Why We Should Not Use ChatGPT To Shame Alexa
As Microsoft started rolling out new Bing and Google launched Bard, I have seen many comparing these new generative AI services to digital assistants like Alexa, Google Assistant and Siri, only to point out the gap in capabilities between the two. Even Microsoft’s CEO, Satya Nadella, made the point during an interview with The Financial Times earlier this month: “They were all dumb as a rock,” he told the newspaper last month. “Whether it’s Cortana or Alexa or Google Assistant or Siri, all these just don’t work. So we had a product that was supposed to be the new front-end to a lot of [information] that didn’t work.”
I understand that it is natural wanting to make the comparison because we are talking about AI and also because these new experiences from ChatGPT to Microsoft Copilot have been referred to as chatbots which, as it happens, was also how we used to refer to digital assistants.
There are two main reasons why making this comparison is unfair.
The Job To Be Done
The first is that digital assistants have a different job to do. Even new Bing agreed when I asked, “Are you like Alexa?” It answered: “Alexa is Amazon’s voice AI that can help you with various tasks and requests. I am Microsoft Bing’s chat mode, that can help you find information and generate content. We are both powered by AI, but we have different features and capabilities 😊”
New Bing is correct. By and large, digital assistants were created to perform tasks for us, whether it was to provide simple information like the weather, add something to our shopping list or help us navigate our smart homes.
What started as simple, transactional voice-based interactions developed into pre-emptive, contextual suggestions. Navigating our private life, especially our home, is no easy tasks as privacy and different comfort level come into the equation more often than you think. There is also a core difference in our exchange with new Bing and ChatGPT versus Alexa, Siri or Google Assistant. In the former, the knowledge needed for a successful task delivery is with them. In the latter, it is with us.
This leads me to the next component of success.
Maybe an even more critical component of why these experiences are so different is the investment made in the chatbots vs. the assistants. I am not only talking about the financial investment that the likes of Open AI and Microsoft have made, or the compute power these exchanges require but the investment we, as users, have made and, consequently, the data available to the services.
ChatGPT or any other generative AI service, they are fueled by tons of data points, and their performance depends on the information they hold. So we interact with them by either leveraging their knowledge database, asking a question and getting information packaged in a format that makes the information easy to digest, or we task them with creating content for us still based on all the available information they have.
With digital assistants, the investment has to come from individual users. There is no way for a brand, whether it is Amazon, Apple or Google, to know all our personal information to help the digital assistant be more efficient or conduct more human-like interactions.
The focus over the years has been on conversational AI so that the prompts that we have to give to digital assistants are not strict, basically shifting tom learning how to speak to a digital assistant to just being understood. But as a user, I had to invest. I had to decide that I wanted to train the assistant with my data and interactions. This is an aspect of the relationship that we have with digital assistants that is not necessarily clear.
The more I do, the more I share, and the better the system becomes. This is no different than what a real human assistant would do. Whether digital or real-life, an assistant learns from your behavior, learns your preferences, and over time it starts doing things for you. As a result, our confidence in them grows and empowers them to do even more as they get it right.
Digital assistants and these new chatbots share the same opportunity: as their behavior results in more correct output to satisfy our needs, we trust them more. The more we trust them, the more we demand of them. This is where some friction started to arise in the early years of digital assistants when simple tasks were performed flawlessly, and users began to want more, but expectations were not always met. That “more” took a long time coming. We haven’t gotten there yet with these new chatbots as we are just getting, and for now, it’s just a wow moment. But let’s be honest with ourselves. It was a similar wow moment, the first time that you used Alexa to get an answer to a question spoken into a smart speaker or saw your lights go out through a voice command. It all seems a little dull now that most users keep on performing the same tasks they did back in 2014.
We want more, but we’re unwilling to put much effort into it. And this is another aspect of his interactions that will also be true about generative AI. How much effort will I put in as a user to vet some of the information I get back from my queries? Is it comparable to how much effort I need to put into creating a Siri shortcut so that when I say good night, my lights go out, my doors lock, and my thermostat changes to a lower setting? Or is the output so brilliant that my effort will seem less because of the higher reward? Will my trust in these new chatbots wither when I find out the information is not correct?
Generative AI is magic to some extent, but the real value comes out when there is a consistent investment in it, whether it is about Microsoft and Google updating their models and continuing to inject fresh data so that the information that we have is current and is correct or whether it is us teaching our digital assistant what our morning routine is.
There’s always something fascinating about a thing, whether it is a smart speaker, a computer or a search bar, behaving in a human-like manner. Both chatbots and digital assistants, whether you call them copilots or give them a human name, are here to enhance us, remove friction from mundane tasks and ultimately try to make our everyday life a little bit easier. That doesn’t happen, though, without us changing. Whether it is changing the comfort level in hearing an unprompted question about reordering our toilet paper or seeing an automated thank you email sent to all our guests who attended this weekend’s party. What we can get from our experiences will always be proportional to our acceptance level. Easier to do that in a consumer context with simple tasks that do not pose a threat than in a corporate setting where we might have to rethink the way we work and what our worth is.