Welcome to our This Week in AI roundup. Our goal with this roundup is to provide an overview of the week's most important news, papers, and industry developments.
This week we have stories about natural language processing, sentiment analysis for SEC filings, and the most in-demand AI jobs.
China Releases Largest Language Model to Date
A Beijing-funded AI institute released the largest natural language processing (NLP) model to data, surpassing both OpenAI and Google in terms of training parameters.
Trained using 1.75 trillion parameters, WuDao 2.0 is a pre-trained AI model that simulates conversational dialogue, writes poems, understands photos, and generates recipes using 1.75 trillion parameters.
The researchers claim to have broken Google's record set in January of 1.6 billion parameters with Switch Transformer and OpenAI's GPT-3 model, which was trained on 175 billion parameters.
10 Most In-Demands AI Jobs
Indeed compiled a list of the ten most in-demand AI jobs in the United States today, along with their median compensation, using data from its platform. According to the platform, the top 10 most in-demand jobs and median yearly salaries in the US are:
- Data scientist - $110,000
- Senior software engineer - $120,000
- Machine learning engineer - $125,000
- Data engineer - $122,060
- Software engineer - $100,000
- Software developer - $95,000
- Software architect - $135,107
- Senior data scientist - $127,500
- Full stack developer - $108,730
- Principal software engineer - $155,000
How to Use Sentiment Analysis for SEC Filings
In this guide, we discuss how traders and investors can use sentiment analysis and natural language processing (NLP) for SEC filings to speed up their research process.
In regards to similarities and differences in language metrics, the paper we review writes:
The highest (lowest) similarity decile is not always the most profitable (unprofitable). This paper is focused on the usability of a positive similarity score in a trading strategy.
The general idea of using these metrics in a trading strategy is that a low similarity of positive language metrics can positively impact future performance of the company.
Emerging Technologies & Wealth Management
During the past year, wealth management companies have been pushed to innovate at a faster rate than ever before. As the managers surveyed in this article highlight, machine learning and natural language processing have piqued the curiosity of wealth management firms.
Advisors can use AI-enabled predictive analytics to better understand each client's unique needs based on past data, or even anticipate which clients are most likely to walk out the door. This is a true hybrid approach, in which technology aids advisers in making better decisions in all areas.
That's it for this edition of This Week in AI, if you were forwarded this newsletter and would like to receive it you can sign up here.