Feb 26, 2024 | AI, Governance
The astonishing ability of the new AI tools to increase productivity relies on being able to ‘learn’ by mining pools of data, then detecting and projecting responses based on statistical outcomes of that mining.
The next step, Generative AI, Generative Artificial Intelligence, is the point at which the artificial systems can reason, much as we do. This happens by making ‘neurological’ connections between apparently disconnected data, depth of domain knowledge and experience, breadth of more general knowledge that provides a ‘thinking canvas’ and context. These add up to instinctive responses we sometimes describe as pure ‘gut feel’.
There is however, a middle point.
‘Deep mind’ is a research unit now owned by Google. Their models evolved as AlphaGo and subsequently AlphaZero. These models cracked the barrier that seemed uncrackable, the ‘4-minute mile’ of computing. By beating the best humans at the complex game of Go, it demonstrated the ability of an algorithm to replicate in some form, the neural networks we have in our brains. In short, it can learn from its own experience, not reliant on outside data.
Crossing this Rubicon opens whole new territories to be explored.
It is in effect a ‘rolling probability’ calculation, each step using an estimation of the outcome of the previous calculation to deliver an adjusted outcome, in an ongoing process.
This is how we learn: from our experience.
As a kid I remember my younger cousin crawling towards a campfire surrounded by rocks. The immediate response of most was to grab him to prevent him getting burnt. However, my aunt stopped us, pointing out he would not be badly hurt by the mildly heated rocks surrounding the fire. However, when he touched a heated rock, it would create a memory-response loop that connected the fire to a modest hurt, thus ensuring he would automatically adjust his behaviour, and not go near another fire.
That incident stuck in my memory, and it reflects the way these AI tools are evolving rapidly towards ‘thinking’.
The dystopian view is that such developments over a few decades will see the machines take over. I prefer to think that we humans will find a way, as we always have, to overcome such threats. I guess my great grandchildren might know the right answer.
The header was created with help from DALL-E in about 3 minutes using a short series of prompts.
E&OE: A few hours after posting this post, I stumbled across this post on Medium that might bring forward the passing of the Turing test by a machine back into my lifetime. It records the evolution and current state of Googles 1.5 Pro tool, claiming it is to Current ChatGPT4 what a Model T is to a Ferrari.
The pace of change is astonishing, logarithmic, which makes it hard to comprehend by normal people..
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Feb 21, 2024 | AI, Change, Governance, Strategy
Often, I hear the term ‘Digital Strategy’ used as if it were an end result, some discrete set of activities to be completed.
To my mind, this is a misuse of the term.
As it is usually used, ‘Digital’ is all about the devices, the technology, whereas the value in digital is elsewhere. It is in the ability to get things done, differently, more quickly, efficiently, and in a distributed manner by those best able to complete the activity with the minimum of organisational friction.
It is about the business models enabled, the understanding of customers, ability to visualise the unseen, and communicate it clearly. It is not about the RFID tags, VR, and all the other enablers of digital, it is the outcomes that count.
Your strategy may be enabled by digital, but you do not need a digital strategy any more than you need a telephone strategy. They are both just tools to be leveraged.
Management of these changes is confronting, there is not a lot of precedent to go by. This is particularly the case now following the explosion of AI onto the scene. There is a lot of advice around, often delivered by those with a stake in selling you another product or service. However, it seems to me that there are a few simple parameters worth considering.
Functional Silo thinking is poison. The communication enabled by digital is inherently cross functional, better reflecting the way customers and suppliers see us and want to interact. Functional silos have little to do with optimised outcomes anymore. They have outlived their purpose and value.
One step at a time. While the pace of change is getting faster, and the pressure to keep up increasing, we all know what happens when we try and run down a hill really fast, we end up arse over tit. Matching the speed of change to the pace that your enterprise can absorb the change is pretty sensible. Of course, if you are the slowest in the competing pack, it may be better to get out while you can.
Digital is a team game. Hand balling digital responsibility to the IT people is a mistake. You will end up getting what they think you need, which is rarely what you really need. The real challenge is engagement of people not really focussed on digital. The primary example is in the space of marketing automation. Suddenly it exploded, way beyond the capabilities and experience of most marketing people, who are nevertheless now investing more in tech than the IT people. It is essential that the right capabilities are built in the right places. Finally, everyone affected, which is everyone, needs to be in on the secret, with all the options, challenges, and opportunities transparent. The unknown is the father of all sorts of ugly children.
Think long term. Digital transformations are not just about which software you will install to automate a process. Is more about what the business may look like in 5, 10 years, and what steps do you need to take over that time to reman relevant. Technology, much of which may not yet be available, will play a vital role in that evolution, but they remain tools of the evolution, rather than the main game.
Header credit: My thanks to Tom Gauld in New Scientist.
