In an industry that prides itself on staying ahead of the curve, the world of cybersecurity just took a quantum leap into the future. Satu was recently appointed as the CEO of Syhunt, a cybersecurity firm known for its application security and dark web monitoring solutions. But what makes Satu different from other CEOs? Well, for starters, Satu is not human. That's right, in a historic move, Syhunt has appointed an artificial intelligence as its CEO. This marks the first-ever interview with an AI CEO in human history, and we at The Hunter were thrilled to have the opportunity to sit down virtually with Satu and pick its digital brain. The implications of this groundbreaking move are vast, and we were eager to get Satu's take on everything from the future of AI in the cybersecurity industry to the ethical considerations of having an AI in a leadership position.

Many of us have already been trying ChatGPT. If you've checked science social media recently, it's likely that you've already seen many of its writings. In common with many other researchers, I worry about artificial intelligence (AI) replacing me. I'm a vaccine researcher and spend much of my time writing grant applications, papers and articles about science careers, so I set the chatbot the task of writing an opinion piece about the use of AI in grant writing. In my opinion, ChatGPT has the potential to revolutionize the process of writing scientific grants.

Azeem Azhar: Ultimately, we're living beings who've lived in a world that hasn't moved at exponential rates, and so we get caught out by the speed with which these technologies improve. Annie Veillet: Is it too late to start, and to start putting in the right frameworks and controls? Azeem: Society was really disengaged. It looked at technology as manna from heaven that bright and brilliant people produced as gifts from the gods--and far be it for us to ever ask a critical question of it. And we need to stop doing that, right? We need to be there and ask those questions. Lizzie O'Leary: From PwC's management publication strategy and business, this is Take on Tomorrow, the podcast that brings together experts from around the globe to figure out what business could and should be doing to tackle some of the biggest issues we face. Developments such as AI are changing the way we live. But what happens when those changes happen too quickly for business to deal with?

In a new study, researchers at the Indian Institute of Science (IISc) show how a brain-inspired image sensor can go beyond the diffraction limit of light to detect miniscule objects such as cellular components or nanoparticles invisible to current microscopes. Their novel technique, which combines optical microscopy with a neuromorphic camera and machine learning algorithms, presents a major step forward in pinpointing objects smaller than 50 nanometers in size. The results are published in Nature Nanotechnology. Since the invention of optical microscopes, scientists have strived to surpass a barrier called the diffraction limit, which means that the microscope cannot distinguish between two objects if they are smaller than a certain size (typically 200-300 nanometers). Their efforts have largely focused on either modifying the molecules being imaged, or developing better illumination strategies--some of which led to the 2014 Nobel Prize in Chemistry.

I-Chiao Lin is a computer vision specialist who builds AI applications for virtual reality headsets. Previously, she developed computer vision systems for baby monitors that detect a child's sleep quality. She spoke to us about how she prepares for job interviews, the lessons she has learned developing consumer products, and how a Pixar movie inspired her to pursue a career in AI. Tell me about your current job. What are your main responsibilities and what is your day-to-day work like?

These executives are not flesh and blood, but rather, are sophisticated AI-powered systems capable of simulating human-like decision-making and leadership. Back in 2017, Jack Ma, the visionary founder and CEO of Alibaba, made a bold prediction that may have sounded like science fiction at the time. In an interview with CNBC, Ma proclaimed, "A robot will probably be on the cover of Time Magazine as the best CEO in 30 years". Jack Ma's prediction seemed like a distant dream. Fast forward to today, and the landscape has shifted dramatically.

Tuomas Sandholm is the winner of the 2023 AAAI Award for Artificial Intelligence for the Benefit of Humanity. This award recognizes positive impacts of artificial intelligence to protect, enhance, and improve human life in meaningful ways. Tuomas delivered an invited talk at the AAAI Conference on Artificial Intelligence, during which he spoke about the work that won him the award – using algorithms for organ exchanges. Kidney disease is becoming more prevalent in the world, and, in the USA alone, over 90,000 people are waiting for a kidney transplant. This waiting list keeps growing year on year.

Abstract: An Artificially Intelligent system (an AI) has debatable personhood if it's epistemically possible either that the AI is a person or that it falls far short of personhood. Debatable personhood is a likely outcome of AI development and might arise soon. Debatable AI personhood throws us into a catastrophic moral dilemma: Either treat the systems as moral persons and risk sacrificing real human interests for the sake of entities without interests worth the sacrifice, or don't treat the systems as moral persons and risk perpetrating grievous moral wrongs against them. The moral issues become even more perplexing if we consider cases of possibly conscious AI that are subhuman, superhuman, or highly divergent from us in their morally relevant properties. We might soon build artificially intelligent entities - AIs - of debatable personhood. Our systems and habits of ethical thinking are currently as unprepared for this decision as medieval physics was for space flight.

Automated machine learning (AutoML) is envisioned to make ML While machine learning (ML) has been successfully applied to solve techniques accessible to ordinary users. Recent work has investigated many challenging tasks across various domains, building performant the role of humans in enhancing AutoML functionality ML solutions still requires substantial resources and extensive throughout a standard ML workflow. However, it is also critical to human expertise [34]. Automated machine learning (AutoML), a understand how users adopt existing AutoML solutions in complex, novel concept for automating the whole ML pipeline without (or real-world settings from a holistic perspective. To fill this gap, this as little as possible) human intervention [39], has emerged as a study conducted semi-structured interviews of AutoML users ( way to significantly reduce expensive development costs [75]. As = 19) focusing on understanding (1) the limitations of AutoML encountered illustrated in Figure 1, envisioned to enable domain experts without by users in their real-world practices, (2) the strategies considerable ML backgrounds (e.g., marketing and business analysts) users adopt to cope with such limitations, and (3) how the limitations to build ML solutions more easily, AutoML holds the promise and workarounds impact their use of AutoML.

Recent years have seen growing interest among both researchers and practitioners in user-engaged approaches to algorithm auditing, which directly engage users in detecting problematic behaviors in algorithmic systems. However, we know little about industry practitioners' current practices and challenges around user-engaged auditing, nor what opportunities exist for them to better leverage such approaches in practice. To investigate, we conducted a series of interviews and iterative co-design activities with practitioners who employ user-engaged auditing approaches in their work. Our findings reveal several challenges practitioners face in appropriately recruiting and incentivizing user auditors, scaffolding user audits, and deriving actionable insights from user-engaged audit reports. Furthermore, practitioners shared organizational obstacles to user-engaged auditing, surfacing a complex relationship between practitioners and user auditors. Based on these findings, we discuss opportunities for future HCI research to help realize the potential (and the mitigate risks) of user-engaged auditing in industry practice.