The change being driven by Uber, and where it’s going: Best of the Web

The change being driven by Uber, and where it’s going: Best of the Web

When it comes to disruptive tech companies, it’s hard to think of a better example at the moment than Uber.

For the uninitiated, Uber is a mobile app that links people wanting to take a lift with potential drivers. It is making headlines for upsetting taxi and public transport authorities the world over.

With the company currently travelling at high speed, it’s certainly not difficult to be caught up in the hype. But are we really being taken for a ride?

Jeff Bercovici from Forbes takes a look at where it could all go wrong for Uber:

A world with less pollution and no traffic jams, where taxis are cheap and safe and you never have to wait for a pickup: It’s a rosy vision Uber is peddling, and venture capitalists, those professional optimists, are fully on board. An app-based service that lets anyone in need of a ride summon one within minutes, Uber recently raised $1.2 billion at a valuation of $18 billion, making it, on paper, one of the world’s biggest transportation companies, more valuable than such venerable competitors as Hertz, Avis and United Airlines.

Bercovici explains that barriers including overbearing government regulations, increasing competition and the potential emergence of new competitors mean the success of Uber is far from assured.

A brief history of autocorrect

Most mobile phone users have a love-hate-laugh relationship with autocorrect.

But who invented it and where did it come from?

It’s a question investigated by Gideon Lewis-Kraus of Wired:

Invoke the word autocorrect and most people will think immediately of its hiccups—the sort of hysterical, impossible errors one finds collected on sites like Damn You Autocorrect. But despite the inadvertent hilarity, the real marvel of our mobile text-correction systems is how astoundingly good they are. It’s not too much of an exaggeration to call autocorrect the overlooked underwriter of our era of mobile prolixity.

According to Lewis-Kraus, if you’ve ever suffered at the hands of an incorrectly corrected text message, the person you need to thank is Dean Hachamovitch.

Hachamovitch first came up with the concept while devising new features for Microsoft Word during the 1990s:

The notion of autocorrect was born when Hachamovitch began thinking about a functionality that already existed in Word. Thanks to Charles Simonyi, the longtime Microsoft executive widely recognized as the father of graphical word processing, Word had a “glossary” that could be used as a sort of auto-expander. You could set up a string of words—like insert logo—which, when typed and followed by a press of the F3 button, would get replaced by a JPEG of your company’s logo. Hachamovitch realized that this glossary could be used far more aggressively to correct common mistakes.

How to fool a facial recognition system

Are you concerned about increasingly intrusive new technologies such as facial recognition tracking your every digital move?

According to Robinson Meyer of The Atlantic, the answer could be as simple as applying some children’s face paint, or perhaps a touch too-much makeup.

Basically, while computer facial recognition is now good enough to automatically identify the friends in your photo album by picking out patterns.

However, the strategic use of cosmetics can break the patterns facial recognition algorithms rely on. The technique is known as a “CV dazzle”:

The patterns are called computer vision dazzle (or CV dazzle). When it works, CV dazzle keeps facial-recognition algorithms from seeing a face. The technique takes its name from the dazzle camouflage of the two World Wars: The Great Power navies sought to protect their ships not by hiding them among the waves but by obscuring their size and movement.

The idea behind CV dazzle is simple. Facial recognition algorithms look for certain patterns when they analyze images: patterns of light and dark in the cheekbones, or the way color is distributed on the nose bridge—a baseline amount of symmetry. These hallmarks all betray the uniqueness of a human visage. If you obstruct them, the algorithm can’t separate a face from any other swath of pixels.

While I personally wouldn’t recommend the use of face paint at work, Meyer’s piece does illuminate where some of the limits of current generation facial recognition technology lie.

The question you need to ask when assessing a small business database

Are you looking at upgrading the customer database your business uses?

Before you do, it might be worth checking out this article in Forbes, by Henry DeVries:

Every small business, my own included, struggles with its database. Some of it is in MS Outlook, another part is on the ConstantContact email program, and another chunk resides on LinkedIn. What is the best database program to handle it all?

Not so fast. Below are the questions Gitchel says a small business should consider asking before trying to solve the database stumper.

DeVries runs through some of the essential questions every small business needs to ask before making the decision, including whether you really want a customer relationship management system or an email marketing platform such as MailChimp.


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