I recently accepted an invitation from Google to try out its conversational assistant Bard, currently only available in the UK and US.
I live in Madrid (Spain), but I got the invitation by simply logging in via my VPN to simulate a connection from London, and had to wait about six days until my request was approved. After receiving the invitation message, I had to reconnect the VPN to access Bard: without it, it simply informs you that it is not yet available from your country.
Nor is it available at the moment in any language other than US English. But in addition to the language, which therefore prevents — for the moment — asking for translations, Bard has another limitations: it is unable to incorporate fonts in its answers, neither natively as Perplexity.ai does, nor by requesting them, as ChatGPT can do. The chatbot also has difficulties in referring to previous moments in a conversation: if you ask it to go back to something you asked it ten minutes before, you may find that it simply tells you that it cannot do it, or even that it repeats the question asked by another user (and apologizes when you tell it).
According to the FAQ, it can’t help you to write code either, at least, not for the moment (although I have verified that it can do some simple things), and those same FAQ warn that not only it is incapable of explaining how it works, but that, in addition, it is likely to “hallucinate” (in the sense applied to algorithms) if you ask it questions about itself.
After the answer to each prompt, Bard adds a thumbs up and a thumbs down so that you can rate it, and a “Google it” button, which suggests related searches in the conventional search engine.
Considering the limitations, and after many exchanges, it is striking how far Bard is from developments such as ChatGPT or Perplexity.ai. I had read some comparisons about it, and I’m afraid they’re pretty much on the money: it lacks the capabilities of other assistants, which is surprising for Google, which was once not only a pioneer in understanding the radical importance and possibilities of machine learning, but even in training all its staff to use it. As I have said before, this is very much the innovator’s dilemma: a company whose leadership prevents it from seeing off a new competitor.
OpenAI did not appear out of nowhere, nor was it especially a surprise: we all knew what it was working on, the kind of language models with billions of parameters it was training, and we could even see them long before ChatGPT jumped onto the scene at the end of November last year, the moment it went viral and became the fastest-adopted technological phenomenon in history. How is it possible that Google had to bring its founders out of retirement and could only scramble this botched response to ChatGPT? How is it possible that a relatively small company like OpenAI has a chatbot on the market right now that handles an infinite number of languages, writes reasonably competent code, keeps references to previous conversations, accepts images or videos as inputs and that provides reasonable translations, while Google’s product is so inferior?
Three fundamental reasons: concerns about launching a still-unfinished product (compared to its competitors, who could accept that risk), concerns about assuming a higher search cost (which again, its competitors were happy to do), and the problem of reimagining a business model that eliminates clicks on sponsored results (an important basis of Google’s revenue). One way or another, Google, a company that has been working on machine learning for years, was clearly left behind and now finds itself with a significantly inferior product. Who knows, in the future maybe it will manage to reverse the situation… but for the moment, what remains for history is this. And knowing Google, it must hurt.
(En español, aquí)
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This post was previously published on MEDIUM.COM.
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