REAL-LIFE AI APPLICATIONS: BEST PRACTICES FOR DEVELOPMENT
AI is no longer just a buzzword in the tech industry - it's a part of our everyday lives. From voice assistants like Siri and Alexa to personalized recommendations on Netflix and Amazon, AI is all around us. And as a software development company, we know firsthand just how transformative AI can be for businesses of all sizes.
If you're a startup founder, you may be wondering how you can leverage the power of AI to improve your product, operations, gain insights from your data, and stay ahead of the competition. By following the best practices described below, you can unlock the full potential of AI and achieve their business goals with confidence.
In this guide, we'll explore the best practices for developing and deploying real-life AI applications, how AI can transform your startup, as well as the tools and techniques you need to develop and deploy real-life AI applications with confidence. So, let's get started - AI is waiting for you!
Topics covered in this article:
- Existing real-life AI applications
- AI products development
Let’s start the AI topic exploration from existing solutions that help us to improve our lives in different industries.
1. The most trending and rapidly growing AI based industry is Natural Language Processing.
Have you ever talked to Siri, Alexa, or Google Assistant and wondered how they understand what you're saying and respond so quickly? The answer is Natural Language Processing, or NLP for short. NLP is an AI technology that enables computers to understand, interpret, and generate human language.
If you would like to use NLP to grow your business, you can use real-life applications of NLP that can help you achieve these goals.
- Chatbots (one of the most popular applications of NLP) that simulate human conversation. You can implement them in different fields, starting from customer service, lead generation and add to your product roadmap. With NLP, chatbots can understand natural language queries and respond in a conversational tone, making them a valuable tool for startups looking to scale their operations and provide better customer experiences.
- Sentiment analysis helps to analyze text data to determine the emotional tone or attitude of the writer. This can be useful for startups in a variety of industries, such as social media monitoring, customer feedback analysis, and brand reputation management. From one perspective you can gain insights from large volumes of text data in real-time to better understand your customers. And additionally you can enhance your product with sentiment analysis. For example, if you have a psychological product, chat and need to understand your customers emotions, you can use sentiment analysis to recognise. Check how we implemented the above for the WriteEmote project.
2. Another highly useful AI technology is Predictive Analytics that uses statistical algorithms and machine learning techniques to predict future outcomes based on historical data. These are: Apple's Women's Health feature, Amazon recommendations, Netflix recommendations, Google Maps traffic, Waze navigation, Spotify Discover Weekly and much more.
One of the key benefits of predictive analytics is its ability to anticipate future trends and identify patterns that might not be immediately apparent. By analyzing large amounts of data, predictive analytics can identify trends and patterns that humans might miss, allowing businesses to stay ahead of the curve and capitalize on emerging opportunities.
Overall, predictive analytics has applications in nearly every industry, providing businesses with valuable insights and helping them make more informed decisions. If you deal in retail, it can help you to sell more to your customers and to identify the best upsell opportunities. In marketplaces you can help customers to match quicker, etc.
If you would like to check if there is a space in your business to add AI technologies or tools, contact us for the consultation and we will make sure you receive a detailed research of such opportunities.
3. Another trending topic nowadays is ChatGPT. It’s a powerful tool that can be used in a variety of ways to enhance business and product development. And there are different areas how to implement ChatGPT to enhance your business.
Basic stuff that you can do with Chat GPT for your business is quick generation of high-quality content. By training Chat GPT on relevant topics, you can generate blog posts, social media posts, and other types of content that are tailored to your audience and industry.
One potential and the most popular use case for Chat GPT is customer service. By integrating a chatbot powered by Chat GPT into your website or app, you can provide instant customer service and support. This can improve customer satisfaction, increase loyalty to your brand and can help you win a few additional points against your competitors.
Additionally you can perform market research with ChatGPT. ChatGPT can easily analyze customer conversations and feedback. Your startup can gain a lot of not obvious insights into customer preferences and pain points. This can help you to develop better products and services that are tailored to your audience and industry.
Chat GPT can be also used to personalize marketing and sales messages. By analyzing customer data and behavior, you can use Chat GPT to generate personalized recommendations and offers that are more likely to resonate with customers, based on previous information. This can improve customer engagement and drive sales. For example, if you have an e-commerce shop with clothes, personalized recommendations can be implemented as additional upsell points: system can offer products (eg. a belt) that can be bought with the chosen position (eg. a bag). The possibilities are endless and you should just use your imagination to add new features to your current product.
4. Computer vision is a branch of artificial intelligence that focuses on enabling machines to interpret and understand visual information from the world around us. It's like giving machines eyes so they can analyze and interpret what they're seeing. It has many real-world applications, from self-driving cars to facial recognition and image search.
Computer vision is used in different industries:
- Retail stores use cameras to watch shoppers and figure out the best way to lay out the store and display products to increase sales and make customers happy.
- Healthcare industry uses it by analyzing medical images like x-rays and MRI scans. Doctors can diagnose diseases more accurately and quickly, which helps patients get better faster.
- In cars, the computer is watching the road and making sure you stay safe while driving and computer vision helps with things like collision avoidance systems and warnings for when you're drifting out of your lane.
- In the entertainment industry it can help with things like automatically generating descriptions and tags for movies and TV shows. This makes it easier for people to find new things to watch and for creators to reach their audience.
5. The next area is very much connected with the previous one. Autonomous vehicles.
These are cars that can drive themselves without human input (yeah, Tesla uses this technology). They use a variety of sensors, cameras, and software to perceive their surroundings and make decisions on the road. AI is used to teach autonomous vehicles to make different functions:
- recognize different objects and people,
- interpret road signs and signals,
- navigate complex road environments.
The software powering these vehicles can analyze massive amounts of data in real-time to make quick decisions and adjustments to keep passengers and other road users safe.
In the automotive industry, companies like Tesla, Waymo, and GM Cruise are leading the way in autonomous vehicle development.
Basically, autonomous vehicles are functioning through machine learning algorithms. These algorithms analyze large amounts of data from various sensors and use it to train the vehicle's AI system to make accurate predictions and decisions. For example, the vehicle can learn to recognize different types of objects like pedestrians, other cars, and traffic lights, and respond appropriately based on its training.
Less advanced system that only partially covers the autonomy is ADAS (advanced driver assistance systems) in traditional vehicles. These systems can help with tasks like lane keeping, adaptive cruise control, and automatic emergency braking. ADAS systems are a stepping stone towards fully autonomous vehicles and are already being integrated into many new cars on the market today.
6. Another AI technology is Personalization and Recommendations. AI-powered personalization is becoming popular across many industries, like e-commerce, healthcare, and finance. It helps to customize your product or service to fit your customer's unique needs. With the help of AI, you can analyze heaps of data to understand customer's preferences, interests, and behaviors. This helps to create personalized experiences that can make your customers happy and keep them coming back for more.
For example, online stores like Amazon use AI to recommend products based on what you've searched for or purchased in the past. Doctors use AI to analyze patient data and create personalized treatment plans that work best for each patient. In finance, AI helps create customized investment portfolios that fit your financial goals and risk tolerance.
7. One more area that uses AI implementation is Education.
AI is changing the way we learn by creating personalized educational experiences. With AI tools, teachers can adapt their teaching style and assessments to better match the individual needs of each student. For instance, with adaptive learning software, students can receive customized lessons and assessments based on their progress and proficiency.
AI is also helping educators identify struggling students by analyzing data such as attendance, grades, and behavior. This way, teachers can intervene early and provide targeted support to keep students on track. For example, the University of Georgia uses AI software to predict which students may drop out and provides them with personalized assistance to help them graduate.
If you develop an Ed-tech product, you should start to implement AI technology to students’ and teachers’ experience to improve educational content. For example, apps like Duolingo use AI to create customized language lessons that provide instant feedback on pronunciation and grammar. Another example is Carnegie Learning, which uses AI to make personalized math lessons for K-12 students. These AI-powered tools provide an engaging and effective learning experience that improves outcomes for students.
These are the most popular AI technologies that are used in different industries. As you see, there are a lot of areas to implement and develop advanced AI algorithms. But it’s also important to make a deep research for the topic of AI in your product, how it will enhance it and if it’s worth to build algorithms.
Here are some best practices for AI products development:
1. The most important step for the development of AI technology is to choose the right problem to solve. It will impact the whole development process and also the possibility to create an advanced technology for your product.
Find more information about research and market analysis in the article HOW TO BUILD MVP FOR YOUR STARTUP.
2. Another important step that will make the product successful is high-quality data collection.
Having high-quality data is crucial for the success of AI applications. It's because AI models learn from the data they are trained on, and if the data is flawed, then the results will be flawed too. That's why it's important to ensure that the data used for machine learning is of high quality.
By collecting, preprocessing, and labeling high-quality data, you'll be able to build more accurate and effective AI models that can provide valuable insights and solutions for your business.
To collect high-quality data, start by identifying the data that is relevant to your AI model. You can collect data from various sources, such as social media, surveys, and sensors. Make sure to collect enough data to create a diverse dataset that represents the problem you're trying to solve. After collecting the data, it's important to preprocess it to ensure that it's ready for machine learning. This can include cleaning the data, removing duplicates, and converting it into a format that's suitable for machine learning models.
Lastly, labeling the data is crucial for supervised learning models. This involves manually adding tags or labels to the data to help the model understand what it represents. The quality of the labels is important, so it's important to ensure that the labels accurately represent the data.
3. Once previous steps are completed, you will need to choose appropriate algorithms.
Generally AI algorithms are designed to process large amounts of data and extract meaningful insights. There are different types of AI algorithms, each with its own strengths and weaknesses. For instance, decision trees are a popular choice for classification tasks, while deep learning algorithms are often used for image and speech recognition. It's important to choose the right algorithm for the task at hand to ensure the best possible results.
When selecting an algorithm, it's important to consider the size and complexity of the dataset, the task being performed, and the desired level of accuracy. Some algorithms are better suited for small datasets with simple structures, while others are designed to handle large, complex datasets. Customizing the algorithm for specific applications can also help to improve accuracy and performance.
In addition to choosing the right algorithm, it's also important to continually evaluate and optimize it over time.
4. So, if the correct algorithm is chosen, start to think on the scalable product architecture.
Scalable architecture is crucial for building successful AI applications that can grow with increasing demand. In simpler terms, it means having a flexible and robust infrastructure that can handle large amounts of data and user traffic without crashing or slowing down. This will save time and resources in the long run, as you won't have to restructure your system every time it outgrows its original capacity.
To build a scalable architecture, it's important to use technologies that are known for their scalability and reliability, such as cloud computing and containerization. These technologies allow for easy deployment and management of applications, and can automatically adjust resources based on user demand.
If it’s the first time you decide to develop an AI algorithm, you will definitely need an experienced person who will be able to advise you about each step and especially about the product architecture. If you are looking for such experts, Appricotsoft team, will be happy to advise in any IT related topic.
5. Once you are done with previous steps, you are ready to jump directly to product development and there are a few more points that should be considered and well planned.
- Ensure model interpretability and transparency of the AI algorithm.
- Testing and validation of chosen models
- Monitoring and maintaining
- Consider ethical and legal implications of your AI application. Make sure that your model is fair, unbiased, and compliant with relevant regulations.
- User training and support
Appricotsoft team understand the importance of following these key steps in developing AI applications. We believe your businesses can leverage the power of AI to gain valuable insights, improve customer experiences, and drive growth. We are dedicated to providing the guidance and support needed to ensure successful AI implementation, and to helping businesses stay at the forefront of technological innovation. Contact us today to learn more about our development services and how we can help you to enhance your products with the power of AI.