#DeepSeek R1 on Perplexity

[free-ai-tools-by-jazzy-content]

Good News from Perplexity: DeepSeek R1 model is now available across every Perplexity platform

We're excited to announce that the new DeepSeek R1 model is now available across every Perplexity platform. You can experience the latest breakthrough in AI by turning on Pro Search with R1 on web, mobile, or MacOS. I highly recommend you try it out today — the experience is truly remarkable.

This model is hosted on servers based in the US and Europe, meaning that your data is not shared with the model provider or with China. Furthermore, we have eliminated all censorship on answers. You can ask it about any topic, even ones that are censored on the DeepSeek app, giving you unbiased and accurate answers.

In the past few years, there have been a handful of revolutionary moments in AI that have transformed the landscape. I wholeheartedly believe that this is yet another moment. We will continue to find ways to make this technology available to our users safely, so we can put knowledge at your fingertips and provide accurate, trusted, answers to every question.

Pro subscribers have access to 500 DeepSeek R1 Pro Searches per day. All other users have 5 free uses per day.

[business-management-softwares]

Open Deep Search (ODS)

XUẤT HIỆN ĐỐI THỦ OPEN SOURCE NGANG CƠ THẬM CHÍ HƠN PERPLEXITY SEARCH

XUẤT HIỆN ĐỐI THỦ OPEN SOURCE NGANG CƠ THẬM CHÍ HƠN PERPLEXITY SEARCH

Open đang phả hơi nóng và gáy close source trên các mặt trận trong đó có search và deep search. Open Deep Search (ODS) là một giải pháp như thế.

Hiệu suất và Benchmarks của ODS:

- Cải thiện độ chính xác trên FRAMES thêm 9.7% so với GPT-4o Search Preview. Khi xài model DeepSeek-R1, ODS đạt 88.3% chính xác trên SimpleQA và 75.3% trên FRAMES.

- SimpleQA kiểu như các câu hỏi đơn giản, trả lời đúng sai hoặc ngắn gọn. ODS đạt 88.3% tức là nó trả lời đúng gần 9/10 lần.

- FRAMES thì phức tạp hơn, có thể là bài test kiểu phân tích dữ liệu hay xử lý ngữ cảnh dài. 75.3% không phải max cao nhất, nhưng cộng thêm cái vụ cải thiện 9.7% so với GPT-4o thì rõ ràng ODS không phải dạng vừa.

CÁCH HOẠT ĐỘNG

1. Context retrieval toàn diện, không bỏ sót

ODS không phải kiểu nhận query rồi search bừa. Nó nghĩ sâu hơn bằng cách tự rephrase câu hỏi của user thành nhiều phiên bản khác nhau. Ví dụ, hỏi "cách tối ưu code Python", nó sẽ tự biến tấu thành "làm sao để Python chạy nhanh hơn" hay "mẹo optimize Python hiệu quả". Nhờ vậy, dù user diễn đạt hơi lủng củng, nó vẫn moi được thông tin chuẩn từ web.

2. Retrieval và filter level pro

Không như một số commercial tool chỉ bê nguyên dữ liệu từ SERP, ODS chơi hẳn combo: lấy top kết quả, reformat, rồi xử lý lại. Nó còn extract thêm metadata như title, URL, description để chọn lọc nguồn ngon nhất. Sau đó, nó chunk nhỏ nội dung, rank lại dựa trên độ liên quan trước khi trả về cho LLM.

Kết quả: Context sạch sẽ, chất lượng, không phải đống data lộn xộn.

3. Xử lý riêng cho nguồn xịn

Con này không search kiểu generic đâu. Nó có cách xử lý riêng cho các nguồn uy tín như Wikipedia, ArXiv, PubMed. Khi scrape web, nó tự chọn đoạn nội dung chất nhất, giảm rủi ro dính fake news – đây là công đoạn mà proprietary tool ít để tâm.

4. Cơ chế search thông minh, linh hoạt

ODS không cố định số lần search. Query đơn giản thì search một phát là xong, nhưng với câu hỏi phức tạp kiểu multi-hop như "AI ảnh hưởng ngành y thế nào trong 10 năm tới", nó tự động gọi thêm search để đào sâu. Cách này vừa tiết kiệm tài nguyên, vừa đảm bảo trả lời chất. Trong khi đó, proprietary tool thường search bục mặt, tốn công mà kết quả không đã.

5. Open-source – minh bạch và cải tiến liên tục

Là tool open-source, code với thuật toán của nó ai cũng thấy, cộng đồng dev tha hồ kiểm tra, nâng cấp. Nhờ vậy, nó tiến hóa nhanh hơn các hệ thống đóng của proprietary.

Tóm lại

ODS ăn đứt proprietary nhờ: rephrase query khéo, retrieval/filter xịn, xử lý riêng cho nguồn chất, search linh hoạt, và cộng đồng open-source đẩy nhanh cải tiến.

[sensaiacademy]

MANUS : chiński agent AI. Czy to już AGI? | Gemma 3 od Google oraz inne newsy AI | AI Lunch #24

Podcast AI Lunch w którym posłuchasz na temat:

Manus agent AI prosto z Chin

Manus oparty jest na Claude Sonnet

Manus ma 29 agentów AI do różnych zadań

Manus jest jak gotowy samochód z różnymi funkcjami

Manus korzysta z Browser Use

OWL czyli Manus OpenSource

Co myślisz o chińskich narzędziach AI i bezpieczeństwie?

OpenManus na GitHub

Agent AI od Claude do programowania

Open Interpreter

Claude Code przykład działania

Różnica konsoli ChatGPT do Claude Code

OpenAI Agent SDK - biblioteka do tworzenia agentów AI

Gemma 3 od Google - co potrafi ten model AI?

DeepSeek R1 vs Gemma 3 - porównanie możliwości parametrów

Problem szybkości modeli AI

Perplexity - aplikacja na MacOS, iOS, Android i Google Chrome

Mistral OCR - omawiamy możliwości tego narzędzia

Sam Altman ogłosił przełomowy model do pisania kreatywnych treści

Przyszłość rynku modeli AI

Same - Copy any UI - kopiowanie strony

AI Knowledge graph - narzędzie do grafów wiedzy

Pytania i odpowiedzi dla widzów

Scena SensAI podczas SEO Vibes Whitepress

[marketingprofitmedia]

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[fundgruber]

The method I used here might be of interest, using a team of LLMs and then finally letting the two with the best "style" and "character" condense the final result in a battle mode. I used Mistral, Llama, ChatGPT-Omni, Perplexity Sonar, Gemini-Flash, DeepSeek-R1, and Anthropic Claude Sonnet in an iterative group think, moderating them as hard as possible. In the "collaborative" process, most of them clearly indicated that they had been trained with openly available nettime data (without permission). In this way, letting AI write the text takes longer than writing it yourself. It might address the points you intended, but it will certainly add new points and cut away others, only reproducing your own style of thinking from a meta perspective.

Pit Schultz on Nettime: https://www.nettime.org/Lists-Archives/nettime-l-2502/msg00029.html

The result: "The Baudrillardian Superintelligence Paradox: Capital's Terminal Simulation" https://www.nettime.org/Lists-Archives/nettime-l-2502/msg00019.html

Also you can see he runs a reply-guy experiment with this setup right now on X: https://x.com/pitsch/with_replies

Ocassionally referencing the models:

https://x.com/pitsch/status/1883438261756129755

https://x.com/pitsch/status/1894197403009380625

[tumnikkeimatome]

Perplexity Deep Research Agentをリリース、今後メモリ機能も実装へ - DeepSeek-R1とHugging Faceのオープンソース技術を活用した無料で使える次世代AIリサーチツールが実現

はじめに Perplexity AIは2025年2月13日、Deep Research機能の正式リリースを完了し、続いてメモリ機能の実装を予告しています。 これらの機能は、AIによる情報��析の可能性を根本から変革する画期的な進歩として���目を集めています。 特に、大規模データ処理と文脈理解を組み合わせたこの新機能は、専門家レベルのリサーチを一般に広く提供するツールとして期待が高まっています。 Deep Researchの特徴 Perplexity AIが提供する「Deep…

[llbbl]

AI Reasoning - Gemini, DeepSeek, and OpenAI Compared

So, the Gemini Flash Thinking models are out, and they simulate how a chain of thought is supposed to work in humans, reasoning their way to an answer instead of simply providing one.

DeepSeek R1 and OpenAI O3 also have CoT. 

How does Google Flash Thinking compare? Well, it’s fast and free and doesn’t send your data to China… but it may be a bit too early to tell. 

Chain of thought makes models more flexible and capable of handling various tasks by breaking down complex problems, considering possibilities, and explaining reasoning.

Transformers are the foundational neural network architecture for many modern large language models. They are what enable models to understand context and generate human-like text.

Chain of Thought (CoT) enhances the reasoning capabilities of transformer models by prompting them to produce intermediate steps in their reasoning process before giving a final answer.

Perplexity Pro now has the Open Sourced DeepSeek R1 model re-hosted in the US. I’ve been using Perplexity Pro for a few weeks. My conclusion: Perplexity is a better Google than Google.

[theenterprisemac]

Please Don't Do This

"I just canceled my ChatGPT & Perplexity subscriptions. Here's how to best use DeepSeek instead: Step 1: go to chat . deepseek . com. Step 2: activate "DeepThink-R1" for reasoning. Step 3: click on the search icon to search the web. I just used it to make a perfect Linkedin post: Prompt it to "Find the trends for [niche]". Copy all of its answers, and paste it on EasyGen. Generate 3-5 times until you have a Linkedin post. EasyGen is the best AI for Linkedin… … if you have the right tool to find trends/research. I can write a post for anyone in 5-10 minutes now. So why DeepSeek is better than ChatGPT: ☑ It can analyze a PDF AND reason. ChatGPT can't. ☑ It can reason AND search the web. ChatGPT can't. ☑ It's faster and free. Their API is 96% cheaper. "But Ruben, it's Chinese!" says the American. But American LLMs are biased. But ChatGPT, Claude, Gemini are biased. Every LLM is biased. DeepSeek makes no difference. Being Chinese isn't the problem. Americans must do (much) better."

I recently encountered this post on my LinkedIn. Please don't do this and please don't follow this advice. Let me rephrase this a different way and you will understand why:

Go to some AI platform: ask it for trends in your choice of niche.

Paste the output into EasyGen a few times

Post whatever you got into LinkedIn and claim credit for the idea. (If you read the posters "blog" he says you refine the output yourself, but AI did the heavy lifting.)

Let me ask you how is this contributing to human knowledge, or showing that you have your own ideas/mastered the topic? You didn't do the work, and you don't even have to pick anything beyond what is popular to post about. Your post is not original, and not even representative of your ideas–the fact that you like the ideas presented doesn't grant you scholarship over them–when you did none of the work to formulate them.

If you understand how LLMs work, then you know that what they generate is a mixture of already known things presented to you as a bunch of text. Remember LLMs are just pattern engines, and that's why they present the sort of content they do.

If you want to be an influencer on LinkedIn, then share your own ideas. Don't be this sort of person–this is the intellectual equivalent of a get rich quick scheme. Do your own work and scholarship and this will deepen your understanding and ideas–it will actually make the world a better place.

[critchpodcast]

Perplexity AI integriert DeepSeek R1 zur Erweiterung der KI-Funktionalität

Perplexity AI integriert DeepSeek R1 zur Erweiterung der KI-Funktionalität: https://www.it-boltwise.de/perplexity-ai-integriert-deepseek-r1-zur-erweiterung-der-ki-funktionalitaet.html

[black7375]

Phind vs Perplexity

두 AI 검색 엔진 서비스가 최근에 업데이트하여 간단히 비교정리해본다.

1. Phind

phind는 검색결과를 보여주는 능력이 매우 뛰어나다.

표는 물론이고 Mermaid 다이어그램 표시,

코드 샘플을 보며주며 선택지와 Pros & Cons 정리

마지막으로 정리와 Rember 노트까지..!!

2. Perplexity

Perplexity는 뭔가 전형적(?)인 AI 검색 느낌이다.

이미지 검색도 같이 해주고, 동영상 검색이나 이미지 생성도 할 수 있다는 점이 좋다.

끝에는 추가로 검색할 쿼리도 제공해준다.

3. 추론 비교

두 서비스다 추론 기능이 있다.

다만 Phind는 일�� LLM이나 사람처럼 다소 긴 문장을 쓰며 판단하고

처음뿐만 아니라 중간중간에도 필요하면 사고한다는 장점이 있다.

반면 perplexity는 Phind보다 더 많은 검색소스를 쿼리 및 체크하고, 무려!!! Reasoning 모델!!!!!!!을 쓸수 있다는 장점이 있다.

심지어 딥씩이까지 지원한다.

딥씩이 장점은 역시 CoT 과정을 자세히 볼 수 있다는 것이다.

각각의 사고과정을 독자를 위해 번역기로 돌려 보여주자면 다음과 같다.

Phind

(1) == 검색1 == (2) == 사고1 == 이 비교 요청을 체계적으로 분석하겠습니다. 1. 첫째, React Virtuoso의 기술 세부 사항 및 가격 모델에 대한 중요한 정보가 누락 된 것을 알았습니다. 이를 위해 대상 웹 검색을해야합니다. 2. 비교는 시각 보조적으로 큰 혜택을 받을 것입니다. - 각 라이브러리 사용시기를 보여주는 의사 결정 흐름 차트 - 구현 차이를 보여주는 코드 예제 - Citation 0 : 1의 통계 테이블 3. 구조 계획 : - 가상화가 무엇인지 설명하는 간단한 소개 - 주요 차이 테이블 - 의사 결정 흐름도 - 상세한 비교 - 구현 예 - 권장 사항 React Virtuoso에 대한 누락 된 정보를 수집하는 것으로 시작하겠습니다. (3) == 검색2 == - react virtuoso license pricing commercial use cost - react virtuoso technical documentation features limitations (4) == 사고2 == 웹 검색 결과는 특히 React Virtuoso에 대한 귀중한 추가 정보를 제공했습니다. 1. 라이센스 세부 사항 및 가격 구조 2. 기술 구현 세부 사항 3. 성능 고려 사항 이제 포괄적 인 비교를 만들 수 있습니다. 수집 된 정보를 살펴보면 다음을 기반으로 이러한 라이브러리 간의 의사 결정 흐름을 보여주는 인어 다이어그램을 만들 수 있습니다. - 프로젝트 요구 사항 (가변/고정 크기) - 팀 규모 및 예산 제약 - 특정 사용 사례 (테이블, 그리드, 채팅) 이 다이어그램은 기본 비교를 소개 한 후에도 세부 구현으로 다이빙하기 전에 가장 유용합니다. 사용자는 코드를보기 전에 어떤 라이브러리가 자신의 요구에 가장 적합한 지 이해하는 데 도움이되므로. 나는 다음과 함께 답을 구조화 할 것이다. 1. 간단한 소개 2. 비교 테이블 3. 의사 결정 흐름도 4. 특수 코드 대안 구문을 사용한 자세한 구현 예제 5. 특정 권장 사항 정보는 이제 답을 진행하기에 충분히 완성 된 것 같습니다. (5) == 컨텐츠1 == - 설명과 비교테이블 - 의사결정 흐름 다이어그램 (6) == 사고3 == 다이어그램은 논리적으로 흐르며 명확한 결정 지점과 결과로 자명합니다. 각 경로는 주요 기능을 요약한 불렛 포인트를 사용하여 특정 권장 사항으로 이어집니다. 컬러 코딩 (질문용 핑크, 솔루션용 녹색)은 어떤 노드가 의사 결정 지점 대 최종 선택을 시각적으로 명확하게 합니다. Special Code Alternatives Syntax를 사용하여 구현 예제를 계속하여 두 라이브러리의 구체적인 사용법을 보여 드리겠습니다. 이를 통해 사용자는 구현의 실질적인 차이를 이해하는 데 도움이됩니다. (7) == 컨텐츠2 == - 구현예시 - 권장사항 - Remember

Perplexity (Deepseek R1)

(1) == 검색1 == React-virtuso와 @tanstack/react-virtual의 기능 비교 - react-virtuso features vs @tanstack/react-virtual features - react-virtuso vs @tanstack/react-virtual comparison - react-virtuso vs @tanstack/react-virtual capabilities (2) == 검색2 == React-virtuso 및 @tanstack/react-virtual에 대한 사용자 리뷰 또는 의견 찾기 - react-virtuoso user reviews - @tanstack/react-virtual user reviews - react-virtuoso vs @tanstack/react-virtual opinions (3) == 검색3 == React-virtuso 및 @tanstack/react-virtual의 성능 및 사용 사례 결정 - react-virtuoso performance - @tanstack/react-virtual performance - react-virtuoso use cases - @tanstack/react-virtual use cases (4) == R1 추론 == 1) 좋아요, 이제 React-virtuoso와 @tanstack/react-virtual 간의 비교를 다루겠습니다. 먼저, 검색 결과를 살펴보며 각 라이브러리에 대한 핵심 사항을 모아야 합니다. 2) React-virtuoso로 시작: 소스에서는 가변 크기 항목을 자동으로 처리하는 것으로 알려져 있으며 이는 큰 장점입니다. 문서는 좋은 예를 포함하여 포괄적인 것으로 평가됩니다. 무한 스크롤, 고정 헤더, 그리드 레이아웃과 같은 기능이 강조됩니다. 다른 제품에 비해 구성이 더 쉽다는 언급도 있습니다. 특히 채팅과 같은 역방향 스크롤 시나리오에서는 성능이 탄탄해 보입니다. 그러나 일부 사용자는 렌더링이 약간 지연되어 사용자 경험에 영향을 미칠 수 있다고 지적했습니다. 3) 이제 @tanstack/react-virtual(이전의 React-virtual)을 살펴보세요. TanStack 생태계의 일부이므로 반응 테이블과 같은 도구와의 통합이 강점입니다. 라이브러리에는 헤드리스가 있어 마크업과 스타일에 대한 더 많은 제어 기능을 제공하므로 사용자 정의에 적합합니다. 가볍고 여러 프레임워크를 지원하지만 더 많은 설정이 필요합니다. 사용자들은 이 방법이 효율적이지만 복잡한 구성 요소로 인해 성능 문제가 발생하여 스크롤 중에 지연이 발생할 수 있다고 보고합니다. 문서가 상세하지 않아 일부에게는 장애물이 될 수 있습니다. 4) 기능 비교: React-virtuoso는 기본적으로 기능이 더 풍부한 것처럼 보이지만 @tanstack/react-virtual은 유연성을 제공하지만 더 많은 수동 구성이 필요합니다. 커뮤니티 피드백은 사용 편의성과 고급 기능을 위해 반응 거장 쪽으로 기울지만, 다른 TanStack 도구와 통합하거나 헤드리스 접근 방식이 필요할 때는 @tanstack이 선호됩니다. 5) 각 라이브러리의 장단점을 간략하게 설명하고 기능, 성능, 사용 편의성, 사용 사례와 같은 주요 영역에서 비교하여 답을 구성해야 합니다. 관련 출처, 특히 비교표와 사용자 평가를 인용하세요. 다양한 크기의 복잡한 목록에는 React-virtuoso가 더 적합하고, @tanstack은 TanStack 생태계와의 사용자 정의 및 통합이 필요한 사용자에게 적합하다는 점을 강조하세요.

4. 기타

Phind는 자체 모델임에도 불구하고 생각보다 한글을 잘 출력해준다. 군데군데 한자가 있지만 말이다.

무엇보다 읽기가 매우 즐겁다!!

Thought 과정을 보면 Python 계산 실행등의 기능도 쓸 수 있어보인다.

관련 기능을 캐물어보니 내부 Doc을 찾아 다음과 같이 답변해준다.

3단계 사고모드는 전혀 예상 못했는데 재밌는 결과였다.

계산기도 써봤는데 실제 Python 코드를 돌려서 보여준다.

중간중간 사고과정이 꼭 사람같다.

Perplexity는 정말 강력한 검색기능을 가지고 있다.

다양한 검색범위를 제한할 수 있으며, 특히 학문과 수학은 전문 검색엔진을 트리거한다!!

학문모드: arXiv와 Semantic scholar를 사용한다.

수학모드: Wolfram Alpha를 사용한다.

비디오모드:

소셜모드:

5. 결론

이번에 새로 나온 Phind의 요약 기능은 내가 원하던 AI 검색엔진 형태와 가장 유사하다. 작년 11월부터 Phind 2.0이 나온다 나온다만 반복하고 계속 모델 학습중이라는 답변만 하길래 디자인 Refresh + PDF 첨부 기능만 먼저 릴리즈하자고 속으로 욕 좀 했었는데 보고서야 왜 2.0인지 깨달았다. 멋지다.

Perplexity는 20개 넘게 참고할정도로 많은 검색 쿼리양, Semantic Scholar/Wolfram Alpha와 같은 전문 검색엔진 사용 가능, 리즈�� 모델인 R1/o3-mini 제공를 고려하면 정말 돈을 아낌없이 써서 유저에게 정보를 제공해주는 착한 회사 같다. 이정도면 자선단체 아닐까? ㅋㅋㅋㅋ 이왕에 Kagi API로 검색까지 해주면 좋겠다 싶다.

평시에는 Phind 쓰다가 어렵거나 신뢰성이 상대적으로 필요한 작업할때면 Perplexity를 이용할 것 같다. 항상 Claude 3.5 Sonet에 종속되어 살았었는데 드디어 다른 모델들도 열심히 써볼 수 있는 계기가 마련되어 즐겁다.

[cleverhottubmiracle]

[ad_1] Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More DeepSeek-R1 has surely created a lot of excitement and concern, especially for OpenAI’s rival model o1. So, we put them to test in a side-by-side comparison on a few simple data analysis and market research tasks.  To put the models on equal footing, we used Perplexity Pro Search, which now supports both o1 and R1. Our goal was to look beyond benchmarks and see if the models can actually perform ad hoc tasks that require gathering information from the web, picking out the right pieces of data and performing simple tasks that would require substantial manual effort.  Both models are impressive but make errors when the prompts lack specificity. o1 is slightly better at reasoning tasks but R1’s transparency gives it an edge in cases (and there will be quite a few) where it makes mistakes. Here is a breakdown of a few of our experiments and the links to the Perplexity pages where you can review the results yourself. Calculating returns on investments from the web Our first test gauged whether models could calculate returns on investment (ROI). We considered a scenario where the user has invested $140 in the Magnificent Seven (Alphabet, Amazon, Apple, Meta, Microsoft, Nvidia, Tesla) on the first day of every month from January to December 2024. We asked the model to calculate the value of the portfolio at the current date. To accomplish this task, the model would have to pull Mag 7 price information for the first day of each month, split the monthly investment evenly across the stocks ($20 per stock), sum them up and calculate the portfolio value according to the value of the stocks on the current date. In this task, both models failed. o1 returned a list of stock prices for January 2024 and January 2025 along with a formula to calculate the portfolio value. However, it failed to calculate the correct values and basically said that there would be no ROI. On the other hand, R1 made the mistake of only investing in January 2024 and calculating the returns for January 2025. o1’s reasoning trace does not provide enough information However, what was interesting was the models’ reasoning process. While o1 did not provide much details on how it had reached its results, R1’s reasoning traced showed that it did not have the correct information because Perplexity’s retrieval engine had failed to obtain the monthly data for stock prices (many retrieval-augmented generation applications fail not because of the model lack of abilities but because of bad retrieval). This proved to be an important bit of feedback that led us to the next experiment. The R1 reasoning trace reveals that it is missing information Reasoning over file content We decided to run the same experiment as before, but instead of prompting the model to retrieve the information from the web, we decided to provide it in a text file. For this, we copy-pasted stock monthly data for each stock from Yahoo! Finance into a text file and gave it to the model. The file contained the name of each stock plus the HTML table that contained the price for the first day of each month from January to December 2024 and the last recorded price. The data was not cleaned to reduce the manual effort and test whether the model could pick the right parts from the data. Again, both models failed to provide the right answer. o1 seemed to have extracted the data from the file, but suggested the calculation be done manually in a tool like Excel. The reasoning trace was very vague and did not contain any useful information to troubleshoot the model. R1 also failed and didn’t provide an answer, but the reasoning trace contained a lot of useful information. For example, it was clear that the model had correctly parsed the HTML data for each stock and was able to extract the correct information. It had also been able to do the month-by-month calculation of investments, sum them and calculate the final value according to the latest stock price in the table. However, that final value remained in its reasoning chain and failed to make it into the final answer. The model had also been confounded by a row in the Nvidia chart that had marked the company’s 10:1 stock split on June 10, 2024, and ended up miscalculating the final value of the portfolio.  R1 hid the results in its reasoning trace along with information about where it went wrong Again, the real differentiator was not the result itself, but the ability to investigate how the model arrived at its response. In this case, R1 provided us with a better experience, allowing us to understand the model’s limitations and how we can reformulate our prompt and format our data to get better results in the future. Comparing data over the web Another experiment we carried out required the model to compare the stats of four leading NBA centers and determine which one had the best improvement in field goal percentage (FG%) from the 2022/2023 to the 2023/2024 seasons. This task required the model to do multi-step reasoning over different data points. The catch in the prompt was that it included Victor Wembanyama, who just entered the league as a rookie in 2023. The retrieval for this prompt was much easier, since player stats are widely reported on the web and are usually included in their Wikipedia and NBA profiles. Both models answered correctly (it’s Giannis in case you were curious), although depending on the sources they used, their figures were a bit different. However, they did not realize that Wemby did not qualify for the comparison and gathered other stats from his time in the European league. In its answer, R1 provided a better breakdown of the results with a comparison table along with links to the sources it used for its answer. The added context enabled us to correct the prompt. After we modified the prompt specifying that we were looking for FG% from NBA seasons, the model correctly ruled out Wemby from the results. Adding a simple word to the prompt made all the difference in the result. This is something that a human would implicitly know. Be as specific as you can in your prompt, and try to include information that a human would implicitly assume. Final verdict Reasoning models are powerful tools, but still have a ways to go before they can be fully trusted with tasks, especially as other components of large language model (LLM) applications continue to evolve. From our experiments, both o1 and R1 can still make basic mistakes. Despite showing impressive results, they still need a bit of handholding to give accurate results. Ideally, a reasoning model should be able to explain to the user when it lacks information for the task. Alternatively, the reasoning trace of the model should be able to guide users to better understand mistakes and correct their prompts to increase the accuracy and stability of the model’s responses. In this regard, R1 had the upper hand. Hopefully, future reasoning models, including OpenAI’s upcoming o3 series, will provide users with more visibility and control. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured. [ad_2] Source link

[tumnikkeimatome]

Perplexity 100万トークンのコンテキストウィンドウ&画像・ファイルアップロード&米国内にホストされたDeepSeek-R1やGemini2.0も有効活用可能なAutoモードを無料ユーザーにも解放

大容量コンテキスト処理の実現 Perplexityは2025年2月のアップデートで、100万トークン対応のコンテキストウィンドウを無料ユーザー向けに開放しました。 この機能強化により、約70万単語のテキスト解析や3万行のコード解析が可能になりました。 過去30日間の会話履歴を保持できるため、長期的なプロジェクト管理や継続的な研究作業に最適な環境が整っています。 マルチモーダル機能の全面進化 JPEG/HEF/PNG形式の画像ファイルとPDF/テキストファイルのアップロード機能が強化されました。 デスクトップではドラッグ&ドロップ、モバイル端末ではギャラリーからの選択やリアルタイム撮影が可能です。 25MBまでのファイルを1日3回までアップロードできるため、学術論文の分析から業務資料の即時翻訳まで多様な用途に対応しています。 米国ホスティングによるセキュリティ強化 米国のデータセンターでホ…

[tumnikkeimatome]

PerplexityがDeepSeek-R1を使った新APIサービス「Sonar Reasoning」を発表：米国のデータセンターを利用し、完全なプライバシー保護を実現

Sonar ReasoningがAI開発の新時代を切り拓く Perplexityが発表した次世代APIサービス「Sonar Reasoning」は、AI開発の常識を覆す革新的なソリューションです。 DeepSeek-R1の高度な推論モデルを基盤に、リアルタイムのインターネット検索と信頼性の高い引用機能を統合した本サービスは、企業のAI開発を加速させる強力なツールとなります。 開発者はSonar…

[tumnikkeimatome]

PerplexityのR1推論モードが天安門事件について回答出来る理由とは？検閲の多くは「chat.deepseek.com」独自の追加フィルターで実施、DeepSeek-R1モデルそのものには制限無し

DeepSeekのAI検閲システムの特徴と仕組み DeepSeekは、AIチャットボットの応答に対して独自の検閲システムを実装しています。 このシステムは特にセンシティブな政治的トピックに対して厳格な制限を設けており、天安門事件や尖閣諸島問題、中国の政治指導者に関する質問に対して選択的な情報制限を行います。 ユーザーが制限対象のトピックについて質問すると、システムは自動的に回答を中断し、「回答できない」という通知を表示します。 プラットフォーム側での制御メカニズム 注目すべき点は、この制限がAIモデル自体の制限ではなく、chat.deepseek.comというプラットフォーム独自の追加フィルターによって実現されているという点です。 プラットフォーム運営側は、特定のキーワードやフレーズを検知する独自のフィルタリングシステムを実装しており、このシステムがユーザーとAIモデル間のコミュニケーシ…

[tumnikkeimatome]

PerplexityにR1推論モードが登場！欧米データーセンターにホストされたDeepSeek-R1で安全に無制限利用可能、o1推論モードも利用制限大幅緩和【Sonnet高速検索との使い分け便利】

DeepSeek-R1の安全性とプライバシー保護 Perplexity AIは、DeepSeek-R1モデルを米国とヨーロッパのデータセンターでホスティングする形で導入を開始しました。 ユーザーのデータは欧米のデータセンター内でのみ処理され、中国のサーバーには一切送信されません。 オープンソースモデルのDeepSeek-R1は、高度なセキュリティ基準を満たしつつ、無制限で利用できます。 セキュリティとコンプライアンス Perplexity AIが採用するDeepSeek-R1推論モードは、EU一般データ保護規則（GDPR）とカリフォルニア州消費者プライバシー法（CCPA）に準拠したデータ管理を行っています。 フランクフルトとバージニア州のTier…