According to Gartner, companies working with AI should stop combining training and learning activities, one of the reasons being the slowing down of conversational agents’ learning processes. The most recommended course of action for data managers is exploring emerging middleware tools that allow them to use the same training data set for multiple AI service providers.
When searching for innovative solutions, it is crucial for leaders and decision makers to have the information that allows them to make informed decisions. Bitext is currently at the forefront of technology since it has been mentioned lately in no less than 20 Gartner reports and was selected as Cool Vendor in AI Core Technologies in 2018. But we keep working hard and Gartner, once again, mentioned Bitext in 4 new Hype Cycle reports.
Those familiar with stock markets surely know how traders communicate with each other via chatrooms in order to keep up with the trends and get actionable insights from peers. Most of these platforms, especially those offering free access, are a cluster of activity that needs to be moderated to ensure the interactions are civil and legal. Why do it manually through professional moderators when you can use an automated AI system?
People who use financial databases are aware of the hardships of ensuring information is structured and legible. Don’t worry! Knowledge graphs are here to help.
Thousands of financial news headlines appear every day on your phone screen. Do you have time to read them all? We dare to say you probably don´t. What if you could ask your news provider to inform you on your interests? Now that’s possible thanks to our AI technology for chat-based financial news based on NLP.
Training data is the data that is used to train an NLU engine. An NLU engine allows chatbots to understand the intent of user queries.
The training data is enriched by data labeling or data annotation, with information about entities, slots…
This training process provides the bot with the ability to hold a meaningful conversation with real people.
After the training process, the bot is evaluated to measure the accuracy of the NLU engine. Evaluation identifies errors in the bot behavior and these errors are then fixed by improving training data. This cycle is repeated
When working on AI projects, owning data to nurture your solution is key for good performance.
