Data Science Talent | Newsletter Edition 3

Welcome to issue 3 of the Data Science Talent Newsletter. A monthly publication bringing you valuable Insights from the world of enterprise Data and AI.

Editor's comments and article picks

The Future of Jobs

February saw some major breakthroughs in AI and in recent days the big news was the Klarna press release about how they are automating customer service work equivalent to the workload of 700 humans. What does this mean for the jobs market in the future?? I think it’s clear that there will be some job displacement, however it is unlikely to be as bad as most fear.

Editor's Picks for February:

1. Andrew Ng WSJ Interview - AI’s Potential Effect On the Workforce - Feb 14th 2024

https://www.youtube.com/watch?v=-mIjwN1o7nE&ab_channel=WSJNews

Andrew Ng’s interview with the Wall Street Journal is a must watch on this topic.? One of his key points is that AI is good at automating tasks, but will not necessarily automate entire jobs.? He talks about the impact of AI on the workforce, emphasizing that while AI will lead to job losses, it will also boost productivity and create new roles, suggesting the situation might not be as dire as perceived.

He advocates for a task-based analysis of jobs to identify opportunities for AI automation and augmentation, using radiologists as an example to illustrate how AI can automate certain tasks without replacing the entire job.

2. Gitclear Study - Coding on Copilot:2023 Data Suggests Downward Pressure On Code Quality

https://www.gitclear.com/coding_on_copilot_data_shows_ais_downward_pressure_on_code_quality In 2023, GitHub Copilot emerged as a pivotal AI programming assistant, significantly accelerating the pace at which developers write code, reportedly by 55%, across numerous businesses.

However, a study by GitClear on roughly 153 million changed lines of code from January 2020 to December 2023 revealed troubling trends for code maintainability, including a projected doubling of code churn in 2024 compared to 2021 and an increase in "added" and "copy/pasted" code.

The study likens the AI-generated code quality to that of a disjointed short-term contractor rather than a seasoned developer.

The findings suggest a need for managerial strategies to uphold code quality amidst the growing reliance on AI tools like Copilot.? Thank you to Nayur Khan for bringing this to my attention.

Enjoy the rest of our newsletter, Damien Deighan - Editor.

Our latest issue 6 of The Data Scientist Magazine was launched on the 21st February.

The issue includes articles and case studies from various contributors and companies, featuring topics such as:

• Transforming Freight Logistics with Advanced Machine Learning by Dr. Luís Moreira-Matias from sennder
• Navigating the Future of Ads: Large Language Models and Their Impact on Programmatic Strategies by Javier Campos from FENESTRA
• Measuring the Effect of Change on Physical Assets by Colin Parry from Head for Data
• How Language Models Are the Ultimate Database by Francesco Gadaleta, Ph.D. from Amethix Technologies
• A Journey from Data Analyst to Data Science Leader by Rebecca Vickery from EDF
• The 5 Use Cases for Enterprise LLMs by Colin Harman from Nesh- Using LLMs in Language for Grammatical Error Correction (GEC) by Bartmoss St. Clair from LanguageTool
• Leveraging Context for Sustainable Competitive Advantage with GenAI by Michel de Ru from DataStax

Don't miss out on the latest updates in data science and technology. Issue 6 of The Data Scientist Magazine is available in digital and print, and you can SUBSCRIBE FOR FREE.

Our Latest Podcast - The Using Open Source LLMs in Language for Grammatical Error Correction (GEC)

We interviewed Bartmoss St. Clair at LanguageTool .

Bartmoss St Clair (Head of AI) is pioneering the use of Large Language Models (LLMs) for grammatical error correction (GEC), moving away from the tool’s initial non-AI approach to create a system capable of catching and correcting errors across multiple languages.

LanguageTool supports over 30 languages, has several million users, and over 4 million installations of its browser add-on, benefiting from a diverse team of employees from around the world.

We discussed the following topics:

• LanguageTool decided against using existing LLMs like GPT-3 or GPT-4 due to cost, speed, and accuracy benefits of developing their own models, focusing on creating a balance between performance, speed, and cost.?
• The tool is designed to work with low latency for real-time applications, catering to a wide range of users including academics and businesses, with the aim to balance accurate grammar correction without being intrusive.
• Bartmoss discussed the nuanced approach to grammar correction, acknowledging that language evolves and user preferences may vary, necessitating a balance between strict grammatical rules and user acceptability.
• The company employs a mix of decoder and encoder-decoder models depending on the task, with a focus on contextual understanding and the challenges of maintaining the original meaning of text while correcting grammar.?
• A hybrid system that combines rule-based algorithms with machine learning is used to provide nuanced grammar corrections and explanations for the corrections, enhancing user understanding and trust.
• LanguageTool is developing a generalized GEC system, incorporating legacy rules and machine learning for comprehensive error correction across various types of text.
• Training models involve a mix of user data, expert-annotated data, and synthetic data, aiming to reflect real user error patterns for effective correction.
• The company has built tools to benchmark GEC tasks, focusing on precision, recall, and user feedback to guide quality improvements.?
• Introduction of LLMs has expanded LanguageTool’s capabilities, including rewriting and rephrasing, and improved error detection beyond simple grammatical rules.?
• Despite the higher costs associated with LLMs and hosting infrastructure, the investment is seen as worthwhile for improving user experience and conversion rates for premium products.?
• Bartmoss speculates on the future impact of LLMs on language evolution, noting their current influence and the importance of adapting to changes in language use over time.
• LanguageTool prioritizes privacy and data security, avoiding external APIs for grammatical error correction and developing their systems in-house with open-source models.

To listen to Bartmoss's interview, please click on the link below:

Our Latest Articles:

1 - HOW AI IS DRIVING THE ERADICATION OF MALARIA BY ARNON HOURI-YAFIN.

Arnon Houri-Yafin is the Founder and CEO of Zzapp Malaria, a developer of software tools for malaria elimination. Arnon’s prior roles include Director of Research at Sight Diagnostics and Lecturer in Statistics at The Hebrew University of Jerusalem.

His specialisms include data analysis, statistics and machine learning. In this interview, Arnon discusses the breakthroughs Zzapp Malaria has made in the fight against this deadly disease. He explains how AI and data modelling have played crucial roles in developing Zzapp’s strategies:

Read the full article:

2 - BUILDING A FEATURE PLATFORM GUIDED BY YOUR ETHOS BY ANDREU MORA.

Andreu Mora is SVP of Engineering at Adyen, where he’s responsible for data (platform, ML, AI, experiments and analytics). Andreu’s prior roles include VP of Engineering for Data Science and ML.

Andreu has also held roles as Tech Lead, Data Scientist, and Engineer where he worked on products relating to network-based pattern recognition and scalable time series forecasting. Andreu holds an MSc in Telecommunication Engineering from Universitat Politecnica de Catalunya.

In this post, Andreu explores the different factors involved in choosing the right tech stack for your business. Citing a real-world example, Andreu shares his insights learned from implementing the new ‘feature store’ at Adyen:

Read the full article:

3 - DIGITAL TWINS: THE FIRST FRUIT OF THE METAVERSE, POWERED BY GENERATIVE AI BY AAKASH SHIRODKAR.

Aakash Shirodkar is the Senior Director of AI & Analytics at Cognizant. Aakash has over 20 years of experience helping businesses achieve global success through the application of AI. Aakash’s previous roles include Data Science Portfolio Leader at IBM and Senior Manager at Idea Cellular.

In this post, Aakash discusses the ways in which the metaverse is set to transform business. Digital twins are integral to this transformation, allowing businesses to flourish in the virtual space. Aakash demystifies the concept of the digital twin and explains how you can harness its power to revolutionise your business in the metaverse:

Read the full article:

Thank you for reading this month's Newsletter. We look forward to seeing you next month!

Data Science Talent Editorial Team.