A SIMPLE GUIDE TO ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING
Artificial Intelligence and Machine Learning have recently become popular topics in the technology industry. But, perhaps more than in our daily lives, Artificial Intelligence (AI) impacts the business world. In 2014, about $300 million in venture capital was invested in AI startups, a 300 per cent increase over the previous year.
AI is present everywhere, from gaming stations to managing complex data at work. Engineers and scientists are working hard to instil intelligent behaviour in machines, allowing them to think and respond in real-time situations. As a result, AI moves from a research topic to the early stages of enterprise adoption. Google and Facebook have made significant investments in Artificial Intelligence and Machine Learning and are already incorporating them into their products. However, this is only the beginning. Over the next few years, we may see AI creep into one product after another.
WHAT IS ARTIFICIAL INTELLIGENCE?
According to John McCarthy, a Stanford researcher, "Artificial Intelligence is the science and engineering of creating intelligent machines, particularly intelligent computer programmes." AI is related to the similar task of using computers to understand human intelligence, but AI does not have to limit itself to biologically observable methods."
Simply put, the goal of AI is to make computers/computer programmes intelligent enough to mimic human mind behaviour. Therefore, knowledge Engineering is a critical component of AI research. Machines and programmes require a wealth of information about the world to act and react like humans. Therefore, AI must access properties, categories, objects, and relations to implement knowledge engineering. In addition, AI instils common sense, problem-solving abilities, and analytical reasoning in machines, which is difficult and time-consuming.
AI SERVICES ARE CLASSIFIED AS EITHER VERTICAL OR HORIZONTAL AI.
So what exactly is Vertical AI?
These services focus on a single task, such as scheduling meetings or automating repetitive tasks. Vertical AI Bots only do one thing for you, but they do it so well that we might mistake them for humans.
What exactly is Horizontal AI?
These services are designed to handle a variety of tasks. There is no single task to be completed. Horizontal AI is demonstrated by Cortana, Siri, and Alexa. These services operate on a larger scale as questions and answer settings, such as "What is the temperature in New York?" or "Call Alex." They are useful for a variety of tasks rather than just one.
AI is achieved by analysing how the human brain works while solving an issue and then using analytical problem-solving techniques to build complex algorithms to perform similar tasks. AI is an automated decision-making system that continuously learns, adapts, suggests and takes actions automatically. At the core, they require algorithms that can learn from their experience. This is where Machine Learning comes into the picture.
WHAT IS MACHINE LEARNING?
Artificial Intelligence and Machine Learning are two popular but often misunderstood terms. Artificial Intelligence (AI) is a subset of Machine Learning (ML). ML is the science of designing and implementing algorithms to learn from previous cases. If a certain behaviour has occurred in the past, you can predict whether it will occur again. However, there can be no prediction if there are no previous cases.
ML can solve difficult problems such as credit card fraud detection, self-driving cars, and face detection and recognition. ML employs complex algorithms that iterate over large data sets indefinitely, analysing patterns in data and allowing machines to respond to situations for which they were not explicitly programmed. The machines learn from history to produce reliable results. The ML algorithms use Computer Science and Statistics to predict rational outputs.
THE 3 MAJOR AREAS OF MACHINE LEARNING
Supervised Learning - In supervised learning, the system is given training datasets. Data is analysed by supervised learning algorithms, which produce an inferred function. The resulting correct solution can be used to map new examples. One example of a Supervised Learning algorithm is credit card fraud detection.
Unsupervised Learning - Unsupervised Learning algorithms are much more difficult to implement because the data to be fed is unclustered rather than in the form of datasets. The machine's goal is to learn independently, without any supervision. There is no correct solution to any problem. Instead, the algorithm discovers patterns in the data. Recommendation engines, which are present on all e-commerce sites, are examples of supervised learning, as are Facebook friend request suggestion mechanisms.
Reinforcement Learning - This type of Machine Learning algorithm enables software agents and machines to automatically determine the best behaviour in a given context to maximise their performance. Reinforcement learning is defined by defining a learning problem rather than learning methods. We consider the reinforcement learning method to be any method that is well suited to solving the problem. Reinforcement learning presumes that a software agent, such as a robot, computer programme, or bot interacts with a dynamic environment to achieve a specific goal. This technique chooses the action that will produce the desired result in the shortest amount of time.
THE RISE OF MACHINE LEARNING
Two significant breakthroughs resulted in the emergence of Machine Learning as the vehicle propelling AI development forward at the current rate.
One of these was the realisation, attributed to Arthur Samuel in 1959 that rather than teaching computers everything they need to know about the world and how to perform tasks, it might be possible to teach them to learn for themselves.
The second, more recent event was the advent of the internet, which resulted in a massive increase in the amount of digital information generated, stored, and made available for analysis.
Engineers realised that rather than teaching computers and machines how to do everything, it would be far more efficient to code them to think like humans and then plug them into the internet to give them access to all of the world's information once these innovations were in place.
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