Why Waze shows you the past

As usual, when driving back home during rush hour, I turned on Waze to get insights on the road conditions.  After a few minutes Waze diverted me to a side road to avoid a traffic congestion.  However as I looked at the highway, traffic was flowing freely while I was stuck in a traffic light on the diversion.  It then dawned on me that the success of Waze in directing people to the diversion, means that Waze is blind to the changing conditions on the highway.

Various solutions to this problem are possible.  One example is A/B testing, i.e. having some users diverted while others are not diverted.  In this manner Waze can keep up with traffic conditions, but at the expense of its users and it risks loosing its users trust if they are used as experimental pawns.  Another example is reducing the persistence time of events, i.e. in the absence of active reports or driving speeds Waze reverts back to normal conditions.

While the consequences of this in Waze are mainly loss of time, a future with autonomous cars may be more complicated.  Autonomous vehicles will pass through three phases

  • a minority of autonomous cars and limited functionality
  • a majority of autonomous cars and car to car and car to device communications
  • only autonomous cars allowed on roads

Its reasonable to assume that there will be at most a handful of car operating systems and probably all will meet some basic criteria.  This means that the diversity of driving patterns we see on the roads today will disappear. So we have a situation of similar objects with many degrees of freedom and interacting in a non linear way.  In physics such system are invariably chaotic.  A simple way to understand the complexity of the system consider a single road.  When the distance between cars is large and there is no interaction the traffic flows.  However as more cars populate the road they start to interact.   When one car brakes so do all the others.  We see this even in human driving, as highways become congested the traffic is erratic even without visible obstructions.  For autonomous cars this will be accentuated since all cars have the same underlying logic, so certain situations are prone to trouble.  Since this is a road specific and multi car scenario, it requires extensive and accurate simulations to create safeguards in the car operating system which identify and change the interaction rules to delay the onset of chaos. These situations will be exasperated in mixed driving conditions when humans try to take advantage of the predefined rules of the autonomous vehicles.  For example, if an autonomous car always keeps it distance, there will always be a human driver cutting in, which will push the autonomous car further back in line.  The mixed driving period will be challenging in every aspect as even innocent behavior can have far reaching consequences raising questions of liability and fault.





Who will drive the driverless car

When RCA invented the car radio a century ago the household names of today, Google, Facebook, Apple Samsung and many others existed at best as an abstraction in a science fiction story. However a coalescence of trends is brewing a disruption to From the VW debacle to Apple I Car activity the car industry seems ripe for disruption.     

There is only one small thing missing. The business case. Autonomous cars fall under one of two categories 

– enhancing safety and convenience 

– replacing taxi drivers by car sharing 

The business case of replacing taxi drivers is interesting since it hinges on the gig economy and the success of Uber and its fellow companies. The unanswered question is will this increase or decrease overall car sales.   Initial logic implies decreasing. As “public transportation” prevails, car ownership goes down. However hype crowding logic says “as every car becomes a profit center, more cars will be bought”. Time will tell, what prevails. 

The first aspect of increasing safety and convinience is the practical route and it seems that at least for image based technologies the price point is right and ripe for inclusion in the basic package of cars.

Those who can, do

In the world of trading stocks, a well known adage is

If you have a good trading system you trade your money, if it’s mediocre you trade other people’s money and if it’s lousy you write a book about it. 

In inventing as in stocks, to make money, talent alone does not work and luck is an important factor.  But to come up with ideas is certainly a learnable trait. I have met many people who teach how to invent. One would imagine that such teachers can generate ideas effortlessly, however the reality is that inventors need at least two aspects of knowledge to be prolific. The first is indeed systematic methods of inventing. The second, which in today’s lean and quick world is often forgotten, is a wide knowledge base. If we compare inventing to quick reading, than the methods of systematic inventing are methods of quick reading, but the knowledge is the books.  

The biggest driver for knowledge is curiosity, which is why curious people make good inventors. 

(Small) Engineering Oversights

A few days ago I participated in a panel about “things developers overlook”.  The panel consisted of a great group of people and many smart things were said such as

  • listening to your customers, both internal as well as external
  • the crucial importance of a system architect or system engineer
  • the art of engineering communication through specifications, requirements and presentations
  • having a structured process in place such as New Product Introduction (NPI)

After the panel I thought of the many examples where all these things existed, yet an engineering error (or decision) permeated through the development process and emerged as a product catastrophe and a major embarrassment to the relevant company.  These occurrences are not rare and indicate that in the complex world we live in and develop for, even the best methods and plans fall short of expectations.  Its well known that released software has bugs, either large or small, its also widely accepted that products may be recalled. yet we find it hard to accept that engineering is limited in its ability to execute without flaw in the development cycle.

I though of giving some examples from the best of companies to remind us to be wary of data, plans and to constructively question our development activities

In 2010 left handed people reported having a significant percentage of dropped calls when using their iphone 4.  It seems the problem was related to a choice of antenna placement in the phone.  In 2014 when Apple released the iphone 6 and 6+ users complained of bending phones.  The problem was apparently related to engineering and design trade-offs made in the phones.

The Pentium FDIV bug is a bug in the Intel P5 Pentium floating point unit (FPU). Because of the bug, the processor can return incorrect decimal results, an issue troublesome for the precise calculations needed in fields like math and science. Discovered by Professor Thomas R. Nicely at Lynchburg College.  Intel attributed the error to missing entries in the lookup table used by the floating-point division circuitry.

Most recently the Volkswagen Emission Scandal is an amazing lesson in realizing and managing the limitations of engineering.

if you want to sell ideas …

From the dawn of mankind, ideas have pushed the boundaries of reality and expanded our knowledge and capabilities. The first person who realized how fire can be used to cook food, or the first person to invent the wheel had a huge challenge in reaping the rewards of their invention. The reduction to practice may have been simple, but the dissemination and protection of ideas had to wait a few thousand years to the era of patents and writing.
Today, inventors and idea originators have a whole system to protect their ideas, but the reduction to practice, and commercialization are often a daunting task.
The simple case is an idea which can become a company. In this case, the idea originator either forms a company, builds a team and raises money to make the idea happen or he should find an entrepreneur who has the drive to create a company around the idea.
Other ideas are challenged to become companies. These ideas typically exist within an ecosystem of constraints, or within established markets (e.g. toys, food etc) which require extensive capital to bring products to market. In these cases while the option of forming a company is possible alternatives such as open innovation are attractive. the following is a partial list of sites which offer challenges and are seeking ideas. These sites present a lower effort route than forming a company towards commercialization of ideas.

Quirky is a site mostly aimed at gadgets
NineSigma typically provides technical problems from large companies
Innocentive also offers less technical challenges
Kaggle is geared towards the data scientist
Intellectual Ventures focuses on building long term relationships with its inventors