Inbenta Uses AI To Create Meaningful Chatbot Conversations скачать в хорошем качестве

Inbenta Uses AI To Create Meaningful Chatbot Conversations

Online marketing, eCommerce, and call centre managers face some pretty tough challenges these days. Tasked with transforming emails and calls into web traffic, this task can take a long time. And if a company’s website is lacking, visitors will leave in the middle of a transaction! This sounds like a no-win situation, but Jordi Torras, CEO and Founder at Inbenta, has a solid solution for senior execs in this predicament: Inbenta. Jordi and host Hans van Dam explore how Inbenta helps companies automate conversations by chatbots. Initially, Jordi tackled the big problem of frequently asked questions (FAQs) on websites. Most companies have them, but users aren't fans, mostly because even with a search engine the results are all the answers containing every word in the question, and that can be a pretty long list! Back in the 2010s, Jordi and his team developed successful technology to match user questions with answers. When the age of conversational AI arose, the FAQ-search engine problem remained the same. Jordi’s early work placed him and his team in the perfect position to create solutions. Inbenta can match user questions with intent, and there’s no need for any training of company staff. In fact, with customer data Inbenta can go live within 24 hours. Ibenta understands the meaning of what someone says, and that can be mapped to intent. Inbenta is built on a linguistic model, rather than a statistical one. Inbenta has the linguistic model that requires no training and it extracts potential intent. It then uses machine learning for disambiguation, based on user behavioral patterns. Highlights 06:53 We can use our engine to match user questions with intent—this is the mantra of the conversational AI space. . 07:25 We don’t require customers to give the AI examples over and over. It’s prebuilt into the system. 08:49: Machine learning has been amazingly successful with image and pattern recognition. But when it comes to natural language processing, it has yet to prove itself. 09:54: Natural language processing is one of the most difficult challenges in AI. 12:05: With language, what machine learning is supposed to do is learn an entire logic by throwing examples at it. It memorizes these examples, but it can take a long time for customers to collect all this data. 17:19: When Inbenta is live, our clients can start measuring and understanding what users are asking, and what content they need to create.