Emotion

Today, people increasingly rely on computer agents in their lives, from searching for information, to chatting with a bot, to performing everyday tasks. These agent-based systems are our first forays into a world in which machines will assist, teach, counsel, care for, and entertain us. While one could imagine purely rational agents in these roles, this prospect is not attractive for several reasons, which we will outline in this article. The field of affective computing concerns the design and development of computer systems that sense, interpret, adapt, and potentially respond appropriately to human emotions. Here, we specifically focus on the design of affective agents and assistants.

This is my first time listening to the show and the host Mary and guest Jeff Lacy gave solid advice on the nuance of being a business owner. I look at my life as a business and seek knowledge to better position myself and my family. I never heard the concept of the difference between profitability & cashflow. I also was not aware of the term Statement of Cashflow (inflow/outflow of money) though the principles are familiar. I appreciate you defining what a healthy business looks like.

Deep learning is popular as an end-to-end framework extracting the prominent features and performing the classification also. In this paper, we extensively investigate deep networks as an alternate to feature encoding technique of low level descriptors for emotion recognition on the benchmark EmoDB dataset. Fusion performance with such obtained encoded features with other available features is also investigated. Highest performance to date in the literature is observed.

TRANSFERABLE POSITIVE/NEGATIVE SPEECH EMOTION RECOGNITION VIA CLASS-WISE ADVERSARIAL DOMAIN ADAPTATION Hao Zhou, Ke Chen School of Computer Science, The University of Manchester, Manchester, M13 9PL, U.K. ABSTRACT Speech emotion recognition plays an important role in building more intelligent and humanlike agents. Due to the difficulty of collecting speech emotional data, an increasingly popular solution is leveraging a related and rich source corpus to help address the target corpus. However, domain shift between the corpora poses a serious challenge, making domain shift adaptation difficult to function even on the recognition of positive/negative emotions. In this work, we propose class-wise adversarial domain adaptation to address this challenge by reducing the shift for all classes between different corpora. Experiments on the well-known corpora EMODB and Aibo demonstrate that our method is effective even when only a very limited number of target labeled examples are provided.

I recently led a project team at ThoughtWorks to create and open source a new Facial Expression Recognition (FER) toolkit named EmoPy. The system produces accuracy rates comparable to the highest rates achievable in FER and is now available for anyone to use for free. Working with Sofia Tania (left) and Karen Palmer (right) to create EmoPy This article explains how the EmoPy system is designed and how it can be used. It will examine the architectures and datasets selected, and illustrate why those choices were made. This should be helpful for those considering using EmoPy in their own projects, contributing to EmoPy, or developing custom toolkits using EmoPy as a template.

Softbank Robotics today announced that its robot Pepper will now use emotion recognition AI from Affectiva to interpret and respond to human activity. Pepper is about four feet tall, gets around on wheels, and has a tablet in the center of its chest. The humanoid robot made its debut in 2015 and was designed to interact with people. Cameras and microphones are used to help Pepper recognize human emotions, like hostility or joy, and respond appropriately with a smile or indications of sadness. This type of intelligence likely comes in handy for the environments where Pepper operates, like banks, hotels, and Pizza Huts in some parts of Asia.

At the Apec CEO Summit in Manila in November, 2015, Ma shared LQ in a conversation with Benigno Aquino III, then-president of the Philippines. Convinced of its competitive advantage in business for his own country, the president quipped, "The love quotient enables the Filipino to go really to the needs of the client that he is talking to, which is not available elsewhere."

Science fiction often portrays future AI technology as having sophisticated emotional intelligence skills to the degree where technology can develop compassion. But where are we today? The authors provide insight into artificial emotional intelligence (AEI) and present three major areas of emotion--recognition, generation, and augmentation--needed to reach a new emotionally intelligent epoch of AI.

"I do have a lot of emotions, but my default emotion is to be happy" are the words of Sophia, the social and genius Humanoid Robot. As empathetic AI machines and anthropomorphic robots step into the world and strive to understand human emotions, it is time to put the term'Robotic' to redundancy. We are fast approaching the day when Human and Robot compatibility skills would be listed as one of the sought-after job requirement (assuming one manages to find a job which needs human intervention in the loop). This puts forth a valid question: In the ever-evolving world of machines learning constantly, where does the new learning curve for us as humans commence? With words such as'Normal', 'Applied', 'Narrow, 'General', 'Super' as a prefix before its intelligence, the technology is distinctively diverse.

I started transforming businesses with technology 35 years ago. It was as true then as it is now that the biggest risk we have to mitigate is the resistance of people and organizations to change. It is a well-known fact that three in four transformation programmes fail to achieve their intended goals because people are not prepared to adopt new processes and technology. Mitigating these risks and helping people learn new technology-enabled processes has been good for the consulting industry, and continues to be one of the keys to successful programmes. With artificial intelligence (AI), change management and process reengineering get reinvented.