If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The basis for using technology to analyze human emotions according to facial cues was inspired by Paul Eckman's research into facial expressions and emotion in the 1960s and 70s. Intrigue and solutions based on the scientific link between emotion and facial expressions has continued ever since Eckman's early published work. Debate and scrutiny are the foundation of scientific inquiry, and scientific conclusions and inferences are built on observation and data. Such is the case with facial coding and Emotion AI technology. Recently, publications like Nature and the Verge have taken a close look at the merits and misconceptions of facial coding and emotion AI.
As the chief scientist of an AI and natural language processing company, I don't just work with engineers and data scientists. I also talk with the heads of marketing departments daily. This field of AI, generally referred to as emotion detection or recognition technology, or emotion analysis, is being put to use in this age of coronavirus. Just this month, researchers from the University College London published a paper purporting to accurately approximate emotional states of research participants using automated analysis of text responses to questions regarding the pandemic. Without getting into the specifics of that study, it underscores the continued growth of the technology and the new ways it is being applied.
It has been suggested in developmental psychology literature that the communication of affect between mothers and their infants correlates with the socioemotional and cognitive development of infants. In this study, we obtained day-long audio recordings of 10 mother-infant pairs in order to study their affect communication in speech with a focus on mother's speech. In order to build a model for speech emotion detection, we used the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and trained a Convolutional Neural Nets model which is able to classify 6 different emotions at 70% accuracy. We applied our model to mother's speech and found the dominant emotions were angry and sad, which were not true. Based on our own observations, we concluded that emotional speech databases made with the help of actors cannot generalize well to real-life settings, suggesting an active learning or unsupervised approach in the future.
The ability for Artificial Intelligence to make decisions based upon large datasets is largely changing the way organizations operate in today's world. Each roadmap, each goal and decision has to be supported by a certain statistic or justification as to why the organization is headed in that direction. This is another reason why Data Engineers and Scientists are heavily paid throughout the world for they have the job to analyze and make predictions, which can potentially put the company's future at stake. However, that's not all that AI can do. Utilizing Computer Vision and Facial Recognition capabilities, AI is able to detect human emotions accurately.
Face recognition is one of the applications of Deep Learning which has witnessed successful practical implementations within a short span of time. From secure transactions to facial detection, the future of facial recognition looks bright and we're currently at the beginning of the facial recognition revolution. Currently, the applications focus moreover strengthening the security systems but its potential applications can bring a change to many other services. Cloud computing, edge processing, and Artificial Intelligence have played a crucial role in the advancement of computer systems. Here, we will be listing out some prime applications and impact of facial recognition.
The growing ubiquity of Social Media data offers an attractive perspective for improving the quality of machine learning-based models in several fields, ranging from Computer Vision to Natural Language Processing. In this paper we focus on Facebook posts paired with "reactions" of multiple users, and we investigate their relationships with classes of emotions that are typically considered in the task of emotion detection. We are inspired by the idea of introducing a connection between reactions and emotions by means of First-Order Logic formulas, and we propose an end-to-end neural model that is able to jointly learn to detect emotions and predict Facebook reactions in a multi-task environment, where the logic formulas are converted into polynomial constraints. Our model is trained using a large collection of unsupervised texts together with data labeled with emotion classes and Facebook posts that include reactions. An extended experimental analysis that leverages a large collection of Facebook posts shows that the tasks of emotion classification and reaction prediction can both benefit from their interaction.
Messages in human conversations inherently convey emotions. The task of detecting emotions in textual conversations leads to a wide range of applications such as opinion mining in social networks. However, enabling machines to analyze emotions in conversations is challenging, partly because humans often rely on the context and commonsense knowledge to express emotions. In this paper, we address these challenges by proposing a Knowledge-Enriched Transformer (KET), where contextual utterances are interpreted using hierarchical self-attention and external commonsense knowledge is dynamically leveraged using a context-aware affective graph attention mechanism. Experiments on multiple textual conversation datasets demonstrate that both context and commonsense knowledge are consistently beneficial to the emotion detection performance. In addition, the experimental results show that our KET model outperforms the state-of-the-art models on most of the tested datasets in F1 score.
Amazon announced a breakthrough from its AI experts Monday: Their algorithms can now read fear on your face, at a cost of $0.001 per image--or less if you process more than 1 million images. The news sparked interest because Amazon is at the center of a political tussle over the accuracy and regulation of facial recognition. Amazon sells a facial-recognition service, part of a suite of image-analysis features called Rekognition, to customers that include police departments. Another Rekognition service tries to discern the gender of faces in photos. The company said Monday that the gender feature had been improved--apparently a response to research showing it was much less accurate for people with darker skin.