This paper is the second in a planned series aimed at envisioning a path to safe and beneficial artificial intelligence. Building on the conceptual insights of "Common Sense Is All You Need," we propose a more formal litmus test for common sense, adopting an axiomatic approach that combines minimal prior knowledge (MPK) constraints with diagonal or Godel-style arguments to create tasks beyond the agent's known concept set. We discuss how this approach applies to the Abstraction and Reasoning Corpus (ARC), acknowledging training/test data constraints, physical or virtual embodiment, and large language models (LLMs). We also integrate observations regarding emergent deceptive hallucinations, in which more capable AI systems may intentionally fabricate plausible yet misleading outputs to disguise knowledge gaps. The overarching theme is that scaling AI without ensuring common sense risks intensifying such deceptive tendencies, thereby undermining safety and trust. Aligning with the broader goal of developing beneficial AI without causing harm, our axiomatic litmus test not only diagnoses whether an AI can handle truly novel concepts but also provides a stepping stone toward an ethical, reliable foundation for future safe, beneficial, and aligned artificial intelligence.

Naive Bayes is a probabilistic machine learning algorithm that is based on the Bayes Theorem and is used for a wide range of classification challenges. In this blog, we will learn about the Naive Bayes algorithm and all of its core concepts so that there are no gaps in the information. As we all know, machine learning is the technology that predicts goal B using characteristics A, i.e., computing the conditional probability P(B A). Then, for the discriminative model, we only take into account assessing the conditional probability. This establishes the classifier under the condition of a limited sample, without evaluating the sample's generative model, instead of learning the prediction model, like in the binary classification problem.

The use case for Artificial Intelligence (AI) in the workplace is there. Deloitte's Tech Trends 2021 found AI and machine learning technologies are helping financial services firm Morgan Stanley use decades of data to supplement human insight with accurate models for fraud detection and prevention, sales and marketing automation, and personalized wealth management, among others. For marketing and customer experience, in particular, organizations are using AI and machine learning to improve internal business processes and workflow, automating repetitive tasks and to improve customer journeys and touchpoints, among other use cases. The CMO Survey by Duke University reports a steady increase as far as the extent to which companies are reporting implementing AI or ML into their marketing toolkits. However, the majority of marketers know AI is very important or critical to their success this year, according to Paul Roetzer, founder and CEO of the Marketing AI Institute and PR 20/20.

Let's say that I find it curious how Spotify recommended a Justin Bieber song to me, a 40-something non-Belieber. That doesn't necessarily mean that Spotify's engineers must ensure that their algorithms are transparent and comprehensible to me; I might find the recommendation a tad off-target, but the consequences are decidedly minimal. This is a fundamental litmus test for explainable AI – that is, machine learning algorithms and other artificial intelligence systems that produce outcomes that humans can readily understand and track backwards to the origins. Conversely, relatively low-stakes AI systems might be just fine with the black box model, where we don't understand (and can't readily figure out) the results. "If algorithm results are low-impact enough, like the songs recommended by a music service, society probably doesn't need regulators plumbing the depths of how those recommendations are made," says Dave Costenaro, head of artificial intelligence R&D at Jane.ai. I can live with an app's misunderstanding of my musical tastes.

In our last article, Lifecycle mapping: uncovering rich, predictive data sources, we discussed the importance of mapping out your customer lifecycle to better understand where your most predictive customer data is hiding. Lifecycle mapping is the first step to using artificial intelligence (AI) to optimize your customer lifecycle marketing initiatives. Now, we'll pose some questions to help identify your predictive customer attributes and lifecycle events, pinpoint where that data is located, and recognize patterns to predict outcomes for future prospects, leads, and customers. Data discovery is the second stage in the customer lifecycle optimization (CLO) process. The primary task of this stage is to expand on your lifecycle map to identify authoritative data sources that establish progress.

The recent killing of the Taliban Chieftain, Mullah Akhtar Mansour, by a drone inside the Pakistan province of Baluchistan, is a striking reminder that we have entered a futuristic world where war is waged by flying killer robots and that we have witnessed a massive leap forward in the history of human conflict. Given that war accelerates history and the Islamic world is incapable of producing the cell phones on which its Islamists plot to kill us, the mullah's death by drone reminds us of immutable laws governing the fall of civilizations. Declining civilizations will always face superior firepower from ascending civilizations because sovereignty is only temporarily uncontested. The U.S. agency that conducts drone warfare worldwide, the Joint Special Operations Command (JSOC), was constituted in 2002 and has grown ten-fold since its inception. Staffed by both the CIA and military, it now operates in super-secret locations across the globe. For the first ten years of its existence, JSOC conducted operations which were largely under reported and therefore garnered very little public scrutiny.