AI Text Generation: Past, Present, and What's Next

Kari Jaaskelainen

12 Nov 2025 — 1 min read

AI that writes: past, present, future

In a sweeping survey, Zhang, Guo, Wang, Liang, Hao, and Yu trace how text generation has evolved - from simple templates to neural networks that can draft emails, stories, and more.

From fluency to personality: systems now aim to reflect tone, style, and user intent.
From rules to learning: methods span templates, probabilistic models, encoder-decoder nets, and Transformers.
Applications: chatbots, translation, summarization, data-to-text, and assistive writing.
What's inside: a unified framework, widely used models, and a map of the state of the art.

Why it matters: better control, personalization, and evaluation will shape safer, more helpful human-machine communication.

Read the survey: http://arxiv.org/abs/1905.01984v1

Paper: http://arxiv.org/abs/1905.01984v1

Register: https://www.AiFeta.com

#AI #NLP #TextGeneration #DeepLearning #HumanComputerInteraction #Survey #ML

Read more

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

Automating GDPR Compliance: A Roadmap for Companies and Law Firms

GDPR compliance is more than checkboxes. A new roadmap from the Privatech project shows how automation and machine learning can help companies and law firms assess—and even generate—privacy compliance. * Shift the focus to data processors’ real workflows: drafting policies, mapping data uses, documenting decisions. * Break compliance into machine-ready

FPGAs for Faster, Leaner Deep Learning: A Review of CNN Accelerators

FPGAs for Faster, Leaner Deep Learning: A Review of CNN Accelerators

Deep learning drives image search, robots, and medical scans. Most systems lean on CPUs and GPUs. This review asks: what if we run convolutional neural networks (CNNs) on FPGAs—reconfigurable chips you can tailor to the model? * Why FPGAs: custom dataflows, low latency, and strong energy efficiency—great for cameras,

Dynamic-K: Recommendations That Know When to Stop

Dynamic-K: Recommendations That Know When to Stop

Most apps show a fixed number of “top” items—say 10 movies or 20 products—assuming there are always enough good options. But that’s not always true: sometimes there are few relevant items, or some users are extra picky. The result? Filler recommendations. Dynamic-K flips the script. Instead of

Teaching chatbots to stop contradicting themselves (DECODE)

Teaching chatbots to stop contradicting themselves (DECODE)

Teaching chatbots to stop contradicting themselves Ever had a bot say one thing, then the opposite a few turns later? This study introduces DECODE—a new task and dataset for spotting contradictions in everyday conversations, drawn from both human-human and human-bot chats. * New data beats existing natural language inference (NLI)