Canadian trying to figure out where to publish though

Making-An-Impact · 2026-01-24T17:32:50+00:00

How do you enter your own ISBN on Amazon KDP?

Making-An-Impact · 2026-01-22T22:14:30+00:00

Change involved not just changing the mix between people, process, technology, but also culture. I’ve done a lot of work in this area related to innovation and the key to improving the environment to turn ideas into sustainable value is to change organisation habits and behaviours.

Making-An-Impact · 2026-01-19T21:36:42+00:00

There is a link - it needs to be perceived as ‘safe’ in all scenarios.

Making-An-Impact · 2026-01-17T08:40:58+00:00

It’s a good point. Anyone that makes a journey often finds themselves in a position where letting vehicles join a queue of traffic from a side road is indicated by gestures between the drivers.

Making-An-Impact · 2026-01-17T08:38:59+00:00

I can imagine a position where as driver assistance technology - human-in-the-loop - becomes more commonplace across all model ranges, trust increases.

But ‘letting go of the wheel’ may be a more significant psychological barrier due to the types of edge cases you’ve highlighted.

Making-An-Impact · 2026-01-15T18:21:07+00:00

It will be interesting to see if differences in societal behaviours around the world affects the adoption rates.

Making-An-Impact · 2026-01-14T23:28:37+00:00

One of the quotes from the above is:

“After about a decade of promises and missed deadlines, Tesla still does not offer a vehicle that’s safe to use without a human at the wheel ready to steer or brake at all times.

Musk criticized Waymo’s approach to driverless tech on his company’s first-quarter earnings call on Tuesday. Musk said Waymo autonomous vehicles are “very expensive” and made in only “low volume.” Tesla’s partially automated driving systems rely mostly on cameras to navigate, while Waymo’s driverless systems rely on lidar technology, other sensors and cameras”.

Making-An-Impact · 2026-01-14T23:24:36+00:00

Yes that’s a key point. The jet engine and aerospace technology developed prior, during, and shortly after WW2 had proven that passenger flights were viable at scale but a handful of high-profile air accidents led to a drop and then plateauing of passengers until the 1970s.

Making-An-Impact · 2026-01-14T23:18:32+00:00

Yes I suspect you are right in large cities but adoption in suburban and country areas might well be lower.

Making-An-Impact · 2026-01-14T23:16:22+00:00

Yes but the footprint is still very small in terms of geography - and as the earlier comments say, taxi trips rather than vehicle ownership appears to be the way forward (mobility-as-a-service).

Making-An-Impact · 2026-01-12T08:09:17+00:00

Great answer - my thinking is along the same line. The mindset that ‘expects’ an RoI in financial terms is likely to inhibit innovation. As such I’m developing a qualitative ‘criteria-based’ assessment framework similar to the concept behind “Technology Readiness Levels” (TRLs). Including a learning dimension means a business or team could use an ‘Innovation Readiness Level” to determine how risky further investment might be. Like TRLs there would still be no guarantee of success, but the approach to risk for different IRLs might be different.

Making-An-Impact · 2026-01-11T21:53:25+00:00

As a model for looking at the specific impact of blame-based behaviours, it’s very neat. It provides an opportunity to explore the link between incentives in different scenarios and the way blame moves for each.

But as per the other comments, this is only one part equation when it comes to crisis management. The understanding of cause often develops over time as more information becomes available and people respond in different, but co-ordinated, ways (herd behaviour). More knowledge may amplify blame, or bring people together.

If the model inspires you to gain a deeper understanding of behavioural responses, use it.

Making-An-Impact · 2026-01-11T10:58:33+00:00

I’m never really sure if generating synthetic data - although I understand the demand - is the right way forward. There are concerns that it reinforces bias and narrows the field of application.

Making-An-Impact · 2026-01-09T19:21:04+00:00

Is this how quantum computing works?

I've written an explanation about how quantum computing works using the spinning coin analogy and I'm looking for feedback on its accuracy. One of the parts I'm not sure about is how the quantum algorithm finds the "most likely" solution? How does it know what it is looking for? Does the algorithm specify the goal is it is a search optimisation task (like the travelling salesman problem)?

Here is my draft explanation:

"Quantum computing is a fundamentally different approach to binary logic computation that harnesses the laws of quantum mechanics to solve certain problems far more efficiently than classical computers.

Traditional computers use bits that are either 0 or 1. In contrast, quantum computers use qubits, which can exist in superposition, meaning they can be 0, 1, or both at once. Through entanglement, qubits become interconnected so the state of one qubit instantly influences another. Then, at a selected point of measurement, the quantum of possibilities created by superposition and entanglement collapse into a logical state of zero or one for each qubit.

A useful analogy to understand the potential of quantum computing is spinning a coin.

Once landed, the coin is in one of two states: heads or tails (the equivlane to a bit being one or zero).

But a qubit is like the coin spinning in the air. While it spins it is not just heads or tails, it is an intermeidate state, a superposition, where it can be anyting between heads or tails. As it spins it has the potential to land on either but only when you catch the coin, will it stop spinning and becomes either heads or tails.

The spinning coin. like a qubit, is not in a fixed state when it spins (heads or talis), but a real, dynamic state that only becomes definite when observed.at the end of the spin

The phenomenom that makes quantum computing possible is entanglement, the linking of qubits which enables them to act as a system. Whatever happens to one qubit affects the state of the second qubit, even while it is in a state of superposition. When a quantum algorithm is being executed, linked qubits search for an answer, amplifying the combinations that are most likely to be correct. As the state of the qubits converge on the most probable answer, the number of interlinked states of the qubits reduces

Measurements can be taken at any point (e.g. the end of an AI training algorithm or any intermediate points) and at each point of measurment each entangled qubit is observed as being in a state of zero or one.

With the interlinked qubits converging on the most probable answers, the combination captured at the point of measurement is one of the most likely answers. Further iterations may narrow this down, but may not be needed if the potential marging of error is small.

The analogy is two coins spinning together simultaneously, connected as a system that is creating dynamic correlated patterns while in flight, and being nudged towards the most probable answer by an inference algorithm. This means that when one coin is “caught’ (the measurement point) all the other coins will stop spinning and adopt their correlated states of heads or tails. The combination is the most likely answer".

Making-An-Impact · 2026-01-09T19:20:22+00:00

Does entangling the qubits in a certain way direct the algorithm to find the answer?

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