Almost Timely News: 🗞️ Next Generation AI Models

(2024-09-15)

Sep 15, 2024

Almost Timely News: 🗞️ Next Generation AI Models (2024-09-15) :: View in Browser

👉 Watch my new talk from MAICON 2024 about why open models are your answer to data privacy and AI

Content Authenticity Statement

100% of this week's newsletter was generated by me, the human. Learn why this kind of disclosure is a good idea and might be required for anyone doing business in any capacity with the EU in the near future.

Watch This Newsletter On YouTube 📺

Click here for the video 📺 version of this newsletter on YouTube »

Click here for an MP3 audio 🎧 only version »

What's On My Mind: Next Generation AI Models

At the start and end of the 2024 Marketing AI Conference (MAICON), Paul Roetzer and team asked these ten questions:

How will the next generation of Al models affect you, your team and your company?
How will generative Al model advancements impact creative work, and creativity?
How will consumer information consumption and buying behaviors change?
How will consumer changes impact search, advertising, publishing, etc.?
How will Al-related copyright and IP issues affect marketers?
How will Al impact marketing strategies and budgets?
How will Al impact marketing technology stacks?
How will marketing jobs change?
How will Al impact agencies?
How will Al impact brands?

Each of these questions is practically a book unto itself, so over the next few pieces of content, we’ll tackle some of them. Every person will and should have their own answers to these questions - your answers should vary from mine based on how you use AI.

So let’s dig (I should start using delve unironically) into the first big one:

How will the next generation of Al models affect you, your team and your company?

Part 1: What is a Next Generation AI Model?

The first big part of the question we have to tackle is what constitutes a next generation model. What exactly does this mean?

Today’s models fall under two fundamental architectures: transformers and diffusers. Transformers predict the next token in a sequence based on all the previous tokens. Diffusers essentially compare noise to images they've already seen and chip away at the noise until they arrive at a blend of whatever the prompt is.

After the release of OpenAI o1, many people are talking about the next generation of models as reasoning models, in alignment with the march towards artificial general intelligence, the ability for AI to be smarter than people at any given task. That's certainly one dimension of next generation models, but not the only one.

What is reasoning, and why do we care? Today's models, when naively prompted, do not do anything more than they're told to do. Give them a simple prompt, they generate a simple answer. New models like o1 have a certain type of reasoning, known as chain of thought (aka "think things through step by step") built it, as a way to get generally better results out of AI that require thought.

An interesting side effect of "reasoning" pointed out by my friend Ruby King is that reasoning is only applicable to some tasks. Others have noted, for example, that o1 is producing better reasoning but less creative writing. This makes logical sense; reasoning is all about finding logical steps to solve a problem. Logic inherently involves some level of probability.

Creativity, in many ways, involves the antithesis of probability. What makes something creative is often something low probability. A piece of music that is made entirely of high probability notes is boring and uninteresting. A piece of music that has surprises in it - key changes, tempo changes, things that are less common - is more interesting.

That's one dimension of a next generation model - foundation models may split into different types of cognitive tasks. Some models may have more creative outputs at the expense of logic, and others may have the reverse.

But that's only one dimension. Other next generation models may ford significant gaps in today's model capabilities. For example, transformers and diffusion models don't work well together. If you've ever tried to make a slide deck with generative AI, you know what a frustrating and ultimately fruitless task that is.

Why? Because transformer models - predicting the next word, effectively - are fundamentally different than diffusion models, which predict what pixels should be nearby based on words in the prompt. The net result is that you either get slide decks that are all words, or you get clip art slides that are trite and meaningless.

On top of that, creating a slide deck is both art and science, both reasoning - creating a logical flow of a presentation - and creative, creating surprises along the way.

Today's models may be multimodal, but they have exceptional difficulty crossing multiple boundaries at the same time. Future models, next generation models, should be able to do this more fluently, but for today, easily creating a logical AND creative slide deck is out of reach for many models and tools.

Next generation models will also have substantially larger working memories. Already, Google's Gemini 1.5 Pro has a working memory of up to 2 million tokens, or 1.5 million words. They've extended that window experimentally to 10 million tokens, or about 7.5 million words. Once models get that large, they start to take on even greater capabilities and draw even more connections within data.

Finally, next generation models will be taking in multiple different data types. Right now, transformers models handle tokens, and mostly text. Diffusers handle mostly images. But we're seeing the advent of models that have sensory inputs - vision, audio, spatial awareness, tactile awareness, olfactory sensors. As more sensory data makes its way into model training, expect models to have greater capabilities that rely on sensory knowledge.

Part 2: How Will We Use Next Generation Capabilities?

So we have several different dimensions of next generation models, from reasoning vs. creativity, true multimodal, and sensory data. How will we use these capabilities?

Every time we add new capabilities, we can infer several things. First, we'll use those new capabilities at an increasing rate, proportional to how we think the models will do. People leapt to use models like GPT-3.5-Turbo back in the day, even when it was clear it had substantial limitations. Today, we use models like GPT-4omni or Gemini 1.5 at much greater capacities because of the models capabilities.

This in turns mean that we'll turn over more tasks to machines based on those capabilities. Suppose, for example, we have models that have true olfactory understanding. A perfect use case for such a model would be detecting things like spoiled food, gas leaks, etc. Anything that a person could smell, a model that has olfactory data could also smell. What does that change? How will we use it differently?

Smell and taste, for example, are highly correlated. Today, language models are capable of processing enormous amounts of text data. It's trivial to write a book review with a language model. Could we have a model with olfactory data provide food reviews? Yes.

The potential of splitting foundation models into reasoning versus creative has already happened in the open models world; many people have done fine-tunes of open models like Llama 3.1 to make them more creative writers (less reasoning) or better coders (less improbability). Foundation models following suit is a logical thing.

The big change will be overcoming boundaries between model types. There are experiments in labs now on "transfusion" models that blur the line between transformer (words) and diffuser (images). How well these perform compared to their pure progenitors remains to be seen, but early research suggests strong capabilities.

Part 3: What are the Second Order Effects of Next Generation Models?

Now, let's dig into those second order effects. For those unfamiliar, a second order effect is a consequence, often unforeseen, of a major change. For example, a second order effect of the mobile phone was that the offline world became part of the online world, a hybridization we see today. Go to a restaurant and scan a code to download the latest menu, or order something in an app that arrives at your front door.

Job loss is one such second order effect of generative AI. We see this in professions like software development, which has had massive declines in hiring demand over the last two years. This happens in part because AI is so empowering to developers, it increases their productivity 2x-5x easily. What happens when you have an employee who does the work of five people? You don't hire four more people.

What this means for you and me is that we have to continue identifying what value we provide that a machine cannot. The biggest, easiest win is our ability to build meaningful relationships with each other.

New job creation is also a second order effect. A colleague of mine who has a PhD in a relatively arcane field has been working for an AI company writing text just for AI. Their work is never made public, never released, never consumed by another human. Instead, it helps this company make a bespoke fine-tune with data that no one else has.

Election tampering and disinformation are second order effects, and as models become more capable, the ability to do bad things with them increases at exactly the same rate as the ability to do good things.

As I often say in my keynotes, paraphrasing the Captain America movie: AI is an amplifier. It makes the good into better and the bad into worse. Every capability we add to AI amplifies what we can do with the tools, for good or ill.

How Was This Issue?

Rate this week's newsletter issue with a single click. Your feedback over time helps me figure out what content to create for you.

Here's The Unsubscribe

It took me a while to find a convenient way to link it up, but here's how to get to the unsubscribe.

If you don't see anything, here's the text link to copy and paste:

https://almosttimely.substack.com/action/disable_email

Share With a Friend or Colleague

If you enjoy this newsletter and want to share it with a friend/colleague, please do. Send this URL to your friend/colleague:

https://www.christopherspenn.com/newsletter

For enrolled subscribers on Substack, there are referral rewards if you refer 100, 200, or 300 other readers. Visit the Leaderboard here.

Advertisement: Bring Me In To Speak At Your Event

Elevate your next conference or corporate retreat with a customized keynote on the practical applications of AI. I deliver fresh insights tailored to your audience's industry and challenges, equipping your attendees with actionable resources and real-world knowledge to navigate the evolving AI landscape.

👉 If this sounds good to you, click/tap here to grab 15 minutes with the team to talk over your event's specific needs.

If you'd like to see more, here are:

ICYMI: In Case You Missed it

Besides the recently updated Generative AI for Marketers course I'm relentlessly flogging, this week, I had some fun messing around with mics and talking about generative AI with disinformation. Check out the new Youtube video.

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.

Premium

Free

Advertisement: Generative AI Workshops & Courses

Imagine a world where your marketing strategies are supercharged by the most cutting-edge technology available – Generative AI. Generative AI has the potential to save you incredible amounts of time and money, and you have the opportunity to be at the forefront. Get up to speed on using generative AI in your business in a thoughtful way with Trust Insights' new offering, Generative AI for Marketers, which comes in two flavors, workshops and a course.

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.

👉 Click/tap here to book a workshop

Course: We’ve turned our most popular full-day workshop into a self-paced course. Use discount code ALMOSTTIMELY for $50 off the course tuition.

👉 Click/tap here to pre-register for the course

If you work at a company or organization that wants to do bulk licensing, let me know!

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.

Advertisement: Free Generative AI Cheat Sheets

Grab the Trust Insights cheat sheet bundle with the RACE Prompt Engineering framework, the PARE prompt refinement framework, and the TRIPS AI task identification framework AND worksheet, all in one convenient bundle, the generative AI power pack!

Download the bundle now for free!

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

Listen to my theme song as a new single:

Advertisement: Ukraine 🇺🇦 Humanitarian Fund

The war to free Ukraine continues. If you'd like to support humanitarian efforts in Ukraine, the Ukrainian government has set up a special portal, United24, to help make contributing easy. The effort to free Ukraine from Russia's illegal invasion needs your ongoing support.

👉 Donate today to the Ukraine Humanitarian Relief Fund »

Events I'll Be At

Here are the public events where I'm speaking and attending. Say hi if you're at an event also:

Lab Products Association, Philadelphia, September 2024
INBOUND, Boston, September 2024
MarketingProfs B2B Forum, Boston, November 2024
Social Media Marketing World, San Diego, April 2025
Content Jam, Chicago, April 2025

There are also private events that aren't open to the public.

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.

Required Disclosures

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.

Thank You

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