3 Most Important AI Trends to Watch in 2023

The last decade has accelerated the democratization of artificial intelligence (AI) in the enterprise space. Balakrishna DR, his EVP and Head of AI and Automation at Infosys, discusses the top five AI trends businesses should be aware of in the near future.

Many companies are developing their AI activities more and more. Interestingly, I think some positive trends could take center stage next year as AI becomes more embedded in the business landscape and new use cases emerge. Here is my bird's eye view.?

1.???Text, voice, and image AI continues its journey into the mainstream

Conversations that customers have with contact center executives are rich in information. These unstructured voice and text-based conversations are rapidly becoming one of the easiest sources of information. In some scenarios, it is possible to derive key consumer insights, improve products and services, develop virtual assistants that help employees tackle complex customer issues, and improve customer satisfaction. can. Other information that may be of value includes identifying frequently asked questions, creating appropriate self-service channels for them, increasing customer loyalty, identifying and prescribing cross-selling and up-selling opportunities; , and many other related possibilities. Language and accent neutralization capabilities also help executives serve customers in different geographies.

In building these solutions, we were able to obtain clean transcriptions from different languages, different dialects and accents, identify different types of contextual vocabulary, filter out ambient noise, and record conversations. There are some existing hurdles such as using different channels such as mono and stereo for Over the years, large technology companies have developed many solutions. They have built a powerful proprietary model with very high accuracy. The biggest challenge, however, is the need to send data through the cloud. This can conflict with confidentiality and privacy concerns.Also, these proprietary models have a limited amount of training on domain-specific customizations. A future differentiator is the use of powerful deep learning to build encoder-decoder-transformer networks using pretrained components and transfer learning. These compute-intensive models leverage the hardware acceleration of high-performance GPU computing to avoid translation challenges and linguistic nuances.

Large language models such as BERT and GPT-3 will become much more sophisticated in the coming days, expanding their ability to handle a wide range of semantic similarities and contextual relationships, text summarization and generation, chat Existing applications will be enhanced in areas such as bots, improved translation accuracy, and more. Improvements in sentiment mining, search, code generation, and more

In the field of computer vision, new and powerful models for object detection, segmentation, tracking, and counting are being developed, achieving previously unimaginable accuracies. These models, complemented by very powerful GPUs, are becoming more popular. Taking advantage of all the above advances, we can look forward to hybrid solutions that enable the next generation of AI assistants. These solutions have the warm touch of human conversation combined with rapid execution and reasoning capabilities, ultimately leading to lower operating costs and significantly higher customer satisfaction.?

2. Generative AI in art and creative space

Attracting and retaining the attention of your customer base is a challenge that most businesses struggle with all the time. To improve brand recall, you must consistently create high-quality content that is relevant, engaging, and suitable for distribution to a wide range of retail outlets. Generative AI is here, bringing new ways to augment content creation. With the help of generative AI, businesses can create a wide variety of content such as images, videos, documents, etc. to reduce turnaround time. Generative AI networks use transferive learning or general adversarial networks to create immersive content from a variety of sources. Aside from the obvious marketing use cases, it has the potential to revolutionize the media industry. Whether it's creating and restoring old movies in high definition, advanced special effects skills, or creating avatars in the metaverse, the uses are endless.

This is where major language models like GPT-3 come once again to create compelling content in fiction, non-fiction, and academic papers. Many public websites are already capable of generating high-quality images of abstract ideas rendered from simple written prompts from users. In areas such as speech synthesis, narrations and voices can be created with thousands of tones and frequencies. One potentially emerging malicious use that we must be wary of is the creation of deepfakes (artificially generated fake images and videos), which can be used to distribute fake news or It leads to new threats such as promoting harmful propaganda. Techno Generative AI will therefore be a transformative force that will amplify our innate creativity in various business activities.

3. Explainable AI for Ethical and Responsible AI

Organizations are increasingly recognizing the need for explainable AI to increase transparency, establish accountability, and uncover biases in automated decision-making systems. Explainable AI is also an important tool for mitigating the risks associated with enterprise AI. It has also been proven that explainable AI will increase AI adoption across the enterprise. This is because people feel more comfortable when AI models provide inferences and inferences along with predictions. This becomes even more important in settings such as healthcare and financial services, where the rationale for recommending a treatment or diagnosis or denying a loan application needs to be understood and articulated.

Some techniques, such as LIME, increase the interpretability of the model by perturbing the inputs and evaluating the effect on the output. Another popular technique, SHAP, uses a game theory-based approach to analyze feature combinations and their impact on the resulting delta. Create an explainability score to highlight aspects of your input that contributed significantly to your output. For example, image-based prediction can highlight the dominant regions or pixels that led to the output. As the impact of AI in business and society continues to expand, we also face various ethical issues arising from these complex use cases. Appropriate data governance frameworks, bias detection tools, and transparency factors are explored to maintain compliance with legal and social structures. Models are thoroughly tested for deviation, humility, and bias. Appropriate model validation and testing mechanisms with built-in explainability and reproducibility checks are becoming a standard for avoiding ethical misconduct.