Several months ago, I arrived at the office at 8:45 a.m., sat at my desk, and was about to start my day. The only problem was that I, for the life of me, could not remember my password. This is a bit laughable because for the past year, I had typed that exact password almost daily, sometimes from muscle memory. I had done it at the beginning of that week, all the days before, and even late the previous night. But in that moment, I could not remember the first letter.

Michio Okada first conceptualised "weak robots", which have limited capabilities themselves, and are framed as objects or "social others" which people are invited to assist and take care of. In Okada's work, such robots are used to invite pro-social behaviour from people, such as encouraging them to pick up trash to assist a trash can robot (Okada (2022)). We conceptualise human-robot interaction (HRI) as a stage where weak robots-- designed to be "cute" and vulnerable--play the role of incidental actors that subvert the person engaging with them. Caudwell and Lacey (2020) argue that cuteness as a design choice for robots can encourage users to trust and form relationships with those robots, which introduces ambivalent power dynamics through the production of intimacy . In fact, cuteness can also be seen as a deceptive or "dark" pattern, due to the utilisation of cuteness to prompt affective responses which can be used to collect emotional data, as well as some degree of reduction of user agency (Lacey and Caudwell (2019)). The ability and affordances of cute and weak robots to influence user behaviour merits the discussion of their ethicality, which we do in this paper through design fiction. Unlike traditional HRI research, often confined to laboratory settings, our focus is on spontaneous, real-world interactions that transform everyday environments into sites of performative potential. We argue that the theatricality of these encounters is central to understanding their impact: the presence of a weak and/or cute robot, such as the trash can robot, developed by Okada and the Interaction and Communication Design Lab of the T oyohashi University of T echnology, acts as a disruptive interloper that introduces an observer's effect and, thus, affects the human interlocutors. First, we examine the concept of weak robots through the lens of performativity theory as well as concepts of machine (dys)function.

The AlphaGarden is an automated testbed for indoor polyculture farming which combines a first-order plant simulator, a gantry robot, a seed planting algorithm, plant phenotyping and tracking algorithms, irrigation sensors and algorithms, and custom pruning tools and algorithms. In this paper, we systematically compare the performance of the AlphaGarden to professional horticulturalists on the staff of the UC Berkeley Oxford Tract Greenhouse. The humans and the machine tend side-by-side polyculture gardens with the same seed arrangement. We compare performance in terms of canopy coverage, plant diversity, and water consumption. Results from two 60-day cycles suggest that the automated AlphaGarden performs comparably to professional horticulturalists in terms of coverage and diversity, and reduces water consumption by as much as 44%. Code, videos, and datasets are available at https://sites.google.com/berkeley.edu/systematiccomparison.

In my experience, there are two serious mistakes that many business leaders make when they start thinking about digital transformation. Firstly, believing that it's a process that an organization begins, then goes through, and then comes out of the other side in a "digitally transformed" state. The second is that it's all about technology. In reality, digital transformation is an ongoing process that never truly ends. Businesses were just getting acclimatized to computers being an essential everyday tool since the 80s when the internet emerged in the nineties and forced them to rethink technology from the ground up.

Ever since Corey Walker, MD, became a spine surgeon, the traditional measure of success focused on how well a patient was able to walk, bend or move after spine surgery. Now, with the help of artificial intelligence, Walker is measuring success differently. "The unique thing we're doing with this project is really focusing in on the pain medication part of it, because opioid addiction continues to be a challenge, and we are looking for ways to improve pain management after surgery," Walker said. Walker's team, in collaboration with the Cedars-Sinai Department of Computational Biomedicine, is using artificial intelligence and machine learning to predict which patients are most likely to successfully manage their pain post-surgery, and which patients might need additional assistance. "This project uses artificial intelligence algorithms to analyze millions of data points and predict which patients may need additional help with pain management after surgery," said Jason Moore, PhD, chair of the Department of Computational Biomedicine and acting professor of Medicine.

One of the central goals of AI is to generate a model representing patterns in data which can be used for something practical like prediction of the behavior of a complex biological or physical system. Models are usually evaluated using objective computational or mathematical criteria such as execution time, prediction accuracy, or reproducibility. However, there are many subjective criteria which may be important to the human user of the AI. For example, a model relating genetic variation to disease risk might be more useful if it included genes with protein products amenable to drug development and targeting. This is a subjective criterion which may only be of interest to the person utilizing the AI.

IT automation is the new normal. With the market for automation technologies ready to exceed $20 billion in 2022, automation is already playing a considerable role in business operations from invoice processing to customer support, as well as IT operations like deploying systems and automating recovery. But the area continues to grow, unlocking new opportunities to automate that depend on previous initiatives. According to Gartner, by 2023 most organisations will be able to automate an additional 25% of their tasks on top of those they have already automated. Until relatively recently, automation was relegated to the most mundane of tasks and used only by companies with extensive IT capabilities.

As enterprise continues the process of digital transformation, it's becoming clearer than ever that data itself has become a form of currency. What do I mean by that? Everything generates data these days and that data has intrinsic values attached to it. I see data as the common language of humanity itself. We make decisions based on data.

As an emerging technology, AI faces and will continue to face its fair share of challenges. On the one hand, consumers remain wary about adopting new tech. Envisioning a world where humans are displaced by AI-empowered machines gone amuck may be haunting a few late adopters. On the other hand, companies express frustration that AI has yet to prove itself to be the magic pill that will streamline every business process and pave a path to bountiful profits. Here is tapping into the human side of AI.

This year's AI Ethics, Policy, and Governance event brought together more than 900 people from academia, industry, and government to discuss the future of AI (or automated computer systems able to perform tasks that normally require human intelligence). Discussions at the conference highlighted how companies, governments, and people around the world are grappling with AI's ethical, policy, and governance implications. In this panel, Expanding Human Experience, Susan Athey, the Economics of Technology Professor at Stanford Graduate School of Business and faculty associate director at Stanford HAI, spoke about AI's impact on the economy. It's critical, she said, that AI creates shared prosperity and expands -- rather than replaces -- the human experience in life and at work. Humans, after all, understand things in a way that may be difficult to codify in AI.