Macaw

General Purpose Question-Answering with Macaw

Devopedia

Devopedia

Bitcoin Investing

There has been a recent influx of interest in bitcoin investing in the markets. People that had invested $10 per bitcoin back in the day are likely now worth in thousands. This may be a grandiose return but at the end of the day it is not investment it is more of a speculation. Although rewards are high, the returns can be exceptionally risky in the higher margin of volatility. Not to mention the fact that capital gains tax calculations can be tricky. And, then there is a lot of fraud associated with cryptocurrencies. The valuation can also be difficult to calculate for bitcoin in real terms. There is also that aspect of limited acceptance within a wide spectrum of markets which makes conversion into cash at times difficult. The magic percentage to bitcoin is 1%, any more and one has an exceptionally high risk to return ratio which may not often provide the right level of long term expectations on the highly volatile cryptomarkets.  Undeniably, if one looks at it, paper money tends to be worthless, but digital currency in real terms is even more worthless. Any security needs to be backed by something. What is a bitcoin backed by? Some form of currency? What is the asset value of a bitcoin? There is no sensible regulation, no sense of protection. At least, not one that has been unanimously agreed across jurisdictions. It is the underground currency so to speak in the digital universe.

Biases in Transformer Models

Transformer models are notoriously trained on biased data, which they then propagate through the training, test, validation cycle and in production use cases. There are many types of biases at various stages of the process. The below highlight the different bias cases in the cycle that could evolve and provides a few suggestions for resolution. 

Training Data is Collected and Annotated: 

• Reporting Bias
• Selection Bias 
• Stereotyping 
• Racism 
• Underrepresentation 
• Gender Bias 
• Human Bias

Model Trained: 

• Overfitting 
• Underfitting 
• Default Effect 
• Anchoring Bias 

Media is Filtered, Aggregated, and Generated: 

• Confirmation Bias 
• Congruence Bias 

People See Output: 

• Automation Bias 
• Network Effect 
• Bias Laundering 

How to Resolve Transformer Bias: 

• Feedback Loop 
• Model Cards for Model Reporting 
• Open Review 
• TLDR

Black and White Biases of Media

Black and White Biases of Media

Benchmark Semantic Textual Similarity

Semantic Textual Similarity

Microsoft Academic Knowledge Graph

Makg

Organizational Web of Data

There are a lot of terms flying around in the data world. Many organizations struggle to find ways of effectively tackling their insurmountable data growth where they want to be able to constructively derive insights and meet their digital transformation needs. Terms like data hubs, data mesh, data fabric, and data lakes have been thrown around in many organizational data architectures. However, many don't utilize the concept of linked data in most of their data architecture frameworks. A data fabric could be used to cover for the semantic knowledge graph that has a data centric view of the organization. And, a decentralized data hub could be extended from this to cover for business functions that form a connected linked data in the conceptual world of organizational web of data. Each business function can then serve, protect access, and make available to share queryable linked data internally within the organization. A further transposition could be made in the context of a focused data mesh that acts as a data as a product strategy for defining a more focused view for analytics and business intelligence requirements. Over time organizations form into internal abstractions of a linked data world across their business functions and have a very connected view of all things with a flexible control for governance and provenance.