It’s funny reading even expert reports that wrap up with statements like “That will require us to tap into a superpower that can’t be programmed into a robot: imagination.” which are already proving false.

One only has to look at any image generation program to see AI imagination at work. Yes, it’s currently human directed but with advances in internal AI monologues we can see that AI will soon be deliberately daydreaming to find novel solutions.

More and more we are able to transfer the evolved mechanisms of brains directly to advances in machine learning. Using sleep to consolidate weights from one task before learning the next is helping with generalization across tasks.

And the reverse is true as well. Seeing and experimenting with ML models gives us insight into the daily machinations of the wet neural networks we each carry around. That led to the following question.

How much of age related cognitive decline is due to the normally beneficial process of generalization?

When your Mom goes through her sibling’s names before getting to your own, those similar concepts have been grouped together and are less differentiated. Commonality becomes generalization. Names in particular are highly specific, high resolution information that map to things like “that tall blond guy I met at that neighbor’s party”.

Perhaps you visit the Botanic Gardens. How many petals did that flower have? What was its latin name written next to it? Neither fact is as useful as the vague memory that it was purple with a yellow center and pretty, so the petal count and latin name may not even make it to long term memory. As time passes the specific high resolution information about each flower on the trip is consolidated into mosaics of areas you passed through, that flower is now just a purple dot. “Yes, it was beautiful, lots in bloom. You should stop by.” Eventually the whole trip is mostly a dot in the timeline of memory, “Yes, I’ve been to the botanic gardens many times”. You probably remember where it’s located, the general layout, some highlights of visits, and have some very general emotional impressions of how you feel about the place.

All of which is a useful process. Knowing the exact petal counts of thousands of flowers is fairly useless compared “it’s pretty there in June and makes me feel relaxed”. The process of learning is powered by that consolidation. All these things are flowers whether they have 3, 5, or 10 petals.

What expectations then should we have of memory? The more one lives, the greater the surface area grows of subjects we expect to know and remember. Are most memories stored with 5 or 10 years of detail before being compacted into more general knowledge or erased to make room for more? The existence of Highly Superior Autobiographic Memory indicates there could be space for all the detail but that forgetting or at least generalizing is useful for getting through life.

It is theorized that memory storage is similar to holographic storage. A scratch across the surface of digital holographic storage will introduce noise rather than destroying specific information; reducing the fidelity and detail. Whether due to cell death or age related decrease in neuron performance, we expect similar effects on our memory as we age.

With two mechanisms, generalization and aging, that have similar results, how do we tell the difference? Your mom eventually retrieves your name but that may not be the case for more obscure information. If aging is the predominant force, perhaps in our knowledge driven economy we should focus more on understanding the cellular mechanisms and developing treatments. If generalization is stronger we could adapt in other ways: changing our cultural expectations of memory or spending time reviewing memories to curate the level of specificity vs generalization we want in areas of our memory.

In an age where we’re able to store more knowledge and detailed photographic memories outside of our brain perhaps generalization is ok and we should remember the bare minimum.

Ok, you’ve had a great strategy retreat, identified the major challenges and opportunities you see coming in the next five years. There will be many specific initiatives and roadmaps to build but the fundamental question is “how do we equip our workforce to deal with that future?”

The current wave of change coming at us makes this need abundantly clear. Machine learning services and AI assistants will sweep through the economy: aiding, changing, or eliminating every job. Computers are becoming programmable in English (and every other language). Information, predictions, optimization paths, and a dozen suggestions of how to do every task are coming to every worker’s information sphere, whether that revolves around a phone, cash register, augmented reality, or ambient computing.

In the next five years this force will sweep through innovators. In the next 10 it will be a constant pressure on companies to adapt or lose out. In 20 years AI assistants will be pervasive in the economy. Walmart needs to adapt just as much as Google will (and is). 

Companies are still catching up on the skills needed for remote work and the changed technology expectations of customers. How are they going to going to handle this next shift? By equipping their workforce with skills so that workforce can do the adapting themselves.

In 6th grade, an insightful school librarian drilled into my class that we were growing up in the information age. “You need to know some things but most importantly you have to be able to find information and to learn”. The shift for everyone now is similar: how to apply and guide machine intelligence to accomplish goals.

I know the bear market is top of mind right now for most executives and boards. However the companies that will lead their markets in the coming years are the ones thinking right now about their talent pipelines and how to maximize success for that talent. We don’t know what tactics will be needed and what key business decisions our businesses will need to make in the next five years. Experience tells us that our best planning is still going to encounter an ocean of uncertainty and changing circumstances.

My strategy for the future is to give my colleagues every opportunity to learn and upskill so they handle the coming challenges and opportunities better that anyone.

Like many kids I loved reading. I was probably lucky to grow up before the constant digital gamification of our world, a quiet corner and a the pages of another world made for a happy afternoon. So it was a crushing realization that I could never read all the books in the world. Probably not even all the books in our small local library.

Being a “full stack” engineer often feels that way.

Even more daunting now that I’ve been away from it for a while. I’m doing a technical rotation this year, refreshing my sense of assembling code and being on the front lines of tech. In many ways it’s a refreshing feeling, learning tons of new things feels fresh and exciting.

On the other hand it’s an embarrassment of choices. In 2006 I became highly proficient in MySQL. Barely seems useful in the age of global causal plus consistency. In 2008 I was writing JavaScript unit tests and running layered javascript builds. Thanks to the Dojo framework I was five years ahead of most of the industry. But it means nothing now, a dozen years later it’s a full time job to keep up with the changes in React.

Oh for simpler times!

Just kidding. Better to be awash in the great tech firehose than standing around thirsty. Pass the quantum homomorphic encryption please.

Prompt engineering of humans continues to be a popular pastime, and career.

I was reminded of this while reading an article on (eventual) chip shortages (if there is conflict around Taiwan). Given that prompt I thought, “ I should buy a new GPU”. Though I then dismissed it I could feel the generated idea still being added to my training set of world data.

Of course this is the domain of marketing, sales…management, parenting…human interaction.  It was a reminder though of why negative advertising works; why Facebook posts could influence an election. 

Thoughts become actions become deeds. Even our small choices, deliberate or not, adjust the multi-level dataset of humanity and that of our technological progeny.