Tag Archives: Futurism

Planning for a post-work future

It’s that time of year where many bloggers make their predictions for next year. Rather than do that, I wanted to look a generation out, when those who entering college today send their children to college, and think about the events of automation on our future. This is not a prediction per se. Instead it is more of a RFI (a request for ideas).

As a caveat, I work in automation (machine intelligence and machine learning, sensor and computer vision, automated controls and planning systems). I also have a prior background in policy—one that is driving me to think about the bigger picture of automation. However, this post is not about the work I am doing now. It is about the near-term “practical realities” I can imagine.

We are at the onset of an “Automaton Renaissance.” Five years ago, most people outside of tech thought about self-driving cars as something from the Jetsons. Last week, the governor of Michigan signed a bill allowing live testing of self-driving cars without human testers. Chatbots are not just the stuff of start-ups. Last month, I attended a conference where Fortune-500, large-cap companies were sharing results of pilots to replace back office help desks and call centers with chatbots. Two weeks ago, I was at a supply chain conference where we discussed initial pilots that a replacing planning and decision-making with machine learning (pilots involving billions of dollars of shipments). Automation is not coming—it is here already, and accelerating. Last week, I was at a conference for advanced manufacturing, we the speakers discussed the current and future impacts (good and bad) on jobs in the US.

So what will life (and work) be like in 20 years? Here are just a few things that we already have the technology to do today (what is left is the less-futuristic problems of mass production, rollout, support and adoption):

  • If you live in the city and suburbs, you will not need to own a car. Instead you can call an on-demand autonomous car to pick you up. No direct insurance costs, no auto loans, less traffic and pollution. In fact the cars will tell Public Works about detected potholes (street light and infrastructure sensors will tell Public Works when maintenance is needed).
  • If you work in a manufacturing plant, you will have fewer workers who are monitoring and coordinated advanced manufacturing (automation + additives). The parts will have higher durability and fewer component suppliers—also a reduction in delays, cost and pollution.
  • If you work on a farm you will demonstrate (supervised learning) to drones how you want plants pruned and picked, holes dug, etc. These drones will reduce back-breaking labor, reduce accidents and automatically provide complete traceability of the food supply chain (likely via Block Chain)
  • If you do data entry or transcription, your work will be replaced with everything from voice recognition-based entry, to Block Chain-secured data exchange, to automated data translation (like the team is doing at Tamr)
  • 95% of call centers will be chatbots. Waiting for an agent will be eliminated (as well as large, power-hungry call centers). The remaining 5% of jobs will be human handling escalation of things the machines cannot.

These are just five examples. They are all “good outcomes” in terms of saving work, increasing quantity and quality of output, reducing cost (and price), and even improving the environment. (If you are worried about the impact of computing on energy, look at what Google is doing with making computing greener.)

However, they will all radically change the jobs picture worldwide. Yes, they will create new, more knowledge-oriented jobs. Nevertheless, they will greatly reduce the number of other jobs. Ultimately, I believe we will have fewer net jobs overall. This is the “post-work future” — actually a “post-labor future”, a term that sounds a bit too political. What do we do about that?

We could ban automation. However any company or country that embraces it will gain such economic advantage that it will force others to eventually adopt automation. The smarter answer is to begin planning for an automation-enhanced future. The way I see it, our potential outcomes fall between the following two extremes:

  1. The “Gene Roddenberry” Outcome: After eliminating the Three D’s (dirt, danger, and drudgery) and using automation to reduce cost and increase quantity, we free up much capacity for people to explore more creative outcomes. We still have knowledge-based jobs (medicine, engineering, planning). However, more people can spend time on art, literature, etc. This is the ideal future.
  2. The “Haves vs. Have Nots” Outcome: Knowledge workers and the affluent do incredibly well. Others are left out. We have the resources (thanks to higher productivity) but we wind up directing this to programs that essentially consign many people to living “on the dole” as it was called when I lived in the UK. While this is humane, it omits the creative ideas and contributions of whole blocks of our population. This is a bad future.

Crafting where we will be in 20 years is not just an exercise in public policy. It will require changes in how we think and talk about education, technology, jobs, entitlement programs, etc. Thinking about this often keeps me up at night. To be successful, we will need to do this across all of society (urban and rural, pre-school through retirement age, across all incomes and education levels, across all countries and political parties).

Regardless of what we do, we need to get started now. Automation is accelerating. Guess how many autonomous vehicles will be on the roads in the US alone by 2020 (virtually three years from now):

10 million

Note: The above image is labeled for re-use by Racontour. Read more on the post-work word at The Atlantic magazine and Aeon magazine.

The Expanding (Digital) Universe: Visualizing How BIG a Zettabyte Really Is

Note: This post was originally published at Oulixeus Consulting

A lot of news articles recently (Google News currently shows 1,060 articles) are citing the annual EMC-IDC Digital Universe studies of the massive growth of the digital universe through 2020. If you have not read the study, it indicates that the digital universe is now doubling every two years and will grow 44-fold 50-fold now 55-fold from 0.8 Zettabytes (ZB) of data in 2009 to 35 40 now 44 Zettabytes in 2020. (Every year IDC has revised the growth curve upward by several Zettabytes.)

Usually these articles show a diagram such as this:

DigitalDecade

This type of diagram is great at showing how much 44-fold growth is. However it really does not convey how big a Zettabyte really is—and how much data we will be swimming (or drowning in) by 2020.

A Zettabyte (ZB) is really, really big – in terms of today’s information systems. It is not a capacity that people encounter every day. It’s not even in Microsoft Office’s spell-checker, Word “recommended” that I meant to type “Petabyte” instead 😉

The Raw Definition: How big is a Zettabyte?

A Computer Scientist will tell you that 1 Zettabyte is 270 bytes. That does not sound very big to a person who does not usually visualize think in exponential or scientific notation—especially given that a one-Terabyte (1 TB) solid state drive has a capacity to store 240 bytes.

Wikipedia describes a ZB (in decimal math) as one-sextillion bytes. While this sounds large, it is a hard to visualize. It is easier to visualize 1 ZB (and 44 ZBs) in relation to things we use everyday.

Visualizing Zettabytes in Units of Smartphones

The most popular new smartphones today have 32 Gigabytes (GB) or 32 x 230 bytes of capacity. To get 1 ZB you would have to fill 34,359,738,368 (34.4 billion) smartphones to capacity. If you put 34.4 billion Samsung S5’s end-to-end (length-wise) you would circle the Earth 121.8 times:

1ZB-Earth-Distance
Click to see a higher resolution image and the dot that represents Earth to-scale vs. the line

You can actually circumnavigate Jupiter almost 11 times—but that is not obvious to visualize.

The number of bytes in 44 Zettabytes is a number too large for Microsoft Excel to compute correctly. (The number you will get is so large that Excel will cut off seven digits of accuracy–read that as a potential rounding error up to one million bytes). Assuming that Moore’s Law will allow us to double the capacity of smartphones three times between now and 2020, it would take 188,978,561,024 (188+ trillion) smartphones to store 44 ZB of data. Placing these end-to-end- would circumnavigate the world over nearly 670 times.

This is too hard to visualize, so lets look at it another way. You could tile the entire City of New York two times over (and the Bronx and Manhattan three times over) with smartphones filled to capacity with data to store 44 ZBs. That’s a big Data Center!

Clik
Amount of Smartphones (with 2020 tech) you would need to store 44 ZB (click for higher resolution)

This number also represents 25 smartphones per person for the entire population of the planet. Imagine the challenge of managing data spread out across that many smartphones.

Next Page: Visualizing Zettabytes in Units of Facebook