Artificial Intelligence — Living in the Age of the Algorithm.
Artificial Intelligence (AI) might mean the end of the mankind?! Maybe we should head for the hills! Or maybe the movie theater…
There is an abundance of information, speculation, hype and futuristic fantasies surrounding Artificial Intelligence (AI). Albeit, AI itself is nothing new to the world, having become officially recognized as an academic field way back in 1956.
But what is AI? Stanley Kubrick’s “A Space Odyssey” circa 1960, seems like a good place to start. It tells a mind-bending story (back then) about a group of astronauts sent on a mysterious space mission. When their ship’s computer system, HAL, started displaying unusual human-like behavior, it lead up to an epic showdown between humans and machine. Despite the horrifying story-line, the old-pal-HAL had become the fearful pioneer of an Artificial Intelligence ever shown on a big screen. That was over a half of century ago. A lot has changed since we got a glimpse of AI for the first time.
What’s new is the way we think about Artificial Intelligence and the way we experience it. We now recognize the virtually limitless practical applications of intelligent machines, because we interact with them on a daily basis. Our smartphones, and various applications seem to have an uncanny ability to relate to us, to adapt to our life, that makes them look intelligent. And let’s be real, there are moments when we all feel like a toaster has more compassion and intelligence that some political leaders. Ahem!
Artificial Intelligence is a display of intelligence by a nonliving object, such as machine, as opposed to Natural Intelligence, which is seen in living creatures, including humans.
The whole discipline of Artificial Intelligence is based on a belief that human intelligence could be defined so precisely that a machine could be designed to replicate it. Another words, intelligence is the ability to continuously “learn”, thereby improving at certain skills over time. And what sets the learning process in motion are sets of instructions in a form of complex computer programs called algorithms. They are at the heart of Artificial Intelligence and Machine Learning.
Algorithm (noun): Word used by programmers when they don’t want to explain what they did.
Essentially, Algorithms are designed to make computers perform in such a way, allowing a device that perceives its environment to take actions to optimize its chance of success at given task, ultimately leading to the appearance of intelligence.
Algorithms have already penetrated far and wide permanently in our daily life. All the new and transformative electronic devices, social media platforms, apps, games and digital avenues make our lives better, more productive, more informed and more fun.
Think about the globally burgeoning world of online dating, also driven by algorithms. Without them, these sites would be little more than boring bulletin boards. With them, we submerge ourselves into an exciting ( and yes, occasionally disappointing) realm where the prospects of finding true, long-lasting love — or even a hot date for Friday night — dangles right in front of us. The irony is, we fear AI and yet we trust it to deliver what most of us want foremost in life: a human partner, whether it’s a friend or a lover.
“Artificial Intelligence will probably most likely lead to the end of the world, but in the mean time, there’ll be great companies”
Nevertheless, it’s mind blowing how sophisticated AI algorithms have become in the last 65 years. Starting from a simple tree-model program, called “The Logic Theorist”, that was able to solve a problem simply by selecting the branch that would most likely result in the correct conclusion; to the state of the art prosthetic robots with exceptional sense of touch, self-driving cars and brilliant yet, oh-so-annoying chatbots. How many times have you started talking to a caller, only to realize you were talking to a machine?
Artificial Intelligence is a very complex, challenging and continuously evolving area of computer science.
There are two key features of Artificial Intelligence known to be the most fascinating: Natural Language Processing (NLP) and Natural Language Understanding (NLU). Both powered by Machine and Deep Learning algorithms and present the most challenging areas in the field.
Natural Language Processing and its subtopic Natural Language Understanding, are an Artificial Intelligence capabilities in which computers interact with humans using natural-sounding language, either in written or spoken form.
NLP is an emerging technology and had only become viable in the recent years. It drives many forms of AI we are used to seeing. Ironically, more often than not, NLP goes unnoticed by us — so deep it has been embedded into our daily routine. For example, when you’re typing on an iPhone, like many of us do every day, you’ll see word suggestions based on what you type and what you’re currently typing. That’s natural language processing in action.
It’s such a little thing that most of us take for granted, and have been taking for granted for years, but that’s why NLP becomes so important.
At its core, Natural Language Processing is a subset of Machine Learning. The role of machine learning and AI in natural language processing and text analytics is to improve, accelerate and automate the underlying text analytics functions and NLP features that turn unstructured text into usable data and insights.
Machine Learning (ML) involves natural language processing as well as computer vision and image recognition.
The whole ML process is based on adjustment of computer’s outputs based on its own UX (user experience), like a chess algorithm that gets better at chess the more it continues to play, whether against a human chess player or a digital one.
Machine learning uses statistics to develop self-learning algorithms that work by trial and error. By recognizing patterns in an enormous batches of existing data (a.k.a. Big Data), it’s able to use this information to identify similar patterns in future data. ML-powered algorithms are widely used in marketing, manufacturing, medical research, speech recognition, and other fields.
Another, more recent form of Machine Learning is called Deep Learning (DL).
Deep Learning involves an artificial “neural network”, which is a digital network, that supposedly mimics a biological nervous system.
Neurons are basic brain cells, the building blocks of our brain that enable us to do everything that we do, from breathing to composing symphonies.
The difference between machine and deep learning is that DL machines don’t require a human programmer to tell them what to do with the data. They basically analyze data in a structure very much as humans do. This is made possible by the extraordinary amount of data we collect and consume — data is the fuel for deep-learning models.
Deep Learning techniques have been widely and successfully used in signal processing, voice understanding, text understanding, and image recognition to name a few. The growth of deep-learning models is expected to accelerate and create even more innovative applications in the next few years.
For all it seemingly magical powers, AI machine is just a machine. A machine that merely mimics certain cognitive functions that human beings recognize in themselves and in other human beings, such as seeing, hearing, learning, and problem solving. At the end of the day that vacuum cleaner can’t really see (and doesn’t really care about) your cat. And a car that drives itself has no idea where it’s going. In fact it has no ideas at all. It has only a series of sophisticated algorithms, which the car’s computer has been programmed to follow.
And despite the fear and uncertainty involving Artificial Intelligence, one thing is definite — human creativity is unmatched, and will remain unmatched at least for now. Machines merely augment, support and facilitate the expression of human genius, by making data accessible and enabling humanity to build learning capacity across generations.
