Tag: genAI

Let gen AI and the Internet be your travel guide in this day and age

In the old days of travel (the 1980s), travellers would depend on printed guides from Fodor’s or Lonely Planet to be your travel guide. They were good, but very general.

Now with tools like ChatGPT and CoPilot, you can make your own highly specific guides just by asking them for it. For example, if you were travelling to Paris, you can ask it for walking tours of neighbourhoods like the Marais and prompt it to give you cafes to see or places to shop or museums to visit along your walk.  (Sure you can also Google that information, but by starting with gen AI, you can get it in a format you want.)

Likewise, you may think: what do I say to people once I get there? For that, I asked CoPilot:

Hello. You are a French to English translator. Please provide me with some common French phrases for that are useful for 1) when you arrive at the airport 2) when you come to your hotel 3) at a cafe 4) at a restaurant 5) at a museum 6) in a cab or uber

and it came back with such phrases as:

1. At the Airport

Bonjour, où est la sortie ? — Hello, where is the exit?
Où sont les taxis ? — Where are the taxis?
Où est la station de métro ? — Where is the subway station?
Je cherche la porte d’embarquement. — I’m looking for the boarding gate.
Où est la récupération des bagages ? — Where is baggage claim?
…
3. At a Café

Un café, s’il vous plaît. — A coffee, please.
Je voudrais un croissant. — I’d like a croissant.
Est-ce que je peux avoir l’addition ? — Can I have the bill?
C’est délicieux ! — It’s delicious!
Est-ce que vous avez du lait végétal ? — Do you have plant-based milk?

….

These were good, so I followed up with

Can you expand each list from 5 items to 10? Merci!

and it replied with this and more.

Parfait, Bernie ! Here’s an expanded list of 10 useful French phrases for each of the six travel situations, with their English translations:
…
4. At a Restaurant

Une table pour deux, s’il vous plaît. — A table for two, please.
La carte, s’il vous plaît. — The menu, please.
Je suis allergique à… — I’m allergic to…
Quel est le plat du jour ? — What is the dish of the day?
L’addition, s’il vous plaît. — The bill, please.
Je voudrais réserver une table. — I’d like to book a table.
Puis-je voir la carte des vins ? — May I see the wine list?
Est-ce que le service est compris ? — Is the tip included?
Je prends ce plat. — I’ll have this dish.
C’était excellent, merci ! — It was excellent, thank you!

Those are just two examples of how you can gen AI to help you with your travel plans. But really, the sky is the limit on what advice you can get from them.

N.B. Do I recommend you double check these guides outside of gen AI to make sure they are correct? Bien sûr! I found the answers we got back were good, but you’d be wise to use Google/Bing/Duck Duck Go  directly to confirm the advice you got.

How generative AI works (it’s not designed, at least the way you think)

How generative AI works is likely not how you think it works.

What led me to state this was two discussions I’ve had this week about the “design” of gen AI. I think the “design” conclusion that people come up with is based on emergent behaviours of the system. You can shape this behaviour in a number of ways, based on the data you feed the system or some ways you configure the software being trained. However at best you are influencing the behaviour of the system, vs designing the behaviour of the system.

In some ways it’s like taking a bucket of seeds and tossing them around a large area of a field. If you took only seeds of one or two flowers and distributed the seeds so that only these one or two flowers and grew there, you could say you designed the garden to grow these flowers. Likewise, if you divided up the land into rows and planted one type of seed in each row, you can say you designed the garden. However if you have a wide range of seeds included in your bucket and you don’t target the planting of the seeds but just toss them into the ground, it will no longer be considered designed.

That’s why I think gen AI is not really designed. It’s a alot like a big bucket of random seeds not planted in any order. What design you see there is likely how you look at it.

P.S. If you want to explore more on how gen AI works, see this. For a great example of how a gen AI system is built from the ground up, see this.

 

A guide to generative AI and LLM (large language models), February 2025


I decided to go through all my posts on AI and pull out information that would be useful to anyone wanting to learn more about generative AI (often referred to as gen AI or genAI) and the LLMs they run. If you have used chatGPT, you have used genAI. But there’s much more to the technology than what you find on that site. To see what I mean, click on any of the blue underlined text and you will be taken to a site talking about something to do with gen AI.

Enjoy!

Tutorials/Introductions: for people just getting started with gen AI, I found these links useful: how generative AI works, what is generative AI, how LLMs works,  sentence word embeddings which kinda shows  how LLM works, best practices for prompt engineering with openai api,  a beginners guide to tokens, a chatGPT cheat sheet,  demystifying tokens: a beginners guide to understanding AI building blocks, what are tokens and how to count them, how to build an llm rag pipeline with llama 2 pgvector and llamaindex and finally this: azure search openai demo. (Some of these are introductory for technical people – don’t worry if you don’t understand all of them.)

For people who are comfortable with github, this is a really good repo / course on generative AI for beginners. (and check out these other repositories here, too). This here on the importance of responsible AI. and here’s a step by step guide to using generative AI in your business, here.

Prompts and Prompt Engineering: if you want some guidance on how best to write prompts as you work with gen AI, I recommend this, this, this, this, this, this, this, and this.

Finally:  Here’s the associated press AI guidelines for journalists. This here’s a piece on how the  Globe and Mail is using AI in the newsroom. Here’s a how-to on using AI for photo editing. Also, here’s some advice on writing better ChatGPT prompts. How Kevin Kelly is using  AI as an intern, as told to Austin Kleon. A good guide on  how to use AI to do practical stuff.

Note: AI (artificial intelligence) is a big field incorporating everything from vision recognition to game playing to machine learning and more. Generative AI is a part of that field. However nowadays when we talk of AI people usually mean gen AI. A few years ago it was machine learning and before that it was expert systems. Just something to keep in mind as you learn more about AI and gen AI in particular.

 

AI: from the era of talking to the era of doing

AI a year ago was mostly talking about AI. AI today is about what to do with the technology.

There are still good things being said about AI. This in depth piece by Navneet Alang here in the Walrus was the best writing on AI that I’ve read in a long time. And this New York Times piece on the new trend of AI slop got me thinking too. But for the most part I’ve stopped reading pieces on what does AI mean, or gossip pieces on OpenAI.

Instead I’ve been focused on what I can do with AI. Most of the links that follow reflect that.

Tutorials/Introductions: for people just getting started with gen AI, I found these links useful: how generative AI works, what is generative AI, how LLMs work, best practices for prompt engineering with openai api,  a beginners guide to tokens, a chatGPT cheat sheet, what are generative adversarial networks gans, demystifying tokens: a beginners guide to understanding AI building block, what are tokens and how to count them, how to build an llm rag pipeline with llama 2 pgvector and llamaindex and finally this: azure search openai demo.

Software/Ollama: Ollama is a great tool for experimenting with LLMs. I recommend it to anyone wanting to do more hands on with AI. Here’s where you can get it. This will help you with how to set up and run a local llm with ollama and llama 2. Also this: how to run llms locally on your laptop using ollama. If you want to run it in Docker, read this. Read this if you want to know where Ollama stores it’s models. Read this if you want to customize a model. If you need to uninstall Ollama manually. you want this.

Software/RAG: I tried to get started with RAG fusion here and was frustrated. Fortunately my manager recommended a much better and easier way to get working with RAG by using this no-code/low-code tool, Flowise. Here’s a guide to getting started with it.

Meanwhile, if you want more pieces on RAG, go here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, and here. I know: it’s a lot. But I found those all those useful, and yes, each “here” takes you to a different link.

Software/embedding: if you are interested in the above topics, you may want to learn more about vector databases and embeddings. Here are four good links on that: one  two,  three, four.

Software/models: relatedly, here’s four good links on models (mostly mixtral which I like alot): mixtral, dolphin 25 mixtral 8x7b,  dolphin 2 5 mixtral 8x7b uncensored mistral , Mistral 7B Instruct v0.2 GGUF,plus a comparison of models.

Software/OpenAI: while it is great to use Ollama for your LLM work, you may want to do work with a SaaS like OpenAI. I found that when I was doing that, these links came in handy: how OpenAI’s billing works, info on your OpenAI  api keys, how to get an OpenAI key, what are tokens and how to count them, more on tokens, and learn OpenAI on Azure.

Software/Sagemaker: here’s some useful links on AWS’s Sagemaker, including pieces on what is amazon sagemaker, a tutorial on it, how to get started with this quick Amazon SageMaker Autopilot, some amazon sagemaker examples , a number of pieces on sagemaker notebooks such as creating a sagemaker notebook, a notebooks comparison, something on distributed training notebook examples and finally this could be helpful: how to deploy llama 2 on aws sagemaker.

Software in general: these didn’t fit any specific software category, but I liked them. There’s something on python and GANs, on autogen, on FLAML, on python vector search tutorial gpt4 and finally how to use ai to build your own website!

Prompt Engineering: if you want some guidance on how best to write prompts as you work with gen AI, I recommend this, this, this, this, this, this, this, and this.

IT Companies: companies everywhere are investing in AI. Here’s some pieces on what Apple, IBM, Microsoft and…IKEA…are doing:

Apple:  Microsoft copilot app is available for the iphone and ipad.

IBM: Here’s pieces on ibm databand with self learning for anomaly detection;  IBM and AI and the EI; IBM’s Granite LLM; WatsonX on AWS; installing watsonX; watsonx-code-assistant-4z; IBM Announces Availability of Open Source Mistral AI Model on watsonx; IBM’s criteria for adopting gen AI ;probable root cause accelerating incident remediation with causal AI; Watsonx on Azure; Watsonx and litellm; and conversational ai use cases for enterprises 

IKEA:  here’s something on the IKEA ai assistant using chatgpt for home design.

Microsoft:  from vision to value realization –  a closer look at how customers are embracing ai transformation to unlock innovation and deliver business outcomes, plus an OpenAI reference.

Hardware: I tend to think of AI in terms of software, but I found these fun hardware links too. Links such as: how to run chatgpt on raspberry pi; how this maker uses raspberry pi and ai to block noisy neighbors music by hacking nearby bluetooth speakers; raspberry pi smart fridge uses chat gpt4 to keep track of your food. Here’s something on the rabbit r1 ai assistant. Here’s the poem 1 AI poetry clock which is cool.

AI and the arts: AI continues to impact the arts for ways good and bad. For instance, here’s something on how to generate free ai music with suno. Relatedly here’s a piece on gen ai, suno music, the music industry, musicians and copyright. This is agood piece on artists and AI in the Times. Also good:  art that can be easily copied by AI is meaningless, says Ai Weiwei. Over at the Washington Post is something on AI image generation. In the battle with AI, here’s how artists can use glaze and nightshade to stop ai from stealing your art. Regarding fakes, here’s a piece on Taylor Swift and ai generated fake images. Speaking of fake, here’s something on AI and the porn industry. There’s also this  piece on generative ai and copyright violation.

Finally: I was looking into the original Eliza recently and thought these four links on it were good: one, two, three and four. Then there’s these stories: on AI to help seniors with loneliness, the new york times / openai/  microsoft lawsuit, another AI lawsuit involving air canada’s chatbot. stunt AI (bot develop software in 7minutes instead of 4 weeks) and a really good AI hub: chathub.gg.

Whew! That’s a tremendous amount of research I’ve done on AI in the last year. I hope you find some of it useful.