5 Useful Python Scripts for Automated Data Quality ChecksKDnuggets Bad data leads to bad decisions. These Python scripts will help you catch data quality issues before they cause problems.
Bad data leads to bad decisions. These Python scripts will help you catch data quality issues before they cause problems. Read More
Designing Data and AI Systems That Hold Up in ProductionTowards Data Science A system-level perspective on architecture, agents, and responsible scale
The post Designing Data and AI Systems That Hold Up in Production appeared first on Towards Data Science.
A system-level perspective on architecture, agents, and responsible scale
The post Designing Data and AI Systems That Hold Up in Production appeared first on Towards Data Science. Read More
Nano Banana 2: Combining Pro capabilities with lightning-fast speedGoogle DeepMind News Our latest image generation model offers advanced world knowledge, production ready specs, subject consistency and more, all at Flash speed.
Our latest image generation model offers advanced world knowledge, production ready specs, subject consistency and more, all at Flash speed. Read More
Learnings from COBOL modernization in the real worldArtificial Intelligence Delivering successful COBOL modernization requires a solution that can reverse engineer deterministically, produce validated and traceable specs, and help those specs flow into any AI-powered coding assistant for the forward engineering. A successful modernization requires both reverse engineering and forward engineering. Learn more about COBOL in this post.
Delivering successful COBOL modernization requires a solution that can reverse engineer deterministically, produce validated and traceable specs, and help those specs flow into any AI-powered coding assistant for the forward engineering. A successful modernization requires both reverse engineering and forward engineering. Learn more about COBOL in this post. Read More
Google AI Just Released Nano-Banana 2: The New AI Model Featuring Advanced Subject Consistency and Sub-Second 4K Image Synthesis PerformanceMarkTechPost In the escalating ‘race of “smaller, faster, cheaper’ AI, Google just dropped a heavy-hitting payload. The tech giant officially unveiled Nano-Banana 2 (technically designated as Gemini 3.1 Flash Image). Google is making a definitive pivot toward the edge: high-fidelity, sub-second image synthesis that stays entirely on your device. The Technical Leap: Efficiency over Scale The
The post Google AI Just Released Nano-Banana 2: The New AI Model Featuring Advanced Subject Consistency and Sub-Second 4K Image Synthesis Performance appeared first on MarkTechPost.
In the escalating ‘race of “smaller, faster, cheaper’ AI, Google just dropped a heavy-hitting payload. The tech giant officially unveiled Nano-Banana 2 (technically designated as Gemini 3.1 Flash Image). Google is making a definitive pivot toward the edge: high-fidelity, sub-second image synthesis that stays entirely on your device. The Technical Leap: Efficiency over Scale The
The post Google AI Just Released Nano-Banana 2: The New AI Model Featuring Advanced Subject Consistency and Sub-Second 4K Image Synthesis Performance appeared first on MarkTechPost. Read More
Prompt Engineering Mastery Series Phase 1: Zero-Shot Phase 2: Few-Shot Phase 3: Chain-of-Thought Phase 4: Agents 1. What Phase 4 Actually Is Agent systems move beyond single-prompt interactions to automated workflows where the model decides which tools to use, executes actions, and adapts based on results. You’re no longer writing prompts. You’re designing decision loopsThe […]
Prompt Engineering Mastery Series Phase 1: Zero-Shot Phase 2: Few-Shot Phase 3: Chain-of-Thought Phase 4: Agents 1. What This Phase Actually Is Reasoning strategies teach the model to show its work before giving an answer. Instead of jumping directly to a conclusion, the model breaks down the problem, documents each step, and builds toward the […]
Prompt Engineering Mastery Series Phase 1: Zero-Shot Phase 2: Few-Shot Phase 3: Chain-of-Thought Phase 4: Agents 1. What Phase 2 Actually Is Few-shot promptingA technique where you provide 2-5 input-output examples to demonstrate the desired pattern, then the model replicates that pattern for new inputs. shifts your approach from telling the model what to do […]
Prompt Engineering Mastery Series Phase 1: Zero-Shot Phase 2: Few-Shot Phase 3: Chain-of-Thought Phase 4: Agents 1. What Phase 1 Actually Is Zero-shot promptingA prompting technique where you give the model a task without providing any examples. The model completes the task based solely on your instructions. is where you write a single instruction and […]
Shapley Value Computation in Ontology-Mediated Query Answeringcs.AI updates on arXiv.org arXiv:2407.20058v3 Announce Type: replace
Abstract: The Shapley value was originally introduced in cooperative game theory as a wealth distribution mechanism. It has since found use in knowledge representation and databases for the purpose of assigning scores to formulas and database tuples based upon their contribution to obtaining a query result or inconsistency. The application of the Shapley value outside of its original setting relies upon defining a numeric wealth function that captures the phenomenon of interest. In the case of database queries, recent work has focused on the so-called drastic Shapley value, obtained by translating a Boolean query into a 0/1 function based upon whether the query is satisfied or not. The present paper explores the use of the drastic Shapley value in the context of ontology-mediated query answering (OMQA). We present a detailed complexity analysis of the drastic Shapley value computation (SVC$^{dr}$) problem in the OMQA setting. In particular, we establish a dichotomy result that shows that for every ontology-mediated query (T,q) composed of an ontology T formulated in the description logic $mathcal{ELHI}_bot$ and a connected constant-free homomorphism-closed query q the corresponding SVC$^{dr}$ problem is either tractable (in FP) or #P-hard. We further show how the #P-hardness side of the dichotomy can be strengthened to cover possibly disconnected queries with constants. Our results exploit recently discovered connections between SVC$^{dr}$ and probabilistic query evaluation and allow us to generalize existing results on probabilistic OMQA.
arXiv:2407.20058v3 Announce Type: replace
Abstract: The Shapley value was originally introduced in cooperative game theory as a wealth distribution mechanism. It has since found use in knowledge representation and databases for the purpose of assigning scores to formulas and database tuples based upon their contribution to obtaining a query result or inconsistency. The application of the Shapley value outside of its original setting relies upon defining a numeric wealth function that captures the phenomenon of interest. In the case of database queries, recent work has focused on the so-called drastic Shapley value, obtained by translating a Boolean query into a 0/1 function based upon whether the query is satisfied or not. The present paper explores the use of the drastic Shapley value in the context of ontology-mediated query answering (OMQA). We present a detailed complexity analysis of the drastic Shapley value computation (SVC$^{dr}$) problem in the OMQA setting. In particular, we establish a dichotomy result that shows that for every ontology-mediated query (T,q) composed of an ontology T formulated in the description logic $mathcal{ELHI}_bot$ and a connected constant-free homomorphism-closed query q the corresponding SVC$^{dr}$ problem is either tractable (in FP) or #P-hard. We further show how the #P-hardness side of the dichotomy can be strengthened to cover possibly disconnected queries with constants. Our results exploit recently discovered connections between SVC$^{dr}$ and probabilistic query evaluation and allow us to generalize existing results on probabilistic OMQA. Read More