Artificial Intelligence(AI) has transitioned from being a concept in research papers and sci-fi stories to something feasible and realistic in its early developmental stage and enterprise adoption. With the increase in computational efficiency and the availability of big data, tech giants like Google and Facebook have already implemented Artificial Intelligence and Machine Learning to create Chatbots and personal assistants in smart mobile applications that we use in our day-to-day lives.
Basics of Artificial Intelligence and Machine learning
Artificial Intelligence is an attempt to simulate the human mind and way of thinking to solve complex problems. AI is mainly aimed to have problem-solving and analytical reasoning power in machines. Currently, AI is used as Digital Assistants, customer support using chatbots, online RPG games, and ultimately creating an intelligent humanoid robot. Machine learning is a subdomain under artificial intelligence, which associates the ability of a machine to learn from experience without needing to be explicitly programmed to achieve a predefined conclusion.
The traditional statistical machine learning algorithms had limited capacity to process valuable information about training data. However, Deep Learning upgraded the machine to create its structured algorithms and develop its intelligent solution without a predefined algorithm. Deep learning (DL) is a Sub-discipline of machine learning where multiple layered neural networks are constructed to deliver intricate tasks such as speech recognition, language translation, even recognising handwritten text and digitising them. Presently, the main application of DL is that they should be able to learn and extract the features automatically and interpret any given type of data set from images to video or text.
Classification of Artificial Intelligence
Artificial intelligence(AI) is a discipline under computer science associated with building smart technologies capable of performing tasks that generally require human assistance. Intelligence at the elementary level is the ability to read data, process it, and form an insightful conclusion on your own. The AI is classified depending on how well a machine can mimic humans in terms of versatility and performance. Under this division, the system is more human-like Functionality and is considered a more evolved type of AI. There are four types under this category.
They are the most basic types of AI systems that are purely reactive and can’t use past experiences to influence current decisions. They can only focus on the real-time scenarios and react to them and come up with the best possible action. One great example is IBM’s Deep Blue – a chess-playing computer developed with complex algorithms. It derived its playing prowess mainly from high computing power by running through all the possible moves and finding the most favourable outcome. It was the first computer to win a chess game against a reigning world champion.
2. Limited Memory
As the word suggests, limited memory machines can store past experiences or some data for a short period. The autopilot mode in cars is the best example of how the system can observe other car’s speed and direction. That can’t be done in just one moment but instead requires identifying specific objects and monitoring them over time. These observations are added to the autopilot mode of the cars preprogrammed with 3D representations of the street, including lane markings, traffic lights, and other essential elements, like curves and trajectories in the road. Allowing that car to make appropriate decisions like changing lanes or parks smoothly without damaging any other vehicles.
3. Theory of Mind
Theory of mind is the bridge between existing AI and future intelligent robots. Scientists and research scholars create a humanoid AI capable of human emotions and can empathise with people, beliefs and interact socially like humans. To create these machines, scientists focus primarily on understanding memory, learning and the ability to make decisions using previous experiences and understand human intelligence as a whole.
4. Self Awareness
Self-awareness is the ultimate goal of AI development to build systems that can form representations about themselves. Once created, these machines will be super intelligent and will have their consciousness, sentiments, values and self-awareness. These machines will be more intelligent than the human mind and are generally the main plot of many sci-fi movies where robots take over the world. But till today, it’s just a hypothesis.
The other type of classification widely used by Tech companies is based upon the capabilities of an AI.
1. Weak AI or Narrow AI:
Narrow AI is a type of AI that can perform a dedicated task with intelligence. This spectrum includes all the AI that has ever been created to date. Anything from Google Assistant to Google Translate, Siri and Alexa are great examples of narrow AI. These applications nowhere possess any kind of intelligence. They only process the human language, enter it into a search engine and return to us with results. Currently, existing AI doesn’t have the fluidity to process information and come up with solutions as we do.
2. General AI:
General AI is expected to perform tasks with intellectual knowledge and the flexibility of a human. These systems should be able to independently build multiple connections across domains and grasp the data efficiently. As of now, no such general AI system exists which could perform a task that is akin to a human
3. Super AI:
This could be dubbed as the God of AI. These machines should be able to replicate the multi-faceted intelligence of human beings and will be exceedingly better at everything. They come with high computational power, large memory, faster data processing and analysis, and precise decision-making capabilities, No matter what type of AI at the grassroots level, all the machines need to process the data. This is where machine learning comes into play.
What is ML: Types and examples
Machine learning gathers data in any form from the database suitable for processing. The better structured the data, the easier it will be for modelling, but unstructured data gives more flexibility. Machine learning happens in four different stages — data processing, model preparation, data training, monitoring and evaluating the model. The desired data set is fed through predefined complex algorithms to attain the best-optimised solution and other possible predictions for the given problem. With the massive boom in the internet and social media, companies have access to a large amount of data about their customers online. Big data is long-standing and difficult to process manually, so automated learning using Machine learning algorithms has proven to be an asset for many companies to gain valuable insights and use the predictions and form sound decisions. ML algorithms are trained using three main methods.
1. Supervised Learning
In this method, the input data has been already labelled manually and machine-readable. The algorithm is given a small training dataset that serves as a model to provide the algorithm with an idea of the problem, solution, and future data points to be dealt with. Eg: Predicting petrol prices for the next week with monthly crude oil and petroleum stock prices.
2. Unsupervised Learning
In unsupervised learning, unlabeled data is used without any intervention from humans manually. This algorithm is capable of adapting to dynamically changing hidden structures. Let’s take for example that you have a picture of a dog and cat unlabeled. This algorithm memorizes the features of the two different animals and can predict the closeness if you input an image of a pomeranian. Like when we were kids, we knew the difference between a dog and a cat. They are different species and are identified based upon their distinct features without looking at every dog or cat picture in the world.
3. Reinforcement Learning
Reinforcement learning data doesn’t have any fixed data set or end goal established at the beginning but instead learning to operate using the feedback. Alpha Go is the best example of reinforcement learning here; it plays both student and teacher. The system starts off with a deep learning artificial neural network that has no grasp of the game. It then plays games against itself. With the help of its ANN and complex search algorithm, it plays and fine tunes for updating and predicting the moves and eventually learning all the tricks and becoming the winner of the games. Lifecycle of ML Machine learning life cycle is a cyclic process to build an efficient and most optimised solution for the given problem. The Cyclic process includes the following steps
1. Data gathering
The process of collecting databases from various sources, from social media analytics to direct customer files and integrating them, is very crucial. The quality and quantity of the data is used to determine the choice of algorithm and further the prediction accuracy.
2. Data preparation and preprocessing
Now that we have the raw data from various sources, we need to prepare it by what nature of data that we have to work with, find trends and discovering patterns to get a better understanding of the data leads and preprocess it to make sure the is no inconsistency or garbage data which can interfere the learning process.
3. Data analysis
This is the core of machine learning, where you choose a suitable algorithm based upon the problems and select the machine learning techniques such as regression, classification, clustering, Support vector machine etc.Then you can build the model using prepared data to evaluate the model.
4. Model training and testing
From the vast amount of data you prepared, you will take batches of data test cases and train the model using various machine learning algorithms. A training model is always required to identify the general pattern and metrics of the dataset and form segmented clusters to learn about additional features. And once the model has been trained, we test it with input and check the prediction to find the accuracy and error percentage.
5. Deployment of the Model
This is the last stage of the ML cycle when the model has attained greater accuracy and higher speed, and it is deployed to the real-time system for the project. And this cycle continues to add further upgradation and optimize the technology.
Importance of Digital Marketing for this industry and how Vajra can help
AI and ML can contribute to the digital marketing industry. According to a Business Insider’s report in 2019, 51% of marketers are already using AI.
With the growth rate of social media and the internet of things (IoT), there is a flood of information utilized by AI and ML industries. Digital marketing companies like Vajra bring the data lead required by these AI and ML technologies. AI and ML can utilize the content marketing platform and use the database to train their models to generate content. With more traffic to your application, you can also improve the ability of your AI chatbot to answer open questions and achieve a natural and correct response. Vajra can provide you with lead analytics & generation, top SaaS metrics and many other services.