OpenAI vs Anthropic

The role and impact of AI in product management and design with Rafie Faruq, CEO and Product Manager, and Nitish Mutha, CTO and Product Designer. Watch Using AI on the Genie AI YouTube channel (and see our AI-generated background images each week, and any screen shares of tools we tried): https://www.youtube.com/@genieai We discuss: AI in Product Management, Influence of AI on design, Knowledge based jobs, Automation of low-value work, AI in discovery phase, AI in design brainstorming, Exploration with AI, Avoidance of personal bias with AI, AI in Graphic design, AI in User Research, tagging user interviews with AI, AI in graphics creation, AI for UI wireframes, AI tools for low level tasks, AI for quick templating and filling content, AI in image/video editing, Genie AI Links: - Galileo AI - https://www.usegalileo.ai/explore - Canva’s AI features - https://www.canva.com/newsroom/news/using-canva-ai/ Links to follow guests / the show / Genie - Rafie on Linkedin - https://www.linkedin.com/in/rfaruq/ - Nitish on Linkedin - https://www.linkedin.com/in/nitishmutha/ - Alex D on Linkedin - https://www.linkedin.com/in/alexdenne/ - Alex P on Linkedin - https://www.linkedin.com/in/alex-papadopoulos-korfiatis/ Contact the show: Genie AI on Linkedin - https://www.linkedin.com/company/genie-ai Genie AI on Twitter - https://twitter.com/genieai Sign up for a free Genie account here: https://www.genieai.co/ Genie AI Careers Page (We are hiring AI roles) - https://www.genieai.co/careers

Rafie is back! In this episode, we delve into Multilingual AI and AI-enabled translation services, machine learning translation techniques, and we discuss and compare translation tools such as Google AutoML, GPT-4, Marian NMT, DeepL, Welocalize, Llama 2, Mistral and BLOOM Furthermore, we discuss the potential and impact of multilingual AI for global startups (the andreesen horowitz view is that the company of the future is 'default global'). 06:00 - Machine Translation vs LLMs 16:10 - Where GPT-4 and LLMs perform badly 29:10 - Mistral Model Weights Leak We discuss: Multilingual AI, Machine Translation, Parallel Data, Marian NMT, DeepL, Welocalize, WMT, GPT-4, Translating Prompts, Multilingual Utterances, Localization, Prompt Pipelines, GPT-5, Bloom, Parameters, Mistral, LLMs, MT Engines, Token use Links: - https://marian-nmt.github.io/ - https://www.deepl.com/en/translator - https://www.welocalize.com/do-llms-or-mt-engines-perform-translation-better/ - https://machinetranslate.org/wmt - https://machinetranslate.org/wmt23#czech--ukrainian - https://mistral.ai/news/mixtral-of-experts/ - https://cloud.google.com/translate - https://www.reddit.com/r/MachineLearning/comments/1452ziq/d_llms_in_languages_other_than_english/ - https://instruct-multilingual-frontend-dtjnk4f6ra-ue.a.run.app/ - https://heidloff.net/article/llm-languages-german/ - https://www.theverge.com/2022/11/2/23434360/google-1000-languages-initiative-ai-llm-research-project - https://www.theverge.com/2022/7/6/23194241/meta-facebook-ai-universal-translation-project-no-language-left-behind-open-source-model We mentioned as a possible example the idea of translating vs locally-generating a French employment contract in France. This was in relation to our AI legaltech startup https://www.genieai.co **Watch Using AI on YouTube (and see our daft AI-generated background images)**: https://www.youtube.com/@genieai **Share the show with friends and family using the all-platforms show link here:** https://podcasters.spotify.com/pod/show/using-ai Please follow the show, and leave us a review when you get a chance! Thanks, Alex, Alex and Rafie

Two more notable mentions in our AI market leaders series: Perplexity makes a name for itself by aggregating famous models from major entities like Google, Mistral, Anthropic, and OpenAI, posing a new alternative to giants like ChatGPT and Google search. NVIDIA's Nemo is a model-building toolkit that allows on-premise AI deployment and is hidden behind an NDA while in early access. We discuss: Perplexity, Rabbit R1, NVIDIA's Nemo, Artificial Intelligence, Large Language Models, Chatbots, Google Search, E-commerce, Machine Learning, AI Hardware, On-premise Deployment, Open AI, ChatGPT Links: - TechTarget News on Perplexity's Funding https://www.techtarget.com/searchenterpriseai/news/366565352/Perplexity-AI-secures-736-million-more-for-AI-search - Perplexity's Official Website https://www.perplexity.ai/ - NVIDIA's Nemo Developer Page https://developer.nvidia.com/nemo-llm-service-early-access - 75 Nemo Models on Hugging Face Model Hub https://huggingface.co/models?sort=trending&search=nemo **Watch Using AI on YouTube (and see our daft AI-generated background images)**: https://www.youtube.com/channel/UCHsQu4IipA7Ri2AqKcQZ1Yw

Two startups have boldly stepped into the ring with OpenAI - Cohere and Mistral. We delve into Cohere's enterprise-focused approach, backed by a rumoured $1bn funding round, and Mistral's pioneering efforts with open-source models. We also look at the broader landscape, pondering the all-important question: can these startups truly contend with market leaders in the AI space? We discuss: Cohere, Mistral, Transformer Architecture, Chatbots, Enterprise AI, Open-Source Models, OpenAI competition, The Bitter Lesson, Richard Sutton, Links: - Cohere's rumoured $1bn funding round: https://www.inc.com/sam-blum/what-coheres-possible-1-billion-investment-signals-for-ai-startups-in-2024.html - Cohere's CEO is the co-author of foundational paper "Attention Is All You Need": https://en.wikipedia.org/wiki/Attention_Is_All_You_Need - Cohere's official website: https://cohere.com/ - Mistral's official website: https://mistral.ai/ - Mistral's models on Hugging Face: https://huggingface.co/models?sort=trending&search=mistral **Watch Using AI on YouTube (and see our daft AI-generated background images)**: https://www.youtube.com/channel/UCHsQu4IipA7Ri2AqKcQZ1Yw

We discuss: AI Tool, HuggingChat, AI startup valuations, Open Source Machine Learning, Transformers Library, LLM Repository, Model Library, Git, Github, Model Hub, Llama 2, Launching AI models, business moat, deploying AI models, AI in the cloud, AI model leaderboards, Image classification, language models, sentiment analysis, ai music, nocode AI, low-code AI, model space, AI community, AI ecosystem, ML Ops, Emergent capabilities, generalist models, specialist models, GPT-3.5, GPT-4, ChatGPT, AI Exploration Links: - Competitors showing increased enquiries following the leadership Farce at Open AI with Sam Altman https://www.cnbc.com/2023/11/28/openai-competitors-hugging-face-and-cohere-report-increased-inquiries.html API Token issues https://www.theregister.com/2023/12/04/exposedhuggingfaceapitokens/ Interesting Hacker News post on hugging face (with ex-HF workers weighing in on strategy): https://news.ycombinator.com/item?id=37248895 Reddit post: Does Hugging Face do too many things?: https://www.reddit.com/r/MachineLearning/comments/160ts9g/d_is_it_me_or_huggingface_do_too_many_things/ transformers library: https://github.com/huggingface/transformers Model hub: https://huggingface.co/docs/hub/models-the-hub BLOOM model: https://huggingface.co/bigscience/bloom BigScience group: https://bigscience.huggingface.co/ In this episode of Using AI, we delve into the capabilities and offerings of Hugging Face, the leading AI repository often likened to GitHub for AI enthusiasts. Hosting over 250,000 datasets and 500,000 AI models, Hugging Face has revolutionised the AI world with its open-source initiatives. Watch Using AI on YouTube (and see our daft AI-generated background images): https://www.youtube.com/channel/UCHsQu4IipA7Ri2AqKcQZ1Yw

On this episode: Llama, AI models, Weights Leakage, Comparison, Usage, Meta vs Open AI, Data management by Meta, Open Source AI, AI research groups, Instruction, fine-tuning, RLHF, Hosting Llama, AWS Bedrock, Azure AI Models, local finetuning, and guardrails, ChatGPT, GPT-4, GPT-3, GPT-3.5 Watch Using AI on YouTube (and see our daft AI-generated background images): https://www.youtube.com/@genieai Links: Llama Access Form (meta.com): ⁠https://ai.meta.com/resources/models-and-libraries/llama-downloads/⁠ Llama 2 on Hugging face: ⁠https://huggingface.co/meta-llama⁠ Llama 2 7B Chat on Hugging Face ⁠https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat⁠ Meta uses copyright ignored books on AI training: ⁠https://www.reuters.com/technology/meta-used-copyright-ignored-books-ai-training-despite-its-own-lawyers-warnings-authors-2023-12-12/⁠ Meta and IBM's Open Source AI partnership and it's lack of inclusion of OpenAI, Google, Microsoft: ⁠https://www.theguardian.com/technology/2023/dec/05/open-source-ai-meta-ibm⁠ Yann LeCun's social media attack on OpenAI, Google, Microsoft etc.: ⁠https://www.reddit.com/r/technology/s/IumG20ZOKz⁠ Model Weights Leakage: ⁠https://www.theverge.com/2023/3/8/23629362/meta-ai-language-model-llama-leak-online-misuse⁠ “We have no moat” - Google AI researcher: ⁠https://www.semianalysis.com/p/google-we-have-no-moat-and-neither⁠ Restrictions on using Llama: ⁠https://spectrum.ieee.org/open-source-llm-not-open⁠ Welcome to another episode of Using AI. I'm your usual host, Alex Denne, and today, I'm accompanied by Alex Pap and Nitish Mutha (Founder of Legaltech Genie AI). We start by introducing Llama and discuss its weights leaking incident. We also elaborate how Llama compares to other AI models and explain how to use it. The conversation takes a turn towards the Meta vs Open AI dispute, shedding light on their differences and impact in this space. We also discuss Meta's data management and how it can actually come up trumps on both privacy strategy here, and non-copyrighted multi-lingual training data.

We don't delve too deep into the already covered demo-gate scandal, don't worry! This episode features insights from Senior ML Research Scientist Alex Pap and AI Startup Founder and CTO Nitish Mutha. We discuss: Google vs OpenAI for the long-term. GPT4 Vision, GPT5, Multimodal AI, Gemini Ultra, Gemini Pro, Gemini Nano, OpenAI Whisper, DALL·E 3, Chain of thought, Google, OpenAI, Bard, AI technology, Machine Learning Welcome to Episode 17: and the 3rd episode in our AI Market Leaders mini-series - focusing on Google vs OpenAI. This episode dives into all the details of the release in Gemini’s Ultra, Pro, and Nano (and how that affects Alphacode2, and Bard). We also delve into multi-modal technology and its promise for the future. Watch This Episode of Using AI on youtube⁠⁠https://www.youtube.com/channel/UCHsQu4IipA7Ri2AqKcQZ1Yw⁠⁠ Topics Discussed: GPT4 Vision, GPT5, Multimodal AI Gemini’s announcements and releases Google’s catch up play with OpenAI (and a little bit about what they did wrong!) Additional Resources: Gemini Technical Report in full (PDF): https://storage.googleapis.com/deepmind-media/gemini/gemini_1_report.pdf Reddit post: Testing the Gemini demo video screenshots with GPT-4: https://www.reddit.com/r/ChatGPT/comments/18d9wgn/asked_gpt4_some_logical_questions_from_the_gemini/ GPT4 + Gemini Pro for coding: https://www.reddit.com/r/ChatGPT/comments/18d773r/gpt4_and_gemini_cocreated_code_better_than_gpt4/ AI Explained’s breakdown on Youtube: https://www.youtube.com/watch?v=toShbNUGAyo&ab_channel=AIExplained GPT4 comparison controversy https://twitter.com/kenshin9000_/status/1734238211088506967?s=46 Alex D's Midjourney background (Godzilla walking through a town in the style of 'The Starry Night Painting by Vincent van Gogh: https://www.reddit.com/r/midjourney/comments/18gt00b/exactly_what_i_expected_and_more_amazing/ Running an LLM on your Pixel 8 Pro: https://store.google.com/intl/en/ideas/articles/pixel-feature-drop-december-2023/ Deep dive into AlphaCode 2 on TechCrunch: ⁠https://tcrn.ch/46G5u8w⁠

Alex and Alex discuss the use cases for OpenAI tools such as GPT-4, compared to Anthropic's Claude 2. We also offer some sneaky insights on Anthropic and their roadmap. Topics discussed: AI Startup Competition, Anthropic reportedly working on 1 million context window, ensemble LLMs, cohere, london, san francisco, Dario Amodei, Hallucination, Refusing to answer, API Tooling, Claude 1, Claude 2, Sam Altman Kebab challenge images on reddit: https://www.reddit.com/r/midjourney/comments/183hv0m/the_forgotten_kebab_challenge_1979/ Using AI on youtube - see what Alex Pap was laughing so hard at at the start of this episode https://www.youtube.com/@genieai/podcasts Links: Claude 2.1: 200K context, 2x decrease in hallucination rates, API Tool use, system prompts, cheaper prices: https://www.anthropic.com/index/claude-2-1 Anthropic will use AWS, Amazon to invest $4B https://www.aboutamazon.com/news/company-news/amazon-aws-anthropic-ai Google to invest $2B https://news.crunchbase.com/ai/google-anthropic-openai-funding-wars/

It's been a year since ChatGPT was released (yeah, feels longer than that doesn't it?). We’re going to look through it’s achievements, and then run a few episodes taking a closer look at the competition hot on OpenAI's heels So this will be the first in a short 5-part mini series where we’re talking about the alternatives to Open AI - given the Fiasco which has dominated headlines for the past 3 weeks - details of which are still emerging. Some stats on ChatGPT from the past 12 months ChatGPT was an overnight success, attracting one million users in the first five days of its launch, according to Sam Altman. Today, the chatbot has more than 100 million users. OpenAI’s website is visited more than 1.5 billion times every month, according to SimilarWeb data as of September. Nearly half of Americans have heard about ChatGPT, according to a recent poll by YouGov. In February 2023, OpenAI released ChatGPT Plus at $20 per month. Plus users can access GPT-4, while the free version of ChatGPT is powered by GPT-3.5. OpenAI is valued by private investors at up to $90 billion. That’s nearly 35 times what the company was worth two years ago. OpenAI’s headcount has more than doubled since launching ChatGPT. Currently it employs about 770. Using AI on youtube - see what Alex Pap was laughing so hard at at the start of this episode https://www.youtube.com/@genieai/podcasts Topics discussed: GPT store, Sam Altman, Dario Amodei, Anthropic, Elon Musk, Greg Brockman, Darth Vader, Fired by the board, Open AI CEO Links: timeline of the Open AI Fiasco https://www.axios.com/2023/11/22/openai-microsoft-sam-altman-ceo-chaos-timeline Open AI board tried to hire Anthropic CEO https://arstechnica.com/tech-policy/2023/11/report-openai-tried-and-failed-to-hire-anthropic-ceo-to-replace-sam-altman/

We discuss: AI Coding Tools, Code-Interpreter, Python, Regex, Network errors, Machine Learning, ChatGPT4, HTML Parsing, Github Copilot vs GPT-4 In this episode, we delve into a fascinating experiment where I, Alex Denne, under the watchful guidance of ML Research Scientist Alex Pap, try to get AI to writing some regex that can be run locally on my machine using python, on millions of documents. The goal? To extract matching text from millions of HTML files. It all inadvertently unfolds into an intriguing journey of trial and error. For the no-code listeners, this episode offers first-hand insights into the application and limitations of AI coding tools and code interpreters (and why, for now, you probably still need technical help like Alex D did!) At the outset, we were greeted by a seemingly promising result - a neat CSV file with the right column names but no entries as the AI successfully claimed to extract definitions only to produce an empty result. In an attempt to further probe, the AI was prompted to read the first 100 characters for potential matches. Alas! In lieu of any found matches, it concluded the document must be lengthy and gracefully tapped out. In addition, we had to deal with several network errors that may be attributed to the reported DDoS attacks on OpenAI. After multiple hits and misses, we decided to start afresh with a new approach. We didn't exactly strike gold, but we learned a lot. Through this episode, we touch upon topics like ChatGPT4 and the wonderful feature of 'dragging and dropping' files into GPT-4 Turbo. Watch USING AI on youtube: https://www.youtube.com/channel/UCHsQu4IipA7Ri2AqKcQZ1Yw