Using AI Alex Denne, Rafie Faruq and Alex Papadopoulos Korfiatis

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

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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


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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


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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


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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


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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.


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