Do you ever wonder what happened to Alexa? Mihail Eric, a former research scientist from Alexa AI, wrote a tell-all post about why Alexa is no longer at the forefront of voice assistants, which is particularly true in this era of rapid advancements.
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While Siri got ChatGPT integration, Eric undertook to find out why its prime competitor, Alexa, failed. A key reason that came up was it getting embroiled in bureaucracy due to its commitment to pushing an ethical product.
“We had all the resources, talent, and momentum to become the unequivocal market leader in conversational AI. But most of that tech never saw the light of day and never received any noteworthy press,” Eric said on X.
Eric was part of the Conversational Modelling team at AlexaAI in 2019. He worked on improving Alexa’s capabilities through the power of AI. However, since this was much before ChatGPT, the necessary requirements for compute and other infrastructure were not met.
However, this is not what effectively killed Alexa as an AI competitor. While only a few years ago, parents were panicking about their kids learning the name “Alexa” before the word “mama”, things nosedived rapidly.
Eric says this could be attributed to several issues, most notably to several bureaucratic hurdles, including a huge focus on ethically sourcing user data.
Alexa Serves as a Cautionary Tale
While the commonly held belief at the moment is that AI should be built on ethical frameworks, Alexa’s story shows that it is easier said than done.
Data privacy is one of the most significant issues raised regarding ethical AI. Eric stated that in a bid to ensure data privacy was preserved, the company inadvertently set up several roadblocks for itself, effectively halting any advancements in training the voice assistant.
“Definitely a crucial practice, but one consequence was that the internal infrastructure for developers was agonisingly painful to work with. It would take weeks to get access to any internal data for analysis of experiments,” he said.
Interestingly, his next point goes into another major problem that companies face today – the issue of poorly annotated data. Eric stated that despite repeated attempts to ensure that data was properly annotated, this was again bogged down by layers of bureaucracy, leading to further delays in development.
Data Plays a Major Factor Too
Currently, major AI companies have begun a mad scramble for functional datasets. Data as a product (DaaP) has slowly become a point of consideration, especially with customer-facing companies that accrue data.
Meanwhile, companies like OpenAI and Google have struck several partnerships with media companies to have reliable datasets that they can train their LLMs on. However, Alexa largely relied on crowd-sourced data, as well as data taken from Alexa users and employees to train it. This data needed to be properly annotated.
“I remember, on one occasion, our team did an analysis demonstrating that the annotation scheme for some subset of utterance data was completely wrong, leading to incorrect data labels,” Eric said.
However, correcting this proved to be even worse, as it required approval from several teams and a proper justification for why it needed to be done, apart from just “it’s scientifically the right thing to do and could lead to better models for some other team”.
This proved to be true, as accurate datasets are hard to come by and worth their weight in gold. This is precisely why OpenAI has partnered with several media organisations over the past year. They’ve always needed quality datasets that can be reliably used to train ChatGPT.
Meanwhile, Google was an example of what poor datasets could do, as their integration of the Reddit API led to baffling answers to some really innocuous queries from users.
Obviously, there were several other issues with the company that ultimately led to the fall of Alexa. This included a lack of communication between teams as well as a mindset that leaned towards the consumer side rather than the scientific side.
As Eric put it, “The success metric imposed by senior leadership had no scientific grounding and was borderline impossible to achieve.”
With Google, OpenAI and Apple all announcing major upgrades to their multi-modal capabilities, it seems that Alexa is nowhere in the race. However, not all is lost.
How Do You Avoid This?
Eric’s post, while critical of Alexa, also proves to be a valuable lesson for AI companies and startups. As mentioned before, one of the biggest talking points surrounding the industry is keeping in mind ethical AI, but again, this is easier said than done.
However, this doesn’t mean that ethical AI as a whole is impossible and should be abandoned. Eric believes that better data infrastructures need to be put into place to ensure that better models are built.
Further, he said that rapid advancements mean that both companies and startups feel the pressure to ship products quickly. “Of course, you should conduct research aggressively, but don’t have delivery cycles measured in quarters, as this will produce inferior systems to meet the deadlines,” he concluded.
With everyone rushing to stay ahead in the AI race, this can also be remedied if startups work to ensure their products stay relevant and sustainable in the long run.
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