What is Artificial Intelligence?
Artificial Intelligence is a specific domain of Computer Science that trains machines to think and decide about the tasks that require human intervention. Artificial Intelligence is now widely used in our surroundings. Smart assistants like Siri, Alexa, self-driving cars, conventional bots, feed recommendations are all types of AI.
How Does AI Work?
With the breaking of Enigma encryption, Alan Turing asked the question back in the 1950s, whether a machine can think or not. Artificial Intelligence is the enhancement of Machine Learning (ML) and Deep Learning (DL) to the extent where it can be used as a decisive tool. That is why AI acts as a superset of ML and DL.
Artificial Intelligence works by learning the patterns in a dataset. It combines and accumulates a large set of data for a specific task by an intelligent and iterative collection process. It then finds patterns in the data to predict the outcome for specific input. With each iteration, the AI model tests its activity and measures the confidence level of the decision. Due to its computational power, it can run through millions of data in a very little amount of time. In short, if you want a computer to solve problems on its own, you will need AI to fix it.
Artificial Intelligence as a method can be classified into the following categories:
- Artificial Narrow Intelligence: This is probably the most lenient form of Artificial Intelligence used in practical applications. This concept primarily deals with making the computer learn to reduce one specific problem on its own.
- Artificial General Intelligence (AGI): AGI deals with computers mimicking similar instincts of human cognition.
- Artificial Super Intelligence: This is an abstract form of AI.
Different branches of Artificial Intelligence cumulatively develop algorithms:
Machine Learning: A particular application of Artificial Intelligence that helps computers learn patterns from experiences. ML can be of two different types. Supervised Machine Learning is the type where the computer learns from a labelled dataset and identifies patterns. Whereas, in unsupervised learning, the computer distinctly separates different types of data to divide them into clusters.
Deep Learning: A specific type of Machine Learning that improves the solved results based on mathematical estimations.
Neural Network: It is an algorithm that mimics human brain actions in determining results. A special category of Neural networks named Convolutional Neural Network helps us to compute and process images to identify patterns in them.
Cognitive Computing: A similar model of Neural Network that uses the interaction of humans and machines to train the AI and perform accordingly.
Natural Language Processing: An Artificial Intelligence model that interacts with humans via text, call or any other medium and tries to understand human communicative language through machine learning algorithms.
Computer Vision: This model tries to understand or recognise an image through various methods. It breaks down an image into smaller segments and classifies different patterns of RGB values in the image. Computer Vision is applied in lots of modern AI models as a tool to manipulate media.
There are primarily four different approaches that define the path of AI:
Reactive Machines: This is the most basic concept of Artificial Intelligence. This machine uses its intelligence only to interpret external stimuli and react accordingly. But it cannot learn from past experiences to improve its response even more. Deep Blue is an example of such a machine.
Limited Memory: This category of machine has the capabilities of learning from previous experiences and changing its further decisions accordingly. Primarily three significant ML algorithms include Limited Memory Artificial Intelligence:
- Reinforcement learning
- Long Short-Term Memory (LSTM)
- Evolutionary Generative Adversarial Networks (E-GAN).
Theory of Mind: It is just an impractical concept till now based on recognising the socio-psychological responses of organisms.
Self-Awareness: The final step of Artificial Intelligence will be self-awareness where it can understand its existence.
Importance of AI
With the advancement of Computer Science, automation in every task is becoming a primary need. With the implementation of Artificial Intelligence, the need for automation reduces to a problem of pattern recognition.
In our everyday world, we find AI at every step:
- In the retail industries, we see digital markets using the 'you may like' option to help feed us new interests, which is a gift of AI.
- The Healthcare industry uses Artificial Intelligence to personalize medicines and activate proper schedules in check-ups.
- In transactions, AI helps to detect fraudulent activities and prevent them.
- AI can also be implemented to identify actions in financial markets to develop further investment strategies.
In this era of Big Data, processing large sets of data and identifying patterns in them within a small amount of time takes a huge computational power. Usually, these tasks are hard to implement on local processing units. However, cloud architectures provide a suitable platform to develop an artificial intelligence model. E2E Cloud has been providing the best solution for this problem for years now. E2E Cloud's High-Performance cloud platform can be accessed via a self-service portal where you can provision,manage, and monitor Linux/Windows/GPU Cloud Machines Monitors with a high-performance processor, large memory (RAM) or smart dedicated calculation with dedicated CPU cores.