AlphaBlues is featured in StartUs Insights’s latest research article on emerging technologies and up-and-coming AI startups. From the post: We analyzed 109 virtual assistant solutions. Visor.ai, ServisBOT, Samim.ai, Claimbot & AlphaBlues develop 5 top solutions to watch out for. Learn more in our Global Startup Heat Map! Read the full story here.
Quantitative evaluation is a vital part of an AI project, but there is little to no standardization within the topic today. In this post, we will look into the ways how Conversational AI can be measured, how the procedure varies between different roles & users and what to do with all of this information. Why measure the performance of a virtual assistant (VA)? Developing a solution based on Conversational AI can introduce a lot of uncertainty to the process when starting out. The early days of a VA can even be characterized as controlled chaos. One of the easiest ways
You can now replay our webinar on the topic of internal IT virtual assistants. Discussion topics: What is the difference between chatbots and internal IT virtual assistants? Which kind of company can benefit from the use of internal IT bots? Which platforms can be used to integrate internal IT bots to your workflow?
You can now replay our webinar on how to measure your virtual assistant. Discussion topics: Why Measure Your Virtual Assistant? What Should You Measure In Your Virtual Assistant? Things To Keep In Mind. What To Do With Your Statistics?
Not every chatbot project is a success story. While every company is different and unique in their own way, there are some common pitfalls that should be avoided. ❌ Too High Expectations Our experience has shown that when customers start out with projects, their expectations tend to differ from ours. As a vendor we try to set realistic expectations right away and try to sync with our customers. Going into the project with wrong mindset and expectations will create misunderstandings later on. One common assumption is that chatbots are easy to make. There is some truth to that. A simple
You can now replay our webinar about virtual assistants on WhatsApp. Discussion topics: WhatsApp basics – the difference between browser-based WhatsApp virtual assistants The benefits of using WhatsApp as a channel Detailed functionalities in WhatsApp virtual assistant (natural language input, location sharing, multiple choice, voice) Which kind of companies and business areas can benefit from WhatsApp bots
We are excited to announce that Statistics Estonia has begun using our multi-bot system, which enables users to have a frictionless conversation with a virtual assistant that suits the person the most. Although official language of Estonia is Estonian, there are several prominent other languages present. To offer services for any resident regardless of their native language, we have developed a multi-bot system (Multi-Bot Handover) for Statistics Estonia’s digital public services. With this kind of a solution virtual assistants understand the language of the user message and automatically hand over the conversation to an appropriate bot. And should the virtual
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