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An incomplete Goliath, Google, to launch undercooked tools

 


Google announced a slew of AI product integrations at their I/O 2023 keynote event this week. It seems that the core technology behind these will be its new PaLM2 LLM. That's a problem, as The Guardian article concluded:

In its preliminary research, the company warned that systems built on PaLM 2 “continue to produce toxic language harms”, with some languages issuing “toxic” responses to queries about black people in almost a fifth of all tests, part of the reason the Bard chatbot is only available in three languages at launch. 

Hinton wouldn't have approved. PaLM 2 will steal a march on OpenAI/Microsoft as it will be the first Multimodal GPT to be launched to the public. According the a Google blog the model will have the following capabilities:

  • Multilinguality: PaLM 2 is more heavily trained on multilingual text, spanning more than 100 languages. This has significantly improved its ability to understand, generate and translate nuanced text — including idioms, poems and riddles — across a wide variety of languages, a hard problem to solve. PaLM 2 also passes advanced language proficiency exams at the “mastery” level.
  • Reasoning: PaLM 2’s wide-ranging dataset includes scientific papers and web pages that contain mathematical expressions. As a result, it demonstrates improved capabilities in logic, common sense reasoning, and mathematics.
  • Coding: PaLM 2 was pre-trained on a large quantity of publicly available source code datasets. This means that it excels at popular programming languages like Python and JavaScript, but can also generate specialized code in languages like Prolog, Fortran and Verilog.
It seems quaint that Fortran is an included programming language. The paper Google published alongside the launch of PaLM 2 is rather opaque. It doesn't indicate how the model was trained for instance. What the paper states is PaLM 2 is trained on a dataset that includes a higher percentage of non-English data than previous large language models, which is beneficial for multilingual tasks (e.g., translation and multilingual question answering), as the model is exposed to a wider variety of languages and cultures.

  • In addition to non-English monolingual data, PaLM 2 is also trained on parallel data covering hundreds of languages in the form of source and target text pairs where one side is in English.
  • The inclusion of parallel multilingual data further improves the model’s ability to understand and generate multilingual text.
  • Even though PaLM 2 has a smaller proportion of English data than PaLM, we still observe significant improvements on English evaluation datasets, as described in Section 4.
  • PaLM 2 was trained to increase the context length of the model significantly beyond that of PaLM.
I don't know what the user base of all the Google products is, from maps to docs and search, but it's likely that more people will be exposed to Google AI tools than any other GPT, once the roll out is complete. Doing so with such an incomplete model seems a high risk strategy. 
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