Inspiration
Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision.
What it does
- Automation Automation is one of the most commonly cited benefits of AI technology, and it has had significant impacts on the communications, transportation, consumer products, and service industries.
- Smart Decision Making Artificial Intelligence has always been used for making smarter business decisions. AI technology can coordinate data delivery, analyze trends, develop data consistency, provide forecasts, and quantify uncertainties to make the best decisions for the company.
- Enhanced Customer Experience AI-powered solutions can help businesses to respond to customer queries and grievances quickly and address the situations efficiently. The use of chatbots that couple conversational AI with Natural Language Processing technology can generate highly personalized messages for customers, which helps to find the best solution for their needs.
- Medical Advances The use of Artificial Intelligence solutions in the healthcare sector is becoming increasingly popular these days. Remote patient monitoring technology, for instance, allows healthcare providers to perform clinical diagnoses and suggest treatments quickly without requiring the patient to visit the hospital in-person. AI can also be beneficial in monitoring the progression of contagious diseases and even predict their future effects and outcomes. 5.Research and Data Analysis AI and Machine Learning technology can be used to analyze data much more efficiently. It can help to create predictive models and algorithms to process data and understand the potential outcomes of different trends and scenarios. Moreover, the advanced computing capabilities of AI can also speed up the processing and analysis of data for research and development, which could have taken too long for humans to review and understand. 6.Solving Complex Problems The developments in AI technologies from basic Machine Learning to advanced Deep Learning models have made it capable to solve complex issues. 7.Business Continuity Business forecasting using AI technology not only helps companies make critical decisions but also prepares them for any emergency to ensure business continuity. As risk management heavily relies on data management and analysis today, AI-powered tools can help organizations to respond to the crisis proactively. AI and Machine Learning can also create scenarios to help businesses plan for a speedy disaster recovery strategy.
- Managing Repetitive Tasks Performing recurring business tasks is not just time-consuming but it can also be monotonous and reduce the productivity of the employees over time. AI- powered Robotic Process Automation tools can automate interactions between different business systems and make the tiresome work easy for the company.
- Minimizing Errors Another great benefit of automating regular business tasks using AI tools is that it helps to reduce the chances of manual errors. As Robotic Process Automation tools take care of the data entry and processing jobs, it can make the digital systems more efficient and less likely to run into or create any problems due to data processing mistakes. 10.Increased Business Efficiency Artificial Intelligence can help to ensure 24-hour service availability and will deliver the same performance and consistency throughout the day. Taking care of repetitive tasks will not make AI tools get tired or bored either. This can help to improve the efficiency of the business and reduce the stress on the employees, who can be re-assigned to perform more complex business tasks that require manual intervention.
How we built it
Steps to design an AI system 1.Identify the problem. 2.Prepare the data. 3.Choose the algorithms. 4.Train the algorithms. 5.Choose a particular programming language. 6.Run on a selected platform.
Challenges we ran into
1.Lack of technical knowledge 2.The price factor 3.Data acquisition and storage 4.Rare and expensive workforce 5.Issue of responsibility 6.Ethical Challenges 7.Lack of Computation Speed 8.Legal Challenges 9.AI Myths & Expectation 10.Difficulty of assessing vendors
What we learned
We learn about Artificial Intelligence
Built With
- c++
- haskell
- java
- javascript
- lisp
- prolog
- python
- r
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