Ontology is a general term used by researchers who want to share information in a specific domain. One of the hallmarks of the greatest success of a powerful manager of an organization is his ability to interpret unplanned and unrelated events. Tools to solve this problem are vital to business growth. Modern technology allows customers to be more informed and influential in their roles as patrons and critics. This can make or break a business. Research shows that businesses that employ a customer-first strategy and prioritize their customers can generate more revenue. Even though there are many different Ontologies offered to businesses, none of it is built from a cognitive perspective. The objective of this study is to address the concept of strategic business plans with a cognitive ontology approach as a basis for a new management tool. This research proposes to design a cognitive ontology model that links customer measurement with traditional business models, define relationships between components and verify the accuracy of the added financial value.

Additional Feedback: ### Comments My overall sense about this paper is that there is an interesting result here that would be significantly improved if the relationship to OBS were clarified, \mathcal{P}_0 were clarified, and the empirical results held up stronger. Particularly, on the last point, given that the method seems to not work particularly well with standard hyperparameters, I am less enthusiastic about directional pruning as a valuable pruning definition even though it seems natural. The results presented in the main body of the paper with non-standard hyperparameters and reduced accuracy for the initial network give me pause as well and, so perhaps the methodology of these experiments could be improved as well. Also, an alternative narrative that would make for a stronger result -- if true -- would be to map the OBS objective to solutions of this algorithm. In which case a reader needs not be concerned about if directional pruning itself is a valuable concept as OBS is already well established.

For years, Chromebooks have served as the loyal opposition to PCs. Google's laptops offer many of the same Google services as you can find via the Web, but integrated into an inexpensive package for consumers and students. I sat down with John Solomon, vice president of ChromeOS and education at Google, to ask about the new wave of AI PCs and how Google responds. We talk about how "generic" Chromebooks survive as Google pushes Chromebook Plus, how kids can be encouraged to game on Chromebooks as well as learn, and what Google is cooking up in response to Microsoft's Recall for Copilot PCs. This interview has been lightly edited for length and clarity. Mark Hachman, PCWorld: I saw your presentation at Computex as a way to remind people that there are more than just AI PCs. So, in light of those products, what is the value proposition of a Chromebook these days? John Solomon, Google: As you know, we have Chromebook and Chromebook Plus. In Chromebook, it has always been about and continues to be about delivering really great value, the best place to experience access to Google services. Whether it's Google Workspace, or more broadly, Chrome, we work very hard to make sure that first-party products as well as the Play Store work well on Chromebook.

Academic writing is an indispensable yet laborious part of the research enterprise. This Perspective maps out principles and methods for using generative artificial intelligence (AI), specifically large language models (LLMs), to elevate the quality and efficiency of academic writing. We introduce a human-AI collaborative framework that delineates the rationale (why), process (how), and nature (what) of AI engagement in writing. The framework pinpoints both short-term and long-term reasons for engagement and their underlying mechanisms (e.g., cognitive offloading and imaginative stimulation). It reveals the role of AI throughout the writing process, conceptualized through a two-stage model for human-AI collaborative writing, and the nature of AI assistance in writing, represented through a model of writing-assistance types and levels. Building on this framework, we describe effective prompting techniques for incorporating AI into the writing routine (outlining, drafting, and editing) as well as strategies for maintaining rigorous scholarship, adhering to varied journal policies, and avoiding overreliance on AI. Ultimately, the prudent integration of AI into academic writing can ease the communication burden, empower authors, accelerate discovery, and promote diversity in science.

A/B testing is a widely-used paradigm within marketing optimization because it promises identification of causal effects and because it is implemented out of the box in most messaging delivery software platforms. Modern businesses, however, often run many A/B/n tests at the same time and in parallel, and package many content variations into the same messages, not all of which are part of an explicit test. Whether as the result of many teams testing at the same time, or as part of a more sophisticated reinforcement learning (RL) approach that continuously adapts tests and test condition assignment based on previous results, dynamic parallel testing cannot be evaluated the same way traditional A/B tests are evaluated. This paper presents a method for disentangling the causal effects of the various tests under conditions of continuous test adaptation, using a matched-synthetic control group that adapts alongside the tests.

Machine learning is a promising technology for improving the customer experience. Why? It’s simple: because it can predict customer behaviors. Prediction as a capability is the Holy Grail for foreseeing each customer need and personalizing products and services accordingly. From the consumer’s perspective, when ML’s ethical pitfalls are avoided, prediction can be the ultimate antidote to the information overload that we all face every day. By deploying ML to predict which content is most relevant for each individual, customers can receive better recommendations, less junk mail, very little inbox spam, and higher quality search results, among many other things. These improvements to customer experience aren’t only a nice-to-have, pleasant side-effect of profit-driven ML deployments. They pursue the raison d’etre of any company — to serve customers — and will ultimately translate into further benefits for the business. After all, a happier customer is a more loyal customer, and a higher customer retention rate means a higher customer growth rate.

The successes of foundation models such as ChatGPT and AlphaFold have spurred significant interest in building similar models for electronic medical records (EMRs) to improve patient care and hospital operations. However, recent hype has obscured critical gaps in our understanding of these models' capabilities. We review over 80 foundation models trained on non-imaging EMR data (i.e. clinical text and/or structured data) and create a taxonomy delineating their architectures, training data, and potential use cases. We find that most models are trained on small, narrowly-scoped clinical datasets (e.g. MIMIC-III) or broad, public biomedical corpora (e.g. PubMed) and are evaluated on tasks that do not provide meaningful insights on their usefulness to health systems. In light of these findings, we propose an improved evaluation framework for measuring the benefits of clinical foundation models that is more closely grounded to metrics that matter in healthcare.

Creating products that rely on technologies such as machine learning comes with different considerations, risks and constraints than normal products. You're really excited about the new project at work. You are working with a team of your company's strongest engineers and scientists to develop a cool new product, which at its core is using a bespoke machine learning approach to deliver the outcomes. To make it concrete it could be a tool determining when to restock your retail stores and automatically planning the truck transportation, or it could be a new app helping your customers preview how your furniture would look in their home before buying, an automated fault-inspection system for your company's production line or something completely different. Everybody starts working on the project.

In a popular blog post titled "The Bitter Lesson," Richard Sutton argues that AI's progress has resulted from cheaper computation, not human design decisions based on problem-specific information. Sutton diminishes researchers that build knowledge into solutions based on their understanding of a problem to improve performance. This temptation, Sutton explains, is good for short-term performance gains, and such vanity is satisfying to the researcher. However, such human ingenuity comes at the expense of AI's divine destiny by inhibiting the development of a solution that doesn't want our help understanding a problem. AI's goal is to recreate the problem-solver ex nihilo, not to solve problems directly.[1]

All it takes is two photos of the dog. Then the funny, colorful illustrations come to life with an artificial intelligence program that creates life-like drawings of the pet and inputs the reader into scenes. "Every dog [in the book] will look like your dog … that's our value proposition, it's not a cartoon," Cohen says. "It's a hand-drawn sketch of your dog, very similar to if you commissioned something." All it takes is two photos of the dog.