Quantum Intelligence Part 2.
Utilizing quantum mechanics to create the future of AI, Part 2. AI on a high level
Quantum computing and artificial intelligence are probably some of the world’s most heavily recognized buzzword technologies to date. These different emerging technologies present a plethora of new applications for society, some of which still remain untapped. However, when we begin to make these new developments, we will reach some extraordinary unexpected outcomes. This new future is approaching rapidly, and will likely be a catalyst for development in our lives. This is an introductory part 2 of quantum artificial intelligence.
Artificial intelligence, or AI, was first used by a cognitive simulation called the Logic Theorist, by developers Newell and Simon in 1955. However, the now used buzzword AI was first coined by who is considered the father of AI, John McCarthy, who thought of the term in the 1950s as well. Later, we have now gotten developments from AI, such as deep learning which originated later through the 1900s, and have advanced further during the 21st century, with computerized systems that can think and express themselves coherently without explicit programming or specific prompts.
The field of artificial intelligence describes the computational paradigm of automating non-biological systems and making them intelligent, without using explicit programming. This allows for the machines and systems developed using AI to respond properly to different and foreign use cases by being trained with a standard set of data. In a developmental sense, we will be able to train technology to think, work, and operate like we humans do. Artificial intelligence contains a wide array of subfields that have given rise to some of the biggest companies, and are implemented in trillion dollar markets such as healthcare, mobile technology, nanotechnology, data analysis, security, and more. These subsections are called: machine learning and deep learning. We will be discussing these on a high level, and really delving into it in part 3 of the series.
Machine learning 🦾
Instead of designing the program to complete a certain task, a machine learning system is able to learn the task itself by interpreting data. Machine learning has multiple subsets, all of which determine the type of learning that the ML is capable of.
The first is reinforcement learning, in which the AI is trained sequentially, able to make decisions in movement, decision-making, and even automatic functions in games. It is trained through multiple datasets, in which the success is measured by the AI’s ability to optimize the yielded reward, which promotes the growth and understanding of a computationally “correct” task based on the data modeling statistics.
The next ML branch is supervised learning, which contains the classification and regression subsets. The classification subset allows for the identification of objects, such as disease diagnosis in medicine, and customer retention in retail. Classification AI is developed sequentially, where an AI is given the task to assign variables to their respective groups. The regression subset is an AI’s ability to understand relationships between variables and extrapolate that data to make conclusions about different data aspects that are fed into it.
The final branch of machine learning is unsupervised learning, which consists of dimensionality reduction and clustering subsets. Dimensionality reduction in AI is achieved by taking randomized number groups and being able to isolate them down to principal values, through which feature extraction and selection take place. Dimensionality reduction can be used to visualize large data sets and print them graphically for extremely efficient interpretation. Clustering algorithms are used to push similar plot points together while separating unique ones. Clustering AI allows for many opportunities in marketing technology such as smart customer segmentation, user-based recommendations, and targeted marketing.
Deep Learning 🧠
The most computationally sophisticated level of AI is currently Deep Learning. Deep Learning uses ML as its foundation, giving it the capability to model the human nervous system near the brain by reimagining the biophysical process as a series of weights and biases using artificial nodes and neurons for comprehensive connections, and is fueled by a series of matrix calculations using linear algebra. We now have the perceptron model as a basis, which has allowed us to complete a variety of computer-based feats.
This technology is known as an Artificial Neural Network (ANN), in which the technology becomes near- autonomous so that it can formulate answers to questions, scour data networks, and receive and conceive information in a similar manner to that of humans and animals.
These algorithms are extremely promising and advanced, as they are able to absorb sensory information and process it by organizing raw input through clusters or labels. By doing so, machines resembling humans, such as humanoid robots like Sophia (a social machine created by Hanson Robotics; it may not be cognitive), are able to sketch portraits in real-time, craft and orate jokes, learn and interpret from human interaction, and even have access to a repository of human knowledge, such as songs, books, and famous history.
Deep Learning is especially useful when it’s recursive, meaning that the learning is reinforced perpetually to allow the system to function unsupervised. Recursive Neural Networks (RNN’s; not to be confused with Recurrent Neural Networks) have had high success rates in building functions essential to amalgamating this technology into the real world, allowing the for implementation of computers to mimic and interpret natural human impulses, such as language undertones and body movement significance. In addition, we now have models like CNN for image style transfer and image identification, and concepts like computer vision, which allow for cameras to collectively understand real time images on their own (I’ve personally worked with these and will be sharing projects on them).
Artificial intelligence is completely revolutionizing the way we live. Right now, we have such a huge AI cloud that is only scratching the surface of what we can do, like with home automation or disease detection. The true beuaty of AI is that’s its applicable in any situation or able to tackle any problem, and we can combine with stronger systems, like quantum computers, to create quantum machine learning algorithms (QML), Quanvolutional Neural Networks (QCNN), and my favorite, quantum neural networks. The concept of quantum intelligence will be discussed in part 3 of this series.
With everything said, AI will serve as an impetus for propelling our civilization into the advanced technological world that will revolutionize the way human society goes about its daily life. Think about these
- Intelligent and preemptive cryptosecurity
- Utilizing AI for more advanced MRI imaging
- Artificial intelligence powered smart cities
- Using AI to test for aging in super-capacitor cells
- AI in healthcare and accelerated drug discovery
- Neuromorphic computing
There are millions of applications!
In the meantime, you can start researching more on your own, or stay excited and wait to see whats next. The artificial intelligence space is so exciting, and its going to completely disrupt the way that we’ll live our lives. Just think about the vision.
“A quantum central intelligence”
My name is Okezue Bell, and I’m a 14 y/o innovator/entrepreneur in the quantum computing and AI spaces. I’m also currently making developments in foodtech and cellular agriculture, as well as biocomputing! Contact me more:
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