Posts tagged machine learning
Taxonomy v Folksonomy
Taxonomy v Folksonomy

The concepts of taxonomy and folksonomy hold significant implications, especially in the context of emerging technologies like OpenAI. While traditional taxonomies offer structured hierarchies of knowledge, allowing for a systematic approach to information organization, folksonomies represent a more fluid and emergent way of categorizing information based on user-generated tags and metadata.

However, the challenge arises when technological advancements fail to incorporate divergent thinking and promote groupthink through convergent taxonomies. This phenomenon is particularly evident in language models, where developers' linguistic and cultural biases can influence the interpretation and representation of (the dominant) language.

Read More
data trends, driving data projectsChristine Haskelltaxonomy, folksonomy, machine learning, unintended consequences, unconsidered consequences, large language models, LLMs, natural language models, artificial intelligence, ai, data trends, DEI, groupthink, bias, linguistic bias, exclusion, data projects, IT teams, project management, empathy, human skills, interpersonal skillsComment
Data Trend: From Spreadsheets to Algorithms
Data Trend: From Spreadsheets to Algorithms

The transition from traditional spreadsheets to sophisticated data management and analysis algorithms represents a significant evolution that has revolutionized how businesses process and leverage information. Algorithms have reshaped the landscape of data-driven decision-making. Facebook's filter bubble is an early example of a machine learning system individualizing the user experience based on user patterns.

Read More
driving data projects, data trendsChristine Haskellspreadsheets, algorithms, machine learning, data fluency, data literacy, facebook filter bubble, bias, data ethics, data trends, data supply chainComment
Who is pacing this race?
Who is pacing this race?

Employees have been encouraged to ‘automate their roles’ to demonstrate self-direction and continuous learning. In the past, an employee's skills, motivation, and business interests determined the pace of change. Soon, the pace may be beyond their control, risking job loss before they can adapt to consider the next set of problems. If they can’t find problems faster than the pace of automation, they are not adequately prepared for transition.

Read More
driving data projects, data trendsChristine Haskellai, automation, upskilling, reskilling, workforce development, machine coworkers, machine learning, job replacement, artificial augmentation, future of work, data trendsComment