How Does AI Achieve This?

From a technical perspective, the terms artificial intelligence, machine learning, and deep learning tend to be confusing and one is sometimes unsure about how they relate. Artificial intelligence encompasses different techniques to synthesize intelligence.

The following sections describe different types of approaches where each approach uses specific principles to achieve a goal. The respective approach is selected based on the data available, the goal that’s trying to be achieved, and the nature of the problem. There are many other approaches, but these are most popular ones used today.

Evolutionary Algorithms

These algorithms are based on concepts of biological evolution. From scientific studies, we have observed the process and outcomes of reproduction, mutation, and individual selection in natural organisms.

Essentially, these algorithms are based on the premise that organisms reproduce to create more organisms, the children of the original organisms are comprised of a combination of the genetic make-up of them. However, there are slight variants in the children; this is called mutation. Given the mixed genetic make-up of the children and their mutations, they could potentially be “better” than their parents even in the case that their parents as individuals are considered “inferior”. Individuals are selected to live on based on their fitness which is derived from how “good” they are. This is the general process that most living organisms have followed over millions of years to be what they are today, including us humans.

Evolutionary algorithms are suited for problems where a single result is comprised of permutations of finite things. These algorithms are geared towards finding incrementally better solutions, but cannot guarantee finding the most optimal solution.

As an example; consider the problem of optimizing package delivery by drones from warehouses to customers where there are constraints on weight that the drones can carry. Each action for a specific drone is finite — let’s call this a gene. Permutations of sequences of possible actions across all drones can be generated — let’s call this a chromosome. And each chromosome will have a different performance — let’s call this fitness.

These chromosomes are generated, reproduce new sequences, and the fitness of each is evaluated to determine which should live on. This happens for a number of generations or a specified stopping condition is reached. The most fit chromosome is then used as the most optimal solution. This means that an optimal sequence of actions for drones will eventually emerge.

Machine Learning

The underlying algorithms used for machine learning are essentially based around statistics. Machine learning is similar to the concepts around data mining. An algorithm attempts to find patterns in data to classify, predict, or uncover meaningful trends. Machine learning is only useful if enough data is available, and if the data has been prepared correctly.

As a toy example, consider that evaluation of password strength depends on the length of the password, if it contains numbers, and if it contains special characters. Let’s also assume that we have a list of passwords and their respective strength. Simply using the raw textual representation of the password for a machine learning algorithm to learn what makes a password strong or not will not work.

Extraction of metadata such as the number of characters, the number of special characters, and the number of numeric digits is required before a machine learning algorithm can learn any trends. This metadata and the process of preparing it is imperative to successful machine learning.

Machine learning consists of two categories, namely supervised learning, and unsupervised learning.

Supervised Learning: Most practical solutions use supervised learning. Supervised learning encompasses approaches to satisfy the need to classify things into categories — known as classification. It also includes approaches to address the need to provide variable real-value solutions such as weight or height — known as regression.

Unsupervised Learning: The goal of this type of learning is to model data and uncover trends that are not obvious in its original state. This type of learning is used to learn about data.

There are no answers that the algorithm tries to guess. It discovers “hidden” structures and correlations that are not apparent at face value. This is useful for finding groups of data that are similar — known as clustering. It is also useful for discovering rules that govern portions of the data — known as association.

Deep Learning and Neural Networks

Deep learning is a term that sounds very mysterious and complex, and it is to an extent. It is similar to machine learning in that it classifies things and discovers patterns in data, however, deep learning algorithms constantly improve their knowledge on what they have already learned in the past. These algorithms may consist of chaining a number of different AI approaches to achieve its goal.

As an example; consider that a large image database exists and there is a need for an algorithm to describe the objects in pictures. Using deep learning, an algorithm is able to find similar objects in different pictures and group them. After a human labels that group, the algorithm understands what that object is going forward, however, it can create further subgroups within that object for different variants. If a grouping of cars is discovered, the algorithm may find different variations of cars such as sedans, hatchbacks, SUVs, etc. Given enough data, these subtle variants can be discovered.

Neural network algorithms are heavily used in deep learning due to their adaptive nature. Neural networks are based on our understanding of how the human brain and nervous system works. It is the concept of a layered hierarchy of neurons that accept an input, influences the input, and then directs the result to other neurons based on the weightings of the neuron.

The weighting on each neuron changes over time as the network becomes better at classifying the input. A higher-weighted neuron will have more influence on the input it receives and thus could strongly impact the outcome of the network. Neural networks are useful for classification problems where classification can change or be refined in the future.

Conclusion

Artificial intelligence is an exciting concept that will shake industries and the way we live. It’s unlikely that we will create human hating robots that go bananas and destroy us, if we focus on the benevolent uses of it. This piece attempts to make AI concepts more clear to you and demystify the buzzwords. Equipped with this knowledge, I challenge you to learn more about AI, and find valuable practical uses for it in your work and everyday life.