Flexible cognition demands discovering hidden rules to quickly adapt stimulus-response mappings. Standard neural networks struggle in such tasks requiring rapid, context-driven remapping. Recently, Hummos (2023) introduced a fast-and-slow learning algorithm to mitigate this shortcoming, but its scalability to complex, image-computable tasks was unclear. Here, we propose the Wisconsin Neural Network (WiNN), which extends Hummos' fast-and-slow learning to image-computable tasks demanding flexible rule-based behavior. WiNN employs a pretrained convolutional neural network for vision, coupled with an adjustable "context state" that guides attention to relevant features. If WiNN produces an incorrect response, it first iteratively updates its context state to refocus attention on task-relevant cues, then performs minimal parameter updates to attention and readout layers. This strategy preserves generalizable representations in the sensory and attention networks, reducing catastrophic forgetting. We evaluate WiNN on an image-based extension of the Wisconsin Card Sorting Task, revealing several markers of cognitive flexibility: (i) WiNN autonomously infers underlying rules, (ii) requires fewer examples to do so than control models reliant on large-scale parameter updates, (iii) can perform context-based rule inference solely via context-state adjustments-further enhanced by slow updates of attention and readout parameters, and (iv) generalizes to unseen compositional rules through context-state updates alone. By blending fast context inference with targeted attentional guidance, WiNN achieves "sparks" of flexibility. This approach offers a path toward context-sensitive models that retain knowledge while rapidly adapting to complex, rule-based tasks.

Conventionally, salespeople are responsible for juggling tasks like following a playbook, capturing responses, building rapport and updating a customer relationship management (CRM) system during sales calls. As these tend to be repetitive and time-consuming, tedium can quickly set in. The average salesperson spends more than five hours a week updating CRM records, according to a Dooly survey. In search of a solution, sales tech entrepreneur Eldad Postan-Koren and cybersecurity practitioner Bar Haleva co-created Winn.AI, an AI-powered assistant designed to help sales teams automatically track, capture and update CRM entries. Winn.AI monitors sales calls and records key data, in theory reducing the need for salespeople to note-take themselves.

Winn.AI handles the busywork of sales, so salespeople can focus on selling. Capture meeting notes & customer data in real-time and update your CRM in a click.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. In 2014, Microsoft launched Project Alexandria, a research effort within its Cambridge research division dedicated to discovering entities -- topics of information -- and their associated properties. Building on the research lab's work in knowledge mining research using probabilistic programming, the aim of Alexandria was to construct a full knowledge base from a set of documents automatically. Alexandria technology powers the recently announced Microsoft Viva Topics, which automatically organizes large amounts of content and expertise in an organization.

Artificial Intelligence (AI) is helping to improve our society, enhance Australia's wellbeing, improve environmental sustainability and create a more equitable, inclusive and fair society. But as we work to reshape government delivery with AI, are we asking the right questions? The role of AI, including policy implications and the nature of industry in society, is being discussed today in a live-stream event co-hosted by the Institute of Public Administration Australia (IPAA) and the Australian Council of Learned Academies (ACOLA). ACOLA CEO Ryan Winn said the event will feature a keynote presentation by Australia's Chief Scientist, Dr Alan Finkel AO and a panel discussion with ACOLA's AI Expert Group and key Government officials leading AI implementation. "The event will give rise to further discussion on the future opportunities and challenges of AI in industry and Government operations and service delivery, and what implications this will have on society for 2030," Mr Winn said.

Drones and big data are both enterprise tech trendsetters, and the convergence of the two in specific areas of application create new opportunities and challenges for IT. Despite the fact that US drone regulations are still in a state of flux, these drone/big data apps are being deployed because they fill specific niches and it's easy for companies to see the business value in their investments. The drone/big data tech meld is also a great foundational piece from which to launch new tech initiatives. Here are three example use cases in action. Drones can map construction sites, fly over them, and capture information on site activities.