Almost Timely News: The AI Goldmine You Already Own (2023-10-15) :: View in Browser
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What’s On My Mind: The AI Goldmine You Already Own
Something interesting is happening in the use of large language models. As more models become available, as open source models become more powerful, and as support systems and infrastructure pop up around the models, how the most successful companies use models is changing.
Today, most people use large language models (and now large multimodal models) as a self-contained system. You log into Claude or ChatGPT and you just use the system as-is, asking it to be language model, interpreter, source of truth, and output machine all at once. We have a monolithic view of large language models as these giant black boxes that just do stuff that seems like magic.
That’s fine for many tasks, especially tasks that are public, tasks that don’t leverage confidential information, and tasks that require common knowledge. These tools and models work absolutely great for that, and people should absolutely leverage them for that purpose.
But what if you want to use confidential data? What if you need more up to date data? What if you need to work with your data specifically?
This is where the current monolithic view of models falls down – even more advanced applications like fine-tuning. The idea of taking a model and trying to keep using it as some kind of all-seeing oracle is inherently flawed for more and more applications, especially business applications. Keeping a model up to date and capable using tuning methods is cumbersome, slow, and expensive.
What we see the smartest, leanest organizations pivoting to is a hybrid architecture, a hybrid approach. Here’s a concrete example. What happens when you use Microsoft Bing chat?
What Bing does is very clever and the best use of these advanced models. It takes our conversation and our line of inquiry, translates it into queries that work with Bing’s existing search engine, and queries the Bing data store. It gets information back from the Bing search engine, reformats it, and returns it as conversation.
Bing leverages the power of the model’s understanding of language to write expert queries and then parse the information returned from their proprietary datastore. They don’t rely on the model as the source of factual information.
That’s the approach that makes the most sense for most commercial applications of generative AI. We want models that can understand us and talk to the data we already have. We want models that can produce trusted data, data we know we already have, data we know we’ve already audited and vetted in many cases – and data we are probably not too keen on sharing with any external parties.
For example, suppose you’re a medical office. You want the ability for a patient to ask a “virtual doctor” questions in a chat portal even when their real doctor isn’t around. You would definitely want a language model that knew a lot about medicine, but you’d especially want a model that could access the patient’s records and medical history to provide custom answers to that patient. You certainly would not want that medical data going anywhere outside the walls of your office except to the patient themselves. That’s a clear use case where the language model would be useful for translating between the arcane syntax of electronic medical records and everyday language, but the specific, private information of the patient should absolutely never be in the hands of an unauthorized third party.
So how would you go about building something like this for your organization, something that leveraged the data you already have? The answer will depend on the resources you have available, but broadly speaking, you’ll need a few components. First, you’ll want a language model of some kind. You can use the GPT family of models from OpenAI, Anthropic’s system, or an open source model like something from the LLaMa 2 family. This is the part that does all the listening and talking.
Second, you’ll want some kind of compatible database that a language model can talk to. There are special databases called vector databases which contain mathematical representations of your data. If you look in a regular database, you’ll see all the words and phrases and writing of your data. If you look in a vector database, you’ll see that plus all your words represented in numbers:
Third, you’ll need the technology to connect your data to the vector database, and connect the vector database to your language model. The system of record most people use is a technology called LangChain, but you can accomplish pretty much the same thing with any major programming language with varying degrees of effort. Most modern AI-powered companies use LangChain because it’s both free and efficient at its job.
Fourth, depending again on how sophisticated you want to be and the resources you bring to the table, you might want to build an add-on to your language model that contains specific domain knowledge which might or might not be in a bigger model. Going back to the medical office example, suppose your office specializes in allergies. You might have access to repositories of clinical research about allergies that aren’t available on the public internet. You might work with a technical resource to convert those papers into a special kind of add-on called a PEFT, a Prompt Efficient Fine Tune, that can make an off-the-shelf language model much smarter at a specific field of study.
Those folks who’ve done a lot of work with open source image generation models are probably familiar with these technologies; there are many addons that help your AI-generated images look like a specific style, like 1990s cartoons or sci-fi movies.
With that special add-on, you can make a foundation model expert in your field and industry with your repository of non-public research papers that you pay for, and then connect that now-specialized model to your private, proprietary data, creating an AI system that is incredibly expert at the task you’ve set before it. It will know your industry, know your company, know your customers, and be able to answer questions with a high degree of specificity and a lower degree of incorrectness because it’s relying on the data you give it, rather than expecting it to know everything.
This system, this architecture, applies to everything. Imagine starting with a foundation model, then creating that special add-on that knows everything there is to know about how to be a great email marketer, and then connects to your company’s email marketing system. It will be able to craft emails that sound like you and adhere to best practices because it has domain expertise AND it has access to your data.
And here’s the juicy secret, the business secret, the way to make a big pile of money. It’s the AI goldmine you already own. Your data? The data you’ve been generating for years, maybe even decades? That data – once cleaned of anything that is confidential – can be turned into one of those special add-ons as well. Imagine having a special add-on that’s tuned specifically for intellectual property lawyers, or an add-on that’s tuned just for real estate agents. Our companies are all sitting on mountains of non-public data that could become guides, trainers, and refiners for AI.
Now is the time to start auditing the data you own. Now is the time to start experimenting with these tools to understand how to build these customizations, because they can be a powerful strategic advantage – especially if your company has a large amount of legacy data. You might just be sitting on the next AI goldmine.
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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 fairness and bias in AI. We cover a lot of important ground in it.
- You Ask, I Answer: Fairness and Mitigating Bias in AI?
- You Ask, I Answer: What’s The Difference Between Good and Great Data?
- You Ask, I Answer: Different Types of Machine Learning and AI?
- You Ask, I Answer: How To Vet AI Vendors?
- Almost Timely News, October 8, 2023: How To Pilot an AI Deployment
- Now with More Large Multi-Modal Models, and Taylor Swift
- So What? How to become a public speaker
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.
Here’s what one participant said after a working session at one of the world’s biggest consulting firms:
“No kidding, this was the best hour of learning or knowledge-sharing I’ve had in my years at the Firm. Chris’ expertise and context-setting was super-thought provoking and perfectly delivered. I was side-slacking teammates throughout the session to share insights and ideas. Very energizing and highly practical! Thanks so much for putting it together!”
Pricing begins at US$7,500 and will vary significantly based on whether it’s in person or not, and how much time you need to get the most value from the experience.
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.
- Data Scientist, Gt.school (Remote) at Crossover
- Director, Competitive Intelligence at New Relic
- Global Search Engine Optimization Director at DocuSign
- Head Of Analytics & Data Science at Intrepid Digital
- Head Of Data Science at Storm3
- Marketing Director For A Restaurant Job Site at OysterLink
- Marketing Director at Hire Innovative
- Senior Digital Marketing Specialist (W/M/D) at LGT
- Senior Marketing Manager (W/M/D) at LGT
- Social Listening And Social Analytics Manager at Tripwire
- Social Media And Community Manager at Beauty & the Broth
- Sr. Data Analyst at Prudent Technologies and Consulting, Inc.
- Subject Matter Expert – Mede Analytics at KAPITAL
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
- How to Manage Social Media in 18 Minutes a Day
- New social media features and updates to know this week via PR Daily
- How I Designed a LinkedIn Thought Leadership Content Creation System
Media and Content
- 7 Key Metrics In Measuring Content Effectiveness
- How I Designed a LinkedIn Thought Leadership Content Creation System
- Crisis Communications with Gini Dietrich! via Marketing Over Coffee
SEO, Google, and Paid Media
- 5 Marketing Principles To Future-proof Your SEO Strategy
- Competitor Comparison Landing Pages: 3 Unique Strategies
- What are YMYL websites? How does Google evaluate these? Yoast
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
- AI Film and AI Games Festival draws 300 to hear about AI taking the director’s seat via VentureBeat
- New technique makes AI hallucinations wake up and face reality
- Google promises to take the legal heat in users AI copyright lawsuits via The Verge
Analytics, Stats, and Data Science
All Things IBM
- Understanding the different types and kinds of Artificial Intelligence via IBM Blog
- Skills and expertise: Keys to the generative AI engine
- How data stores and governance impact your AI initiatives
Dealer’s Choice : Random Stuff
- Europe outpaces US and China in hydrogen investment, says EU
- How to Take a Full-Page Screenshot in Chrome and Firefox
- This map shows you what Indigenous lands you’re living on | Mashable
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
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 our ongoing support.
Events I’ll Be At
Here’s where I’m speaking and attending. Say hi if you’re at an event also:
- Content Jam, Chicago, October 2023
- SMPS AEC AI, DC, October 2023
- Humanize Your Brand, Online, October 2023
- SiteImprove, Online, October 2023
- DigitalNow, Denver, November 2023
- AImpact, Online, November 2023
- Social Media Marketing World, San Diego, February 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:
- Understand the Meaning of Metrics
- How to Set Your Public Speaking Fee
- Best Practices for Public Speaking Pages
- You Ask, I Answer: Google Tag Manager and Google Analytics Integration?
- How to Measure the Marketing Impact of Public Speaking
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