Good morning. Welcome back to Scaling Greatness, a newsletter from Integreon focusing on amplifying business excellence and innovation.
Once I start focusing on something, I notice it everywhere.
My friend Bill Mais likes to say, “You never notice a green VW Bug until someone points one out to you and then you start to see them everywhere.” While I am not sure how many green VW bugs still exist (dating myself a bit here), I am experiencing this phenomenon right now with the term “greatness”.
State Farm Insurance has an awesome ad highlighting Caitlin Clark and asks the questions “What does it take to achieve greatness?” They go on to say “...it takes passion, heart and a team that is there for you every step of the way.” They close with the tag line “State Farm is proud to cover greatness.”
Click here for video.
Similarly, a Nike campaign for the 2012 Olympics featured a series of ads titled “Find Your Greatness”. One video opens with the footsteps of a runner on a lonely road and the voiceover begins with: “Greatness is just something we made up. Somehow, we’ve come to believe that greatness is a gift, reserved for a chosen few...”. The ad continues, pointing out that greatness is not just for superstars and that “...we are all capable of it, all of us”.
Click here for video.
I have the good fortune to see greatness everywhere - in our clients’ work, my colleagues’ designs, the attention given to support a great idea, the smile from those who discover they are capable of greatness.
Where do you see greatness?
⧉ AI: A Quick Guide to the Basics
The much-discussed OpenAI was inescapable this week as the company celebrated the release of GPT-4o (the “o” is for “omni”), the latest iteration of the firm’s popular ChatGPT chatbot. 4o is accompanied by a “weirdly flirtatious” vocal interface and – just days after release – the news broke that co-founder and chief scientist, Ilya Sutskever is leaving the company.
Due to the sheer intensity of the hype powering OpenAI and its products, making sense of the ever-expanding vocabulary surrounding the $80bn kind-of for-profit nonprofit can prove challenging. What do acronyms like GPT, or LLM, or AGI, or ANI, or ASI mean? Why is every company talking about AI? And how do I decide which AI tool to use? While we won’t attempt to answer all these questions in this edition of Scaling Greatness, a quick run-down of the basics is a good place to start.
Top AI Terms You Should Know
MIT’s introductory program "Introduction to Deep Learning" has about the most concise definition of 3 key terms:
- Artificial Intelligence: Any technique that enables computers to mimic human behavior.
- Machine Learning: Ability to learn without explicitly being programmed.
- Deep Learning: Extract patterns from data using neural networks.
Deep learning is a subset of machine learning, which is a subset of artificial intelligence. Generative AI (see below) is a form of deep learning, but NOT the only form of deep learning.
In addition to the core definitions above, there are a few more common acronyms that are important to understand:
- Large Language Model (LLM): LLMs are language-generating and processing applications based on artificial neural networks – groups of machine learning algorithms designed to replicate the structure of a biological brain. LLMs are “trained” on large volumes of textual data, analyzing the patterns in written language and gauging the probabilistic relationships between words to produce results which, ideally, should sound natural and coherent. Several of today’s prominent chatbots – including OpenAI’s GPT series, Google’s Gemini and Meta’s Meta AI, among others – rely on LLM tech. These chatbots are frequently described as Generative Artificial Intelligence (GAI) because – you guessed it – they generate text.
- Generative Pre-trained Transformer (GPT): GPTs are a type of LLM developed by OpenAI. GPTs are based on the “transformer” machine learning architecture created by Google in 2017 (originally for the purpose of translation), which converts text into numerical tokens to speed up processing. OpenAI’s innovation was to introduce two separate stages of training: an unsupervised pre-training stage, during which the model would digest data on its own, and a human-guided fine-tuning stage.
- Artificial General Intelligence (AGI): This is a big one. AGI – variously referred to as “full AI” or “general AI” – is AI that can complete a large number of tasks to a human standard (or better). AGI is contrasted with Artificial Narrow Intelligence (ANI or “narrow AI”), which is designed for specific jobs. Nobody has developed AGI yet, though there are all manner of guesses and predictions as to when we might see one. There’s also Artificial Super Intelligence (ASI), which is even more hypothetical.
In future editions, we’ll dive into the finer details and larger topics surrounding AI.
