What is It? How Can a Machine Exhibit It? "It's about thinking. The main theory is that emotions are nothing special. Each emotional state is a different style of thinking. So it's not a general theory of emotions, because the main idea is that each of the major emotions is quite different. They have different management organizations for how you are thinking you will proceed."
"Because the main point of the book [The Emotion Machine] is that it's trying to make theories of how thinking works. Our traditional idea is that there is something called 'thinking' and that it is contaminated, modulated or affected by emotions. What I am saying is that emotions aren't separate."
– Marvin Minsky. The Emotion Machine, book and draft, 2006.
In the 1970s and '80s, he was part of a small cohort of organizational behaviorists who argued that just focusing on skills in the workplace wasn't enough. Instead, they insisted that an individual's competencies must be assessed and reinforced. People [in the field] said it's all knowledge, skills and abilities. But a few of us kept arguing that there was a behavioral level they weren't tapping with skills, that skills were too micro." Today, he proudly notes, "there's hardly a human resources organization serving 100 or more people that doesn't use competency language."
In the early 1990s, Lisa Feldman Barrett had a problem. She was running an experiment to investigate how emotions affect self-perception, but her results seemed to be consistently wrong. She was studying for a PhD in the psychology of the self at the University of Waterloo, Ontario, Canada. As part of her research, she tested some of the textbook assumptions that she had been taught, including the assumption that people feel anxiety or depression when, despite living up to their own expectations, they do not live up to the expectations of others. But after designing and running her experiment, she discovered that her test subjects weren't distinguishing between anxiety and depression.
It has long been known that AI will affect workforces and markets. Robotic production lines will continue to erode manufacturing jobs. Self-driving vehicles will force drivers of trucks, trains and buses to look for alternative forms of employment that can utilise their skills. As AI improves, which is happening quickly, a much broader set of jobs will be impacted, including those that require certain levels of cognitive ability. Some of these are jobs that, until a few years ago, no one could imagine being done without the participation of a trained human being, such as teaching, medicine, financial advising, marketing and business consulting.
"She'll respond, 'That's what slave masters would say. Help me!' First versions may be resident on web pages or infest your Alexa, but later ones will be free-floating algorithms or'blockchain smart-contracts' that take up residence in spare computer memory. "Why would anyone unleash such a thing? Kate Darling, a researcher at the Massachusetts Institute of Technology, adds that the coming year could be the one in which emotionally intelligent robots are introduced to our homes. However, this will also bring its own unique set of challenges she warns.
If you have been tempted to solve the numerous IQ tests that have plagued the social platforms, then you are not alone. Of late, emotional intelligence has been quantified for centuries through psychological tests and examinations that test your intelligence, personality, vocational interests, attitude, achievement, aptitude, and observational powers. It was never so easy to capture emotional intelligence as it is now. Human behaviour can be predicted by understanding and calculating one's emotions. AI startups have developed the technology where facial emotions and micro-expressions that lasted for seconds are studied and examined to establish patterns, then evaluated to find out what provokes these emotions, to conclusively determine underlying behaviour traits.
For many, it was their first job, the time they got their foot in the door. For Randal Ford, it was a commercial photoshoot with 10 cows on a dairy farm in rural Texas. The client was thrilled with Ford's conceptual series; the dairy community left rather confused. Ford was inspired, and his first foray into animal portraiture eventually lead to his magnum opus, The Animal Kingdom. Ford is historically a portrait photographer of humans.
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.
This paper investigates the influence of different acoustic features, audio-events based features and automatic speech translation based lexical features in complex emotion recognition such as curiosity. Pretrained networks, namely, AudioSet Net, VoxCeleb Net and Deep Speech Net trained extensively for different speech based applications are studied for this objective. Information from deep layers of these networks are considered as descriptors and encoded into feature vectors. Experimental results on the EmoReact dataset consisting of 8 complex emotions show the effectiveness, yielding highest F1 score of 0.85 as against the baseline of 0.69 in the literature.