As someone who works in many facets of the music industry, I used to seethe with a mixture of anger and jealousy when I would hear people in more “traditional” goods-based industries argue in favor of music content-based piracy. They made all the classic talking points, like “I wouldn’t spend money on this artist normally, and maybe if I like it I’ll spend money on them when they come to town” (which never happened), or “artists are rich and I’m poor, they don’t need my money” (rarely the case), or the worst, “if it were fairly priced and worth paying for, I’d buy it” (not true).  I always wondered if they’d have the same attitude if 63% of the things acquired by customers in their industries weren’t actually paid for, as was conservatively estimated as the case for the music industry in 2009 (other estimations put the figure of pirated music at 95%). Well, we may soon see the answer to curiosities like that. Though one can say with tentative confidence that music piracy is on the decline thanks to services like Spotify and Rdio, it could be looming on the horizon for the entire global, physical supply chain. Yes, I’m talking about 3d printers.

Before I get into the heart of this article, let me take a moment to make one thing clear: I think these machines are incredible. It’s damn near inspiring to think of even a few of their potentially world-changing applications: affordable, perfectly fit prosthetic limbs for wounded servicemen and women; the ability to create a piece of machinery on the spot instead of having to wait for a spare to arrive in the mail, or en route if your car or ship breaks down in a far away place; a company based out of Austin, TX even made a fully functioning firearm from a 3d printer a few months ago.

If these machines become as consumer-friendly and idiot-proof as possible (like computers), it’s possible that in a matter of decades (maybe less), a majority of U.S. households will have their own 3d printer. There’s also the possibility they could take the tech-hobbyist path, one that is much less appealing to the masses. Dale Dougherty of Makezine.com estimates there are currently around 100,000 “personal” 3d printers, or those not owned for business or educational purposes. I don’t think they’ll ever be as ubiquitous as computers, but there are plenty of mechanically inclined, crafty hobbyists out there who would love to play around with a 3d printer if it was affordable enough.

That being said, is there reason to worry about the economic implications of consumers making what they want, essentially for free, instead of paying someone else to produce it? Or will the printers instead be used for unique items more so than replicating and ripping off other companies’ merchandise in mass amounts? The number of people working in industries that would be affected by a development like this is far greater than the number of people who work in content-based industries, so any downturn would probably have a much larger economic implications. Certainly, those times are a ways off, but a little foresightedness never hurt anyone!

With stiff penalties for being caught and the whiff of secretive underground or even nefarious acts, computer hacking can be seen as a somewhat dubious pursuit. Not all hackers operate with the motive of emptying your Paypal account, however; there are many hackers who utilize their skills to aid companies in locating security flaws ("penetration testing") or engage in hacking with the goal of becoming cyber-freedom-fighters that champion simple human freedoms, such as the right to free speech.

Computer hacking is as much an art as it is a skill. At its simplest distillation, hacking is the systematic search for chinks in programming armor. While advanced problem-solving, intuition and sophisticated understanding of programming languages are a distinct advantage, there does exist a number of push-button programs that computing wizards have written allowing those less sophisticated in the art of hacking to break into remote computers in a variety of ways. Because of this new ubiquity, today's hackers no longer need to be a programming Wunderkind; they simply need to know where to download software and be able to turn on a computer. It really is that simple and the implications can be disturbing.

Phishing, Push-Button Programs and Brute Force Tactics

There's no need to crack a company's firewall if you have direct physical access to their computers. One aspect of hacking is the impersonation of an employee or service worker with the goal of gaining access to a company's database, where the hacker can then unleash whatever havoc he or she has planned into the system. Another is to engage in simple phishing techniques, such as impersonating an employee who forgot their password and needs help logging into the system.

Because such impersonations often fail thanks to companies becoming more security-conscious, taking over operations of a computer remotely is often the preferred method of gaining access. Such attempts can be facilitated in a variety of ways. One is the brute-force method, in which a program such as SQLmap, Nmap or Burpsuite is used; running one of these programs is analogous to trying every doorknob in a neighborhood to see which house is unlocked. Using a variety of different parameters, these programs can find access to a vulnerable computer or network in less than a minute.

Hackers can also attempt to gain access with a program like Metasploit. With literally a few clicks of a mouse, access to a remote and vulnerable computer can be achieved by a relative newbie. With a related hacking aid, called Meterpreter, a backdoor is created that allows access into an operating system. It does not install itself onto the remote computer, running instead using the computer's memory; in fact, Meterpreter can hide itself inside the operations of a perfectly valid program, so it cannot be detected even by sophisticated programmers. Once engaged, it allows a remote user carte blanche access to the system in question.

Where to Learn the Art of Hacking

Of course, for those who wish to learn the actual skills rather than download someone else's hack, there are a number of practice sites that pose an increasingly difficult set of challenges intended to train neophytes in the art of hacking. For example, Hack This Site starts beginners with the goal of cracking simple flaws in coding scripts or software such as HTML, Unix, Javascript and Apache. Their structured series of tests increase in complexity, incorporating real-word scenarios and even old-fashioned "phone phreaking" challenges that recall the bygone golden age of hacking skills displayed by Matthew Broderick in "WarGames."

Using just these simple tools and free practice sites, beginners have a powerful array of hacking resources just a simple mouse click away.

Machine learning systems are equipped with artificial intelligence engines that provide these systems with the capability of learning by themselves without having to write programs to do so. They adjust and change programs as a result of being exposed to big data sets. The process of doing so is similar to the data mining concept where the data set is searched for patterns. The difference is in how those patterns are used. Data mining's purpose is to enhance human comprehension and understanding. Machine learning's algorithms purpose is to adjust some program's action without human supervision, learning from past searches and also continuously forward as it's exposed to new data.

The News Feed service in Facebook is an example, automatically personalizing a user's feed from his interaction with his or her friend's posts. The "machine" uses statistical and predictive analysis that identify interaction patterns (skipped, like, read, comment) and uses the results to adjust the News Feed output continuously without human intervention. 

Impact on Existing and Emerging Markets

The NBA is using machine analytics created by a California-based startup to create predictive models that allow coaches to better discern a player's ability. Fed with many seasons of data, the machine can make predictions of a player's abilities. Players can have good days and bad days, get sick or lose motivation, but over time a good player will be good and a bad player can be spotted. By examining big data sets of individual performance over many seasons, the machine develops predictive models that feed into the coach’s decision-making process when faced with certain teams or particular situations. 

General Electric, who has been around for 119 years is spending millions of dollars in artificial intelligence learning systems. Its many years of data from oil exploration and jet engine research is being fed to an IBM-developed system to reduce maintenance costs, optimize performance and anticipate breakdowns.

Over a dozen banks in Europe replaced their human-based statistical modeling processes with machines. The new engines create recommendations for low-profit customers such as retail clients, small and medium-sized companies. The lower-cost, faster results approach allows the bank to create micro-target models for forecasting service cancellations and loan defaults and then how to act under those potential situations. As a result of these new models and inputs into decision making some banks have experienced new product sales increases of 10 percent, lower capital expenses and increased collections by 20 percent. 

Emerging markets and industries

By now we have seen how cell phones and emerging and developing economies go together. This relationship has generated big data sets that hold information about behaviors and mobility patterns. Machine learning examines and analyzes the data to extract information in usage patterns for these new and little understood emergent economies. Both private and public policymakers can use this information to assess technology-based programs proposed by public officials and technology companies can use it to focus on developing personalized services and investment decisions.

Machine learning service providers targeting emerging economies in this example focus on evaluating demographic and socio-economic indicators and its impact on the way people use mobile technologies. The socioeconomic status of an individual or a population can be used to understand its access and expectations on education, housing, health and vital utilities such as water and electricity. Predictive models can then be created around customer's purchasing power and marketing campaigns created to offer new products. Instead of relying exclusively on phone interviews, focus groups or other kinds of person-to-person interactions, auto-learning algorithms can also be applied to the huge amounts of data collected by other entities such as Google and Facebook.

A warning

Traditional industries trying to profit from emerging markets will see a slowdown unless they adapt to new competitive forces unleashed in part by new technologies such as artificial intelligence that offer unprecedented capabilities at a lower entry and support cost than before. But small high-tech based companies are introducing new flexible, adaptable business models more suitable to new high-risk markets. Digital platforms rely on algorithms to host at a low cost and with quality services thousands of small and mid-size enterprises in countries such as China, India, Central America and Asia. These collaborations based on new technologies and tools gives the emerging market enterprises the reach and resources needed to challenge traditional business model companies.

From Brennan's Blog which is no longer up and running: