This paper extends Kleinberg's work on the impossibility of clustering. That is, Kleinberg introduced 3 axioms that any clustering procedure should satisfy and then showed that it is impossible to satisfy all three simultaneously. This work suggests a refinement of Kleinberg's 3rd axiom having to do with consistency. In the original axiom, if the clustering distance function is perturbed such that all within-cluster distances do not increase and all across cluster distances do not decrease, then the optimal clustering should be the same with respect to both the perturbed and unperturbed distance functions. The refined consistency axiom proposed in this work says that under the perturbed distance function, the optimal clustering may be different so long as the number of clusters in the optimal partition is different as well.

Are you looking to optimize your content strategy with the help of ChatGPT? ChatGPT is a powerful tool that can generate keywords quickly and accurately. It can help you create a content strategy that will drive more traffic to your website and increase engagement with your target audience. With ChatGPT, you can easily generate a list of basic keywords and use them to derive more specific long-tailed keywords that are more targeted to your content. This blog post will provide you with step-by-step instructions on how to use ChatGPT to maximize your content strategy.

Improve AI is a machine learning platform for making apps self-improving, meaning they optimize their own data structures and variables to improve revenue and conversions. With Improve AI v7.2, you can now optimize the variables of any Java, Swift, or Python object with reinforcement learning. The new optimize() method finds the best combination of variable values given current conditions. Optimized objects are created immediately, on the fly, with zero network latency. Improve AI can optimize any object or JSON-encodable dictionary in Swift, Java, or Python to find the best combination of variables given current conditions.

Artificial intelligence is becoming less of a futuristic technology and a more integral aspect of today's business landscape. The usage of AI across the business universe is revolutionizing every industry, and Gartner reports that at least 75% of organizations use deep neural networks today. In financial departments, AI is automating menial tasks and reducing errors in traditional manual workflows. There's no doubt that businesses utilizing the right AI for the right reasons are seeing exponential benefits. Unfortunately, not every business unit is as excited about the available AI solutions that finance departments are gifted with.

Thu, Aug 4, 9:00 AM PDT: Join us for the twenty-second Google Open Source Live event in our series; “Machine Learning Day”! Google Machine Learning experts will share updates on everything from To

Google processes more than 8.5 billion searches every day. That's more than 100,000 searches per second, thousands of which could lead a user to a purchase. It's no wonder, then, that 60% of marketers list SEO as their number one inbound marketing priority. But generating organic traffic comes with challenges. Google has hundreds of billions of webpages in its index, competing for the top spots on search result pages.

Rudi Asseer tells Inc. that his Nashville-based supply chain services company, IMI Material Handling Logistics, saves more than 42 hours per job posting thanks to A.I. To optimize those tools, Asseer recommends employers use them in a way that emulates the company's tone and voice if using A.I. to communicate with its workforce. The reason: It maintains consistency in communications but also helps broadcast what you're company is all about. That can contribute to better fit and happier employees who want to stay put.

With modern requirements, there is an increasing tendancy of considering multiple objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a multi-objective optimization scenario comes with an important issue --- how to evaluate the outcome of optimization algorithms, which typically is a set of incomparable solutions (i.e., being Pareto non-dominated to each other). This issue can be challenging for the SE community, particularly for practitioners of Search-Based SE (SBSE). On one hand, multiobjective optimization may still be relatively new to SE/SBSE researchers, who may not be able to identify right evaluation methods for their problems. On the other hand, simply following the evaluation methods for general multiobjective optimisation problems may not be appropriate for specific SE problems, especially when the problem nature or decision maker's preferences are explicitly/implicitly available. This has been well echoed in the literature by various inappropriate/inadequate selection and inaccurate/misleading uses of evaluation methods. In this paper, we carry out a critical review of quality evaluation for multiobjective optimization in SBSE. We survey 717 papers published between 2009 and 2019 from 36 venues in 7 repositories, and select 97 prominent studies, through which we identify five important but overlooked issues in the area. We then conduct an in-depth analysis of quality evaluation indicators and general situations in SBSE, which, together with the identified issues, enables us to provide a methodological guidance to selecting and using evaluation methods in different SBSE scenarios.

Artificial intelligence (AI) is changing the face of business. No longer a futuristic concept, its impact is real. From tech giants like Google, Apple and Amazon to user-centric behemoths like Uber and Starbucks, everyone seems to be using AI technology to transform the customer experience (CX). But, it's not just corporate giants that are deploying AI. Smaller organizations are following suit.