Since the 1940s, when the digital computer was developed, it’s been clear that computers could be programmed to complete extremely complex tasks. For example, they could discover proofs for mathematical theorems or play chess. In fact, computers or computer-controlled robots can perform tasks typical of humans. That’s where artificial intelligence comes into play.
Are you interested in how to build an AI? This article provides a basic understanding of artificial intelligence, its application, and the steps necessary for making an AI.
Artificial intelligence (AI) is the ability of a digital computer or computer-controlled robot to carry out tasks that intelligent beings perform. AI represents a branch of computer science. Siri, Alexa, and similar smart assistants, as well as self-driving cars, conversational bots, and email spam filters, are examples of AI.
Mathematician Alan Turing’s paper, “Computing Machinery and Intelligence,” and the Turing Test express AI’s fundamental goal and vision. Turing wrote his paper on artificial intelligence, arguing that there isn’t any convincing argument that machines can’t think intelligently like humans. Similarly, the Turing Test is a method of determining whether a machine can “think.”
Based on the information theory, intelligence is one’s ability to accept or transfer information and keep it in the form of knowledge. The information theory mathematically represents the conditions and parameters that affect how information is transmitted and processed
According to Shane Legg, co-founder of DeepMind Technologies, intelligence is the agent’s ability to set goals and solve different problems in a changing environment. If the agent is a human, you deal with natural intelligence, and if the agent is a machine, you deal with artificial intelligence.
Increasingly, building AI systems is becoming less complex and cheaper. The principle behind making a good AI is collecting relevant data to train the AI model. AI models are programs or algorithms that enable the AI to recognize specific patterns in large datasets.
The better you make AI technology, the more wisely it can analyze vast amounts of data to learn how to perform a particular task.
The process of analyzing data and performing tasks is called machine learning (ML). For example, Natural language processing (NLP) gives machines the ability to read, understand human languages, and mimic that behavior. The most promising AI apps rely on ML and deep learning. The latter operates based on neural networks built similarly to those in the human brain.
Real-world applications of AI systems are wide-ranging. Below, you can find the most common examples of AI in daily life:
Also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability that uses NLP to process human speech into a written format. For example, Siri utilizes speech recognition to conduct voice searches.
Increasingly, more companies are turning to online virtual agents for customer service, thus replacing human agents. According to Servion Global Solutions, 95% of all customer interactions will involve artificial intelligence by 2025.
In this case, AI technology allows computers and systems to derive meaningful information from digital images, videos, and other visual inputs. You can see its application in photo tagging on social media.
AI algorithms can use consumers’ behavior to discover data trends, allowing companies to build effective cross-selling strategies. As a result, companies can offer relevant add-on recommendations during the checkout process. That’s where predictive analytics software steps in.
Such software allows real-time decision-making with your data. For instance, the software can generate risk assessment models, such as fraud and risk detection, targeted advertising, and product recommendations.
One of the primary problems that artificial intelligence tackles are payment and sensitive information fraud. Companies utilize AI-based systems to detect and prevent this type of fraud effectively.
AI-based high-frequency trading platforms make thousands or, sometimes, millions of trades each day. As of 2020, half of stock market trades in America were automated. According to Allied Market Research, the global algorithmic market size is forecast to account for $31.2 million by 2028.
Gartner, Inc. predicts that worldwide AI software revenue will reach $62.5 billion in 2022, growing by 21.3% from 2021. So, how to build an AI? Let’s go through the basic steps to help you understand how to create an AI from scratch.
Before developing a product or feature, it’s essential to focus on the user’s pain point and figure out the value proposition (value-prop) that users can get from your product. A value proposition has to do with the value you promise to deliver to your customers should they choose to purchase your product.
By identifying the problem-solving idea, you can create a more helpful product and offer more benefits to users. After you’ve developed the first draft of the product or the minimal viable product (MVP), check for problems to eliminate them quickly.
Now, when you’ve framed the problem, you need to pick the right data sources. It’s more critical to get high-quality data than to spend time on improving the AI model itself. Data falls under two categories:
Structured data is clearly defined information that includes patterns and easily searchable parameters. For example, names, addresses, birth dates, and phone numbers.
Unstructured data doesn’t have patterns, consistency, or uniformity. It includes audio, images, infographics, and emails.
Next, you need to clean the data, process it, and store the cleaned data before you can use it to train the AI model. Data cleaning or cleansing is about fixing errors and omissions to improve data quality.
When telling the computer what to do, you also need to choose how it will do it. That’s where computer algorithms step in. Algorithms are mathematical instructions. It’s necessary to create prediction or classification machine learning algorithms so the AI model can learn from the dataset.
Moving forward with how to create an AI, you need to train the algorithm using the collected data. It would be best to optimize the algorithm to achieve an AI model with high accuracy during the training process. However, you may need additional data to improve the accuracy of your model.
Model accuracy is the critical step to take. Therefore, you need to establish model accuracy by setting a minimum acceptable threshold. For example, a social networking company working on deleting fake accounts can set a “fraud score” between zero and one to each account. After some research, the team can decide to send all the accounts with a score above 0.9 to the fraud team.
Apart from the data required to train your AI model, you need to pick the right platform for your needs. You can go for an in-house or cloud framework. What’s the main difference between these frameworks? The cloud makes it easy for enterprises to experiment and grow as projects go into production and demand increases by allowing faster training and deployment of ML models.
For example, you can choose Scikit, Tensorflow, and Pytorch. These are the most popular ones for developing models internally.
With an ML-as-a-Service platform or ML in the cloud, you can train and deploy your models faster. You can use IDEs, Jupyter Notebooks, and other graphical user interfaces to build and deploy your models.
There is more than one programming language, including the classic C++, Java, Python, and R. The latter two coding languages are more popular because they offer a robust set of tools such as extensive ML libraries. Make the right choice by considering your goals and needs. For example:
Finally, after you’ve developed a sustainable and self-sufficient solution, it’s time to deploy it. By monitoring your models after deployment, you can ensure it’ll keep performing well. Don’t forget to monitor the operation constantly.
“How to build an AI” is a question many are interested in these days. To make an AI, you need to identify the problem you’re trying to solve, collect the right data, create algorithms, train the AI model, choose the right platform, pick a programming language, and, finally, deploy and monitor the operation of your AI system.