Every day, highly advanced artificial neural networks (ANNs) and deep learning algorithms scan through millions of queries and dig through the endless flow of big data. They are providing the knowledge required to fuel the many ever-evolving artificial intelligence (AI) that many software houses have incorporated in their products. Machine learning is the instrument through which these newborn computer-based bits of synthetic intelligence process all the info they're nourished with, much like the five senses help a human toddler learn and experience the world.

Eventually, all this information becomes enough to help these AIs provide us with new answers to our questions, and many solutions that are much smarter than those that a human mind might conceive. So, what are some examples where neural networks and machine learning are being effectively used in practice today? Let's have a look.

Self-Driving Cars

Is there anything that screams "future" more than a self-driving car? We've spent the last 30 years dreaming of cyberpunk dystopian worlds where androids who dream of electric sheep run from captors by jumping on driverless vehicles. Okay, maybe those vehicles were also able to fly, but you get the point.

Autonomous vehicles aren't just a dream anymore. Albeit most of them are still just prototypes, they're definitely a reality nowadays. Dozens of different companies have already invested a substantial amount of money to fuel this technology, including the U.K. Department for Transport which is currently backing 15 government-sponsored projects.

How else could those vehicles learn how to drive if not through machine learning? Deep learning algorithms are employed by software developers to power computer vision, understand all the details about their surrounding environment, and make smart, human-like decisions. For years, human-driven cars have been equipped with an array of cameras and sensors that record everything from driving patterns to road obstacles, traffic lights, and road signs. Now, all this data is used to "teach" the autonomous systems how to recognize these objects and how to appropriately react to external stimuli while driving on a real road. (To learn more, check out The 5 Most Amazing AI Advances in Autonomous Driving.)

Network Efficiency

The idea of using artificial intelligence to optimize the efficiency of networks and improve their security dates back to the early '80s. However, modern technologies have made a huge leap forward, and revolutionary machine learning algorithms can mundanely perform complex tasks such as predicting faults and scheduling fixes.

AI is exceptionally efficient at allocating network resources where they're most needed by autonomously analyzing traffic data, and they possess the agility required to integrate themselves with the many internet of things (IoT) devices connected to the network architecture. No one can talk to a machine better than another machine, after all. We can hear humans falling back in the food chain already, can't we?

Cybersecurity

ANNs can also be used to protect organizations from several types of attacks, such as DDoS and malicious software. Malware itself is a huge problem, with at least 325,000 new malicious files being generated every day. Yet, no more than 10 percent of the files change from iteration to iteration, so algorithm-based learning models that can predict these variations are able to detect which files are malware with amazing accuracy.

AI is better than humans at cybersecurity because they automate the most complex processes required for detecting attacks and analyzing the best way to react to breaches. More in general, neural nets could be used to detect any change or anomaly in network traffic to identify potentially malicious activities such as brute-force attacks, unusual failed logins and file exfiltration.

Obviously, hackers started developing their own adaptive AI to deceive security software and exploit vulnerabilities, in a never-ending arms race between attackers and defenders. However, all of this actually benefits AIs, which get smarter and smarter every day they are deployed in the battlefield. (To see how AI is fighting crime in the real world, see How AI Is Helping in the Fight Against Crime.)

Building a Better World

One of the traditional fears of those who oppose technology is that machines will eventually replace human labor and drive millions of people into poverty. As a matter of fact, however, machine learning and neural networks are actually helping many governments build a better, fairer and more equitable society. And while a few may find it unsettling that in the future nationwide decisions will be made by machines, we may always argue that many human-made ones didn't prove to be so smart after all. Machines' decisions are always neutral and unbiased, at least until Skynet starts making them.

In Belgium, an employment and vocational agency crafted an IBM software-powered solution to bring unemployment down for young workers. The machine learning-driven model is able to analyze past data to predict the duration of unemployment for each potential candidate, while devising new, smart ways to direct the government's limited resources where they’re truly needed to boost the economy.

In Colombia, the Instituto Colombiano de Bienestar Familiar is a local welfare organization that provides charity and services to protect destitute families and poor children. Their budget is extremely tight, yet they managed to provide over five million dietary supplements and food rations to tens of thousands of malnourished children. How? Well, predictive analytics and micro-targeting software provide the necessary degree of optimization to assist this organization in reaching Colombia’s poorest and most remote areas.

But that's not the only case where machines teach humans how to do more with less. In the Netherlands, the environmental protection agency DCMR Milieudienst Rijnmond employed a new solution armed with machine-learning sensors that can identify and evaluate environmental hazards in real time. Deep learning algorithms can then identify key risks and sort them by urgency, diverting the resources where they're needed most and improving public safety.

Business and Advertising

This can be summed up in just one word (well... three): personalized product recommendations. Every time we search for something on Google or any other search engine, eventually we start seeing a ton of precisely targeted ads about these things. How could the software understand so well what our interests are and how to entice us into buying those extremely cheap goods we want so badly?

Once again, deep learning is the answer. These highly reactive programs learn by watching our behaviors, such as when we skip to page two of the search results when none of those found on page one satisfied our needs. Machines can crunch demographic data about customers' habits and preferences at a speed that no human analyst can ever hope to reach, and can consume it to optimize pricing, offers, customer experience, and profitability. It should not surprise anyone that one of the biggest lovers of AIs and smart algorithms is none other than Amazon itself.

Yet, the retail giant is using advanced heuristics to optimize its services in many other ways. One of the reasons why Jeff Bezos' creature is so successful, is, in fact, the amazing efficiency of its logistics planning. Other giants such as Walmart and Honda as well as many small-to-medium businesses and factories vastly improved their efficiency by implementing machine learning in the management of orders, stocking, inventory control and warehousing. AIs are incredibly good at detecting quality issues inside and beyond the assembly line, for instance by identifying patterns in the free-text fields of warranty registration cards.

We live in an age where many of the newest digital technologies are assisting many lazy humans in discarding their abilities to learn, communicate and interact with real life. Ironically enough, these same technologies are helping artificial intelligence grow and move forward at an incredibly fast pace.

Just like young and promising kids eager to learn new things every day, our machines are still "attending school" right now. We can only look forward to the day when they will be able to build and perfect their own learning methods and reach their university phase, but in the meantime, the goals they have already achieved are nonetheless amazing.