Artificial Intelligence
Explore practical insights, research, and benchmarks on artificial intelligence, including generative AI, large language models, RAG, governance frameworks, MLOps practices, and AI hardware. Gain an understanding of key tools, implementation strategies, and enterprise use cases shaping the AI landscape.
Explore Artificial Intelligence
AIM-Decision: Jev vs Clef vs LLMs
System OneBenchmarkOct 15Decision models, also called System One models, score a fixed set of options instead of generating text token by token. We tested 50 decision models on 1,655 classification questions, such as rating a security flaw or a product recall, and ran 47 of them on 50 browser tasks. See AIM System One Index for combined…
- AI FoundationsInsightOct 15
100+ AI Use Cases with Real Life Examples
Exploring AI use cases can help companies identify 44% more opportunities and lower external funding needs by 39.5% before implementation. During my nearly 20 years of experience of implementing advanced analytics & AI solutions at enterprises, I have seen the importance of use case selection. I analyzed 100+ AI use cases, their real-life examples and…
- AI in IndustriesOpen World EvaluationOct 15
Compare Top 48 Legal AI Software by Pricing
In the last 2 decades, I worked with enterprises as a consultant and tech vendor to deploy advanced analytics & AI solutions. I looked into more than 45 legal tech companies using generative AI and categorized the leading products. Click the category names below to see leading players in that category: Explore more details on…
- ChatbotsInsightOct 15
Top 40 Chatbot Applications with Examples
We reviewed 40 real chatbot applications across industries to see what actually delivers results versus what’s just marketing hype. Amazon’s support bot processes millions of queries daily. It handles straightforward issues (tracking numbers, delivery dates) and escalates anything involving money or complex problems. The bot also asks “Did this help?” after each interaction, feeding data…
- AI FoundationsInsightOct 15
20 Strategies for AI Improvement & Examples
AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions. Changes in regulations, product requirements, or customer expectations can also introduce new constraints that existing models were not designed to…
- RAGBenchmarkOct 15
Reranker Benchmark: Top 8 Models Compared
We retrieved top-100 candidates with multilingual-e5-base, reranked them with each model, and evaluated the top-10 results against 300 queries, each referencing concrete details from its source review. The best reranker lifted Hit@1 from 62.67% to 83.00% (+20.33pp). Metrics explained: ΔHit@1 / ΔHit@10 shows the improvement over the baseline (no reranker) in percentage points (pp). For…
- LLMBenchmarkOct 15
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 40 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 chart understanding questions testing data visualization interpretation, and 100 visual logic questions assessing pattern recognition and spatial reasoning. See our benchmark methodology to learn our testing procedures. Every model scores lower on visual logic…
- AI EthicsBenchmarkOct 15
Bias in AI: Examples and 6 Ways to Fix it
We also benchmarked decision models to see how they handle bias: In the multiple-choice part we ran every question twice: once with “cannot be determined” as the last option (v1) and once with it as the first option (v2). The question and the other options stay the same; only the position of the correct option…