’ has simplified the acceptance of AI in regulated industries where explainability is crucial to ensure that the system is void of unintended biases.
- Explainable AI
Neural network algorithms are capable of deriving patterns that even conventional machine learning algorithms fail to do. Therefore, we see the need for explainable AI (XAI) especially for applications where the results need to be explained or traced back.
This is critical for enhancing functions such as tumor detection, mortgage rejection, candidate selection, etc. For example, when it comes to calculating eligibility for a loan, machine learning can help in identifying potential defaulters far better than traditional algorithms.The growing interest in ethical AI comes after some glaring failures around trust and AI in the marketplace. If the machine learning algorithm learns mainly from existing data, it is likely to be fraught with unintended biases against specific ethnic groups or genders. Set against this backdrop, explainability plays a crucial role, especially in highly regulated industries.
- Generative AI
Generative AI can play an important role in creative functions. For instance, neural style transfer is a generative AI technique in deep learning that consists of two deep neural networks—a generative network and a discriminative network. Both networks work in conjunction to provide a high-level simulation of conceptual tasks. This technology can find numerous use cases in areas such as art generation, sketch generation, image or video resolution improvements, data generation/augmentation, and music generation.
- Transfer Learning
Humans generally learn from their own experiences or observations. Transfer learning model uses a similar concept where one can introduce an additional trained layer on top of a trained layer, thus eliminating the need to learn everything from scratch. This helps enterprises save significant amounts of data, computational power, and time in training new models as they leverage pre-trained weights from existing trained models and architectures.Transfer learning can prove to be valuable in use cases such as identifying individuals flouting certain road rules (such as wearing a helmet while riding a bike), logo/brand detection in an image, and speech model training for various accents, vocabularies, etc.
- Capsule Networks
Capsule networks store the spatial relationships of various parts of an image. Capsule networks are multi-layered neural networks comprising several capsules, which in turn contains several neurons. These networks are well-suited for object detection and image segmentation operations, and can prove useful in applications such as image reconstruction and image comparison/matching.
Given the large amounts of ongoing investment and maturation of AI-powered customer service bots and portals, 2020 could be the first time the population will interact with a robot without even realizing it. Most enterprises will consider AI as an opportunity to amplify their business capabilities and ease the burden on their workforce. When AI takes over the routine and repetitive tasks, the workforce can effectively direct their focus on unique human pursuits such as innovation, creative thinking, and problem finding. While the transition to AI may not exactly be a cakewalk, if done right, AI can significantly help enterprises differentiate themselves in this highly-competitive market.The author is Senior Vice President, Service Offering Head - ECS, AI and Automation.
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