![]() ![]() It is interesting to note that Google has already added this type of text instruction functionality to their spreadsheet tool (sheets) by which you can ask questions about your data or create a chart.Įven in this case, where the difference in the number of clicks is small versus creating the chart manually, the advantage is large.Ĭreating the chart yourself is slower and requires more mental and physical effort because of the precision involved in selecting the exact cells you want to operate on and ensuring that all operations are done in the exactly the right order. This gives you some idea as to why chatbots are the future but also about how to create bots that look to the future. If they don’t have a lot of domain knowledge then building it yourself is easier. If your colleague understands the references you are making they can complete the task without more information. “Build a sales spreadsheet using the sales template I prepared using the sales data from the XYZ system” If your colleague already has a lot of relevant domain knowledge regarding what you want to be built then it is much quicker to instruct them with voice rather than build the spreadsheet yourself. The spreadsheet example is instructive because it shows you where the value of voice and text instructions starts and ends. If you don’t believe me just try to instruct your colleague on how to build a spreadsheet you want to be built rather than building it yourself. ![]() Not only is it harder (or impossible) to get this right from a technology point of view, but graphical interfaces are a lot better for doing many tasks than using text or voice instructions. Voice doesn’t work so well for doing iterative tasks in a conversational way and probably won’t be used this way much in the near future at least. They also make sense for other devices such as cars and IoT devices where the human is likely to understand more or less exactly what they want to do and will not be in a position to use a touch interface. Whether the chatbot will ever pass the Turing Test is an open debate, but even with the current state of NLP chatbots can be useful.įor augmented reality and virtual reality voice interfaces make sense. It’s not flawless of course and the user still needs to be “trained” to some extent in terms of understanding what the valid phrases to use are, but it works pretty well. A chatbot assistant that is always with you and that assists you in a similar way to the way a human assistant seems a feasible vision of the future to some extent.Īt the very least, even now, natural language processing (NLP) works well especially in capturing one of instructions and intentions. The proliferation of voice interfaces such as Alexa, Siri and Google Home already give us some clues as to the future. It appears at this moment that augmented reality and virtual reality are set to play an increasingly important role in software and chatbots definitely have a role to play here. And the killer use cases to some extent will depend on how chatbot technology develops and how the wider tech ecosystem developers. ![]() Regardless of what we hypothesize here, what will determine the success of chatbots are the killer use cases. In this post, we will go further to discuss the role chatbots will play in the future of software. As we’ve discussed in other posts, defining what a chatbot is is not a straightforward process if you recognize that chatbots functionality can go well beyond the conventional definition of software that converses with humans (and bots) inside a chat platform.
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