As an executive, investor or board member; how should we interrupt, position and understand AI?
Image source: https://medium.com/politics-ai/an-overview-of-national-ai-strategies-2a70ec6edfd and updates https://futureoflife.org/national-international-ai-strategies/
The purpose of this article is to provide a framework which gives you a clear and coherent communication tool to assess or position any AI initiative. The positioning will be on the continuum of AI will either save the human race from extinction or be our nemesis, causing our very eradication.
The framework is built on an explanation of how value is created using an economic model that provides for both stability (BAU) and change (disruption). Critically explored is how the choice is made between the stable and change models. Through exploring stable, change and choice we unpack how the outcome of an AI project can lead to growth and prosperity or dystopia and destruction.
The simplest economic modelFigure 1 presents a very simple process. Raw materials are an input to a process called gain, which creates an output. This simple model describes any process of adding value and a central core of a business. Indeed, the same model can be applied to our human daily life where the inputs are food and water, the gain is our process of living and the output is our activity.
Figure 1. A simple model
This simple model is an open loop system, it is highly unstable and doesn’t exist in isolation but must be present in a complex world where it is continually interacting with many other processes all affecting each other. To create stability, complex systems depend on feedback loops, where the output is feedback.
The simplest stable control system is called a negative feedback loop. Figure 2 shows how you take the output after a process of gain and SUBTRACT (take is away) from the input. (the green loop.) If we took all of the gain away, there would be no value created [output - input*gain = 0], so we attenuate the output (reduce it by a factor) before the closing the loop. This negative feedback loop allows the output to track the input. Imagine the input increases, after the gain process the output increases, however as the output is feedback and taken away from the higher input, the actual input into the process is corrected and controlled. Big changes are avoided and small changes are tracked – it is stable, but enables innovation, agile changes and small adjustments.
This is a control system. The negative feedback loop (subtracting the output from the input) delivers stability and control, it is widely understood having stood the test of time. Technically the system control order of response [1st, 2nd, 3rd or higher orders] will depend on internal dependencies and external complexities; but give rise to different response times and the accuracy in tracking changes.
As a business function we like this negative feedback loop, using planning and controls to keep businesses stable but iterating and growing in a controlled manner; innovation is incremental. In psychology think Nudge. In human terms this loop of control is happening all the time in our cells and is called homeostasis. Our body temperature remains at c.37oC irrespective of environment, food, clothing and activity. Society, politics and economics thrive on this stability cycle, continually making small adjustments to maintain order. In perspective; our lives, societies, businesses and environment remain in this stable loop for over 90% of the time.
Another simple control loop is shown in Figure 3, this is a positive feedback loop. In this case you take the output after a process of gain and ADD it to the input. (the yellow loop) In this case we take the output after the process of gain and amplify (make it bigger) then add this to the input. In audio terms this feedback loop creates that deafening ringing sound or in business planning it is the magical hockey curve or exponential growth for a unicorn. It creates fundamental accelerating change.
Positive feedback loops deliver instability, at the end of each cycle round the loop a new level is achieved. This positive feedback loop creates disruptive innovation and change. The control loop order (how quickly something changes and how much change) will depend on complexity, time and the resources available.
In the human terms, the negative feedback cycle delivered stability which is seen as homeostasis; this positive feedback loop delivers mitosis (cell division and growth) and meiosis (having children) or death when it gets out of control (cancer and virus). In business terms negative feedback loops are times of stability and slow growth, positive feedback loops exist in times of rapid change, disruption and high growth. In perspective; our lives, societies and environment can only remain in unstable change loops for a very short period of time, less than 10% as it is as destructive as well as creative.
Figure 3. A simple positive feedback loop
It is possible to show change by attenuating the output and adding it to the input, rather than amplifying; however often you don’t have an ability to actually control the amplification or attenuation.
Combining these two models (positive and negative feedback loops) is obvious, we need both to understand our world of stability and change. We see this in our bodies every day at a cell level every day or when the disruption of when we are ill or a child arrives. We see the history of societies change across the passing of centuries, and at an environmental/ecology level across the millennium.
The complex model; we need both loops
In business the negative feedback loop allows us to be increasingly smart at one thing in the short term driven through continuous improvement yielding efficiency and getting to the best or optimal solution, whilst the positive feedback loop allows us to change, do radical innovation to create new products or markets, buy companies, and sustain high growth over a long period.
There is much evidence that our behaviours swop between different loops. In Jonathan Haidt’s book “The Righteous Mind” he looks into the psychology of human beings and why they believe what they believe. In his studies, he’s found that humans generally think of themselves and are selfish. They mostly behave like their ancestors, chimpanzees. They’ll take care of their own needs first, then think of others – stability. However, Haidt also explains that humans in certain instances also behave like bees with a hive mind. At times, a switch can be flipped which causes a human to think of the group before him or herself. When this mental state is achieved one will even die for the group they care for. They become a part of something much larger than themselves.
Bringing the models togetherTo bring the models together we need to introduce a third idea, the choice selector, Figure 4. The Choice selector picks which feedback loop, stable or unstable, we should currently be in. How we get to pick which loop we are in now and how to move from stable to change (different) and back to stable at a new level is “complex.” This choice selector is either where intelligence (learning &/or experience) is applied to change the cycle, meaning you are in control of the choice; or it is where you are reactive and forced to change. For humans we can see we can choose to change using a positive feedback loop two ways, we can choose to have a child (intelligence) or become ill (forced). In business we can choose to be disruptive (intelligence) or react (forced) to the disruption.
Figure 4 Choice Selector
How does this apply to AI?The title was “As an executive, investor or board member; how should we interrupt, position and understand AI?” The last section takes this framework of stability, Change and Choice selector and explores where Machine (AI) is best and where Human is best and if there are scenarios where Human + machine (AI) would be a better outcome.
The rational for unpacking these feedback loop is that AI is positioned to solve everything. As a very good book to read is 25 Minds (review here)
Figure 5. The AI matrix
Unpacking the AI Matrix.From Figure 5. In the stable loop (negative feedback loop/ green) without a doubt AI can deliver. AI will be transformative in terms of getting to the best solution, fast. Why: because in stable loops there is lots of data and it has been and will likely remain stable. The past data is an excellent indicator of future outcomes – training data works. Be careful as it is not that easy. The data can be corrupt, but the application of AI in this loop is a winning strategy. If the human is in this loop, we will probably get it wrong (motivations, bias and politics). If Human + AI, the human still likely get things wrong. However, as proven in gaming (chess and Go) human + machine does work really well and can lead to better outcomes.
In the change loop (positive feedback loop/ yellow) AI is just not up to it. The base reason is that we don’t know where we are starting from or where we are going and there is no or very little data. What the machine determines to do and why will be random and unknown. If a project is being presented to you asking for funding to use AI to change anything – walk away. Putting the human as the lead for change (it is what we do today) but we know it is sub optimal as humans have issues and limits. Human and machine (AI) is a really interesting solution as we can eliminate the worst human characterises that leads to bad decision making but getting the AI to support decision but not take them.
The choice selector (which loop) This is where you hear Elon Musk and Bill Gates talk about their fears. It is where we allow the AI/ machine to make a choice for us. Fear, uncertainty and doubt are the best outcomes. If someone is presenting to you an AI to make choices for you – AVOID and run for the hills. The human leads on choice and we have spent the past 5,000 years getting to rules, regulations, governance and government. We and our system have a few problems but we (should be) are in control. Transparency is increasingly important in choice. Finally, Human and AI together to make choices hit the issues of who decides, who decides who decides. Who polices the police? How do you ever know what someone else has put in an AI that is helping you decide? Currently asking a machine to help you decide is not a good choice!
AI in a negative feedback loop for stable environments and rapid improvement is a winning strategy, as long as the data is available. The AI will save the human race.
AI in a positive feedback loop for change is a NO. In cases where the human leads and AI/ machine support decision; a good outcome is possible.
AI in control of the choice selector is an AVOID at all cost. The AI will eradicate us
The framework presented has been evolving over the past year. I have presented and refined it over 100 times and I am therefore very grateful to those who listened to the radical early versions and those who helped finesse these final words.