May 12, 2020
Additive attention in PyTorch - Implementation

Attention mechanisms revolutionized machine learning in applications ranging from NLP through computer vision to reinforcement learning. Attention is the key innovation behind the recent success of Transformer-based language models such as BERT.1 In this blog post, I will look at a first instance of attention that sparked the revolution - additive attention (also known as […]

May 7, 2020
Recommender systems - part 1

The recommender systems have a quite long history. The problem of deciding what content should be presented to a user is important to many companies. It can bring more money and what's important in the long run, it makes the experience of users more delightful. It is also one of the biggest motivators for the […]

April 28, 2020
Interpreting uncertainty in Bayesian linear regression

While vanilla linear regression predicts a maximum likelihood estimate of the target variable, Bayesian linear regression predicts a whole distribution over the target variable, offering a natural measure of prediction uncertainty. In this blog post, I demonstrate how to break down this uncertainty measure into two contributing factors: aleatoric uncertainty and epistemic uncertainty. We will […]

April 21, 2020
AI Business Tools Making an Impact in 2020

With 2020 in full swing, the business world is undergoing major changes in order to accommodate the growth of technology. Artificial intelligence is making waves in modern business operations, forcing organizations to adopt new processes in order to remain relevant. Below is a list of AI business tools that are making an impact this year […]

March 24, 2020
Where Syntax Ends and Semantics Begin. Why should we care?

The relation between syntax (how words are structured in a sentence) and semantics (how words contribute to the meaning of a sentence) is a long-standing open question in linguistics. It happens, however, to have practical consequences for NLP. In this blog post, I review recent work on disentangling the syntactic and the semantic information when […]

March 18, 2020
AI for Event-Driven Stock Prediction: Activist Investors

Event-driven or opportunistic investing is a strategy associated with exploiting stock mispricings occurring before, during or after corporate events (also called catalyst or special situations) like restructurings, M&As, spinoffs or bankruptcies. Mispricings tend to arise when public companies are involved in special situations because the stock can become artificially inflated or depressed due to speculations […]

March 5, 2020
AI in investment management spots event-driven opportunities that others missed

When I first thought about investing I asked my clients in the financial industry what I should be doing to even become an investor? The number one answer was “to find my strategy/niche” or a sector that I can research and develop an expertise in. I decided not to try too hard by starting to […]

February 14, 2018
6 jobs that could see an uptick in demand with the rise of AI - VentureBeat article

Sigmoidal's CEO, Mariusz Kierski wrote an article on VentureBeat about the jobs that can expect higher demand due to the development of AI. "While fears abound that AI automation will lead to big staff cuts in industries around the world, technology will bring forth a plethora of new jobs and services. Similar to the onslaught […]

February 8, 2018
Natural Language Processing Algorithms (NLP AI)

Before Deep Learning Era Back in the days before the era — when a Neural Network was more of a scary, enigmatic mathematical curiosity than a powerful or tool — there were surprisingly many relatively successful applications of classical mining algorithms in the Natural Language Processing (NLP) domain. It seemed that problems like spam filtering […]

December 21, 2017
Portfolio Analysis by Machine Learning

The stock market has always been a core interest in predictive analytics. Thus it came as no surprise when one of our clients seek our assistance in his investment strategy. We designed an intelligent asset allocation system consisting of a Long Short-term Memory Recurrent Neural Network (LSTM), which predicted the expected return for every asset […]

December 21, 2017
Financial Times: Wall Street and AI - an article

Sigmoidal's Head of AI, Marek Bardonski has been featured in the Financial Times in an article about using trading for Machine Learning by John Dizard. The article speaks about AI-powered, automated trading decisions - and what challenges (and opportunities) it poses to Wall Street. “Most of the theoretical work for autonomous driving has been done. […]

December 21, 2017
Use Case: Investment Opportunities Discovery

Challenge Information sourcing and research is a common problem for many companies out there. Obtaining useful information about market opportunities from huge amounts of data require vast resources, both in terms of time and funds. The good news is there is the AI-based technology that is able to perform some parts automatically, and therefore improve […]

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