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.

Deep learning has disrupted nearly every field of research, including those of direct importance to drug discovery, such as medicinal chemistry and pharmacology. This revolution has largely been attributed to the unprecedented advances in highly parallelizable graphics processing units (GPUs) and the development of GPU-enabled algorithms. In this Review, the authors present a comprehensive overview of historical trends and recent advances in GPU algorithms and discuss their immediate impact on the discovery of new drugs and drug targets. R is an increasingly preferred software environment for data analytics and statistical computing among scientists and practitioners. Packages markedly extend R's utility and ameliorate inefficient solutions to data science problems.

A team of researchers from the Pritzker School of Molecular Engineering (PME) at the University of Chicago has recently succeeded in the creation of an AI system that can create entirely new, artificial proteins by analyzing stores of big data. Proteins are macromolecules essential for the construction of tissues in living things, and critical to the life of cells in general. Proteins are used by cells as chemical catalysts to make various chemical reactions occur and to carry out complex tasks. If scientists can figure out how to reliably engineer artificial proteins, it could open the door to new ways of carbon capturing, new methods of harvesting energy, and new disease treatments. Artificial proteins have the power to dramatically alter the world we live in.

A COVID-19 pandemic has already proven itself to be a global challenge. It proves how vulnerable humanity can be. It has also mobilized researchers from different sciences and different countries in the search for a way to fight this potentially fatal disease. In line with this, our study analyses the abstracts of papers related to COVID-19 and coronavirus-related-research using association rule text mining in order to find the most interestingness words, on the one hand, and relationships between them on the other. Then, a method, called information cartography, was applied for extracting structured knowledge from a huge amount of association rules. On the basis of these methods, the purpose of our study was to show how researchers have responded in similar epidemic/pandemic situations throughout history.

Eugene Schieffelin was the eccentric ornithologist who in 1890 shipped 60 starlings from London to New York City and set them free in Central Park. The next year he released 40 more, and today there are maybe 200 million starlings in the United States and Southern Canada. As immigrants go, starlings are shrewd flyers, clever mimics, and often unwelcome. The truth is they're no more than uptown blackbirds, stocky, three-ounce grifters with iridescent blue and green plumage, along with yellow beaks and a long history of displacing woodpeckers and flycatchers, and destroying entire crops of berries and cherries. Not to mention the havoc they cause at many airports.

Chatbots are still considered an emerging technology, but their use is rapidly expanding among all businesses. Because chatbots offer many benefits, including better customer experiences, increased customer engagement and increased sales and conversion rates. Also, with the advancement of artificial intelligence, machine learning and natural language processing, chatbots are poised to become more and more intelligent -- which means even more businesses will adopt them. Business Insider experts predict that by 2020, 80 percent of enterprises will use chatbots. We don't quite know why the other 20 percent is shying away, but we hope the realization dawns upon them soon.

We are at an inflection point where too many vendors are offering too many solutions for moving our AI/ML models to production. The very real risk is duplication of effort, fragmentation of our data science resources, and incurring unintended new technical debt as we bind ourselves to platforms that have hidden assumptions or limitations in how that approach problems. Remember when our biggest problem was getting our models off of data science platforms and into production. Well the market is nothing if not efficient and hundreds of platform companies have been laboring away to help solve your pain point. The problem arising for the CDO, CAO or any other CXX is trying to decide which and how many of these you need.

Increasingly, the data that is relevant for companies' machine learning efforts will be not just some data, but all of it; anything less risks missing what could conceivably be the critical insight down the road, the answer to questions as yet Chief information officers of companies have a strange predicament in an age of AI: They are meant to solve problems for companies by marshaling the relevant data on customers and transactions, but the data itself is going to raise new, unexpected questions. Increasingly, the data that is relevant for companies' machine learning efforts will be not just some data, but all of it; anything less risks missing what could conceivably be the critical insight down the road, the answer to questions as yet unasked. Until recently, the era of "big data," as it's called, has been about providing only the requisite information to answer some straightforward question, where the "known unknowns" are all that matters. For example, if you're a retailer, you might want to know how many of your customers would be likely to return items they've bought based on patterns of purchases. In fact, a group from Indian online apparel retailer Myntra this summer showed off a machine learning model for just such an application.

Increasingly, the data that is relevant for companies' machine learning efforts will be not just ... [ ] some data, but all of it; anything less risks missing what could conceivably be the critical insight down the road, the answer to questions as yet unasked. Chief information officers of companies have a strange predicament in an age of AI: They are meant to solve problems for companies by marshaling the relevant data on customers and transactions, but the data itself is going to raise new, unexpected questions. Increasingly, the data that is relevant for companies' machine learning efforts will be not just some data, but all of it; anything less risks missing what could conceivably be the critical insight down the road, the answer to questions as yet unasked. Until recently, the era of "big data," as it's called, has been about providing only the requisite information to answer some straightforward question, where the "known unknowns" are all that matters. For example, if you're a retailer, you might want to know how many of your customers would be likely to return items they've bought based on patterns of purchases.

Consumers and activists are rebelling against Silicon Valley titans, and all levels of government are probing how they operate. Much of the concern is over vast quantities of data that tech companies gather--with and without our consent--to fuel artificial intelligence models that increasingly shape what we see and influence how we act. If "data is the new oil," as boosters of the AI industry like to say, then scandal-challenged data companies like Amazon, Facebook, and Google may face the same mistrust as oil companies like BP and Chevron. Vast computing facilities refine crude data into valuable distillates like targeted advertising and product recommendations. But burning data pollutes as well, with faulty algorithms that make judgments on who can get a loan, who gets hired and fired, even who goes to jail.