Today we are celebrating Silo.AI’s one-year birthday. During our first year we have had the pleasure to work with as many as 37 clients helping them build artificial intelligence solutions.
With hype around Artificial Intelligence dominating conversations across all industries at the moment, Financial Services is one of the sectors that is most talked about as a target for the
When an industry becomes more competitive, job applicants need to possess additional skills that help them to start smoothly at work and to be distinguished from candidates with similar qualifications
Some weeks ago the International Conference on Machine Learning (ICML2018) concluded. It is one of top 3 conferences in Machine Learning and Artificial Intelligence in the world. This year it
Finland’s TurkuNLP group has reached the highest aggregate ranking in the global natural language parsing Shared Task 2018, beating 25 other top universities and company research groups in performance. Filip Ginter, the head of TurkuNLP, is an AI scientist in Silo.AI’s NLP team.
Bayesian networks can be used to compute probabilities of causes given effects. Bayesian networks belong to a class of models called probabilistic graphical models. Probabilistic models are particularly good in handling uncertainty.
Managing AI projects poses a new set of challenges compared to, for instance, managing traditional software development projects. We examine some of these challenges.
From text-based news, we extract company and network-level signals that show when companies are at higher probability of stock price decline. Using the described methodology, we were able to show that companies which reached the maximum risk value in our risk model, during any given quarter is up to 13 percentage points more likely to have stock price decline one month later than the average company.
The era of information – We live in a time period characterized by the shift from traditional industries to an economy based on information technology. Thanks to this, companies are more aware every day of the value of the information they accumulate in a seemingly natural way.
The purpose of this article is to introduce the concept and explain it by giving examples of its implications for the artificial intelligence sector in line with Silo.AI’s work on the topic and GDPR compliance. And, most especially, to explain the ways to help prevent organisations leaving privacy to chance and encourage them to have it by design instead.
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