Hidden in the conversations between customers and call center executives are treasures of intelligence. These unstructured voice and text conversations are quickly becoming one of the easiest sources of intelligence. In some cases, it may be possible to gain critical consumer insights to improve products and services, design virtual assistants to help employees solve complex customer problems, and increase customer satisfaction. Some other valuable intelligence includes identifying common questions and creating appropriate self-service channels for them, increasing customer engagement, identifying and prescribing cross-sell and up-sell opportunities, and a host of other related opportunities. Additionally, language and accent neutralization capabilities enable managers to serve customers across geographies.
There are several hurdles in building these solutions, such as achieving clear transcriptions from different languages, different dialects and accents, identifying different types of scene vocabulary, removing ambient noise, and using different channels (e.g. mono or stereo) to record dialogue. Big tech companies have come up with many solutions over the years. They have built powerful proprietary models with very high accuracy. But the main challenge is that the data needs to be sent over the network, which can conflict with confidentiality and privacy issues. Additionally, these proprietary models have limited scope in domain-specific customized training.
In the days to come, using powerful deep learning to build encoder-decoder networks using pre-trained components and transfer learning will be a differentiator. These computationally intensive models leverage hardware acceleration of high-performance GPU computing to circumvent the challenges posed by translation and speech nuances.
Large language models like BERT and GPT-3 will become more sophisticated in the coming days, extending their capabilities to handle different semantic similarities and scene relationships, and improving existing text Applications such as summarization and generation, chatbots, improving translation accuracy and enhancing sentiment mining, search, code generation and more.
In the field of computer vision, newer, more powerful models for object detection, segmentation, tracking, and counting are being built that provide previously unimaginable levels of accuracy. With powerful GPUs, these models will become increasingly common.
One can expect to see hybrid solutions leveraging all of the above advancements to bring the next generation of AI assistants to life. These solutions will have the warm touch of human conversation, coupled with fast execution and reasoning capabilities, ultimately lowering operational costs and dramatically improving customer satisfaction.
Capturing and retaining the attention of your customer base is a challenge that most businesses have been grappling with. In order to increase a company's brand awareness, it is necessary to continuously generate high-quality content that is relevant, engaging, and appropriately used for communication in various channels. Generative AI offers new capabilities to enhance content creation. Using generative AI, businesses can create a variety of content, such as images, videos, and written materials, and reduce turnaround time. Generative AI networks employ transfer learning or general adversarial networks to create immersive content from disparate sources. Beyond its obvious use in marketing, it could revolutionize the media industry. Filmmaking and high-definition restoration of old movies, enhancing special effects capabilities, and building avatars in the metaverse are some of the limitless applications.
Here again, large-scale language models like GPT-3 will come into play to create engaging content in fiction, non-fiction, and academic articles. On many publicly available websites, it is already possible to generate high-quality abstract concept images from simple written prompts from users. In areas such as audio synthesis, narratives and sounds can be created in thousands of tones and frequencies. One possible malicious application that people need to be wary of is the creation of deepfakes (artificially generated false images and videos), which will lead to emerging threats such as the proliferation of fake news and further harmful propaganda. As a result, generative AI will become a major transformational force, enhancing people’s innate creativity in a variety of business pursuits.
More and more businesses are realizing that explainable AI is needed to increase transparency and build question accountability and expose bias in automated decision-making systems. Explainable AI is also a primary tool for mitigating the risks inherent in enterprise AI. Explainable AI has also been proven to increase adoption of AI across the enterprise, as people trust AI models more when they give reasons and rationale while making predictions. In settings such as healthcare or financial services, this will gain significant momentum, as the rationale for a recommended treatment or diagnosis needs to be understood and articulated, or why a loan application was denied.
Some techniques, such as LIME, improve model interpretability by perturbing the input and evaluating the impact on the output. Another popular technique (SHAP) uses a game theory-based approach by analyzing combinations of features and their corresponding impact on the increment of results. It creates interpretability scores to highlight aspects of the input that contribute more to the output. For example, in image-based prediction, the dominant regions or pixels that lead to the output can be highlighted. As the impact of artificial intelligence on business and society continues to increase, people are also faced with a variety of ethical issues arising from these complex use cases. Appropriate data governance frameworks, tools to expose bias and transparency factors are being researched to remain compliant with legal and social structures. Models will be thoroughly tested for drift, humility, and bias. Proper model validation and auditing mechanisms, with built-in interpretability and reproducibility checks, will become the norm to prevent ethical lapses.
Industry-leading retailers are investing heavily to improve operational efficiency and customer experience through artificial intelligence. Retail stores will increasingly become focal points for enhancing brand awareness and customer experience rather than simple transaction hubs, and Adaptive AI will be the force behind this shift. Accessible shopping experiences based on computer vision and edge-based AI systems will reduce wait times and hassles and will be a major growth area. Retail stores of the future will also be able to provide highly personalized recommendations and create a seamless customer journey based on real-time insights generated by video analytics powered by built-in infrastructure.
In-store analytics will provide intelligent insights based on dwell time in different aisles in the store. Integrating past shopping history across multiple channels and incorporating demographics will enrich the customer experience, making experiential shopping highly immersive and fun. Omni-channel management will be enhanced by adaptive artificial intelligence, which will provide highly context-relevant assistance. Conversational AI, coupled with emerging technologies like AR and VR, will empower store associates to completely redefine the in-store shopping experience.
Edge artificial intelligence enables ordinary consumer devices to have scene awareness through powerful deep learning, and has the huge ability to change people's daily lives. Edge-based AI will become more affordable due to lighter models and the accessibility of high-performance GPU computing. Edge models use local scene-based learning and synchronize with the central model at the appropriate time, thereby reducing bandwidth and energy requirements. These affordable smart devices will revolutionize various sectors including retail, manufacturing, and energy utilities, for use cases such as quality inspections, predictive maintenance, and health and safety.
Falling costs due to lower computing requirements will create a market for smart and responsive devices. Reduced data requirements will be a boon to industries such as healthcare and finance, where data management is highly regulated. Each edge device's model is customized for the specific edge environment, and critical data never exits the edge network. Edge AI will become ubiquitous in areas such as smart warehouses, manufacturing, and utilities. As enterprises become increasingly aware of the huge energy requirements of bulky models, edge-based AI will be adopted to reduce the carbon footprint of AI and achieve sustainability goals. The anti-lock braking device will cause the car owner to keep pressing the brakes, and it will close and open the brake fluid circuit by releasing and releasing the brakes. The computer of the anti-lock braking device basically applies the brakes about 15 times per second to prevent the car from locking up. Anti-lock braking improves safe stopping over short distances. Because of this, the anti-lock braking device continuously taps the brakes every time, so car owners will have a feeling of pedal jumping when they apply the brakes.
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