Customer satisfaction is a potent metric that directly influences the profitability of an organization. Establishing highly efficient contact centers requires significant automation, the ability to scale, and a mechanism of active learning through customer feedback. There is a challenge at every point in the contact center customer journey--from long hold times at the beginning to operational costs associated with long average handle times. In traditional contact centers, one solution for long hold times is enabling self-service options for customers using an Interactive Voice Response system (IVR). An IVR uses a set of automated menu options to help reduce agent call volumes by addressing common frequently asked requests without involving a live agent.

Despite advances in hierarchical reinforcement learning, its applications to path planning in autonomous driving on highways are challenging. One reason is that conventional hierarchical reinforcement learning approaches are not amenable to autonomous driving due to its riskiness: the agent must move avoiding multiple obstacles such as other agents that are highly unpredictable, thus safe regions are small, scattered, and changeable over time. To overcome this challenge, we propose a spatially hierarchical reinforcement learning method for state space and policy space. The high-level policy selects not only behavioral sub-policy but also regions to pay mind to in state space and for outline in policy space. Subsequently, the low-level policy elaborates the short-term goal position of the agent within the outline of the region selected by the high-level command. The network structure and optimization suggested in our method are as concise as those of single-level methods. Experiments on the environment with various shapes of roads showed that our method finds the nearly optimal policies from early episodes, outperforming a baseline hierarchical reinforcement learning method, especially in narrow and complex roads. The resulting trajectories on the roads were similar to those of human strategies on the behavioral planning level.

Genesys is one of the popular technology companies in India that leverages major cutting-edge technologies like artificial intelligence to enhance customer as well as employee experiences. It provides cloud-based contact center software that includes inbound and outbound analytics, self-service options, screen share, and many more. Genesys is known for connecting customers in marketing, sales, and services on any type of channel efficiently and effectively. Genesys with AI offers automation through different strategies in recent years across 100 countries for more than 11,000 customers. Let's explore some of the top strategies for AI for automation in multiple tasks.

Customer service, while likened to back-office or desk jobs, has long been outsourced to third parties (call centres) for resolving customer queries. Over the years, outsourcing customer support services has hampered organizational flexibility, brand value, and privacy. With some automation, the digital revolution shifted businesses toward adopting Interactive Voice Response (IVR) technology for handling and prioritizing high volume of calls, simplifying customer service processes, and cutting overhead expenses. Although IVR allowed companies to automate their customer support and increase professionalism, the complex routing mechanism and inflexibility resulted in customers dissatisfaction towards an organization or a brand. In today's highly connected and personalized world, customers demand instant resolution of grievances and high-quality customer service at anytime, anywhere.

In Bayesian optimization, accounting for the importance of the output relative to the input is a crucial yet challenging exercise, as it can considerably improve the final result but often involves inaccurate and cumbersome entropy estimations. We approach the problem from the perspective of importance-sampling theory, and advocate the use of the likelihood ratio to guide the search algorithm towards regions of the input space where the objective function to be minimized assumes abnormally small values. The likelihood ratio acts as a sampling weight and can be computed at each iteration without severely deteriorating the overall efficiency of the algorithm. In particular, it can be approximated in a way that makes the approach tractable in high dimensions. The "likelihood-weighted" acquisition functions introduced in this work are found to outperform their unweighted counterparts in a number of applications.

When you think of artificial intelligence, you might think of examples from science fiction like Terminator or The Matrix. Modern AI exists without the limitations that you see in movies, operating on everything from the smartphones in your pocket to the website that uses machine learning to track COVID-19. Artificial intelligence was designed to solve for specific tasks and has applications that can dramatically impact the bottom line in your restaurant, from automating features to the burger-flipping robots of the future. As we look into an uncertain future, the opportunities to employ artificial intelligence to automate your restaurant is an excellent way to put AI to work for you. As the coronavirus has taught us, a robust communications network is critical to ensuring that everyone is kept aware of changes.

Every day, call centers process thousands of calls using live operators. This has a number of disadvantages such as customers spending a long time waiting on the line, low customer satisfaction and a high cost of service relative to other means of communication. The growth of popularity of voice assistants and other NLP (natural language processing) solutions like speech analytics give us a feeling that we can totally automate contact centers without any negative consequences. But what are hidden pitfalls? And how not to get to slippery slope? IVR and IVA -- what and when to use? IVR (interactive voice response) are very popular from 70s.

Webhelp recently commissioned revealing new research from polling experts YouGov, designed to uncover what 2,000 British adults really think about Artificial Intelligence (AI). The report explores the public perception of how AI technology will change the way brands provide customer service. Webhelp's Global Analytics Director, Chris Bryson, takes a closer look at the findings: Rules in CX are being rewritten. It's getting harder to predict the future but we can still try. In the evolving digital marketplace, as customers become more exposed to AI systems, it is critical that businesses consider new strategies for the future of shopping without human-to human contact.

Bayesian quadrature (BQ) is a sample-efficient probabilistic numerical method to solve integrals of expensive-to-evaluate black-box functions, yet so far,active BQ learning schemes focus merely on the integrand itself as information source, and do not allow for information transfer from cheaper, related functions. Here, we set the scene for active learning in BQ when multiple related information sources of variable cost (in input and source) are accessible. This setting arises for example when evaluating the integrand requires a complex simulation to be run that can be approximated by simulating at lower levels of sophistication and at lesser expense. We construct meaningful cost-sensitive multi-source acquisition rates as an extension to common utility functions from vanilla BQ (VBQ),and discuss pitfalls that arise from blindly generalizing. Furthermore, we show that the VBQ acquisition policy is a corner-case of all considered cost-sensitive acquisition schemes, which collapse onto one single de-generate policy in the case of one source and constant cost. In proof-of-concept experiments we scrutinize the behavior of our generalized acquisition functions. On an epidemiological model, we demonstrate that active multi-source BQ (AMS-BQ) allocates budget more efficiently than VBQ for learning the integral to a good accuracy.

A new trend emerges in every industry yearly. In the BPO industry, for instance, the artificial intelligence or AI got the center stage. Experts even believed that it is going to be the next big thing -- and it is quickly happening in the present. For this year, there are also trends in the call center industry. Most of them talk about the different ways to appease consumers and clients. What we are going to list down is more on the technology side.