SOLVVE has talked about artificial intelligence a lot in this blog, we even have a dedicated hashtag for it. However, while we talked a lot about the numerous applications of this amazing technology, we have never really talked through the basics of it. Artificial intelligence might seem complicated and sure involves a lot of technical expertise to develop and deploy AI-powered solutions. Nevertheless, the key concepts are easy to grasp for anyone even without a technical background.
In this article, SOLVVE will provide simple explanations for underlying concepts and principles of artificial intelligence as well as their brief history and main areas of application.
Definition of AI
Although artificial intelligence is widely discussed these days, there is no set definition for this technology. If you look across definitions in Cambridge, Oxford, Merriam-Webster or Britannica dictionary, you will notice that those differ slightly. However, it is still possible to draw the common features that all of them mention. Those are:
- Seeing artificial intelligence as both a theoretical discipline and a practical approach to creating machines and/or computer system;
- A set of methods to mimic functions of human cognition with those systems. For example, the ability to recognize and understand speech or pictures, make decisions, solve problems, discover meanings, reason, learn from the past to assume the future, etc.
For a wide audience, artificial intelligence might be tightly associated with human-like robots or at least some other form of interface that is close to daily human interactions, like voice input or typing in the chatbot window. Nevertheless, the better way to think of artificial intelligence would be as of intelligent agent, or putting it simply, an abstract bundle of algorithms that can be integrated into almost any casket or interface to suit one’s needs, giving it great flexibility in compatibility with various software and hardware.
When was AI born?
Theories about what human intelligence is and whether it is possible to mimic the cognition has been with humanity for the majority of its history. However, the beginning of artificial intelligence as we know it today can be traced back to the conference at Dartmouth College (Hanover, New Hampshire, United States) in 1956. This conference coined the term and gave the official start to artificial intelligence as a separate field of studies.
Ever since scientists have been focusing on training machines in imitation of human reasoning. However, despite optimistic outlooks at the beginning, this field saw ups and downs in research and funding, so-called “AI winters” when the interest in this type of research was close to zero. The last surge in popularity that keeps on growing today came when IBM’s Deep Blue won a chess tournament with Garry Kasparov in 1997, followed by IBM’s Watson that won the Jeopardy show in 2014 competing against the two best players at that time.
So, what are the key concepts and components of artificial intelligence? What is the set of technologies we should keep in mind when we speak of it?
Main ideas and concepts
You might have noticed that whenever someone talks about artificial intelligence they also mention machine learning and data science. Quite often they are used as synonyms or even interchangeably. Yet, those terms have to do with different concepts. Let us take a look at them one by one.
As we have mentioned above, artificial intelligence is seen either as a theoretical field of study or as an applied discipline that tries to replicate human cognition. While this term has gained a great marketing value and sells well, most of the time when someone mentions artificial intelligence what they actually mean is machine learning.
Machine learning is a sub-set of knowledge within artificial intelligence that studies and develops methods of training programs in mimicking intellectual processes in a human brain: forecasting, behavior assessment, modeling, etc. In its turn, machine learning encompasses deep learning as a sub-discipline and one of the methods to train artificial intelligence. What makes this approach stand out from the others is that deep learning can run without human supervision and work with unstructured data.
Nevertheless, both supervises and unsupervised training are done through data sets of various kinds. The more data algorithm has as a reference, the better outcomes it will produce. Over time algorithms can learn from the previous experience and extrapolate that knowledge on the new data sets. These new sets will in turn train algorithms even better and so on.
Where does the data for learning sessions come from? This is a task for data science, a field that deals with the extraction of knowledge from structured and unstructured data. More specifically, data scientists deal with extremely large volumes of information known as big data to separate necessary types of data sorted in specific ways so that it could be used in further research.
Now, when we have figured out the connections between data science, artificial intelligence, machine and deep learning, we can talk about the most exciting thing. If we are to make machines mimic human cognition, it is only natural that to do so we chose systems that mimick the structure of the organ that is responsible for cognition – neural networks.
They replicate structures and processes in the human brain – the original neural network that consists of neural notes and can have many layers. The more layers you have in your artificial neural network, the more analytical power you get. Still, even today, the most powerful computers are lagging behind the power of the actual human brain because it cannot have as many layers and connections as a human brain, and it will take some time before AI can catch up with humans.
What can you do with artificial intelligence today?
Despite the limitation of the capabilities of artificial intelligence today, there are still numerous ways in which many industries can benefit from using machine learning. Here only a few of them.
In our dedicated article about artificial intelligence in eLearning, you find several detailed examples of AI-backed solutions. To give you a brief overview, with this technology one can provide continuous mentoring and support for students, it can generate, refresh, or repurpose the learning content and draw personalized study plans. Moreover, it can also make education more accessible to those who need non-standard solutions in education due to their physical state through voice recognition, speech-to-text transformation, or automated translations.
This fast-growing industry is native to the digital world and that makes it one of the most fruitful soils for artificial intelligence adoption. To begin with, eSports titles themselves incorporate ML-powered features to boost gameplay and supervise players’ behavior. For example, through the detection of cheating behavior, when a certain player progresses through levels or acquires skills faster than the others or in an unusual way. Other applications include assistance in coverage of eSports events and forecasting in betting.
Security in Fintech
Since financial services are no exception to digitalization, granting security in this section is a cornerstone issue for many companies. Artificial intelligence can be handy in business forecasting or assessment of clients’ reliability and trustworthiness. Combined with biometric identifiers it becomes a powerful security tool.
Artificial intelligence has found many applications in Healthcare. From routine tasks like image classification in ultrasound diagnostics, MRI or radiology, to more sophisticated and global tasks as spotting and predicting disease outbreaks, artificial intelligence might become the backbone of the industry in the near future. Not to mention what can be crunched out from the amount of health-related data that wearable devices produce these days.
Social networks are widely adopting artificial intelligence solutions and you have already seen them in action when you get friends recommendations or find yourself tagged on someone’s photo. Boosting your social media platform with machine learning becomes a common practice that grans numerous benefits: targeted advertising, content recommendation systems, chatbots, detection and recognition faces and logos, etc.
As you can see, it does not take a lot of in-depth technical knowledge to grasp the basic ideas of artificial intelligence and the core areas of its application. Nevertheless, the implementation of AI-based solutions requires a very specific set of skills to bring the full potential of this amazing technology to life. Thus, if you have any ideas or questions related to artificial intelligence and how it can be integrated with your project, do not hesitate to contact us. Let us make it happen!