If it’s unlikely for you to go by your day without hearing the mention of AI then you are not alone. In this day and age, AI is being hailed as the disrupter of another sector or facet of our lives. AI is basically used in order for the machines to tackle a problem the same way a human being would. AI has significantly eased difficulties for people in different areas. AI in medical sector helps to reduce medical billing errors or prescribe the most apt medicine for a patient. Similarly, AI in banking helps to protect valuable information and to steer clear of embezzlers and data breaches. To simplify, AI basically enables machines to learn from their experience and put it to use when a similar problem arises in the future. Most of the AI examples you hear about today such as DeepMind’s AlphaGo or Tesla’s AI employ deep learning and natural language processing (NLP).
Why is there a significant mention of the term “learning” everywhere?
The most endearing allure of AI is that it learns from data and performs actions without any code being fed into it. Even though the terminology is misleading because AI doesn’t learn the same way humans do, instead, there is a slew of techniques that go into making the AI able to learn from the data. There are for example, three primary ways a machine learns: supervised learning, unsupervised learning and reinforcement learning. Since we are on the topic of AI, the mention of neural networks is vital.
What’s a neural network?
Artificial neural network is inspired by the workings of a biological neural network in order for the machines to carry out tasks in a similar fashion as the human mind. Artificial neural networks provide a slew of benefits, but the most important and beneficial feature is how they are able to recognize tons of data sets to improve performance. Instead of utilizing data sets, ANNs use data samples to reach an outcome which not only saves a lot of money, but it is a relatively faster process which expedites learning massively.
ANNs comprises of three layers. The first layer sends input neurons to the second layer, which when processed, sends output to the third layer. Training an ANN requires you to employ one of the three learning techniques and using appropriate algorithms.
What is deep learning?
Deep learning is basically the approach to train artificial neural networks. The term “deep” signifies the numerous layers in an ANN that enables the machine to analyze the data set and make use of it. Deep learning is the process behind the plethora of apps we use today such as the speech-recognition ability of Siri and Alexa, self-driving cars such as Tesla and image recognition in Facebook.
The deep layers within the ANNs enable a wide variety of functions to be performed all together. For example, an image recognizing software works this way. Each layer can define a different element of the whole picture, literally. While one layer might consist the outlines of a face, the next layer could have colors related to it. While these layers separately are of not much significance, together they are responsible for forming a whole picture, which is impressive to say the least. Speech recognition works in a similar fashion, by making use of multiple layers that all depict a particular feature. Once the layers combine, they depict understandable speech.
What does a neural network look like on screen?
Neural network looks nothing short of a jumble of computer code on a screen. Software engineers at Google’s AI subsidiary DeepMind use Python to write down all of the code. Python is a programing language which was initially released in 1991.
Python is basically the language behind powerful applications such as YouTube, Instagram, Google etc.
Is deep learning coupled with neural networks the best approach towards training machines?
While some may argue that in this day and age the best technique to train machines is through deep learning, this could eventually change in the upcoming years, since scientists are coming up with new techniques on a daily basis and when they discover something more intuitive than deep learning, it will only be a matter of time before the companies make the switch.
Why does AI matter?
Businesses all around the globe race to provide their customers with a competitive edge over their competition. To achieve that, it is crucial to employ the latest technologies to not only be technically sound, but to also reduce the time of operations and operational cost associated with tasks. AI helps companies to achieve that which is why it is absolutely dire for companies to look into it, if they want to earn a big name for their corporation.
Big corporations today rely on AI to carry out important functions be it customer service, marketing, or product innovation. Companies as old as Heineken which was incepted nearly 150 years ago use AI and Internet of Things to analyze the large amounts of data it gets in order to augment its marketing strategies and customer services.
From managing global logistics to augmenting delivery paths, artificial intelligence is enabling corporations of all sizes and in all sectors enhance productivity and the bottom line at each level of the business lifecycle from procurement of materials to sales and accounting to customer service. It’s enabling establishments to design, create and deliver commodities and service better than ever before.
AI is also being used very commonly in the medical industry in order to tackle life threatening illnesses and come up with treatments for them. Practitioners foresee AI to be able to save lives in the future which is why this technology is quite important in the medical field.
AI has disruptive capabilities and in order for big companies or startups to make their mark in their respective industries, it is crucial to employ the features of AI to make a business more competent and provide it with a competitive edge.