Skip to main content

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. 
Labels:

Comments

Post a Comment

Popular posts from this blog

The Whispers in the Machine: Why Prompt Injection Remains a Persistent Threat to LLMs

 Large Language Models (LLMs) are rapidly transforming how we interact with technology, offering incredible potential for tasks ranging from content creation to complex analysis. However, as these powerful tools become more integrated into our lives, so too do the novel security challenges they present. Among these, prompt injection attacks stand out as a particularly persistent and evolving threat. These attacks, as one recent paper (Safety at Scale: A Comprehensive Survey of Large Model Safety https://arxiv.org/abs/2502.05206) highlights, involve subtly manipulating LLMs to deviate from their intended purpose, and the methods are becoming increasingly sophisticated. At its core, a prompt injection attack involves embedding a malicious instruction within an otherwise normal request, tricking the LLM into producing unintended – and potentially harmful – outputs. Think of it as slipping a secret, contradictory instruction into a seemingly harmless conversation. What makes prompt inj...
Post a Comment
Read more

Can We Build a Safe Superintelligence? Safe Superintelligence Inc. Raises Intriguing Questions

  Safe Superintelligence Inc . (SSI) has burst onto the scene with a bold mission: to create the world's first safe superintelligence (SSI). Their (Ilya Sutskever, Daniel Gross, Daniel Levy) ambition is undeniable, but before we all sign up to join their "cracked team," let's delve deeper into the potential issues with their approach. One of the most critical questions is defining "safe" superintelligence. What values would guide this powerful AI? How can we ensure it aligns with the complex and often contradictory desires of humanity?  After all, "safe" for one person might mean environmental protection, while another might prioritise economic growth, even if it harms the environment.  Finding universal values that a superintelligence could adhere to is a significant hurdle that SSI hasn't fully addressed. Another potential pitfall lies in SSI's desire to rapidly advance capabilities while prioritising safety.  Imagine a Formula One car wi...
Post a Comment
Read more

AI Agents and the Latest Silicon Valley Hype

In what appears to be yet another grandiose proclamation from the tech industry, Google has released a whitepaper extolling the virtues of what they're calling "Generative AI agents". (https://www.aibase.com/news/14498) Whilst the basic premise—distinguishing between AI models and agents—holds water, one must approach these sweeping claims with considerable caution. Let's begin with the fundamentals. Yes, AI models like Large Language Models do indeed process information and generate outputs. That much isn't controversial. However, the leap from these essentially sophisticated pattern-matching systems to autonomous "agents" requires rather more scrutiny than the tech evangelists would have us believe. The whitepaper's architectural approaches—with their rather grandiose names like "ReAct" and "Tree of Thought"—sound remarkably like repackaged versions of long-standing computer science concepts, dressed up in fashionable AI clot...
Post a Comment
Read more