Latitude59 2020 takes place this Thursday and Friday, 27 and 28 August, in Kultuurikatel, Tallinn, Estonia. From the official site of the event: “Latitude59 is THE place to be for various networking opportunities, in-depth discussions with top international players, several pitching rounds for both startups as well as investors, and an overall chance to get together and reflect on the crazy strenuous months behind us and forge new plans for the future.“ We are featured in the Tehnopol Online Demo Area. To have a talk with us, book a meeting via Talque app.
This is the part II of our previous post, where we opened up on the methods and the theory behind the the comparison of learning algorithms. In this post, we are going to dive in to our experiment results and conclusions. As most of the models are made and optimised for English, we started our testing with English data. In our English experiment, we tested the models of BERT, DistilBERT, RoBERTa, XLM, XLNet, ULMFit, MLP. When we increased the input data (i.e. 90:10 vs 10:90 training:test ratio), the prediction accuracy, on average, improved by 20%. The most accurate models RoBERTa
You can now replay our webinar on reasons why chatbot projects fail. Discussion topics: Expectations management The relationship between allocated time and the scope of the project The communication between the vendor and the buyer The importance of carefully designed conversation flows
You can now replay our webinar on how to build a superior customer service chatbot. Discussion topics: How long does it take to get a virtual assistant up and running? How much data is required when starting out? Which kind of existing data can be used initially? Topic Rating Matrix – how can it help you to evaluate the benefits of automation? Why is it important to launch the first version of a virtual assistant silently?
The last decades have been generous for the development of AI, machine learning and all things computer related. We know that the hardware capabilities approximately double every two years. Recent findings show that the pace is even quicker and is continuously speeding up. But how about the software side of it? We have been in the conversational AI business and research for 5 years now. With the rise of AI utilization, the field of machine learning algorithms have grown more diverse. To see how does the algorithms side of the AI have evolved, we decided to test out all of