Archie  Powell

Archie Powell

1625958420

Selecting a Conversational AI Platform

Businesses are quickly acknowledging the importance of Conversational AI (CAI) to increase their customer engagement and revenues. The question is no longer whether to deploy CAI, but rather which platform to use and how to leverage its capabilities.

In this series, Daniel Eriksson, Chief Innovation and Customer Success Officer at Artificial Solutions, gives insight on important aspects of a conversational AI platform that buyers often overlook. For example: what does language support really mean? What is localization? How do different deployment models impact the TCO? And maybe most importantly, how can the CAI platform not only help me during the first development sprints, but across the entire bot lifecycle?

Making Bot Developers More Productive

During the last six months, I’ve had a lot of conversations with companies (clients) and system integrators (partners) who have been building conversational bots. I’ve spoken with conversational bot developers, data linguistics reps, integration engineers, conversational designers, project managers, senior stakeholders, product owners, and many more.

At the same time, I’ve talked to existing, new, prospective, and former clients. These talks included people who had ambitious plans and succeeded and others who have had plans where they have struggled to generate impact.

Four Perspectives to Consider When Selecting your Conversational AI Platform

Select a Tool Your Development Team Can Grow With

See past the buzz-words like “awareness”, “understanding”, and “self-learning”.

Conversational AI is a fascinating space and still holds a lot of potential that is yet to be explored. Yet most companies who have experience of CAI tooling will tell you it’s all about engineering, and actually has a lot of resemblance to regular software or process flow development instead of being something ground-breaking new.

Sure, there are some terminologies both useful and specific for the space, like “intent recognition”, “entities”, and “context”. These words are related to the Natural Language Understanding (NLU) part of a conversational bot.

Find a Balance Between Pure Coding and Drag-and-Drop

Have you ever heard about low-code or no-code? In short, those concepts describe a user interface where a developer can configure or graphically design a process instead of having to write programming code. It is a great way to visualize how a program is executed and can be a quick way to build some things rapidly. Here comes the tricky part — for an effective Conversational AI solution with some ambition, you will still need to code. Your team will need to write code in some scripting language. If not, you will not be able to do the things you expect a bot to do. Do not shy away from this fact, as scripting and coding are super important to make a bot great. So, when you look at a toolset, evaluate it from the standpoint “how will the coding part work?”

Consider Possible Future Limitations

There is a lot of CAI tooling in the market today available to developers. Your job is to make sure that you don’t select tooling that is quick to build only the first MVP but also is useful for every new generation of your bot. When your ambitions grow, and your insights on how you can deliver a better bot user experience start to develop, you might realize that the tool you chose is holding you back.

#ai #artificial intelligence #natural language processing #conversational ai #ai platform #platform

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Selecting a Conversational AI Platform
Archie  Powell

Archie Powell

1625958420

Selecting a Conversational AI Platform

Businesses are quickly acknowledging the importance of Conversational AI (CAI) to increase their customer engagement and revenues. The question is no longer whether to deploy CAI, but rather which platform to use and how to leverage its capabilities.

In this series, Daniel Eriksson, Chief Innovation and Customer Success Officer at Artificial Solutions, gives insight on important aspects of a conversational AI platform that buyers often overlook. For example: what does language support really mean? What is localization? How do different deployment models impact the TCO? And maybe most importantly, how can the CAI platform not only help me during the first development sprints, but across the entire bot lifecycle?

Making Bot Developers More Productive

During the last six months, I’ve had a lot of conversations with companies (clients) and system integrators (partners) who have been building conversational bots. I’ve spoken with conversational bot developers, data linguistics reps, integration engineers, conversational designers, project managers, senior stakeholders, product owners, and many more.

At the same time, I’ve talked to existing, new, prospective, and former clients. These talks included people who had ambitious plans and succeeded and others who have had plans where they have struggled to generate impact.

Four Perspectives to Consider When Selecting your Conversational AI Platform

Select a Tool Your Development Team Can Grow With

See past the buzz-words like “awareness”, “understanding”, and “self-learning”.

Conversational AI is a fascinating space and still holds a lot of potential that is yet to be explored. Yet most companies who have experience of CAI tooling will tell you it’s all about engineering, and actually has a lot of resemblance to regular software or process flow development instead of being something ground-breaking new.

Sure, there are some terminologies both useful and specific for the space, like “intent recognition”, “entities”, and “context”. These words are related to the Natural Language Understanding (NLU) part of a conversational bot.

Find a Balance Between Pure Coding and Drag-and-Drop

Have you ever heard about low-code or no-code? In short, those concepts describe a user interface where a developer can configure or graphically design a process instead of having to write programming code. It is a great way to visualize how a program is executed and can be a quick way to build some things rapidly. Here comes the tricky part — for an effective Conversational AI solution with some ambition, you will still need to code. Your team will need to write code in some scripting language. If not, you will not be able to do the things you expect a bot to do. Do not shy away from this fact, as scripting and coding are super important to make a bot great. So, when you look at a toolset, evaluate it from the standpoint “how will the coding part work?”

Consider Possible Future Limitations

There is a lot of CAI tooling in the market today available to developers. Your job is to make sure that you don’t select tooling that is quick to build only the first MVP but also is useful for every new generation of your bot. When your ambitions grow, and your insights on how you can deliver a better bot user experience start to develop, you might realize that the tool you chose is holding you back.

#ai #artificial intelligence #natural language processing #conversational ai #ai platform #platform

Otho  Hagenes

Otho Hagenes

1619511840

Making Sales More Efficient: Lead Qualification Using AI

If you were to ask any organization today, you would learn that they are all becoming reliant on Artificial Intelligence Solutions and using AI to digitally transform in order to bring their organizations into the new age. AI is no longer a new concept, instead, with the technological advancements that are being made in the realm of AI, it has become a much-needed business facet.

AI has become easier to use and implement than ever before, and every business is applying AI solutions to their processes. Organizations have begun to base their digital transformation strategies around AI and the way in which they conduct their business. One of these business processes that AI has helped transform is lead qualifications.

#ai-solutions-development #artificial-intelligence #future-of-artificial-intellige #ai #ai-applications #ai-trends #future-of-ai #ai-revolution

Using A Fantasy Game World To Boost AI Performance

Recently, Facebook AI Research (FAIR) built and deployed a role-playing fantasy game world to boost the performance of conversational AI models such as virtual assistants. The researchers presented a fully-realised system for improving an open-domain dialogue task by utilising a deployed game for lifelong learning.

Human beings learn languages over their course of life from the interactions they have with other people. Yet, research in sophisticated natural language processing (NLP) models are done using the fixed dataset, without any ability for the model to interact with humans using language at training time at all.

#developers corner #ai using games #conversational ai #conversational ai platforms #facebook nlp research #fantasy game world #natural language processing #nlp models

Murray  Beatty

Murray Beatty

1598606037

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai

This Week in AI - Issue #22 | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.Have fun!

Research Papers

Articles

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai