Data science
fromMedium
2 days agoIs the Data Scientist Role Dead? No, it's Transforming
The data scientist role is evolving, not disappearing, as organizations demand broader skills and system-oriented thinking.
#classifier-models
#classifier-models
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#ai-agent-evaluation
Software development
fromInfoQ
1 month agoEvaluating AI Agents in Practice: Benchmarks, Frameworks, and Lessons Learned
AI agents require system-level evaluation across multiple turns measuring task success, tool reliability, and real-world behavior rather than single-turn NLP benchmarks like BLEU and ROUGE scores.
Artificial intelligence
fromwww.scientificamerican.com
1 month agoAs AI keeps improving, mathematicians struggle to foretell their own future
First Proof, a benchmarking initiative, is launching its second round to evaluate large language models' ability to contribute to research-level mathematics, now requiring transparency and access from participating AI companies.
fromThe Drum
2 months agoData-driven attribution models still lead to gut decisions - here are the alternatives
When discussing their results, they tell us that Facebook's reporting or Google Analytics show the ad campaigns as barely breaking even. Yet they keep investing in this channel. They reason that Facebook can only see a fraction of the sales, so if Facebook is reporting a 1x return on ad spend (ROAS) then it's probably at least 2x in reality.
Marketing tech
fromInfoQ
2 months agoBuilding Embedding Models for Large-Scale Real-World Applications
What happens under the hood? How is the search engine able to take that simple query, look for images in the billions, trillions of images that are available online? How is it able to find this one or similar photos from all that? Usually, there is an embedding model that is doing this work behind the hood.
Artificial intelligence
fromNature
2 months agoMultimodal learning with next-token prediction for large multimodal models - Nature
Since AlexNet5, deep learning has replaced heuristic hand-crafted features by unifying feature learning with deep neural networks. Later, Transformers6 and GPT-3 (ref. 1) further advanced sequence learning at scale, unifying structured tasks such as natural language processing. However, multimodal learning, spanning modalities such as images, video and text, has remained fragmented, relying on separate diffusion-based generation or compositional vision-language pipelines with many hand-crafted designs.
Artificial intelligence
Artificial intelligence
fromEntrepreneur
2 months agoComparing AI Models With This Tool Can Save Your Business Time and Money
ChatPlayground AI aggregates over 25 leading AI models into one interface for instant side-by-side comparisons, streamlined workflows, and a lifetime Unlimited subscription for entrepreneurs.
Artificial intelligence
fromInfoQ
2 months agoAutonomous Big Data Optimization: Multi-Agent Reinforcement Learning to Achieve Self-Tuning Apache Spark
A Q-learning agent autonomously learns and generalizes optimal Spark configurations by discretizing dataset features and combining with Adaptive Query Execution for superior performance.
fromMedium
2 months agoWhy "Data Scientist" is Becoming "AI Engineer" and What That Actually Means
The title "data scientist" is quietly disappearing from job postings, internal org charts, and LinkedIn headlines. In its place, roles like "AI engineer," "applied AI engineer," and "machine learning engineer" are becoming the norm. This Data Scientist vs AI Engineer shift raises an important question for practitioners and leaders alike: what actually changes when a data scientist becomes an AI engineer, and what stays the same? More importantly, what skills matter if you want to make this transition intentionally rather than by accident?
Artificial intelligence
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