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The Challenges of AI Product Development
Developing artificial intelligence (AI) products involves creating models and feeding data to train them, testing the models, and deploying them. Software engineers can support the adoption of AI and machine learning (ML) in companies by building an understanding of the technologies, encouraging experimentation, and ensuring compliance with regulations and ethical standards.
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Is ChatGPT Fit for Every Purpose: Alan Turing Ethics Fellow Presents Checklist in Devoxx UK Keynote
During her keynote at Devoxx UK, Mhairi Aitken talked about the limitations of AI when grappling with the complexities of human language. Further, she provided checklist developers use to inspect the AI Foundations before building on top of them. She urged us to be guided by ethical and social considerations when building on AI, as a general-purpose AI model may not be fit for every purpose.
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Galactica: Large Language Model for Scientific Knowledge
Meta AI and Papers with Code recently released Galactica, a 120-billion-parameter scientific-language model which can search and summarize academic literature, solve math problems, and write scientific code. Galactica’s architecture is based on a transformer, an attention mechanism which draws global dependencies between input and output.
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Experiences from Testing Stochastic Data Science Models
A data science model is a statistical black box; testing it requires an understanding of mathematical techniques like algorithms, randomness, and statistics. To validate data science models you can use thresholds to handle output variance.
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Apollo Data Graph Platform: a GraphQL Middleware Layer for the Enterprise
In a recent InfoQ podcast, Matt Debergalis, founder and CTO at Apollo, discussed the motivations for GraphQL and the Apollo Data Graph platform. Key topics explored included data modelling in an enterprise context, and how incrementally adopting GraphQL can help with decoupling the evolution of frontend and backend systems.
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Protecting Artificial Intelligence from Itself
Applications using artificial intelligence can be fooled by adversarial examples, creating confusion in the model decisions. Input sanitization can help by filtering out improbable inputs before they are given to the model, argued Katharine Jarmul at Goto Berlin 2018. We need to start thinking of the models and the training data we put into them as potential security breaches, she said.
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Agile Data Modeling for NoSQL Databases
Pascal Desmarets recently spoke at Data Architecture Summit 2018 Conference about agile modeling and best practices for NoSQL databases.
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Concept and Object Modeling Notation for Data Modeling NoSQL Databases
Ted Hills hosted a workshop at the recent Data Architecture Summit 2018 Conference about data modeling for relational and NoSQL databases. He said that the NoSQL movement helped the database community realize two things. First, not every application needs ACID properties. Second, the tabular data organization is still a good choice for much data, although not for all datasets.
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Implementing Agile in Data Warehouse Projects
This post talks about using an agile implementation for data warehouse projects.
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Oracle NoSQL Database 3.0 Supports Table Data Model and Secondary Indexing
The latest version of Oracle NoSQL Database supports tabular data model, secondary indexing, security enhancements via Oracle Wallet integration, and data center enhancements. Oracle recently announced the release of version 3.0 of the distributed key-value database.
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Forecasting at Twitter
Arun Kejariwal, from Twitter, talked at Velocity Conf London last month about forecasting algorithms used at Twitter to proactively predict system resource needs as well as business metrics such as number of users or tweets. Given the dynamic nature of their data stream, they found that a refined ARIMA model works well once data is cleansed, including removal of outliers.