Almost Timely News: The Fallacy of the Single AI (2023-08-06) :: View in Browser
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What’s On My Mind: The Fallacy of the Single AI
What’s the biggest blind spot people have about generative artificial intelligence and its capabilities? Beyond fanciful science fiction predictions that don’t really have a basis in reality, the biggest blind spot most people have about artificial intelligence, at least currently, is the fallacy of the single intelligence.
Here’s what this means. The power of generative AI becomes apparent as complexity increases. The first generative AI models were fairly limited in capabilities because they were trained on such little data. As companies like OpenAI and Google added more data, more capabilities of these models began to emerge – the ability to answer more complex questions, solve more complex problems, take on tasks that previously AI models had no hope of achieving.
This created a natural eagerness to build bigger and bigger models, something we see when people talk about AI models and how many parameters they have – 7 billion, 30 billion, 70 billion, etc. The natural assumption is that a bigger model is a better model, and to a great degree, this has proven true. GPT-3.5 was an improvement on GPT-3. GPT-4 is an improvement on GPT-3.5. The newest version of any model generally has more tokens and more parameters and more training time than its predecessor.
So it seems natural that people would assume that bigger is always better, and that whatever problem we’re facing can be solved by making models bigger. And we have ample precedent for this assumption; we saw it during the chip wars when companies like Intel and AMD kept making faster and faster CPUs, touting each chip’s speed as better than the last. I remember long ago when Intel earned massive bragging rights for reaching a consumer chip speed of 1 gigahertz back in March of 2000.
You would think, based on that example, we would have something like 23 gigahertz computers today, but we do not. In the case of CPUs, we ran into literal limits of physics that prevented us from making chips that operated much beyond 4 GHz. For example, the faster a chip goes, the more heat it generates, and the harder it is to keep it synchronized across billions of operations.
We are running into similar limitations with large language models and generative AI. The bigger a model is, the more compute power is needed to make it and operate it. Think of it like manufacturing bigger and bigger cars; not only does the assembly line need to be bigger and consume more resources, so does the final product once it’s built. Yes, a bigger model in general delivers better quality than a smaller model, but we are running into real resource limitations for bigger models. Anyone who’s logged into a system like ChatGPT has seen issues with speed and quality; ChatGPT’s premier model, GPT-4, appears to have gotten dumber over time.
That’s why the fallacy of the single intelligence is problematic; the assumption that bigger is better will lead people to chase after bigger numbers, bigger models. The parallel assumption is that bigger models also mean more and more capabilities, more abilities for models to do ever more complicated tasks.
What happened with computers once we started hitting physical limits of computing power? The industry pivoted from making faster chips to making more of them bundled together. You started seeing companies touting that they had two CPUs – cores, then four cores, six cores, etc. Today, companies like Apple advertise having 12 core CPUs and 32 core neural engines as a way to suggest the total processing power of a system.
And that evolution – from one big chip to a bundle of chips – is what the evolution of generative AI will look like as well. In general, nature addresses complex problems with complex solutions involving many parts rather than one bigger part. One animal’s survival is not guaranteed, but having a pack of animals greatly increases the survival capabilities of every animal in the pack. We do the same thing. Think about it – can one employee power an entire company? Or do companies hire many people?
That’s where generative AI is already going; instead of trying to make a bigger, more magical single model that can do everything, we are seeing more architectures of model ensembles, collections of models chained together to accomplish tasks. Fable Studios showed this off very capably in their recent South Park generative episode; they didn’t try to force one model to do everything. Instead, they built an ensemble of different models that each did individual tasks, sewing them together with some infrastructure.
So what? Why is any of this important to the many folks who just want to use AI to solve problems? It’s important to understand because it frames how we need to think about problems and the AI solutions that we will commission to solve those problems. What we’re seeing with agent networks – ensembles of AI models – is that they’re very capable of taking a complex task, breaking it down into much smaller tasks, and then using individual models to solve those specific smaller tasks. That’s no different than how we humans solve problems; we take a big task like “achieve profitability” and break it down into smaller tasks like marketing, lead generation, deal management, etc. Until we pile up closed sales and achieve our goal.
Right now, many AI companies are little more than a skin or a wrapper on someone else’s model, usually with a pretty interface to make things easier to understand, or with specific prompts under the hood to generate desired outputs. This class of companies will go away fairly quickly. Those companies which will survive and prosper? They’ll have ensembles of models put together in very specific ways to achieve results that exceed what any individual model is capable of alone.
And that’s where the magic will happen, where companies will differentiate themselves. When you have one gigantic all-purpose super-model, there’s no real opportunity for users of the models to differentiate, to show any kind of uniqueness. If you take two people who are equally competent at prompt engineering and have them use such a system, one person will not achieve substantially greater results than another.
When you have ensembled systems? You can achieve significant differentiation, because the models you choose under the hood and the way you connect them together will dictate the results you get.
Here’s how. Suppose you were going to cook dinner and you had one super-appliance that did a bunch of things reasonably well, like every appliance manufacturer often touts. “13 appliances in one, it fries, it bakes, it broils, it makes soup!” We know those appliances can do many of those things, though not necessarily equally well. In that case, the appliance would not be a differentiator for the quality of your food because everyone would be using the same thing. (Said appliance would also be crazy expensive)
Now suppose you have a kitchen full of different appliances – a great blender, a nice induction stove, a deep fryer, etc. Instead of cooking everything in one machine, you’d use different machines for different capabilities, and in different orders. For example, you would probably not want to use a blender of steak. You would absolutely want to use a blender to mix the gravy for a roast, though. You probably would not want to use a deep fryer for soup, but you could absolutely use a deep fryer to make shrimp tempura that you put into an udon noodle soup.
The network of appliances in a kitchen creates more powerful outcomes than one single super-appliance; the network of AI models will create more powerful outcomes than one single super-model.
As end users of AI, we will probably care little what the underlying architecture of systems are, so long as we get the results we want. But as economics begins to take hold, as we begin to hit practical limits of compute power (electricity, cost, etc.), ensembles systems will be the way we continue to expand the power of generative AI – and just as we have with computer CPUs today, this change in strategy will let us keep growing the power of AI over time.
As consumers of AI – particularly if we are working with vendors that we’re paying money to – understanding what’s under the hood will help us understand the value we’re getting and how current a vendor can stay with the latest technologies. For example, if a company’s technology is predicated on a single super-model (like GPT-4), then what tends to happen is that company gets locked into that particular model. The next time something newer and better comes along, it can be challenging for the company to change the model, in the same way that it can be mechanically very challenging to rip the motor out of a car and put a totally different motor in.
If you’re built on ensembled models – particularly if you’ve been clever and smart and built an abstraction layer in – then when something new comes along, you don’t have to start from scratch. You can swap out the affected pieces while leaving the entire system intact, just like you could buy a new blender for your kitchen without it affecting your stove or microwave. That kind of agility is what we want to look for in our AI vendors, the agility to change with the ever-increasing pace of change in AI.
Whether you’re buying AI technology from someone else or building it yourself, consider how the technology is evolving and then decide whether you want to pursue or purchase systems based on one big model or a network of models. I would wager the second choice is probably the one you want to go with.
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ICYMI: In Case You Missed it
Besides the newly-refreshed Google Analytics 4 course I’m relentlessly promoting (sorry not sorry), I recommend the piece on
- You Ask, I Answer: Why Have Content Statements?
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- So What? How to be a good podcast guest
- Now with More Gun Toting Robots!
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.
- 👉 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: Bring My AI Talk To Your Company
I’ve been lecturing a lot on large language models and generative AI (think ChatGPT) lately, and inevitably, there’s far more material than time permits at a regular conference keynote. There’s a lot more value to be unlocked – and that value can be unlocked by bringing me in to speak at your company. In a customized version of my AI keynote talk, delivered either in-person or virtually, we’ll cover all the high points of the talk, but specific to your industry, and critically, offer a ton of time to answer your specific questions that you might not feel comfortable asking in a public forum.
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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.
- Adobe Aep Implementation Engineer at ShyftLabs
- Director Of Marketing at Let’s Roam
- Head Of Marketing – North America at Rizobacter US
- Lead Data Engineer at carwow
- Marketing Director at Wade Marketing
- Product Marketing Director at CAST AI
- Product Marketing Director at Veeva Systems
- Regional Commercial Director – Chem (Usa) at Ravago
- User Researcher – 6 Month Ftc at carwow
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
- Metas AI personas might launch next month via The Verge
- Social Selling Explained: A 2023 Guide to Social Media Sales
- I Launched a New Product Based on a Viral TikTok Trend. Heres Why It Workedand How It Changed the Way I Do Business Forever
Media and Content
- How Womens World Cup campaigns have evolved since 2019 via PR Daily
- The Daily Scoop: Barbenheimer meme reveals the perils of sensitive cross-cultural communications via PR Daily
- How to edit AI-generated content the right way via Ragan Communications
SEO, Google, and Paid Media
- SEO Professionals Should Delegate Website Management
- How to Do an SEO Competitor Analysis
- Introducing a New Chrome Extension by All in One SEO: Analyze Your SEO in a Click
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
- OpenAI Files Trademark Application For GPT-5
- The Impact of AI On Jobs: An Interview With Dr. Craig Froehle
- How AI is fundamentally altering the business landscape via VentureBeat
Analytics, Stats, and Data Science
- Unleashing the Power of Few Shot Learning via Analytics Vidhya
- AI Brain Implants Restore Paralyzed Man’s Movement & Sensation
All Things IBM
- 3 ways IBM and Adobe are transforming content supply chains with generative AI via IBM Blog
- How IBM HR and the Chief Data Office partnered to drive data quality, increased productivity and a move to higher value work via IBM Blog
- How foundation models and data stores unlock the business potential of generative AI via IBM Blog
Dealer’s Choice : Random Stuff
- Why State Farm used influencer marketing to attract Gen Zers to its video game competition via Digiday
- 3 Business Lessons I Learned While Playing Video Games via Entrepreneur
- From tipple to tipping points: A plant geneticist on climate-resilient barley
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:
- ISBM, Chicago, September 2023
- Content Marketing World, DC, September 2023
- Marketing Analytics and Data Science, DC, September 2023
- Content Jam, Chicago, October 2023
- MarketingProfs B2B Forum, Boston, October 2023
- Social Media Marketing World, San Diego, February 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.
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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
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