For Your Consideration: Cotton Robots, Datamining for Literacy, and Childhood Memories

1. Automation and the Cotton Gin

When the cotton gin was invented, many people thought that it would reduce our new nation’s dependence on slavery by removing the painstaking work of separating the usable cotton from seeds, hulls, stems, etc.

But ironically, it resulted in the growth of slavery.

The gin could process cotton so efficiently that more cotton goods could be produced, and it turned out that there was massive latent demand for cotton goods. So while the robots did indeed reduce the reliance on slaves to do the finishing work, they also increased demand for cotton, which resulted in many more cotton fields, and many more slaves to tend them.

I don’t know enough history to know whether this was a core issue that led to our Civil War or just a contributing factor. Probably somewhere in between. But it took us more than 100 years to really process all the implications of just this one technology advance (and I think really you’d argue that we haven’t fully come to terms with them even today.)

So you see where I’m going with this.

Fast forward to our own era, and we’re working our way through software automation instead of cotton processing automation. And it seems obvious to me that as we’re making systems and processes easier and easier to automate, we’re also generating massive new previously latent demand for software driven systems.

I’m not arguing at all that this will result in anything like the growth of slavery in the first half of the 19th century — more that we’re in a time of profound change. And that worries over whether robots will take all our jobs I think will prove to be ultimately misplaced. I think that if you look not just at the cotton gin, but most technology automation advances what you’ll find is that the demand for labor nearly always increases.

2. Mobile Phone Data Reveals Literacy Rates in Developing Countries

One of the millennium development goals of the United Nations is to eradicate extreme poverty by 2030. That’s a complex task, since poverty has many contributing factors. But one of the more significant is the 750 million people around the world who are unable to read and write, two-thirds of which are women.

There are plenty of organizations that can help, provided they know where to place their resources. So identifying areas where literacy rates are low is an important challenge…

The usual method is to carry out household surveys. But this is time-consuming and expensive work, and difficult to repeat on a regular basis. And in any case, data from the developing world is often out of date before it can be used effectively. So a faster, cheaper way of mapping literacy rates would be hugely welcome.

Pål Sundsøy at Telenor Group Research in Fornebu, Norway, says he’s worked out how to determine literacy rates using mobile phone call records. His method is straightforward number crunching. He starts with a standard household survey of 76,000 mobile phone users living in an unidentified developing country in Asia. The survey was carried out for a mobile phone operator by a professional agency and logs each person’s mobile phone number and whether or not they can read.

Sundsøy then matches this data set with call data records from the mobile phone company. This provides data such as the numbers each person has called or texted, the length of these calls, air time purchases, cell tower locations, and so on.

From this data, Sundsøy can work out where all the individuals were when they made their calls or texts, who they were calling or texting, the number of texts received, at what time of day, and so on. This allows him to construct a social network for each user, working out who they called, how often, and so on.

Finally, he used 75 percent of the data to search for patterns associated with users who are illiterate, using a variety of number crunching and machine learning techniques. He used the remaining 25 percent to test whether it is possible to use these patterns to identify illiterate people and areas where there is a higher proportion of illiterate people.

3. Why Childhood Memories Disappear

“People used to think that the reason that we didn’t have early memories was because children didn’t have a memory system or they were unable to remember things, but it turns out that’s not the case,” Peterson said. “Children have a very good memory system. But whether or not something hangs around long-term depends on on several other factors.” Two of the most important factors, Peterson explained, are whether the memory “has emotion infused in it,” and whether the memory is coherent: Does the story our memory tells us actually hang together and make sense when we recall it later?

But then, this event- or story-based memory isn’t the only kind, although it’s the one people typically focus on when discussing “first” memories. Indeed, when I asked the developmental psychologist Steven Reznick about why childhood amnesia exists, he disputed the very use of that term: “I would say right now that is a rather archaic statement.” A professor at the University of North Carolina-Chapel Hill, Reznick explained that shortly after birth, infants can start forming impressions of faces and react when they see those faces again; this is recognition memory. The ability to understand words and learn language relies on working memory, which kicks in at around six months old. More sophisticated forms of memory develop in the child’s second year, as semantic memory allows children to retain understanding of concepts and general knowledge about the world.

“When people were accusing infants of having amnesia, what they were talking about is what we refer to as episodic memory,” Reznick explained. Our ability to remember events that happened to us relies on more complicated mental infrastructure than other kinds of memory. Context is all-important. We need to understand the concepts that give meaning to an event: For the memory of my brother’s birth, I have to understand the meanings of concepts like “hospital,” “brother,” “cot,” and even Thomas the Tank Engine. More than that, for the memory to remain accessible, my younger self had to remember those concepts in the same language-based way that my adult self remembers information. I formed earlier memories using more rudimentary, pre-verbal means, and that made those memories unreachable as the acquisition of language reshaped how my mind works, as it does for everyone.

“Now comes the second machine age. Computers and other digital advances are doing for mental power—the ability to use our brains to understand and shape our environments—what the steam engine and its descendants did for muscle power.” – Erik Brynjolfsson