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Machine Screws and AI

 

The attraction of AI is that it is learns by experience. All learning requires feedback, whether its an animal, a human or a computer doing the learning, The learning-entity needs to explore its environment in order to try different behaviors, create experiences and then learn from them.

For computers to learn about computer-based environments is relatively easy. Based on a set of instructions, a computer can be trained to learn about code that it is executing or is being executed by another computer. The backbone of the internet uses something to similar to ensure data gets from point A to point B.

For humans to learn about human-environments, this is also easier. It is what we have been doing for tens of thousands of years.

For humans to learn about computer-based environments is hard. We need a system to translate from one domain into another. Then we need a separate system to interpret what we have translated. We call this computer programming, and because we designed the computer, we have a bounded-system. There is still a lot to understand, but it is finite and we know the edges of the system, since we created it.

It is much harder for computers to learn about human environments. The computer must translate real-world (human) environment data into its own environment, and then the computer needs to decode this information and interpret what it means. Because the computer didn't design our world it doesn't have the advantage that humans do when we learning about computers. It also doesn't know if it is bounded-system or not. For all the computer knows, the human-world is infinite and unbounded - which it could well be.

In the short term, to make this learning feasible we use human input. Human's help train computers to learn about the real-world environments. I think of the reasons that driver-less car technology is being focused on, is that the road system is a finite system (essentially its 2D) that is governed by a set of rules.

•Don't drive into anything, except a parking spot.

•Be considerate to other drivers, e.g. take turns at 4-way stop signs.

•Be considerate to pedestrians and cyclists.

•etc

This combination of elements and rules makes it a perfect environment to train computers to learn to drive, not so much Artificial Intelligence but Human-Assisted Intelligence. Once we have trained a computer to decode the signals from this real-world environment and make sensible decisions with good outcomes, we can then apply this learning to different domains that have more variability in them, such as delivering mail and parcels.

This is very similar to the role of the machine screw in the industrial revolution. Once we had produced the first screw, we could then make a machine that could produce more accurate screws. The more accurate the screw, the more precise the machine, the smaller tolerance of components it could produce, the better the end-machine. Without the machine screw, there would have been no machine age.

This could open the doors to more advanced AI, it is some way off though because time required to train computers to learn about different domains.

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Building An Asset, Being Strategic, Learning Important Lessons

Since shifting out of a pure-play service company to building a product-led  company, I am now seeing what it is to be strategic.

In building a product, you are investing in an asset. Investing in an asset forces you to make strategic decisions since the product features define the course and goals for a company. When resources are limited, decision-making needs to be better since the direction these decisions impose on your company's direction are costly to undo.

Bootstrapping the development Knowledge Leaps for the past three years has been eye-opening and a great learning opportunity. The top lessons learnt so far are:

  1. Don't invest money in features that don't make it easier to use the product, today.
  2. Use the product, experience the pain points, then write the scope for the next build.
  3. Get the basics done right before moving on to build more advanced features.
  4. Work with the right team.

Fundamentally, I have learnt that if I am  allocating finite resources that have a compounding effect on my company then I am making the right  strategic.

 

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Driver-less Cars: Will They Really Help Uber and Lyft?

I have been thinking about why Uber is so keen to develop driver-less technology.

In their current business model, they recruit drivers who have cars (most of the time). These drivers show up with their cars on the app when there are riders to pick up.  As the rider volume ebbs and flows throughout the day so does the driver volume. It's near-perfect scale-able resource.

Once Uber adopts driver-less car technology, it will no longer need drivers. No more HR and employment headaches. However, it will still need cars.  To survive, Uber will become a fleet taxi operation, albeit a fleet of driver-less taxis. If Uber goes all-out driver-less a key question needs to be answered:

Just how many driver-less taxis will Uber need to have in its fleet?

Does Uber design around peak usage? And, as a result recreate the scarcity problem that it originally solved, a ride when you want it. Or does it design around total usage? In which case it will have costly excess capacity sitting idle.

At the moment Uber owns very few cars. If it buys 100,000 cars worldwide, the cost to the business over 5 years could be $300 million a year (Assuming it pays $15,000 per car fitted with driver-less technology) plus running expenses and insurance. These additional costs could be as $5000 a year or $500mn for a fleet this size, giving rise to annual costs of $800mn to operate this fleet. However, a fleet of 100,000 cars wouldn't give Uber sufficient density across the ~500 cities they operate in worldwide to maintain their market position.

The only scenario that is viable is if Uber were to replace all of its driver-owned fleet with driver-less cars. In this scenario it would need to purchase 1 million driver-less vehicles (one for each of the drivers who reportedly work for Uber). Using the same assumptions as before, this would cost Uber $3bn a year plus annual running costs and insurance of $5bn.  All in all, $8bn a year. This is quite a drag on the business that ride fees will need to cover.

Using publicly available data, Uber's gross bookings in 2016 were $20bn derived from 700mn rides. Each ride averaging at $28.73 - this might be slightly inflated because of UberEats revenue.  On this basis, the revenue from ~280mn rides each year would cover the ownership and running costs of the 1mn driver-less fleet, this represents 40% of gross booking revenues.

At this rate Uber could still be very profitable, but their business-model will shift from a gig-economy business to one with high capital costs baked-in, that is less insulated to changes in technology and culture, as well as being more exposed to threats from direct competitors.

In many ways, the strength of Uber, and similar companies, is their ability to recruit and manage a scale-able resource to meet demand and grow their businesses long term.  Although, as many gig economy businesses are realizing, it is hard to build a company using freelancers, so while the adoption of a driver-less fleet will change the nature of ride-hailing companies' economics, it might be the best next step for Uber, et al. in order to maintain growth.

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Automation: A Bright Future

From reading many articles and posts about the threat of AI to the job market, I am coming to the view that any automation, whether or not it is as result of AI, is good for long term economic prospects. Like most economists I have painted a simplistic view of the economic cycle, none-the-less I have faith that automation is a force for good.

Automation will help decouple the relationship between reducing employment and increasing inflation, a relationship that can quickly turn an economic booms into a recession.

The accepted view is that rising demand not only increases companies' profits, it also raises inflation as prices rise in response to demand. Rising demand for a company's products and services will lead to more hiring to increase output. As economies approach full employment, the cost of labor for companies faces two inflationary pressures; the first is response to increased demand for labor, and the second is in response to increased prices lead to hire wage demands. This leads to a familiar cycle: boom -> increasing inflation -> correction in the economy -> increased unemployment and reduced inflation/prices -> boom -> etc.  

Inserting automation into this cycle will allow companies to increase productivity without increasing labor cost - which erode profits and break the growth cycle. Increasing company profits will lead to increased share prices for public companies. Since many people's retirement savings are invested in the stock market in one form or another, as companies profits grow, so will the value of people's retirement savings. This will help make it easier for people to make the decision to retire. In short, the right amount of automation could a) reduce an economy's overall demand for labor, and b) provide sufficient long term stock market gains to support a growing retired section of the population. This latter point is interesting since automation could reduce the overall demand for labor. If the pool of workers chasing fewer jobs is too large then wages would fall leading to deflation and a stagnated economy. The ideal outcome is that people remove themselves from the labor market, because they can afford to retire sooner, leaving the right balance between jobs and workers. The right balance of labor supply and demand will allow for moderate inflation, GDP growth, and a stock market that can support an growing number of liberated workers.

From an employment point of view, automation may also create the need for new jobs that do not currently exist. For example prior to 2007, a marketing department in a company did not need a Social Media Manager, similarly there were no Gas Station Attendants prior to the invention of the car. In other words, automation will reduce the need for labor in current roles, as companies look to increase productivity without baking in more labor costs, it will also create new roles as the labor force becomes liberated from repetitive tasks.

One area this is happening is in data analysis and data engineering.  My web app Knowledge Leaps is designed to automate the grunt and grind of data engineering. I am building it because I want people working in similar industries to be liberated from the chore of data management, so that they  can focus on interpretation and application of the findings.

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Should Linear Analysis Be The Only Tool We Use

99% of analysis carried out by analysts involves a cross tab - analyzing one piece of data through the lens of another.

The cross tab is the de facto standard tool and while it has limitations from an analytical perspective, the cross tab is produces human readable outputs. The challenge lies in the fact that the cross tab produces linear results but not definitive results. They tell a story but often not a satisfactory one. For instance, if we look at how people voted in the 2016 Presidential Election in the USA using this data we can see a weak story appear. While many commentators wanted to label Trump supporters as white, poor and uneducated, these labels are only partially true. They are not definitive.  Were we to use just these simple descriptors to predict who voted for Trump (or Clinton) and provide a definitive story then the story would be much more convoluted to relay, since it would rely on non-linear transformation of these descriptors.

The challenge for analytics is to find the right blend of Linear Analytics and Non-Linear Analytics that combines predictive power and retains human-readability.

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Patent Update: 792 Days and counting

Source: http://examiner.ninja

My patent attorney sent me through some interesting data about the USPTO patent agent reviewing my application. The agent takes an average of 886 days to first respond to an application and if we follow the average path then we will expect to receive our patent approval on August 3, 2020, a mere 2041 days after filing it.

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Stories Vs. Predictions

Having worked within an industry (market research) for some time, I am intrigued how other occupations use data, particularly data scientists.  After a conversation with a new friend - a data scientist - last week I had a revelation.

Data scientists have created a new words to talk about data analysis. The ones that stand out are features and feature sets.   Quantitative market researchers talk about questions and surveys but never features. Essentially, they are the same thing; features are traits, attributes and behaviors of people that can be used to describe/predict specific outcomes.  The big difference is that data scientists don't care so much that the features are not human-readable (i.e. they can be read and understood like a book), as long as they help make a prediction.  For example, Random Forests make good predictors but aren't easily understandable. The same is true of Support Vector Machines. Excellent predictors but in higher dimensions they are hard to explain.

In contrast, market researchers are fixated on the predictive features being human-readable.  As data science has shown, a market researcher's predictions, their stories, will always be weaker than those of a data scientist. This in-part explains the continued trend of story-telling in market research circles.  Stories are popular, and contain some ambiguity, this ambiguity can allow people to take out from them what they wish. This is an expedient quality in the short term but damaging long term to the industry.

I think market researchers need to change, my aim with Knowledge Leaps is to try and bridge the gap between highly predictive features and human-readable stories.