Shared weblinks

1. Google’s theory is that, as for every query, Google faces competition from Amazon, Yelp, AA.com, Cars.com and other verticals. The problem is that government kept bringing up searches that only Google / Bing / Duckduckgo and other GSs do.

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For example, only general search engines return links to websites with information you might be looking for, e.g., a site explaining how to change the oil on a 1965 Mustang. There’s no way to find that on cars.com.

I’m having a much harder time finding news clips via Google search than a few months ago. Instead, I often get tons of random blogs from law firms, coaching websites, etc. even when I include the name of the outlet in the search

What’s the phrase to describe when an algorithm doesn’t take into effect it’s own influence on an outcome?

What is the equivalent of SEO, but for navigating the physical built environment? And for searching for physical destinations on a GPS map?

For the record, I think Google testing the Discover feed on desktop might be the biggest SEO development we’ve had in a long time.

Why do I think this is bigger than other developments, updates, etc.?

Ask me later when you see how much traffic comes from Discover on desktop.

[ . . . ]

Daniel Griffin, a recent Ph.D. graduate from University of California at Berkeley who studies web search and joined the Discord group in September, said it isn’t uncommon for open source software and small search engine tools to have informal chats for enthusiasts. But Griffin, who has written critically about how Google shapes the public’s interpretations of its products, said he felt “uncomfortable” that the chat was somewhat secretive.

The Bard Discord chat may just be a “non-disclosed, massively-scaled and long-lasting focus group or a community of AI enthusiasts, but the power of Google and the importance of open discussion of these new tools gave me pause,” he added, noting that the company’s other community-feedback efforts, like the Google Search Liaison, were more open to the public.

[ . . . ]

Today @SM00719002 and I are launching a public beta of our project, Collective Cognition to share ChatGPT chats - allowing for browsing, searching, up and down voting of chats, as well as creating a crowdsourced multiturn dataset!

https://collectivecognition.ai

Is there any paper about the increasing “arXivification” of CS/HCI? (eg pre-print cultures and how they relate/intersect w increasingly sped-up processes of technology development and academic paper churn culture)?

When we scrutinize our students’ and colleagues’ research work to catch errors, offer clarifications, and suggest other ways to improve their work, we are informally conducting author-assistive peer review. Author-assistive review is almost always a * scoreless*, as scores serve no purpose even for work being prepared for publication review.

Alas, the social norm of offering author-assistive review only to those close to us, and reviewing most everyone else’s work through publication review, exacerbates the disadvantages faced by underrepresented groups and other outsiders.

[ . . . ]

We can address those unintended harms by making ourselves at least as available for scoreless author-assistive peer review as we are for publication review.7

Anyone seen any interesting examples of things this new instruct model can do that are difficult to achieve using the chat models?

Interesting that @replit Ghostwriter gave a better response than GPT-4 for a coding question. Ghostwriter has gotten noticeably better for me and I find myself using it more than GPT-4 for development

Oooh. This is a slippery one! Because both are right?

They must assume/interpolate:
What does the f [format specifier] [mean/stand for] in printf?
What does the [letter] f [mean/stand for] in [the string] printf?

Anyone got leads on good LLM libraries that can be installed cleanly on Python (on macOS but ideally Linux and Windows too) using “pip install X” from PyPI, without needing a compiler setup?

I’m looking for the quickest and simplest way to call a language model from Python

Prompt engineering is a relatively new discipline for developing and optimizing prompts to efficiently use language models (LMs) for a wide variety of applications and research topics. Prompt engineering skills help to better understand the capabilities and limitations of large language models (LLMs).

Researchers use prompt engineering to improve the capacity of LLMs on a wide range of common and complex tasks such as question answering and arithmetic reasoning. Developers use prompt engineering to design robust and effective prompting techniques that interface with LLMs and other tools.

Prompt engineering is not just about designing and developing prompts. It encompasses a wide range of skills and techniques that are useful for interacting and developing with LLMs. It’s an important skill to interface, build with, and understand capabilities of LLMs. You can use prompt engineering to improve safety of LLMs and build new capabilities like augmenting LLMs with domain knowledge and external tools.

Motivated by the high interest in developing with LLMs, we have created this new prompt engineering guide that contains all the latest papers, learning guides, models, lectures, references, new LLM capabilities, and tools related to prompt engineering.

Can trust be built into systems that users have determined to be untrustworthy? Should we be thinking of trust as something that is declining or improving, something to be built into AI and other data-centric systems, or as something that is produced through a set of relations and in particular locations? Where else, besides large institutions and their technologies, is trust located? How do other frames of trust produce community-centered politics such as politics of refusal or data sovereignty? What can community-based expertise tell us about how trust is built, negotiated, and transformed within and to the side of large-scale systems? Is there a disconnect between the solutions to a broad lack of trust and how social theorists, community members, and cultural critics have thought about trust?

[ . . . ]

In our work together, we aim to move away from a concept of trust that is inherent to the object (e.g. information as trustworthy) or a concept of trust that is overly normative (prescribing trust as a goal that should be achieved), and toward a concept of trust as a relational process. We will work toward an empirical grounding of how trust is stymied, broken, established, reestablished, co-opted, and redirected among the powerful and among communities who have never been able to fully trust the institutions that shape their lives.

It took about 6 months of gradual change (shorter than I expected) but I finally use zero Google products for my personal life. Moved my email to Fastmail with my own domain, browser to Safari, maps to Apple, search to Kagi. Less painful than I expected, no noticeable downsides.

+1
In a “search terms that confirm preconceptions” vignette (Haider & Rödl 2023):
“‘I do not sit down and google “milk good for health”, I google “milk bad”’ … through choice of search terms, Google Search can be said to serve as a tool for confirmation bias (Tripodi, 2022)…”

Keywords based Search can’t help you because it just show you what you want and ignore the real Truth

Should I …… says Yes take the shower but warm

Should I not ……. says no don’t take shower even if it’s warm

Excited to announce that pplx-api is coming out of beta and moving to usage based pricing, along with the first-ever live LLM APIs that are grounded with web search data and have no knowledge cutoff!

Excited to release our online LLMs! These models have internet access and perform very well on prompts that require factuality and up-to-date information.

read more here: blog.perplexity.ai/blog/introducing-pplx-online-llms

We’re thrilled to announce two online LLMs we’ve trained: pplx-7b-online and pplx-70b-online! Built on top of open-source LLMs and fine-tuned to use knowledge from the internet. They are now available via Labs and in a first-of-its-kind live-LLM API.

Q: “What is Google doing”
A: Selling ads.

Dev friends - is there a well known service that expands any arbitrary links for LLM consumption?

example:

input: any url (twitter, youtube, HN, medium, github, discord, reddit)

output: opengraph content, but also post body, optionally summary of the post content

usecase:

when people drop links, its useful for the LLM to “read it a little bit” rather than only read the URL (as @simonw has shown, LLMs can hallucinate content from URLs)

Anecdotal but I realized I don’t personally know a single professional who’d be using GPT for anything seriously pertaining to their work, beyond what I’d call amusement. People are “playing with it”, “probing it”, “experimenting” but nobody is actually “using” it for anything.

Personally I’ve found Bingchat with search, and GPT4 with Browse to be far more confused at all times than the no-RAG model

PARSE YOUR ROBOTS.TXT FILE THE SAME WAY GOOGLE’S CRAWLERS DO

Heads-up, the Quality Rater Guidelines have been updated (as of 11/16)

“Specifically, we’ve simplified the”Needs Met" scale definitions, added more guidance for different kinds of web pages and modern examples including newer content formats such as short form video, removed outdated and redundant examples, and expanded rating guidance for forum and discussion pages. None of these involve any major or foundational shifts in our guidelines." https://developers.google.com/search/blog/2023/11/search-quality-rater-guidelines-update…

Google released a new version of its Quality Rater Guidelines. One of the big changes, IMO:

The Relationship between Page Quality and Needs Met

In the new version, High-Quality pages (often big brands) have less of an advantage on Needs Met

Is this to level the playing field?

Perplexity user: “Microsoft banned your domain, so can’t use it on work devices”.

seeking proposals for a wide variety of research projects which might shed light on what real-world impacts LLM systems could have over the next few years.

…

Below are some examples of project ideas that could make for a strong proposal to this RFP, depending on details:

• Conducting randomized controlled trials…
• Polling members of the public…
• In-depth interviews…
• Collecting “in the wild” case studies…
• Estimating and collecting key numbers…
• Creating interactive experiences…
• Eliciting expert forecasts…
• Synthesizing, summarizing, and analyzing the various existing lines of evidence about what language model systems can and can’t do at present…

Last week, I gave a presentation about Google Search results not being perfect, how we update to improve those results, and how our guidance to creators needs to improve. In this thread, I’ll share my slides and commentary for those who weren’t able to attend my talk…