short halflife on human label data

Extracted Attributes

• Implication

  The value and effectiveness of human-annotated datasets diminish rapidly over time in the context of AI advancement.

• Core Principle

  Scalable computation is more effective than relying on human-labeled data for AI development.

• Related Concept

  The Bitter Lesson.

• Application Area

  Artificial Intelligence, Machine Learning, Large Language Models (LLMs).

• Alternative Data Source

  Synthetic data.

Timeline

• Rich Sutton publishes 'The Bitter Lesson' essay, articulating the principle that scalable computation is more effective than relying on human-labeled data, which forms the theoretical basis for the 'short halflife on human label data' concept. (Source: External Search)

  2019-03-13

• The concept of 'short halflife on human label data' is discussed as a key investment thesis in the context of the AI arms race, particularly with the success of xAI's Grok 4 and the strategies of Tesla FSD, highlighting the shift towards synthetic data. (Source: Document 5a2e1075-6c4e-4a14-9e5b-7bf833bb6674)

  2024-01-01

Prosulfocarb

Prosulfocarb is a pre-emergent herbicide used agriculturally in Australia, the EU, Japan, New Zealand, (since 2020), Morocco and Iran, for control of annual ryegrass and toad rush in wheat and barley crops. It was introduced to the EU in 1988 and is rapidly growing in use, with sales increasing by over 500% in France since 2008. Prosulfocarb is a thiocarbamate, and is absorbed by the roots of germinating seedlings to inhibit growth in the meristem by inhibiting fat synthesis, of resistance HRAC Group J, (Aus), Group N, (WSSA), Group 8. (8 merged into Group 15) (numeric) Applying prosulfocarb repeatedly builds weed resistance but undoes trifluralin (Group 3) resistance, as they have opposing mechanisms: prosulfocarb is resisted by decreasing pesticide metabolism; trifluralin is resisted by increasing metabolism.

Web Search Results

• Human neutrophil kinetics: modeling of stable isotope labeling data ...

  Human neutrophils have traditionally been thought to have a short half-life in blood; estimates vary from 4 to 18 hours. This dogma was recently challenged by stable isotope labeling studies with heavy water, which yielded estimates in excess of 3 days. To investigate this disparity, we generated new stable isotope labeling data in healthy adult subjects using both heavy water (n = 4) and deuterium-labeled glucose (n = 9), a compound with more rapid labeling kinetics. To interpret results, we [...] assumption that R is very large, which is physiologically untenable. We conclude that stable isotope labeling in healthy humans is consistent with a blood neutrophil half-life of less than 1 day. [...] developed a novel mechanistic model and applied it to previously published (n = 5) and newly generated data. We initially constrained the ratio of the blood neutrophil pool to the marrow precursor pool (ratio = 0.26; from published values). Analysis of heavy water data sets yielded turnover rates consistent with a short blood half-life, but parameters, particularly marrow transit time, were poorly defined. Analysis of glucose-labeling data yielded more precise estimates of half-life (0.79 ±

• Proteome-wide mapping of short-lived proteins in human cells - PMC

  A previous protein half-life analysis with pulse-chase metabolic labeling revealed a weak negative association been protein abundance and turnover rates at

• What is Data Labeling? | Google Cloud

  The process of data labeling involves assigning predefined labels to data points based on established guidelines or rules. This task can be performed either manually by human annotators or through automated methods using software or algorithms. Manual labeling involves individuals manually reviewing and assigning labels according to the specified guidelines. This approach often ensures high accuracy but can be time-consuming and labor-intensive. [...] In some cases, a hybrid approach combines manual and automated methods to balance accuracy and efficiency. For example, human annotators may label a subset of data to create a high-quality training dataset, which is then used to train an automated labeling system. This system can then label larger datasets more efficiently while maintaining reasonable accuracy. [...] Data labeling plays a pivotal role in machine learning for numerous reasons. It provides the crucial training data for supervised ML models, enabling them to learn patterns and make predictions from labeled examples. Having high-quality labeled data enhances model accuracy by providing clear and consistent learning signals.

• Data Have a Limited Shelf Life - Harvard Data Science Review

  I will argue that data have a limited shelf life. To see what data look like after too long a time on the shelf, see Figure 1.

• Systematic study of the dynamics and half-lives of newly synthesized ...

  A novel chemical proteomics method was developed to specifically identify newly synthesized proteins and measure their half-lives.