Almost Timely News: How Large Language Models Are Changing Everything (2023-03-19) :: View in Browser
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What’s On My Mind: How Large Language Models Are Changing Everything
Well then, that was certainly a week. First, apologies. It’s another week of all AI, all the time, but it’s worth it. This past week was stuffed to the gills with AI announcements, so let’s talk about these announcements, what they mean, and what you should be doing about them. We have three things to talk about: PaLM, Copilot, and GPT-4.
PaLM is Google’s newest large language model; PaLM stands for Pathways Language Model. Google announced this week that PaLM, a model with 540 billion parameters, would be rolled out soon, especially in productivity software like Gmail and Google Docs. You’ll be able to access the PaLM model through prompts in these software packages as well as developers being able to call the PaLM API through Google Cloud.
The second big development this week was the announcement of Microsoft Copilot. This is a GPT-4 integration of the language model in the Microsoft Office productivity suite. Think about Clippy on steroids and actually useful; with Office 365’s knowledge of your company’s corpus of data, it will be able to construct tuned first drafts based on your data. Some of the examples shown were deriving a presentation from some Word documents, which would be awfully handy for folks like me making a new keynote talk. I could take the transcript from this newsletter and turn it into a deck.
The third big announcement came from OpenAI this week, which was the release of the GPT-4 model. A couple of things set GPT-4 apart from previous models. First, it’s a much bigger model. OpenAI hasn’t said exactly how big, but it’s reasonable to assume it’s in the hundreds of billions of parameters.
A brief aside on parameters. When you hear someone talking about model parameter sizes, what does that mean? A parameter, to simplify it, is a value that describes the relationship between entities in a model. For example, suppose we examine this sentence, which has ten words. A parameter would be the relationship of the first word in the sentence to the second word, the frequency of one word with respect to another. If a sentence, in this very crude example, has ten words, it would in the first pass have nine parameters. Now, it’s more complicated than that, but it gives you a sense of how large these models are – they’re trained on enormous amounts of text, and then the relationships between words are mathematically calculated over and over again until you get billions of parameters – probabilities. Then, when you or I use these models in an interface like ChatGPT, it’s drawing on those parameters, those probabilities, to predict what words to put together. Generally speaking, more parameters means a better performing model.
So GPT-4 has been released and contains a gazillion parameters. It’ll be able to take in more text in prompts and return more text, too. That’s useful. It also has something new, something that hasn’t been made available to the public yet but will be available soon: multimodality. The model will be able to accept an image as an input, and spit out text. Put in a photo of your dog, and GPT-4 will be able to describe the photo, perhaps name your dog’s breed, etc.
Otherwise, it’s a bigger, more accurate model that does everything previous versions have done. The new model is available inside ChatGPT if you’re a paying subscriber.
That’s the facts. Now let’s talk about what it all means. First, let’s address multimodality. Right now, the GPT-4 model can take in images or text and spit out text. It’s not a stretch of the imagination, especially given OpenAI’s development of DALL-E 2, to imagine that GPT-5 will have the ability to spit out multiple formats as well, but that’s down the road. No, the ingestion of images is going to be a very big game changer for a lot of companies and businesses because image data is informationally dense.
The old expression, a picture is worth ten thousand words, is more true than ever. We can pack a tremendous amount of information into a single image, something that requires a ton of words to even approximate. What would you use this capability for? There are the obvious applications, like optical character recognition, or OCR. Put in a picture of page of text and it’ll recognize the text. That’s nothing new. There are things like captions – put in a photo, get a caption accurately describing the photo. Again, nothing new except that the accessibility of these capabilities will be greater than ever.
Now start to expand your mind about what you can put in images that a machine could interpret for us. Suppose you put in a page of music, a score. The machine could read that and interpret it, then return a variation based on what it’s processed. That’s not a capability models have today.
Suppose you took some ancient texts like Sanskrit or Sumerian or Babylonian, stuff where there’s a tremendous amount of public data already but in hard-to-access tools. Amateurs like you and me – assuming you’re not a Sumerian scholar – will be able to use tools like GPT-4 to translate, interpret, and extrapolate from data that’s been locked away in images.
You and I, because we’re marketing folks, are looking at images all the time in our reporting tools. One of the first use cases I plan to tackle once I get access to the API is to feed screenshots from Google Analytics into GPT-4 and have it write a synopsis, a summary of what it sees. Descriptive analytics will be much easier for many of us when a machine does the first pass of describing what happened, freeing us up to derive the insights from the data rather than burn a lot of time processing the data itself.
I wholly expect a company like Adobe to follow suit. I would be shocked if they didn’t; having a large language model available in a tool like After Effects or Audition or Photoshop would be a game changer. Imagine loading an image into Photoshop and just typing a prompt to colorize the photo, fix any defects, and remove your ex from the photo.
That’s all pretty cool. But that’s probably the least interesting thing that happened this week. Copilot and PaLM are big, big deals. Not because they’re better versions of Clippy, but because they fundamentally change the role and nature of the office worker. Pop quiz for those of you who have been keeping up on this topic: what profession is writing prompts for tools like ChatGPT?
Programming. When you write prompts for ChatGPT, you are programming. You are writing instructions to give to a machine to direct that machine to do things. True, it doesn’t look anything like C or Python or R or Java. But it’s still writing instructions to a machine in a specific format to achieve a specific result. Prompt engineering is really programming and development.
Do you get it now? When every office worker is using prompts and large language models in their day to day work, that transforms every office worker into a developer, into a prompt engineer. When you write a prompt that works well to convert an Excel spreadsheet into a set of Powerpoint slides, you are writing software. Just because it doesn’t look like traditional coding doesn’t mean it’s not software. It is.
That means that every role that uses office software will also need coaching, training, and professional development to some degree on prompt engineering and software development. Folks will need to learn how to construct prompts that help them do their jobs better, that help them make the most of these awesome integrations into large language models.
If you’re a software company with complex software – like CRMs, accounting software, etc. – and integration of a large language model isn’t on your roadmap soon, it needs to be. This past week, Hubspot announced ChatSpot, the integration of the GPT models into the Hubspot CRM. That’s the kind of agility every software company needs to be bringing to the table right now.
But it gets bigger than that. What is programming? What is software? It’s intellectual property. It’s valuable stuff. Companies jealously guard their code. Companies file patents, file lawsuits to defend their code. When every employee is a programmer, every employee’s work is software. Every prompt an employee writes, from the CEO to the intern, is code that could be valuable to the company – which means we should be thinking of prompts as software and protecting them as such. I see tons of folks offering downloads and ebooks and tutorials and selections of prompts, and I think that’s cool. They’re essentially open-sourcing their software. You may not want to do that with your prompts, with your employee-generated software. You need to be thinking about that and developing policies and processes around that.
This is also going to radically change our talent wars. Because of the nature of prompt engineering – writing computer code in plain language – we may find that the people who are most successful at writing prompts are not the traditional coders and developers. Folks who are good at writing in general can be very successful writing prompts for machines – and that means your best ideas, your best software may be popping up in departments and employees in your company that you normally don’t look to as software development hot spots. Your administrative assistant will have a book of prompts – software – that work really well for them. They’re a software developer now, and we should be opening our eyes to who in our companies may have exceptional talent developing this kind of software. Your secret sauce, your next big thing, your big idea may not come from the usual places in your company if you’re open-minded. Your janitorial staff that has to enter their time sheets may write a prompt that creates incredible, unexpected results – but only if you know to look for it.
Mind blown yet? We’re not done. So far, we’ve only talked about the human computer interface, the way people interact with these models through prompts. People scale poorly. There’s only so many prompts per minute you can copy and paste into a machine. This week, OpenAI announced the API for GPT-4, and last week made public the API for GPT-3.5-Turbo, aka the model that ChatGPT uses most of the time. Why does this matter? An API allows a prompt to scale. Instead of a person typing a prompt in, a piece of software issues the prompt via the API to one of the GPT models and gets the result back. We wrote an example of this in the Trust Insights newsletter a couple of weeks ago for sentiment analysis. I didn’t sit there and copy/paste 50 articles into ChatGPT. I sent them all in via API with the same prompt – hard coded into my software – and got 50 results back in the blink of an eye. That’s how these large language models scale – we use other software to talk to them.
And that means that when you find prompts that work, prompts that create exceptional and reliable results, you can write additional code to turn those prompts into full-fledged, scalable software. Instead of one employee typing in a prompt to generate a Powerpoint deck from a spreadsheet, you’ll have software that can take hundreds of spreadsheets and assemble hundreds of decks in the blink of an eye.
… and THAT means every prompt that every employee writes has the potential to graduate, to grow up to be real enterprise software. That prompt you wrote that converts a transcript into meeting notes and action items? A piece of code could wrap that prompt into something that can talk to an API and convert your entire meeting recording system into action items for everyone in the company. Prompts are the steering wheels that guide the engine of the large language model, and when they’re put inside the rest of the car, they make the car drivable by anyone. More than that, they allow us to mass-produce the car; in this analogy, that’s mass producing software.
That means the prompts our employees are writing today could be the secret sauce for a totally different way of doing business tomorrow. We should be thinking about how to train employees, how to guide employees, and how to capture all the knowledge that employees will be generating with these new tools very, very soon. As an employee, you should be looking hard at the contracts you sign with employers and talking to your lawyer about intellectual property protections for the work you do, for pay increases tied to software you write – because you’re a developer now, and for strict protections for stuff you do outside of work.
The game has changed, my friend. Tech folks have long said that software will eat everything, and they were right, but not in the way they imagined. Instead, we’ve all become developers thanks to the large language model interface to our computers, and every one of us is or will be writing software very soon. Now is the time to get in front of this, to develop processes, policies, and procedures that are fair and equitable for everyone, and to seize the advantage ahead of slower competitors. Now is the time to get ahead of the talent war that’s incoming as we look for people who can work in these new environments and innovate at every level of work.
As these new tools roll out, it’s anyone’s game to win. Let’s make you and me the winners, shall we?
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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), enjoy the celebrations of Trust Insights’ fifth birthday.
- So What? It’s our birthday – 5 years of Trust Insights
- INBOX INSIGHTS, March 15, 2023: Happy Fifth Birthday, Trust Insights
- Almost Timely News, March 12, 2023: Broken Agreements
- PESO Update with Gini Dietrich
- ChatGPT and Crocs Genius Upsell
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.
- ⭐️ 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
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 five most recent open positions, and check out the Slack group for the comprehensive list.
- Analytics Manager at TRKKN
- Associate Marketing Analyst at CBC
- Chief Digital Officer at Forsyth Barnes
- Consumer Engagement & Content Manager at GoodRx
- Digital Analytics Specialist at REA Group
- Director Of Data Analytics at Location3
- Marketing Manager at Paxera Health
- Paid Media Analytics Specialist at Digital Marketing Specialist
- Search Engine Marketing Manager at WITHIN
- Vp Marketing at Instrumental
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What makes this course different? Here’s the thing about LinkedIn. Unlike other social networks, LinkedIn’s engineers regularly publish very technical papers about exactly how LinkedIn works. I read the papers, put all the clues together about the different algorithms that make LinkedIn work, and then create advice based on those technical clues. So I’m a lot more confident in suggestions about what works on LinkedIn because of that firsthand information than other social networks.
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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
- 9 Ways to Use the TikTok Ad Library to Make Better Ads
- How to Measure Your ROI on Social Media Marketing Campaigns
- How to Generate Leads on Social Media
Media and Content
- 6 tips for creating shareworthy content in 2023and examples of how it works via Agility PR Solutions
- How Remote and Contract Workers Fit Into Agency Culture via Spin Sucks
- Human or Bot? Reflections on Generative AI via Institute for Public Relations
SEO, Google, and Paid Media
- ‘SEO Is Not a Unicorn‘: Crucial SEO Tips for Content Marketers via Contently
- Schema Improvements Show Deliberate SEO Now Activity via BrightEdge
- What is Search Intent and Why Is It Fundamental to SEO?
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What makes this different than other training courses?
- You’ll learn how Google Tag Manager and Google Data Studio form the essential companion pieces to Google Analytics 4, and how to use them all together
- You’ll learn how marketers specifically should use Google Analytics 4, including the new Explore Hub with real world applications and use cases
- You’ll learn how to determine if a migration was done correctly, and especially what things are likely to go wrong
- You’ll even learn how to hire (or be hired) for Google Analytics 4 talent specifically, not just general Google Analytics
- And finally, you’ll learn how to rearrange Google Analytics 4’s menus to be a lot more sensible because that bothers everyone
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Tools, Machine Learning, and AI
- The Realities of Using ChatGPT to Write for You via What to Consider When It Comes to Legalities, Reputation, Search and Originality
- Microsoft Copilot leak shows AI automatically creating a PowerPoint presentation via The Verge
- Microsoft Bing Uses New GPT-4 From OpenAI
Analytics, Stats, and Data Science
- Detect Cyberbullying Using Topic Modeling and Sentiment Analysis
- Accuracy in digital analytics: What marketers need to know
- YouTube Adds Multi-Language Audio Analytics, New Shorts Music Sharing Options via Social Media Today
Dealer’s Choice : Random Stuff
- A Fandom’s Heartbeat – The Home of the #SaveShadowhunters Archive
- The Urgent Need for Ratings Transparency
- How to Build an Actionable Content Marketing Dashboard
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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
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- On Twitter – multiple daily updates of marketing news
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- My free Slack discussion forum, Analytics for Marketers – open conversations about marketing and analytics
Events I’ll Be At
Here’s where I’m speaking and attending. Say hi if you’re at an event also:
- Martechopia, London, March 2023. Use MARSPEAKER20 for 20% off the ticket price.
- B2B Ignite, Chicago, May 2023
- MarketingProfs B2B Forum, Boston, October 2023
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.
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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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