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

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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:

1. Llama Access Form (meta.com): ⁠https://ai.meta.com/resources/models-and-libraries/llama-downloads/⁠
2. Llama 2 on Hugging face: ⁠https://huggingface.co/meta-llama⁠
3. Llama 2 7B Chat on Hugging Face ⁠https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat⁠
4. 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/⁠
5. 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
6. Yann LeCun's social media attack on OpenAI, Google, Microsoft etc.: ⁠https://www.reddit.com/r/technology/s/IumG20ZOKz⁠
7. Model Weights Leakage: ⁠https://www.theverge.com/2023/3/8/23629362/meta-ai-language-model-llama-leak-online-misuse⁠
8. “We have no moat” - Google AI researcher: ⁠https://www.semianalysis.com/p/google-we-have-no-moat-and-neither⁠
9. 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 youtube
⁠⁠https://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/