2025-03-07
When Words Won’t Talk, Sentence Structures Spill the Truth
Statistical parsing proves effective in distinguishing authors by analyzing grammatical structures rather than traditional word-based methods. While The Federalist Papers benefited from dimensionality reduction, Sanditon did not, highlighting the method’s...
Can AI Tell Jane Austen’s Writing Apart from a Fake?
Using statistical parsing, this study confirms distinct stylistic differences between Jane Austen’s unfinished Sanditon and its continuation. Unlike in The Federalist Papers, POS tagging proves effective in distinguishing authorship, revealing...
Deep Syntax and Dead Founders: How AI Deciphered The Federalist Papers
Using statistical parsing and syntactic tree features, this study accurately attributes The Federalist Papers to their authors. Rooted subtrees outperform POS tagging in distinguishing writing styles, proving the effectiveness of...
Great software design looks underwhelming
Years ago I spent a lot of time reviewing coding challenges. The challenge itself was very straightforward - building a CLI tool that hit an…
2025-03-06
Inside the Incredible Potential of Quantum Computing in Drug Development
Drug development is time-consuming, costly, and complex — but quantum computing could allow results in mere months rather than years by accelerating data generation, rapidly solving complex problems, and authentically...
Pentagon Signs Deal to "Deploy AI Agents for Military Use"
The Pentagon has signed a deal with AI company Scale AI, dubbed "Thunderforge," to use AI agents for military planning and operations. Put simply, we've never been closer to giving...
Code Smell 293 - You Should Avoid Adding isTesting or Similar Flags
When you add flags like isTesting, you mix testing and production code. This creates hidden paths that are only active in tests.
There's Some Serious Drama on the Moon
Houston-based space company Intuitive Machines' Athena lunar lander approached its final landing spot on the Moon early Thursday morning. It was meant to touch down autonomously near Mons Mouton, a...
Consent: It’s Not Just for Doctors’ Offices Anymore—Tech Needs It Too
In the world of technology and data privacy, consent plays a crucial role. The principles of medical consent that govern our most serious health decisions also apply to the digital...
The Crypto Industry Got Everything It Wanted, and Now It's in Crisis
Life has never been better for the cryptocurrency industry. In its 16 short years of existence, the industry's eclipsed to over $3 trillion, worm its way into state and federal...
SquareX Unveils Polymorphic Extensions That Morph Infostealers Into Any Browser Extension
Polymorphic extensions work by exploiting the fact that most users interact with extensions via the pinned in the browser toolbar. The attack begins with the user installing the malicious extension,...
Bybit Becomes The First Exchange To List USDtb , Bringing Institutional-Grade Stability To Crypto
Bybit becomes the first platform to include USDtb, a blockchain-based USD stablecoin, on its Spot exchange. Bybit is offering 5% Annual Percentage Rate (APR) for new and existing eligible users...
Answer To Win from $2000: How Does Decentralized Cloud Compare to Traditional Cloud Services?
The #blockchain Writing Contest by Aleph Cloud and HackerNoon offers $700 for the best takes on decentralized cloud computing. Answer key questions on decentralization’s advantages, real-world applications, and challenges. Submit...
The Hidden Power of "Cherry" Parameters in Large Language Models
Not all parameters in LLMs matter equally! This blog explores parameter heterogeneity and how some "cherry" parameters significantly impact performance. Learn how optimizing them can boost efficiency.
Rethinking AI Quantization: The Missing Piece in Model Efficiency
Quantization helps reduce LLM memory demands, but existing methods overlook parameter heterogeneity. Learn how new approaches like CherryQ address this issue for better efficiency.
The Future of AI Compression: Smarter Quantization Strategies
What’s the best way to select parameters for mixed-precision training? This blog compares different approaches and highlights how CherryQ sets a new standard in LLM quantization.
The Impact of Parameters on LLM Performance
Discover how parameter heterogeneity affects LLM performance. Learn how researchers measure the importance of different parameters and optimize mixed-precision training for better results.
Can ChatGPT-Style Models Survive Quantization?
Applying quantization to chat-based LLMs comes with challenges. See how different techniques impact conversational AI and what methods preserve the best response quality.
The Perplexity Puzzle: How Low-Bit Quantization Affects AI Accuracy
Lowering bit precision affects AI performance, but how much? We analyze perplexity scores and task performance across different quantization strategies to find the best approach.
The Science of "Cherry" Parameters: Why Some LLM Weights Matter More
A tiny fraction of LLM parameters—called "cherry" parameters—play a crucial role in model accuracy. Learn how identifying and preserving them can improve AI efficiency.
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