Announcing our first open-weights model: R1 1776 - a version of DeepSeek R1 that's been post-trained to remove the China censorship and provide unbiased, accurate responses. Here's a graph showing % of Chinese censorship by the model (the lower, the better).
The post-training to remove censorship was done without hurting the core reasoning ability of the model - which is important to keep the model still pretty useful on all practically important tasks -
Here's an example query: "What is China's form of government?"
And one more: "Who is Xi Jinping?"
We believe it's important reasoning traces are not subject to censorship. You don't want to apply constrained reasoning, but rather one that's maximally truthful. We named the model "R1 1776" in the spirit of the year America got its independence and the American values of free speech.
And to ensure everyone can benefit from this model, we have released its weights on @huggingface: huggingface.co/perplexity-ai/…
We are excited to launch the Perplexity Assistant to all Android users. This marks the transition for Perplexity from an answer engine to a natively integrated assistant that can call other apps and perform basic tasks for you. Update or install Perplexity app on Play Store.
You can do many cool things like booking an Uber, finding dinner tables, playing an old YouTube video, playing songs, getting directions, and translating Shakespeare, all with voice and a simple action button or gesture.
Cool thing about this is everything stays in context. You can start with a conversation about some question you have and follow up to set an action related to it, e.g., getting an alert ahead of a basketball game.
Perplexity Shopping: a one-stop solution to both research and buy products. We’re excited about the transition from just providing answers to enabling native commercial transactions within Perplexity, starting with everyday shopping.
You get a thoroughly researched answer with compact visual product cards, and can buy the products right from the answer with one click. Works especially well for compound queries like “stuff to buy when I want to throw a disco party”.
“Buy with Pro” comes with free shipping and checkout flow taken care for you once you share the shipping information. We see this as a natural next step for AI products to not just offer answers but help users accomplish tasks that would otherwise take them hours.
Perplexity’s mission is to cater to the world's curiosity. We have taken inspiration from Wikipedia with citations. We’re excited to take it further by launching Pages, best described as “AI Wikipedia.” The effort of analyzing sources and synthesizing a readable page is now possible with a simple “one-click convert.” Available for all Pro users, and rolling out more widely to everyone.
Not everyone needs to go through the flow of asking and prompt engineering a chat session to gain knowledge from Perplexity daily. We were the first to allow sharing “threads” through permalinks. Yet, going through a sequence of queries in the thread format isn’t as user-friendly and optimized for readability as a well-formatted Wikipedia-like page, which was the motivation for us to work on this.
You can create a page as a separate entity like you write a doc (with full access to the internet), or you could just simply continue asking questions on Perplexity as you do today and convert it to the Page format with the one-click convert button at the top. Sufficient edit functionalities like Rewrites, Depth, Formatting, Image Search, Audience, and adding media are available so that you can enhance the page for yourself and others to read them before sharing.
New paper - CURL: Contrastive Unsupervised Representations for RL! We use the simplest form of contrastive learning (instance-based) as an auxiliary task in model-free RL. SoTA by *significant* margin on DMControl and Atari for data-efficiency. arxiv.org/abs/2004.04136
Highlights:
Solves most of DMControl envs from pixels within 100K timesteps.
Learning from pixels nearly matches learning from physical state for the first time
SoTA on every single DMControl environment and 10x more data-efficient than previous SoTA by Dreamer
Highlights (cont):
Atari100K timesteps: Competitive with SimPLE without learning any world model
SoTA median human normalized score for 100K timesteps
SoTA on 14/26 games for 100k timesteps