Artificial Intelligence and Machine Learning (AI/ML)

The very goal of human inventions has been to ease the work that we have to do manually. We invented cars to facilitate transportation, electricity to turn our nights into days and run our industries without manual labor.

The dream is to make a world where the survival of human life is easy and life necessitates are easy to fulfill so that man can focus on himself and explore his capabilities and express his inner self freely in the form of art. And then with that try to tackle more existential questions with utter freedom and a mind that’s free from any kind of daily worries of bread and butter. This is the perfect utopia that we dream of and everything we do collectively is to make that dream come true one day. Artificial intelligence and machine learning have been the recent struggles in achieving this dream.

What are AI and ML?

Artificial intelligence and machine learning (AI/ML) have been the buzzwords of the past couple of years alongside Blockchain and Cryptocurrency. But AI/ML is far beyond just a buzzword. It’s the latest field of computer science where most research and innovation are happening nowadays. Artificial intelligence in technical terms means an agent or an algorithm that does smart work to the level of almost human intelligence. Such an agent that can think rationally and make smart decisions about anything that’s thrown towards it is termed as Artificial General Intelligence (AGI). There hasn’t been much progress in terms of AGI but we have achieved substantial goals in terms of AI that is specific to the task at hand. This area of artificial intelligence is called Machine Learning (ML).

Machine Learning (ML) is a subset of AI where an algorithm is trained on raw data to learn from it and then apply it in automating tedious tasks. It is the most applicable form of AI. Machine learning in technical terms is just a set of algorithms that make sense of raw data and form predictions based on it. This is the main reason why ML applications are enormous in enterprise and business. Machine Learning is classified into different categories based on the kind of task it can achieve. Some of the major ones are as follows:

Supervised learning

This is the type of machine learning algorithms where the training data is labeled and the algorithms learn the pattern from it and then if any unlabeled data comes in, the algorithm does the job of classifying it based on the training data labels. In technical terms, such algorithms do the job of making a relationship between the input variable and the output variable. So, when an unknown input comes in its output can be predicted easily. Its major ML applications are classification and regression problems. Classification is where you have to decide whether a certain thing belongs in a certain predefined category or not. Regression is the process of making a relationship between a dependent and independent variable. And thus it can be used in various prediction models, for instance, stock price prediction based on past behavior.

ML Applications: Most email spam filters use this kind of supervised learning algorithms. These are also used in bioinformatics, speech recognition, and object recognition, etc.

Unsupervised learning

These are the type of machine learning algorithms where the training data is not labeled. The algorithms make some sense of data based on overlapping features and try to cluster the data into different groups based on these features. So, it is the best case of learning the relationship between different attributes in data. Its major ML applications are clustering and association. In clustering the algorithm groups the data provided into categories of same attributes may be color, size, shape or type. Association is where a dependency relation is found between two data points and are associated with each other based on that relation.

ML Applications: Various photo applications, for example, Google Photos use unsupervised learning to group different photos based on their overlapping features. Airbnb uses such algorithms to recommend you places that follow your past likings and experiences. Amazon does its product recommendation in the same way. YouTube, Netflix or any other streaming service displays its feed produced through such algorithms. Basically, any recommendation system mostly uses such algorithms.

Reinforcement learning

This is a very sophisticated set of ML algorithms that fall closer to the General AI category and are mostly classified under the Deep Learning category. In such algorithms, an agent or an Ml algorithm is made that observes the environment and performs and action on it and then you reward it or punish it based upon that action performed. Its ultimate job is to maximize the reward function and hence become the best at the task assigned. There is no dataset needed for such algorithms. Just and agent that learns from itself by doing actions on its environment.

ML Applications: Reinforcement learning is mostly used in robotics. And various automation robots used in manufacturing plants of all kinds. It can be used in traffic light controls, simulation of the environment to see the consequences of different hazards and climate changes. It’s used in the finance sector to make decisions on different stock values for automated trading. It’s used in video games to achieve superhuman performance in particular games and to test different strategies and pave the way to the general artificial intelligence which is the ultimate goal of AI research.

How to apply AI/ML to your company

AI/ML can have a vast impact on the profitability of your company or enterprise. The true Artificial General Intelligence is yet to come true but the use of AI/ML in Enterprise is very much practical. If you have a big company that deals with a lot of customer or client data surely there are ways to make use of such data and channel it into tangible profit with the use of AI/ML. First, we should consider some use cases where AI/ML is being applied in enterprises to the best effect.

AI/ML applications in customer services

Various autonomous techniques are being used to deal with customers and improve their relationship with the company. For example, the use of sophisticated Chabot is common nowadays for any kind of queries about your product. The process of complaint handling can also be automated using AI/ML.

AI/ML in manufacturing

The use of AI/ML in manufacturing will have the biggest impact on the supply chain by automating almost every task and thus cutting short the workforce required to do all those tasks and that workforce can be put to other innovative uses.

AI/ML in sales

In sales, various ML techniques are used to automate the sale process by recording meetings, phone calls and emails of customers and then analyzing them to give better insights about the customers’ emotional states and their interests. Which in turn strengthen the company’s relationship with them.

BigAI/ML in marketing

ML is used in marketing by keeping the record of industry trends and social media biases of people to best strategize the marketing campaign and make targeted ads for specific audiences.

How to integrate AI/ML in your business:

If you have a working business and want to boost the profits the integration of AI/ML in your business is the best investment you are ever going to make. AI/ML is helpful in all fronts of any business.

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  • First of all, you have to decide your use case of AI.
  • Get familiar with all the technologies in AI.
  • List out the problems you want to solve with AI.
  • Assess the potential value you can get by using ai to solve the said problems.
  • The last step is to bring in experts that know what they are doing and start implementing your solutions.

Sunvera helps you with the last step. With your plan to implement AI into your business you can rely on Sunvera to execute this plan in the most cost-effective way. The process of implementing AI/ML in your current business can be very daunting and expensive too. There are two ways you can approach this problem. You either make a custom AI solution for your business or hire a third party to implement your plans for you. The custom AI solutions can cost from 10000 USD to 300000USD while the other solutions can cost you around 0-20000USD. These factors determine the cost of your AI solution: AI type; whether it is a chatbot, a virtual assistant or/and analysis systems, Project type; pre or custom-built solution, and the amount of AI features that you want to incorporate.

Implementing all these tools in-house individually can be very expensive and a long process. Sunvera is here to help you with that. We have experience in implementing any kind of AI/ML solutions at the cheapest rates possible. From workflow automation, supply chain automation to automated CRM implementation all the tasks are done with utmost care and professionalism.

The major AI/ML engines being used in the industry today are Amazon AWS and Microsoft’s Azure platform. Our team has experience in both of these platforms and can help you with any of them. We will help you along in all the developing stages of your project.