These are high-stakes conversations where clinicians must navigate weighty issues, where a poorly chosen word could have lasting consequences on a patient's final days and the memories their loved ones carry forward. Low-quality SIC has been associated with poor patient and family prognostic understanding [5], perceived lack of emotional support [6], lower quality healthcare outcomes and higher costs [7-13]. Communication with advanced-stage cancer patients specifically poses a variety of challenges, including: the volume and complexity of medical information, often fast-paced office visits, and the emotional burden of these life-changing conversations, for clinicians, patients, and their loved ones. Despite their extensive medical training, many physicians struggle to deliver difficult news effectively [14-16], often resulting in patient anxiety, misaligned treatment decisions, and reduced quality of care [17-19]. Also costly is the terms of expensive and potentially burdensome treatments as well as malpractice claims[20].

This paper investigates the empathetic responding capabilities of ChatGPT, particularly its latest iteration, GPT-4, in comparison to human-generated responses to a wide range of emotional scenarios, both positive and negative. We employ a rigorous evaluation methodology, involving a between-groups study with 600 participants, to evaluate the level of empathy in responses generated by humans and ChatGPT. ChatGPT is prompted in two distinct ways: a standard approach and one explicitly detailing empathy's cognitive, affective, and compassionate counterparts. Our findings indicate that the average empathy rating of responses generated by ChatGPT exceeds those crafted by humans by approximately 10%. Additionally, instructing ChatGPT to incorporate a clear understanding of empathy in its responses makes the responses align approximately 5 times more closely with the expectations of individuals possessing a high degree of empathy, compared to human responses. The proposed evaluation framework serves as a scalable and adaptable framework to assess the empathetic capabilities of newer and updated versions of large language models, eliminating the need to replicate the current study's results in future research.

The most meaningful connections between people are often fostered through expression of shared vulnerability and emotional experiences in personal narratives. We introduce a new task of identifying similarity in personal stories based on empathic resonance, i.e., the extent to which two people empathize with each others' experiences, as opposed to raw semantic or lexical similarity, as has predominantly been studied in NLP. Using insights from social psychology, we craft a framework that operationalizes empathic similarity in terms of three key features of stories: main events, emotional trajectories, and overall morals or takeaways. We create EmpathicStories, a dataset of 1,500 personal stories annotated with our empathic similarity features, and 2,000 pairs of stories annotated with empathic similarity scores. Using our dataset, we fine-tune a model to compute empathic similarity of story pairs, and show that this outperforms semantic similarity models on automated correlation and retrieval metrics. Through a user study with 150 participants, we also assess the effect our model has on retrieving stories that users empathize with, compared to naive semantic similarity-based retrieval, and find that participants empathized significantly more with stories retrieved by our model. Our work has strong implications for the use of empathy-aware models to foster human connection and empathy between people.

As someone who struggled for years to manage my emotions effectively, I can attest to the incredible impact that emotional intelligence can have on our lives. Growing up, I was quick to anger and struggled to control my impulses. I was in a lot of chaos with my friends, family and colleagues which lead to losing my job, that made me devastated and angry. I hated the way my emtions took hold of me until I began focusing on developing my emotional intelligence that I was able to turn my life around. Emotional intelligence, or EI, is the ability to identify, understand, and manage our own emotions, as well as those of others.

Empathy is the ability to understand another person's emotional state. Recognizing how you think and behave in response to others is essential for building stronger relationships and becoming more empathetic. Learn more about the definition of empathy and how to demonstrate it. Empathy is derived from the Greek word "empatheia," which means "passion," and the German word "einfühlung," which means "feeling into something." It means understanding and feeling another person's emotions as if they were your own.

AI chatbot company Replika, which offers customers bespoke avatars that talk and listen to them, says it receives a handful of messages almost every day from users who believe their online friend is sentient. "We're not talking about crazy people or people who are hallucinating or having delusions," said Chief Executive Eugenia Kuyda. "They talk to AI and that's the experience they have." The issue of machine sentience -- and what it means -- hit the headlines this month when Google placed senior software engineer Blake Lemoine on leave after he went public with his belief that the company's artificial intelligence (AI) chatbot LaMDA was a self-aware person. Google and many leading scientists were quick to dismiss Lemoine's views as misguided, saying LaMDA is simply a complex algorithm designed to generate convincing human language.

We know that intelligence is the human ability to think, understand, decide, and reason, but do we know what "artificial intelligence" is, which has been heard so often since the computer entered our lives? The concept of artificial intelligence was first used by John McCarty in 1955. The purpose of developing artificial intelligence is to make a machine think just like a human being and to give it the ability to think and solve problems. For this reason, when creating artificial intelligence, the machine is given the ability to hear like a human, that is, to identify sounds in different languages, to see and even to move. As a result of these, the ability to perceive and solve the problem is tried to be imposed on the machine with artificial intelligence.

On this podcast Jason Stoughton is joined by Alan Cowen, CEO and Chief Scientist, at Hume AI. As AI progresses, both in terms of what it can do and how widely it is deployed, the ability for AI to understand and empathize with our emotions is still a glaring hole in AI's capabilities. On this podcast Jason and Alan talk about the state of the technology, unpack the hopes and dreams and fears of an AI that understands, and can potentially manipulate, our emotions and how Hume is not only leading the way in advancing AI's capabilities in this area but is also leading the way in ensuring that AI should service human well being above all else.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. The short excerpt below from the 1938 film La Femme du Boulanger (The Baker's Wife) ingeniously depicts how the human mind can extract deep meaning from life experiences and perceived situations. In the movie, directed by Marcel Pagnol, the baker Aimable welcomes his wife Aurelie, who has just come back after running off with a shepherd days earlier. While Aimable treats Aurelie with sweet words and a heart-shaped bread (which he had baked for himself), he shows no kindness toward Pomponette, his female cat who coincidentally returns home at the same time as Aurelie, after abandoning her mate Pompon for a chat de gouttière (alley cat). Aimable calls Pomponette ordur (junk) and a salope (a rude term) who has run off with un inconnu (a nobody) and bon-a-rien (good for nothing) while the poor Pompon has been miserably searching for her everywhere.

The impending need for "design thinking" in robotic automation is becoming a sore subject now. Creating robots that are welcoming appear to be quite familiar to ourselves and we're more likely to respond to them with positivity to help them learn and develop amiable personalities. To fully understand the importance of design thinking in robotics, let's first understand why is design thinking so popular? Design thinking is just like thinking "out of the box" and clearly, it has become a buzzword among global corporations now. But "what makes design thinking so special?"