AI continues to grow exponentially as a field. If you’re a business leader who wants to jump onto this quickly moving train - use this guide as a resource to help get you started.
Topics: AI at Work
Artificial intelligence, much like human intelligence, is only as good as the education it receives. That's why training data -- the information used to "teach" deep learning algorithms how to perform complex tasks -- is key to AI success.
At Talla, we had a customer struggling with their support reps productivity. I’ll paint a picture of the problem they were struggling with…
We are constantly working on two things at Talla: 1) Automating away ever more Service and Support tasks, and 2) Working everywhere that you work. In order to fulfill both of these pieces of the Talla vision, we have to continue to integrate with more tools. Today we are excited to announce integrations with HubSpot, Zendesk, and Jira.
“The workplace is shifting, and it’s changing at an extremely rapid rate. The natural consequence is that workers must adapt to meet those changes...” - (Forbes)
This topic was discussed this week on AI at Work, a weekly podcast takes a look into AI trends and the future of AI in the enterprise and provides insight on how to think about and effectively deploy AI in your organization. Host Rob May, Co-Founder and CEO at Talla, sat down with Jeanne Meister, Founding Partner at Future Workplace. The Future Workplace is an HR advisory and research firm providing insights on the future of learning and working. Jeanne shared valuable insights on HR’s resistance to AI, her experience coming from a non-technical background, the danger of getting left behind, and how quickly HR teams are moving. Check out the three things that all HR leaders need to know about implementing AI at their organization.
Customer support representatives have a challenging job that sometimes requires superhuman patience, speed, and product knowledge to handle -- to the point that many support teams struggle to close calls at an efficient rate.
For the past several years, software vendors and customers have been trained to look for a "wow" moment when trying out new software -- which is an expectation sure to lead to disappointment among adopters of modern artificial intelligence solutions. AI almost never gives you a traditional "wow" moment, which can often lead you to underestimate, overlook, or mistakenly dismiss the value of an AI solution.
The phrase "artificial intelligence is like lily pads in a pond" sounds like a really bad Zen koan, but it is an important concept for properly setting expectations with modern AI solutions. If you expect your business AI solution to be awesome on Day 1, you're setting yourself up for disappointment and, possibly, failure. The lily pads explain why.
This week I'm excited to announce that we are launching 3 new Talla products. Well, technically 2 new products and one major upgrade, but the latter also feels new with all the improvement. Earlier this year we were focused on "customer facing teams," and while we still have customers in Sales and Success, it is clear that the biggest interest in AI automation comes from Customer Service and Support teams. With that in mind we've revamped the Talla Platform into three parts that can be used separately or together, and map more directly to key Service and Support workflows. The three new products are: a knowledge base for support teams, a rep assist tool, and a customer facing self service automation tool. More details will come in future posts, but for now here are the highlights.
Topics: Product Updates
Machine comprehension is one of the fundamental problem spaces in artificial intelligence research. Can we teach a machine to read, comprehend, and answer our questions? Recent advances in deep learning and natural language processing (NLP) have led to promising breakthroughs and novel AI models.
At Talla, we have been actively investigating the capabilities of machine comprehension. We have deployed several beta features that leverage these models in our product around content training, user question-answering, and unsupervised knowledge ingestion. In this series of blog posts, we aim to share our experiences and understanding of this space. In this post, we will provide a more in-depth overview of machine comprehension research.