Almost Timely News: Where Generative AI and Language Models are Probably Going in 2024 (2023-12-10) :: View in Browser
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What’s On My Mind: Where Generative AI and Language Models are Probably Going in 2024
As it’s heading towards the end of the year and a lot of people are starting to publish their end of year lists and predictions, let’s think through where things are right now with generative AI and where things are probably going.
I wrote yesterday on LinkedIn a bit about adversarial models, and I figured it’s worth expanding on that here, along with a few other key points. We’re going to start off with a bit of amateur – and I emphasize amateur as I have absolutely no formal training – neuroscience, because it hints at what’s next with language models and generative AI.
Our brain isn’t just one brain. We know even from basic grade school biology that our brain is composed of multiple pieces – the cerebrum, the cerebellum, the brain stem, etc. And within those major regions of the brain, you have subdivisions – the occipital lobe, the parietal lobe, and so on. Each of these regions performs specific tasks – vision, language, sensory data, etc. and those regions are specialized. That’s why traumatic brain injury can be so debilitating, because the brain isn’t just one monolithic environment. It’s really a huge cluster of small regions that all perform specific tasks.
If you look at the brain and recognize that it is really like 15 brains working together in a big network, you start to appreciate how complex it is and how much we take for granted. Just the simple act of opening this email or video and consuming it requires motor skills, language skills, vision skills, auditory skills, and high level thinking and processing. It’s millions, maybe billions of computations per second just to consume a piece of content.
Why do we care about this? Because this perspective – of a massive network of computer models all integrated together – is where generative AI is probably going and more important, where it needs to go if we want AI to reach full power.
In the first half-decade of generative AI – because this all began in earnest in 2017 with Google’s release of the transformers model – we focused on bigger and better models. Each generation of language model got bigger and more complex – more parameters, more weights, more tokens, etc. This model has 175 billion parameters, that model was trained on 1 trillion tokens. Bigger, bigger, bigger. And this worked, to a degree. Andrej Karpathy of OpenAI recently said in a talk that there doesn’t appear to be any inherent limit to the transformers architecture except compute power – bigger means better.
Except bigger means more compute power, and that’s not insignificant. When the consumer of generative AI uses ChatGPT to generate some text or DALL-E to make an image, what happens behind the scenes is hidden away, as it should be. Systems generally shouldn’t be so complex and unfriendly that people don’t want to use them. But to give you a sense of what’s REALLY happening behind the scenes, let me briefly explain what happens. This is kind of like going behind the lanes at a bowling alley and looking at how absurdly complex the pin-setting machine is.
First, you need to have a model itself. The model is usually just a really big file. For open source generative AI, I keep models on an external hard drive because they’re really big files.
Next, you need a model loader to load the model and provide some kind of interface for it. The two interfaces I use for open source models are LM Studio for general operations and KoboldCPP for creative writing. You then load the model on your laptop and configure its settings. Again, for a consumer interface like ChatGPT, you never see this part. But if you’re building and deploying your own AI inside your company, this part is really important.
You’ll set up things like how much memory it should use, what kind of computer you have, how big the model’s environment should be, how much working memory it should have, and how it should be made available to you:
And then once it’s running, you can start talking to it. When you open a browser window to ChatGPT, all this has happened behind the scenes.
Behind the scenes, as you interact with the model, you can see all the different pieces beginning to operate – how it parses our prompt, how it generates the output one fragment of a word at a time, how much of the working memory has been used up, and how many of these things occur:
Watching these systems do their thing behind the scenes makes it abundantly clear that they are not self-aware, not sentient, have no actual reasoning skills, and are little more than word prediction machines. Which means that a lot of the characteristics we ascribe to them, they don’t actually have.
Bigger models take more resources to run, and at the end of the day, even the biggest, most sophisticated model is still nothing more than a word prediction machine. It’s very good at what it does, but that is literally all it does.
Which means if we have tasks that aren’t word and language-based tasks, language models aren’t going to necessarily be good at them. What we need to be thinking about is what are known as agent networks.
An agent network is an ecosystem of AI and non-AI components, all meshed together to create an app that’s greater than the sum of its parts. It has a language model to interface with us. It has databases, web browsers, custom code, APIs… everything that a language model might need to accomplish a task. If we think of the language model as the waiter interfacing with us, the agent network is the back of house – the entire kitchen and everyone and everything that does all the cooking.
Just as a waiter rarely, if ever, goes to the line and cooks, a language model should not be going to the back of house to do operations that are not language. Except when we think about tools like ChatGPT, that’s exactly what we expect of them – and why we get so disappointed when they don’t do as we ask. We assume they’re the entire restaurant and they’re really just front of house.
So what does this have to do with the future of generative AI? Well, let’s put a couple of things together. Bigger models are better but more costly. Recent research from companies like Mistral have demonstrated that you can make highly capable smaller models that, with some tuning, can perform as good or better than big models for the same task, but at a fraction of the cost.
For example, much has been made of the factoid that’s been floating around recently that generating an image with AI uses the same amount of power as charging your phone. This was cited from a piece by Melissa Heikkila in the MIT Technology Review from a study that has not been peer-reviewed yet. Is that true? It really depends. But it is absolutely true that the bigger the model, the more power it consumes and the slower it is (or the more powerful your hardware has to be to run it).
If you can run smaller models, you consume less power and get faster results. But a smaller model tends to generate less good quality results. And that’s where an agent network comes in. Rather than having one model try to be everything, an agent network has an ensemble of models doing somewhat specialized tasks.
For example, in the process of writing a publication, we humans have writers, editors, and publishers. A writer can be an editor, and an editor can be a publisher, but often people will stick to a role that they’re best at. AI models are no different in an agent network. One model generates output, another model critiques it, and an third model supervises the entire process to ensure that the system is generating the desired outputs and following the plan.
This, by the way, is how we make AI safe to use in public. There is no way under the current architecture of AI models to make a model that is fully resistant to being compromised. It’s simply not how the transformers architecture and human language work. You can, for example, tell someone not to use racial slurs, but that doesn’t stop someone from behaving in a racist manner, it just restricts the most obvious vocabulary. Just as humans use language in an infinite number of ways, so too can language models be manipulated in unpredictable ways.
Now, what is an agent network starting to sound an awful lot like? Yep, you guessed it: the human brain. Disabusing ourselves of the notion of one big model to rule them all, if we change how we think about AI to mirror the way our own brains work, chances are we’ll be able to accomplish more and consume fewer resources along the way. Our brain has dozens of regions with individual specializations, individual models if you will. Networked together, they create us, the human being. Our AI systems are likely to follow suit, networking together different models in a system that becomes greater than the individual parts.
Business is no different, right? When you’re just starting out, it’s you, the solo practitioner. You do it all, from product to service to accounting to legal to sales. You’re a one person show. But as time goes on and you become more successful, your business evolves. Maybe you have a salesperson now. Maybe you have a bookkeeper and a lawyer. Your business evolves into an agent network, a set of entities – people, in the case of humans – who specialize at one type of work and interface with each other using language to accomplish more collectively than any one person could do on their own.
This is the way generative AI needs to evolve, and the way that much of the movement is beginning to. While big companies like OpenAI, Meta, and Google tout their latest and greatest big models, an enormous amount is happening with smaller models to make AI systems that are incredibly capable, and companies & individuals who want to truly unlock the full power of AI will embrace this approach.
It’s also how you should be thinking about your personal use of AI, even if you never leave an interface like ChatGPT. Instead of trying to do everything all at once in one gigantic prompt, start thinking about specialization in your use of AI. Even something as simple as your prompt library should have specializations. Some prompts are writing prompts, others are editing prompts, and still others are sensitivity reader prompts, as an example. You pull out the right prompts as needed to accomplish more than you could with a single, monolithic “master prompt”. If you’re a more advanced user, think about the use of Custom GPTs. Instead of one big Content Creation GPT, maybe you have a Writer GPT, an Editor GPT, a critic GPT, etc. and you have an established process for taking your idea through each specialized model.
As we roll into the new year, think of AI not as “the best tool for X”, but what ensemble, what toolkit has the pieces you need to accomplish what you want. You’ll be more successful, faster, than people looking for the One Model to Rule Them All.
Also, I’m going to take a moment to remind you that my new course, Generative AI for Marketers, goes live on December 13. If you register before the 13th with discount code EARLYBIRD300, you save $300 – a whopping 38% – off the price once the course goes live. The first lesson is free, so go sign up to see what’s inside the course and decide whether it’s right for you or not, but I will say of all the courses I’ve put together, this is my favorite yet by a long shot.
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ICYMI: In Case You Missed it
Besides the new Generative AI for Marketers course I’m relentlessly flogging, I recommend
- You Ask, I Answer: Is the Generative AI Space Ripe for Consolidation?
- You Ask, I Answer: Future of Retrieval Augmented Generation AI?
- You Ask, I Answer: Answering the Same Generative AI Questions?
- Almost Timely News, December 3, 2023: AI Content Is Preferred Over Human Content
- You Ask, I Answer: Is Art Made by AI Really Art?
- You Ask, I Answer: Open Weights, Open Source, and Custom GPT Models?
- Mind Readings: The Dangers of Excluding Your Content From AI
- So What? Generative AI in Sales
12 Days of Data
As is tradition every year, I start publishing the 12 Days of Data, looking at the data that made the year. Here’s the first 5:
- 12 Days of Data 2023 Day 1: Private Social Media Communities
- 12 Days of Data 2023 Day 2: Instagram for Brands
- 12 Days of Data 2023 Day 3: Instagram for Influencers
- 12 Days of Data 2023 Day 4: Global ChatGPT Conversations
- 12 Days of Data 2023 Day 5: Content Republishing
Skill Up With Classes
These are just a few of the classes I have available over at the Trust Insights website that you can take.
- 🦾 Generative AI for Marketers
- 👉 Google Analytics 4 for Marketers
- 👉 Google Search Console for Marketers (🚨 just updated with AI SEO stuff! 🚨)
- ⭐️ The Marketing Singularity: How Generative AI Means the End of Marketing As We Knew It
- Powering Up Your LinkedIn Profile (For Job Hunters) 2023 Edition
- Measurement Strategies for Agencies
- Empower Your Marketing With Private Social Media Communities
- Exploratory Data Analysis: The Missing Ingredient for AI
- How AI is Changing Marketing, 2022 Edition
- How to Prove Social Media ROI
- Proving Social Media ROI
- Paradise by the Analytics Dashboard Light: How to Create Impactful Dashboards and Reports
Advertisement: Generative AI Workshops & Courses
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Workshops: Offer the Generative AI for Marketers half and full day workshops at your company. These hands-on sessions are packed with exercises, resources and practical tips that you can implement immediately.
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Get Back to Work
Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.
- Brand Marketing Manager at Cypress HCM
- Brand Marketing Manager at STAND 8 Technology Services
- Chief Revenue Officer at D33P Search Group
- Chief Revenue Officer at Talence Group
- Growth Marketing Manager at 80Twenty
- Paid Search Director at AMP Agency
- Product Marketing Manager at Incode Technologies
- Segment Marketing Manager at Western Digital
- Senior Salesforce Marketing Consultant at Senovo IT Ltd
What I’m Reading: Your Stuff
Let’s look at the most interesting content from around the web on topics you care about, some of which you might have even written.
Social Media Marketing
- 4 TikTok Analytics Tools That Do the Math for You
- Future of Social Media Predictions for 2024 via Sprout Social
- TikTok from Creator Fund to Creativity Program via Sprout Social
Media and Content
- Mastering Executive Communications: Lessons from OpenAI
- Charting the Course for Communications In an AI-Driven Era
- Navigating the Content Jungle: My Dance with AI in Book Writing
SEO, Google, and Paid Media
- SEO Writing: 8 Steps to Create Search-Optimized Content
- Google Offers 3 Tips For Checking Technical SEO Issues
- Eric Schwartzman on the Myth of Organic SEO via Mktg Over Coffee
Advertisement: Business Cameos
If you’re familiar with the Cameo system – where people hire well-known folks for short video clips – then you’ll totally get Thinkers One. Created by my friend Mitch Joel, Thinkers One lets you connect with the biggest thinkers for short videos on topics you care about. I’ve got a whole slew of Thinkers One Cameo-style topics for video clips you can use at internal company meetings, events, or even just for yourself. Want me to tell your boss that you need to be paying attention to generative AI right now?
Tools, Machine Learning, and AI
- Announcing Purple Llama: Towards open trust and safety in the new world of generative AI
- Google’s Gemini AI launch marred by questions over capabilities via VentureBeat
- New AI tool aims to democratise high-res image generation
Analytics, Stats, and Data Science
- The Ultimate Guide to Power BI Visualizations via Analytics Vidhya
- 8 GitHub Alternatives for Data Science Projects via Analytics Vidhya
- Elon Musk’s xAI Firm Faces Speculation Over Funding
All Things IBM
- Six ways AI can influence the future of customer service via IBM Blog
- How OLAP and AI can enable better business via IBM Blog
- IBM Is Planning to Build Its First Fault-Tolerant Quantum Computer by 2029
Dealer’s Choice : Random Stuff
- ‘Piano Man’ (BILLY JOEL) Song Cover by The HSCC Feat. Danny Lopresto – YouTube
- MLX — MLX 0.0.3 documentation
- David B. Thomas Obituary – Visitation & Funeral Information
How to Stay in Touch
Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:
- My blog – daily videos, blog posts, and podcast episodes
- My YouTube channel – daily videos, conference talks, and all things video
- My company, Trust Insights – marketing analytics help
- My podcast, Marketing over Coffee – weekly episodes of what’s worth noting in marketing
- My second podcast, In-Ear Insights – the Trust Insights weekly podcast focused on data and analytics
- On Threads – random personal stuff and chaos
- On LinkedIn – daily videos and news
- On Instagram – personal photos and travels
- My free Slack discussion forum, Analytics for Marketers – open conversations about marketing and analytics
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Events I’ll Be At
Here’s where I’m speaking and attending. Say hi if you’re at an event also:
- Social Media Marketing World, San Diego, February 2024
- MarketingProfs AI Webinar, Online, March 2024
- Australian Food and Grocery Council, Melbourne, May 2024
- MAICON, Cleveland, September 2024
Events marked with a physical location may become virtual if conditions and safety warrant it.
If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.
Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.
Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.
Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.
My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.
Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.
See you next week,
Christopher S. Penn
You might also enjoy:
- Almost Timely News, November 19, 2023: A Deep Dive on Prompt Libraries
- How To Determine Whether Something is a Trend
- What's the Difference Between Social Media and New Media?
- Google Analytics 4: User Medium vs Session Medium
- What's the Difference Between Social Media and New Media?
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