The global artificial intelligence (AI) market size was estimated at $136.6 billion in 2022 and rose to $196.6 billion in 2023. Apart from key players like IBM Watson Health, NVIDIA, Baidu, Google, Intel, and Microsoft, small and medium businesses are also coming on board and increasingly adopting artificial intelligence technology to reap its incredible benefits.
If you also wish to make your own intelligent assistant or chatbot to access information quickly and improve customer support, you’re in the right place!
In this tutorial, we’ll look at the instructions on how to make AI from scratch, along with some useful knowledge about this technology.
So, let’s sit back and dive right in!
What Is Artificial Intelligence?
It’s essential to understand what artificial intelligence is, its key types, and some related fundamental concepts.
In simple terms, artificial intelligence is a branch of computer science that simulates human intelligence in machines to do tasks that might otherwise need human assistance. AI is an umbrella term referring to a range of technologies, such as machine learning (ML), natural language processing (NLP), computer vision, speech recognition, and many more.
Types of AI
Now, break down the types of AI you should know:
- Artificial narrow intelligence (ANI): ANI or Weak AI is designed for particular tasks like facial recognition, language translation, or playing chess.
- Artificial general intelligence (AGI): AGI, or Strong AI, is a theoretical system able to perform any intellectual task humans can do.
- Artificial superintelligence (ASI): ASI is a hypothetical system that surpasses human intelligence in every possible way.
So, as you can tell, today, we are well capable of creating ANI, but the two other types are merely hypothetical and depicted in science fiction only.
Key Concepts of Artificial Intelligence
Moving on, let’s touch on four important elements required to build an AI system:
- Data: AI needs a lot of data to learn and get better over time. The better the quality and quantity of your data, the more successful your AI systems will be.
- Algorithms: Think of AI algorithms as the human brains in charge of processing data and gleaning insights from it. There are different types of algorithms, including supervised learning, unsupervised learning, and reinforcement learning.
- Models: AI models are like mathematical representations of a system, helping AI make decisions or predictions based on the data it gets. They can be as simple as linear models or as complex as neural networks.
- Infrastructure: This is the support system for your AI solution, encompassing the hardware, like the Central Processing Units (CPUs) and Graphics Processing Units (GPUs), and the software, like the operating systems and frameworks needed to build, train, and deploy your AI models in a production environment.
6 Key Steps to Building Your AI Software
Now that you’ve got a grasp of artificial intelligence, it’s time to learn how to create your own AI system.
Just a heads up – crafting an AI system from the ground up requires a mix of tech know-how and essential tools. No worries, we’ve got a beginner-friendly comprehensive guide down below:
Step 1. Identify Business Problems
To get started, you need to determine the specific case AI can solve for your business. Whether it’s streamlining customer service, automating repetitive resume screening tasks, or enhancing marketing decision-making, the key is to be crystal clear about that business problem and then set practical objectives for your AI system accordingly.
Remember we’ve talked about Artificial general intelligence and Artificial superintelligence as mere hypotheses as of now? Therefore, let’s keep your AI mission real and feasible for now.
Ask yourself:
- Can AI effectively handle this issue?
- What advantages will your organization receive when solving this problem?
- Do you have enough high-quality data on this problem?
Answering these questions will lead you in the right direction toward creating an AI system that’s truly beneficial for your business operations.
Step 2. Collect and Prepare Data for AI Development
Once pinpoint the problem, let’s gather data!
The quality of your AI model depends on the data it’s trained on. Therefore, having the right data is of great significance.
But how do you know your collected data is the right fit?
- The data needs to be relevant to the problem at hand.
- There is enough data to represent all the possibilities and outcomes.
- The data is not biased.
Typically, data comes in two categories: structured and unstructured.
- Structured data is organized, clearly defined, and easy to search and use, like a neat spreadsheet with columns for names, addresses, and phone numbers.
- Unstructured data is less readily available and more complex. Take a transcript from a customer service call as an example. Although there’s lots of valuable information in the transcript, it’s not arranged in a predetermined way – encompassing not just natural language but also various sentence styles and even slang. This data makes it especially tricky for AI models to handle directly.
Most of the time, you’ll be dealing with unstructured data in an artificial intelligence project, which means you have to do some serious data cleaning. Basically, in data cleaning, you need to spot and fix errors, remove duplicates, and make sure all data is in a standard format.
Step 3. Choose The Right Tools and Platforms
Picking the right tools and platforms is a big deal in implementing AI systems.
As for platforms, you’ve got two main choices: in-house and cloud.
In-house frameworks
In-house AI frameworks are custom-made platforms crafted by your internal teams to meet specific development and deployment needs. They cater to unique business requirements, data sets, and technology stacks, bringing various perks like customization, data ownership, data control, and privacy. Some popular in-house frameworks call the name of TensorFlow, Pytorch, Keras, Microsoft Cognitive Toolkit, and Tableau.
When deciding on a framework for your AI system, carefully assess your needs and each framework’s strengths and weaknesses. Here’s a brief overview of 3 major frameworks:
- TensorFlow: An open-source deep learning framework from Google for machine learning applications like neural networks. It’s a great option for large-scale production systems that require scalability and robustness.
- PyTorch: One of the latest deep learning frameworks for Python, developed by Facebook and mainly employed for applications like natural language processing. PyTorch is becoming increasingly popular, especially among academic researchers, for its simplicity, ease of use, and memory efficiency.
- Keras: An open-source Python-based neural network library, perfect for beginners and those who wish to experiment and develop AI models quickly.
Cloud frameworks
AI cloud frameworks, on the other hand, empower developers and data scientists to build, deploy, and manage machine learning models in the cloud. They provide unified computing environments for data processing, storage, model training, and deployments, simplifying the whole artificial intelligence development process. They also boast inherent flexibility and seamless integration with other services.
Notable tools for artificial intelligence product development within cloud frameworks include Amazon Web Services, Google Cloud AI, IBM Cloud, and Microsoft Azure AI.
Programming languages
In addition to the framework platform, you also need to consider which programming language to use. The most popular programming languages are C++, Java, Python, and R. The last two are particularly favored due to their extensive machine learning libraries. However, remember to carefully weigh your goals and requirements to find the most suitable option:
- Python emerges as an excellent starting point for beginners, offering a straightforward syntax that is easily comprehensible even for those without programming backgrounds.
- C++ is known for its high performance and efficiency level, making it ideal for AI in games.
- Java proves easy to debug, user-friendly, and compatible with most platforms. It excels in working with search engine algorithms and handling large-scale projects like building desktop applications.
- R is specifically developed to build deep learning models for predictive analysis. Thus, it’s predominantly used in data science and AI development. Notably, R delivers an array of libraries and tools that streamline the analysis and visualization of data.
Step 4. Write Algorithms or Select a Model
Moving on, you need to write your machine learning algorithms or adopt a pre-existing model. Algorithms, at their core, are mathematical instructions that guide your AI in executing tasks and refining its performance. Normally, writing ML algorithms requires the expertise of seasoned data scientists or software developers.
Alternatively, if creating algorithms seems daunting, opting for pre-trained models is a viable option. Especially for a midsize tech company seeking to – for example – enhance customer support efficiency, leveraging established models from TensorFlow and PyTorch can prove highly efficient.
However, for those keen on crafting their own AI model yet lacking technical expertise, there’s good news! Today, there are various no-code machine learning platforms available on the market. All you need to do is provide your data; the platform will handle the rest. Google Cloud AutoML enables you to create AI models for various tasks like image and text classification, as well as natural language processing.
Step 5. Train The AI Model
Writing algorithms or selecting a model is not enough. You also have to train it using the data gathered previously.
In essence, training an AI model goes from feeding data and identifying data patterns to making a prediction based on those patterns. This complex process necessitates a substantial dataset divided into training and validation sets. Typically, most data scientists allocate 80% of their dataset for training their models and reserve the remaining 20% for validating the model’s predictive capabilities. TensorFlow, PyTorch, or Keras can be instrumental in managing this process.
Keep in mind these key factors during training:
- Data quality: Make sure you have clean and relevant data.
- Overfitting vs. Underfitting: Striking a balance is vital, as overfitting occurs when the model overly learns the training data, resulting in poor performance on new data, while underfitting arises when the model fails to learn from the training set.
- Computational resources: Given the resource-intensive nature of model training, it’s important to ensure your business is equipped with sufficient hardware capabilities or consider cloud-based solutions instead.
Moreover, training is a dynamic, ongoing process – a continuous cycle of learning and adaptation. As a result, iterative training holds great significance, enabling effective enhancement and retraining of your AI model in response to emerging data or trends.
Step 6. Evaluate, Deploy, and Monitor The AI Model
After constructing and training your AI model, the next step is to evaluate, aiming to assess your AI system’s performance in alignment with predefined goals. Techniques such as cross-validation, precision-recall, or confusion matrices prove valuable in measuring its accuracy and reliability.
Also, during the evaluation phase, please take note of key factors such as overfitting/underfitting, model bias, and real-world applicability.
Following model evaluation is model deployment, meaning you integrate it into your existing systems or workflows. Here, you may utilize APIs for system interoperability or choose to build a user interface for end-user interaction.
Important aspects to take into account during deployment include:
- Compatibility: Ensure your AI tool can integrate seamlessly with your current systems.
- Scalability: Assess whether the solution can manage increased data or user load.
- Security: Implement measures to preserve sensitive data and uphold user privacy.
- Monitoring: Set up mechanisms to track performance and promptly identify any issues.
Lastly, don’t forget to monitor your AI system continuously post-deployment. This entails tracking performance, detecting errors or anomalies, and implementing necessary updates.
Advantages of Outsourcing AI Software Development
While attempting to create an AI solution independently, many have faced challenges, with only a handful achieving success. Making AI itself demands tremendous investments in advanced technologies, experienced talent, and much more, as outlined in the six steps above.
In case this entire process seems too intimidating for your business even to get started, consider AI outsourcing services. There are several reputable AI consulting firms for you to consider, including Accenture, Deloitte, KPMG, and Neurond AI. They all offer enormous benefits, paving the way for your transformative AI journey.
Below are four compelling advantages that might prompt you to outsource right away:
- Cost-effective Solution
Undoubtedly, one of the most enticing benefits lies in the cost-effectiveness of outsourcing. The expertise and proficiency of the AI vendor contribute to a transparent approach to project planning, implementation, and other related costs. Additionally, savings also come from reducing expenses on hardware, workspace, training, and employment.
- Access to Expertise
Many enterprises grapple with a scarcity of skilled AI professionals. Particularly for businesses whose primary focus isn’t AI-related, attracting top-tier experts can pose challenges. When collaborating with AI sourcing companies, you can easily get access to elite tech talents without bearing the costs of recruitment and employment.
- Faster Results
Depending solely on in-house software engineers, especially those in the learning phase, can make your AI development process long and sluggish. In a competitive market, time is crucial. Therefore, dedicated teams from outsourcing vendors might be the best solution to help you deliver state-of-the-art technologies in the shortest amount of time, staying ahead of competitors. Furthermore, your employees can focus on their designated tasks while third-party partners handle all the hard work for your AI projects.
- Scalability
Expansion and growth come naturally with the help of professionals. With years of experience, these experts offer insights into leveraging diverse aspects of AI to propel your business into unforeseen profitable trajectories. Not only can they guide you in new ways, but they also carry out those plans flawlessly, assisting you in scaling confidently.
Realize Your First AI Software with Neurond AI Today
Building AI systems might look easy on paper, but admittedly, it truly takes blood, sweat, and tears to deploy them successfully, far beyond the six basic steps above. Yet, many have realized their own AI project ideas, so don’t feel discouraged just yet. There are still many workarounds, including no-code machine learning platforms and outsourcing services.
However, always remember to sit down and weigh the pros and cons of outsourcing and assigning to an in-house team. If your final answer is outsourcing, then move on to find an offshore AI development company. In this instance, look no further than Neurond AI. We are a team of full-stack, experienced technical and analytical engineers in the AI software development market for over 15 years. We commit to delivering to you only the highest quality AI solutions tailored to your exact requirements.
Interested in our end-to-end consultation service? Drop us a line now!