The ability to discern subtle emotional cues is fundamental to human social intelligence. As artificial intelligence (AI) becomes increasingly common, AI's ability to recognize and respond to human emotions is crucial for effective human-AI interactions. In particular, whether such systems can match or surpass human experts remains to be seen. However, the emotional intelligence of AI, particularly multimodal large language models (MLLMs), remains largely unexplored. This study evaluates the emotion recognition abilities of MLLMs using the Reading the Mind in the Eyes Test (RMET) and its multiracial counterpart (MRMET), and compares their performance against human participants. Results show that, on average, MLLMs outperform humans in accurately identifying emotions across both tests. This trend persists even when comparing performance across low, medium, and expert-level performing groups. Yet when we aggregate independent human decisions to simulate collective intelligence, human groups significantly surpass the performance of aggregated MLLM predictions, highlighting the wisdom of the crowd. Moreover, a collaborative approach (augmented intelligence) that combines human and MLLM predictions achieves greater accuracy than either humans or MLLMs alone. These results suggest that while MLLMs exhibit strong emotion recognition at the individual level, the collective intelligence of humans and the synergistic potential of human-AI collaboration offer the most promising path toward effective emotional AI. We discuss the implications of these findings for the development of emotionally intelligent AI systems and future research directions.

In early 2025, Augmented Intelligence - Christie's first AI art auction - drew criticism for showcasing a controversial genre. Amid wider legal uncertainty, artists voiced concerns over data mining practices, notably with respect to copyright. The backlash could be viewed as a microcosm of AI's contested position in the creative economy. Touching on the auction's presentation, reception, and results, this paper explores how, among social dissonance, machine learning finds its place in the artworld. Foregrounding responsible innovation, the paper provides a balanced perspective that champions creators' rights and brings nuance to this polarised debate. With a focus on exhibition design, it centres framing, which refers to the way a piece is presented to influence consumer perception. Context plays a central role in shaping our understanding of how good, valuable, and even ethical an artwork is. In this regard, Augmented Intelligence situates AI art within a surprisingly traditional framework, leveraging hallmarks of "high art" to establish the genre's cultural credibility. Generative AI has a clear economic dimension, converging questions of artistic merit with those of monetary worth. Scholarship on ways of seeing, or framing, could substantively inform the interpretation and evaluation of creative outputs, including assessments of their aesthetic and commercial value.

Today, interactions matter more than ever. According to data compiled by NICE, once a consumer makes a buying decision for a product or service, 80% of their decision to keep doing business with that brand hinges on the quality of their customer service experience, according to NICE research. "I think AI is becoming a really integral part of every business today because it is finding that sweet spot in allowing businesses to grow while finding key efficiencies to manage that bottom line and really do that at scale," says vice president of product marketing at NICE, Andy Traba. When many think of AI and customer experiences, chatbots that give customers more headaches than help often come to mind. However, emerging AI use cases are enabling greater efficiencies than ever.

Machine learning techniques are effective for building predictive models because they identify patterns in large datasets. Development of a model for complex real-life problems often stop at the point of publication, proof of concept or when made accessible through some mode of deployment. However, a model in the medical domain risks becoming obsolete as patient demographics, systems and clinical practices change. The maintenance and monitoring of predictive models' performance post-publication is crucial to enable their safe and effective long-term use. We will assess the infrastructure required to monitor the outputs of a machine learning algorithm, and present two scenarios with examples of monitoring and updates of models, firstly on a breast cancer prognosis model trained on public longitudinal data, and secondly on a neurodegenerative stratification algorithm that is currently being developed and tested in clinic.

By combining the strengths of humans and machines, AI can help us achieve more than we could on our own. AI algorithms can analyze mountains of data with the efficiency of a hundred Sherlock Holmeses (yes, that is the plural of "Holmes"), identifying patterns and insights that humans might miss. In healthcare, this has led to breakthroughs in disease detection and treatment, and in finance, it has helped investors make better investment decisions -- because let's face it, we can't all be Warren Buffet. And let's not forget about AI-powered robots and machines, who have joined forces with human workers to increase efficiency and productivity. According to a recent report from PwC, AI has the potential to contribute a staggering $15.7 trillion to the global economy by 2030.

Claims adjusters make decisions every day--million-dollar decisions that have the potential to change a claimant's life. If anyone needs a second set of eyes--that helpful colleague with tons of experience and sharp attention to detail--it's claims adjusters. Here's the thing: even two of the best claims adjusters with 60 years of combined experience probably haven't seen everything (although they may be pretty close). Every day there are new cases and unseen factors that offer data about the best course for a particular claim. That's where augmented intelligence comes in.

Join top executives in San Francisco on July 11-12, to hear how leaders are integrating and optimizing AI investments for success. Everywhere you look, organizations are facing resource limits. If necessity is indeed the mother of invention, these resource constraints will give birth to new and disruptive innovations in 2023. Every new wave of technology over the past 20 years has responded to the growth in data generated by people and things connected to a worldwide network. Organizations routinely use data from sources as diverse as client transactions, internal systems, external partners, the Web, social media and the physical world to inform their strategies, plans and operations.

AI's tantalizing promise – and its missteps – are more apparent than ever, as OpenAI's ChatGPT makes headlines for its ability to cheat on college exams or conduct an imposter job interview. But, for anyone who feels inclined to dismiss AI's potential, I would urge caution. Bill Gates called recent developments in AI "every bit as important" as the emergence of the internet – a statement that should draw the attention of innovators across every discipline. In the field of healthcare, our relationship with AI has had a mixture of successes and setbacks, particularly in applications for diagnostics. To maximize our successes and realize the potential of AI, we must make a distinction between "artificial intelligence" and "augmented intelligence" to deliver meaningful change to our healthcare system.

Joe DeCosmo has 25 years of experience in technology, machine learning and AI. He is Chief Analytics and Technology Officer at Enova. Technological change accelerated during the pandemic, leading many people to adopt new ways to complete everyday tasks. Online tools and mobile applications have exploded for everything from shopping to food delivery and even financial services. Fintechs have led the way in providing working people with online access to financial services regardless of where they live, what they look like or whether they have an imperfect credit history. Doing so requires technical innovation.

We live in a world in which ever more information systems are drawing upon Artificial Intelligence (AI), influencing different aspects of our everyday life. The algorithm-focused view on the remarkable progress of these technologies opened new opportunities for empowering users but also posed threats such as bias, trust, and privacy, just to name a few. Successful AI strengthens the tie to Human-Computer Interaction (HCI) by creating a demand for new and better interaction. Usable AI might be realized by teams composed of HCI researchers employing AI techniques and AI researchers applying HCI methodologies, and not only! To face this challenge, new research topics emerged for providing new design and engineering methodologies considering users and the AI as a whole (e.g., Human-Centered AI), or pushing the borders between HCI and AI, such as Human-Based Computation (HBC) and Augmented Intelligence, i.e., problems in which humans and machines work together towards solving a common goal, either where the machine is assisted by humans (HBC) or the humans are assisted by the machines (Augmented Intelligence).