The world is reeling with the news of tech giants pulling plugs off their AI projects and research division. While data science and AI quickly recovered from the Covid-19 crisis, the Ukraine war disrupted the supply chain. Inflation went through the roof, bringing in an economic headwind. As obvious, economic crises always go hand-in-hand with layoffs.
The Silver Lining?
Despite the economic crisis, experts believe there is no slowing down of AI projects and innovation.
Infact, Scott Stephenson, CEO at Deepgram told VentureBeat –
AI will continue to be central to business in 2023, by cutting costs and increasing innovation. Simply put, AI will help us do more with less.[source]
AI is so deeply imbibed in our daily life that it is impossible to halt it completely. Experts predict a U-shaped recovery – descent, stagnation, and slow recovery. Also, putting all fears to rest, experts also state that AI will not replace humans entirely.
Vishal Sikka, founder, and CEO of Vian AI, a human-centered AI platform, says –
AI won’t — and shouldn’t — replace humans in the near term.
He strongly believes that AI is nowhere close to human judgment.
What does this mean for you?
More and more systems will be designed to amplify human judgment — to aid people and encourage AI symbiosis, rather than seeking to have AI replace the user.
It is one thing to learn the theory of AI. Looking at how AI is shaping the present and future, the demand for skilled AI professionals will increase – professionals who understand the power of AI and the modern market.
Your chance to show your skills and your understanding of the present market is through your AI project. AI projects can help you take a modern-day problem head-on and show a solution that is not just lean but also scalable. Your AI projects can help you showcase your understanding of an organization’s working or core value proposition.
In this article, we have curated 21 different AI projects for students that take you from simple AI projects to advanced artificial intelligence projects. These projects will help you grasp various techniques such as bag-of-words, random forest, LempelZiv (LZ) algorithm, Markov Model (MM), Neural Networks (NNs), Bayesian Networks, Association rules, Word2Vec approach, k-nearest neighbor classifier, Bonferroni, FDR corrections, and much more.
Benefits of Doing an AI Project
Industries across the world are demanding AI-based software applications like never before. According to a study by Statista, industries will grow to $126 billion by 2025 through AI applications .
No businesses are ready to ignore this opportunity. Companies that have implemented AI-based chatbots have experienced great growth in their businesses. Through AI Projects, you get the upper hand in learning the concepts and applying them practically. In a nutshell, the benefits of doing an AI Project include:
- Self-learning through practical applications
- Understanding market and industries deeply
- Acquire the ability to solve problems with the leanest available solution
- Design AI-based solutions that are scalable
- Create a handsome resume that speaks volumes of your capabilities as a professional
All said and done. Now, let’s get started with AI project ideas.
15 AI Projects For Students and Professionals
There are many interesting AI projects. However, selecting the type of project will depend on several factors. These include your interest, time, budget, and trending topics.
You can also shortlist the projects by understanding the challenges in companies from the domain you are interested in. Solving their problems will give you a hands-on experience with the work they are doing. However, certain artificial intelligence projects are typically necessary for you to gain confidence in AI and its associated topics.
We have put together different AI project ideas to help you start your journey of learning AI.
Also Read: How to Learn AI and ML Tools by Yourself?
Interesting AI Projects in Python
1. Predicting users’ upcoming location
Predicting the user’s most probable next location (next summer vacation or holiday destination, next restaurant, etc.) becomes an important requirement to make better decisions for future services.
This project on artificial intelligence is ideal for services like healthcare applications, network management, travel management, and so on.
Working on this AI project model will help you to understand the LempelZiv (LZ) algorithm, Markov Model (MM), Neural Networks (NNs), Bayesian Networks, and Association rules.
2. Detecting social media scam
The popularity of YouTube, Instagram, and Facebook attract not only genuine users and viewers, but also spammers. As a result, there is an increase in unwanted spam posts, images, videos, and comments. Even bots may be spamming your email, SMS inbox, and the comment section of social media accounts.
The nature of this spam can range from frivolous promotions of products to more problematic hate-mongering designed to incite people by demeaning their political, social, or religious beliefs.
An AI model can be created by training it on simple spam v/s ham messages.
Distance-based algorithms like Euclidean distance or other similarity-finding algorithms (for example, cosine similarity) can help identify spam messages.
Pretrained models like ALBERT can offer better results. Similar models can also be used to auto-flag offensive or hateful messages.
Here is an example of an AI-based YouTube spam comment detection model. It can be devised as a project on artificial intelligence where you will be focusing on text and words.
In this project, artificial intelligence will help classify internet comments as spam or not. Here’s another sample project to detect Twitter Bullying using AI and ML:
The spam detection model can be created using bag-of-words and random forest techniques. You can also predict positive and negative reviews with the Word2Vec approach and the k-nearest neighbor classifier in addition to spam detection.
3. Identifying the genre of a song
One of the popular artificial intelligence mini projects to work upon. In this project, artificial intelligence will be used to identify the genre of a song.
Using an artificial neural network, you will detect the song and find its genre to display it in the correct playlist for users. You will use Librosa (python library) to extract features from the song and Mel-frequency cepstral coefficients (MFCC) to detect the music genre.
Also Read: Understanding Perceptron: The Founding Element of Neural Networks
4. Shock front classification
One of the most critical artificial intelligence projects, it detects shock fronts in computational fluid mechanics (CFD) simulations. The presence of shock results in additional complexities in fluid mechanics; hence, it is necessary to detect and handle shock fronts to deal with fluid mechanics problems.
In the Shock Front Classification AI-based projects, you will be using supervised algorithms for classification, such as classification trees (RPART), linear discriminant analysis (LDA), naive Bayes (NB), support vector machines (SVM), and random forests (RF).
5. Translator app
Another exciting project of AI involving natural language processing can be the creation of a translator app. Such an app will translate a sentence from one language to another. While technically, you can train an AI model from scratch, but that can be difficult, time-consuming, and inefficient.
Several pre-trained models known as ‘transformers’ can be used to create a translator app that makes the project easy even for students.
A pre-trained transformer model will perform feature extraction through tokenization of the input sentences, pass it through the pre-trained model, and deliver the translation in the required language.
You can create a project on artificial intelligence using GluonNLP, a common library available in Python.
Here’s another project idea on the same lines: to create a translator app that can translate sign language.
Simple AI Projects for Beginners
6. Predicting bird species
Birds are ecological indicators, and they respond quickly to environmental changes. Hence, it is important to classify birds to understand the problems in ecology.
Domain experts can classify birds manually, but this traditional classification is a tedious and time-consuming. It is also becoming very difficult due to the tremendous increase in amounts of data.
Here comes the opportunity for those looking for ai projects for students. It is among the easy artificial intelligence mini projects.
The project uses AI-based classification for predicting bird species. It can be approached in two ways. If you are a beginner, you can use a random forest to predict bird species. You can use a convolution neural network if you are looking for an intermediate level.
7. Identifying handwritten mathematical symbols
In this project, artificial intelligence helps comprehend handwriting. It is one of the simplest ai related projects you can work on as a beginner. You will be using a convolution neural network (CNN) to detect handwritten mathematical symbols.
The HASYv2 dataset is the input to the neural network; it contains 168,000 images from 369 different classes. Here is a video to help you get started with an AI project on identifying handwritten text:
8. Scotch Whiskey classification
Scotch whiskey is famous for its distinct flavors. In this project on artificial intelligence, you will classify scotch whiskeys based on their flavor characteristics. Here, we will use datasets of scotch whiskeys from several distilleries and cluster them based on their flavors.
Here is references to the datasets to help you start off – the Whiskey region dataset and the Whiskey varieties dataset.
9. Investigate Enron
Enron is one of the largest energy companies in America that collapsed overnight. Enron investigation is one of the real-life ai related projects for students. In this project, artificial intelligence investigates Enron’s fraud activities with the help of the emails sent by their former senior executives. It has 500 thousand emails from its former employees.
Check the link for the Enron database- Enron Email Dataset.
10. Fake news detector
In social media, deep fakes, news generators, and fake news have become a menace to society. For example, as per NCRB (National Crime Records Bureau, India), there has been a 214% increase in fake news-related cases.
Fake news can flare up all types of pre-existing social unrest or create social tensions out of nowhere. Given the sheer number of unregulated, unaccountable news outlets and people increasingly receiving news from social media portals, manually checking the validity of each piece of news can be problematic as by the time the verdict is out, harm can be done.
On top of this, the validity of the fake news reviewer can be questioned because of their perceived political leanings. These complex social problems can be solved through a fake news detector.
AI can perform social responsibility by cross-checking news contents with official government briefings or prestigious news portals held accountable.
You can create an artificial intelligence project using NLP models like BERT here are helpful and should be explored. A fake news detector model can produce labels such as ‘True’, ‘False’, ‘Mostly False’, or ‘Misleading’ for a news item.
Advanced AI Projects
11. Automated system to detect fashion trends
Coolhunter has gained significant importance in the fashion world. They take advantage of social media platforms to understand new trends in fashion. But, due to irrelevant information, it becomes a challenging task to predict fashion trends.
AI can be used to sort information. This ai based project filters relevant information from irrelevant data and derives insights for predicting fashion trends.
12. Web pattern navigation profiling
Each time when users search for information on the internet, they leave an invisible blueprint of their preferences. These preferences are recorded based on their browsing behavior in a specific sequence of domains. Here, segments of user groups are created based on their browsing habit or social media opinions.
In this project on artificial intelligence for web pattern navigation profiling, you will learn a new perspective on collecting user preferences. Here, different navigation profiles are extracted based on the consecutive sequence of domain visiting order and the route followed within a certain socio-demographic profile.
You will need to define an algorithm to extract frequent contiguous sequences and also use Bonferroni and FDR corrections to retrieve socio-demographic characteristics.
13. Food attribute classification
One of the most interesting ai projects for food lovers. It classifies the diverse array of food based on cuisine and its flavors. Here, we create a deep learning model based on a multi-scale convolutional network.
The food attribute dataset – Yummly48k – is taken from the website Yummly. In addition to the multi-scale convolutional network, it uses Negative Log-Likelihood (NLL) for the model creation.
14. Resume parser
AI has the advantage of being versatile and can be used in various domains. One such domain is Human Resources (HR), where the concerned people need to understand the human resource requirement and shortlist appropriate candidates for further interviews. This issue can be problematic given the number of applications can often be in the hundreds if not thousand.
According to a study, an average recruiter spends approximately 7 seconds reviewing a resume. To utilize these ‘7 seconds’, reviewers look for keywords in the resume that can help them know if the resume is relevant to the job profile. However, candidates can deliberately put these keywords alone, causing the resume to get shortlisted.
Also Read: How to Optimize Your Resume for the ATS
This problem can be solved through AI. You can train an AI model with several relevant and irrelevant resumes. Natural Language processing is involved in such a project, and deep learning algorithms like RNN are the ideal choice. The final product can be a resume parser that provides either a yes/no or a score from 1 to 10 in terms of the relevancy of the resume for a given job profile.
15. Object detection system
Google Images can classify images based on their contents, such as ‘Birthday’, ‘Pet’, ‘Car,’ ‘Nature’ etc. On a more complex level, the model can also label the objects in the image. For example, the model can label all the relevant objects in the image if a human looks at this phone sitting beneath a tree. This is done by creating an object detection system that skims through the contents of the image.
Also Read: Understanding Image Segmentation
Training an AI model on object detection can help companies dealing with autonomous vehicles, smart infrastructure, and security solutions. To work on an artificial intelligence project of this nature, you can use the COCO 2017 dataset available on Kaggle for the output layer and can use an open-source, pre-trained model for this called SSD (Single Shot Detector).
Get Inspired: AI Projects To Explore
1. Healthy diet via Diet4You
Maintaining a healthy lifestyle plays a key role in preventing the cause of chronic diseases. The right amount of nutrition is necessary to maintain a healthy lifestyle, but a major chunk of the population suffers from undernutrition due to a poor diet plan.
Diet4You is an intelligent decision support system (IDSS) that uses different techniques to tailor a personalized menu planner.
It considers the nutritionist’s prescription and various other factors, such as the nutritional guidelines to be followed, the person’s characteristics, health status, habits, food preferences, and allergies.
This AI project combines advanced techniques such as Knowledge Engineering, Case-Based Reasoning (CBR), and Data Analysis. Diet4You consists of two main modules:
- NPG module – tailoring a nutrition plan for a specific person.
- PMP module – a nutrition plan for a specific period.
2. Phone unlocking using Face ID
It is one artificial intelligence project that uses face biometrics to unlock a phone. Using deep learning, the AI application can extract image features. It mainly uses two types of neural networks: Convolution neural networks and Deep autoencoders network. The ai project comprises a four-step process. They are- face detection, face alignment, face extraction, and face recognition.
Here’s how to build a project that uses AI for high-accuracy facial recognition:
3. Forecasting earthquake-aftershock locations
Earthquakes cause massive destruction. It initially occurs as the main shock and is followed by a set of aftershocks. The timing and size of aftershocks can be identified using empirical laws, but forecasting the locations remains challenging.
Google AI project applies deep learning to identify where the aftershock might occur. The project uses information on 118 major earthquakes reported around the world. Here, it uses a neural network to analyze the static stress change of mainshock and aftershock locations.
MEENA is a chatbot that handles various conversational topics and humanizes computer interaction. It can chat about anything and even improve foreign language practice. It is an end-to-end trained neural conversational model with a single Evolved Transformer encoder and 13 Evolved Transformer decoder blocks. These blocks help them to respond sensibly by minimizing the perplexity and uncertainty in prediction.
5. Gmail’s smart reply
Gmail’s Smart Reply uses a machine-learning algorithm to suggest replies to emails. It is based on a novel thinking hierarchy where each hierarchical model can learn, remember, and recognize a sequential pattern.
While responding, it considers whether it is a positive or negative gesture. It uses long-short-term memory (LSTM) recurrent neural networks and semantics.
Tips to Help You Make AI Projects
Making an artificial intelligence project can be an uphill and complex task, and things can fall apart quickly if done in a sporadic and disorganized manner. Therefore having a good roadmap is very important so that when you are working on the project, you must have a clear idea about every stage of the project.
Here are a few tips to help you improve the outcome of your AI-based project:
1. Update Your Concepts and Foundations
Working on an AI-based project is to use your AI knowledge and demonstrate it to others. Therefore, the logical first step is to ensure you are well-versed in all the important AI concepts.
These include an in-depth understanding of numerous deep learning algorithms, their parameters, data evaluation, and validation techniques, along with having a good command of the language you will build in (e.g., python or R).
Also Read: Learn the Best ML Programming Languages
2. Understand the Business Problem and its Significance
The next step is to pick a project topic and understand the problem. This includes defining the problem, identifying the key issues that can hinder the model creation, identifying how the solution will benefit the end user, and, most importantly- what role AI plays. You must understand the value added AI provides to the solution.
3. Get Help
Working on AI-based projects can be complex and time-consuming if done individually. So if you need help, then form a team and solve a complex problem.
This will not only help create an advanced AI model but will also be a learning experience for you on how to work in a team.
It will prepare you for the future, as AI projects in companies often involve a team working on various aspects of model development.
4. Layout Deliverables
You must understand that the solution you intend to provide will be used by someone. Therefore, when starting with the project, you or your team must brainstorm the product you intend to provide to the user.
5. Explore Solutions
One of the most important steps is patiently exploring how the solution will be provided. This includes exploring the type of deep learning algorithm, the model’s architecture, methods of data preparation to be used, types of model evaluation and validation to be deployed, how to implement the model, etc.
6. Create a Roadmap and Design a solution
Creating a roadmap for your AI-based project is crucial to keep it on track. This includes setting up objectives and timelines and assigning responsibilities (if working in a team).
It will help if you also design your solution regarding how your final AI tool will look and what operating procedure the user will be required to follow to use the product.
7. Access Data
The concept of GIGO (garbage-in garage-out) is common in computer science and mathematics, and it is highly relevant to AI. Therefore, identifying or gathering the dataset for training your model is among the most crucial steps.
For your project, you can look at websites like kaggle.com or other repositories like Google dataset and UCI for the dataset.
8. Create a Proof of Concept
Once the model is trained, the next step is to implement it. The implementation can be as simple as running the model on a jupyter notebook or more complex where you create a user interface. UI can be created using libraries like Streamlit, Django, Flask, etc.
9. Perform User Testing
Ideally, before you let others use your AI tool, you or your team must perform user testing to identify if the prototype works appropriately, solves the user’s problem, provides adequate error messages, and has no bugs. If there is any scope for improvement, then it must be perused.
10. Create a Demo and Perform Diligent Documentation
The last step of your AI-based project that may not seem necessary but is essential in practical work life is creating a user demo of the final AI tool. This demo can be video or text with images; you must also document the whole project work process in detail.
While working in an organization, this documentation is vital to demonstrate your work, it is also important when working on an independent project as it can help you explain your project during the interview.
You can adapt to new job trends by pursuing a course in AI. But to excel in your AI-based career, only hands-on experience working on AI projects can make you efficient. It helps you understand the process end-to-end and derive more value.
You will be better prepared to address challenges in designing and implementing AI projects. You can explore the above AI project ideas to gain skills that companies seek and build a successful career in AI.
1. What are Artificial Intelligence Projects?
Artificial Intelligence projects are intelligent projects that make machines capable of executing tasks requiring human intelligence. These intelligent agents’ goals include learning, reasoning, problem-solving, and perception.
AI includes many theories, methods, and technologies. It consists of many subfields, such as machine learning, neural network, deep learning, cognitive computing, computer vision, and natural language processing.
The additional technologies that support AI are a Graphical processing unit, the Internet of Things, Advanced algorithms, and API.
2. How do I start an AI project?
Gaining skills in AI projects opens a lot of opportunities. Plenty of options are available for those who want to start an AI project. One efficient way is to enroll in an online course. Choose an area of the topic you are interested in and opt for a course that offers real-world projects.
3. What are the 4 types of AI?
We can classify AI into the following 4 types:
- Reactive machine- Reactive machines are AI systems that do not use the experience to perform the current task. They do not form any memory and act based on what it sees. Deep Blue, IBM’s chess-playing supercomputer, is an example.
- Limited memory- Limited memory uses experience to act in present situations. An example of limited memory is autonomous vehicles.
- Theory of mind- Theory of mind is a type of AI system that makes machines capable of decision-making. None of them is extremely capable of decision-making as that humans. But it is showing significant progress.
- Self-aware- Self-aware is an AI system that is aware of itself. These types of systems should be conscious of themselves, be aware of their internal state, and be able to predict others’ feelings.
4. How does AI work?
Data is the new oil. AI combines a large amount of data and intelligent algorithms to help the system learn automatically from data models. AI adds intelligence to your existing application through progressive learning algorithms. This algorithm can be a classifier or a predictor.
Hope this helps you ideate your next AI Project. Happy Learning!
It’s highly informative content. I have been looking for AI project ideas. Thanks for sharing
Looking to read more content.