Artificial Intelligence Software Development Strategy

In artificial intelligence software development, a variety of methods and techniques are used to achieve designated goals. In order to ensure success of the final result, artificial intelligence development companies must formulate sound strategies that address the specific needs for each AI project. Long-term observation and refinement is particularly beneficial to AI software development projects. Putting into place a long-term strategy is essential to ensuring that the full potential of artificial intelligence systems is being realized, thus ensuring AI projects are a success.

  1. Categorize the data set.
  2. Determine the optimal AI format for processing information from data set.
  3. Structure the database to capture all relevant data and conform to AI model.
  4. Program AI machine learning algorithms.
  5. Feed initial test data and refine algorithms based on observed machine learning.
  6. Launch AI application to gather real information.
  7. Refine algorithms based on observed machine learning to improve AI outcomes.

1. Categorize the data set.

Categorizing the data set begins the AI software development process. Will the AI app handle textual data, numerical data, or visual information? In some datasets, each particular entry may be a cluster of numerous variables. For example, a customer in a CRM database may be associated with a long list of past purchases, each categorized by product type and number along with price, date of purchase, etc., along with length of relationship, industry type, size, purchase trends, and more. How many variables govern each input entry, and how complex are the relationships between all the variables? How many relationships can be drawn between all the input entries? Questions like these will help determine the structure of the database, and what artificial intelligence methods will produce the best results from AI information processing.

2. Determine the optimal AI format for processing information from the data set.

The next step in AI development is to determine the optimal AI format based upon the dataset categorization. Each form of artificial intelligence has its own strengths and is suitable for solving certain categories of problems. The below list represents the main formats will use in the development of its AI applications.

  1. Apriori Algorithm: this style of machine learning uses association rule learning model to help explain observed relationships between variables in a dataset. These rules can even accommodate large and complex datasets, such as those in a business's CRM, ERP, or other business intelligence system.
  2. Artificial Neural Networks: artificial neural networks are used both to classify data as well as for regression problems, in which relationships between variables are iteratively refined using a measure of error to ensure the highest quality predictions made by the machine learning model. Regression calculations are common in statistical processing and thus are crucial to the ability to produce high quality, actionable business intelligence (BI) analytics.
  3. Collaborative Filtering: this type of machine learning is used in recommendation systems like product recommendation engines that predict and end-user's desired option among many based on statistical associations seen in users with similar demographics and behavior patterns. Collaborative filtering is essential to the development of advanced AI in eCommerce applications.
  4. Decision Trees: this type of machine learning allows for decisions to be based upon values of a dataset. Decision trees are another method of dealing with classification and regression problems, but output is tailored more specifically to end goals. This type of machine learning is important for AI that is used for autonomous decision-making.
  5. Deep Learning: this artificial intelligence method uses artificial neural networks within the context of semi-supervised learning to gain insights on large datasets that contain largely unstructured, unlabeled data. Deep learning AI is important to discovering insights into data that are of a class that cannot be determined prior to implementation of the algorithm. This form of AI helps us learn what we don't know we don't know.
  6. K Means Clustering Algorithm: this is used to reveal structures present in unstructured input data. It may cluster information, reduce dimensionality, or help establish association rules.
  7. Linear Regression: linear regression is used to process statistics using machine learning. The goal when using linear regression AI is to help predict responses based upon predictor variables, and can help businesses determine short- and long-term trends and what they may mean for the immediate or more distant future.
  8. Logistic Regression: logistic regression AI models are used to accept data inputs and determine binary classifications of data. A common example of logistic regression AI is spam detectors on email programs.
  9. Naïve Bayes: this type of artificial intelligence uses the Bayes theorem to more accurately predict probabilities when attributes do not interact. Commonly used to determine class and conditional probabilities, Naive Bayes is used in natural language processing to determine the topic of phrases and documents and their intent. Another example may be classifying fruit based on attributes such as color, shape, and size.
  10. Classifier Algorithm: these algorithms are used to classify data into a distinct number of classes and assign labels. Examples include handwriting recognition, speech recognition, biometric identification, and document classification.
  11. Nearest Neighbors: this is a type of instance-based algorithm, which compares input data to existing data and measures for similarity to match with the closest type. This helps weigh competing input factors in the processing of broader AI algorithms. They can be used to make predictions based upon provided data, issue credit ratings, predict votes, and more.
  12. Random Forests: this is a form of ensemble algorithms that allow for predictions based on the output of multiple other AI models. This follows supervised learning algorithm development. Random Forests are advantageous in producing relatively accurate predictions with less development time than would take to establish an artificial neural network. Examples of real-world use include eCommerce recommendation engines, stock market predictions, fund allocation in bank accounts based on customer use, and clinical decision support (CDS) systems used to help identify diseases.
  13. Support Vector Machine (SVM) Algorithm: SVM is an AI algorithm that is helpful for classifying more complex data, especially those that have outliers or don't obviously cluster as they are initially presented in the dataset. SVM algorithms are especially useful with highly dimensional datasets, where each entry is a conglomeration of a large number of data points. SVM is a supervised learning model and has been important in image classification, handwriting recognition, biological classifications, and more.

3. Structure the database to capture all relevant data and conform to the AI model.

Once the datasets are determined and categorized and the machine learning format is chosen, the database can be structured to maximize the efficiency of information processing required of the AI application. The hierarchy and clustering of information is an important consideration. Data sets need to have an effective entry classification that defines the parent attribute for multidimensional data. Additional data may be classified beforehand, but some data may go unlabeled if the goal of the AI is to effectively categorize information. For example, in eCommerce AI, a customer company may be the lead classification, with dimensional attributes comprising a history of purchases (each with their own dimensions, such as time, price, product, etc.), beginning date of relationship, total purchase volume, main contact (including demographics), etc. Additional data fields should be included to allow for potential classifications produced by the AI system. To extend the previous example, one may classify customers in terms of their potential to leave for competitors, to help businesses focus efforts on retaining their more vulnerable relationships.

4. Program AI machine learning algorithms.

The programming of the actual artificial intelligence machine learning algorithms follows the structuring of the database. The goal is to take the objectives for the AI and break them down into smaller computation steps that mimic human deductive reasoning. For example, on a product recommendation engine, the relative weight given to the likeliness of a purchase based on past purchases may be adjusted according to each buyer demographic dimension. While many companies that purchased a certain type of paper may be likely to buy a certain toner, that toner may be less popular for companies in a certain region or within a certain industry, so individual purchases of toner for that demographic may be weighed against the overall trends in the product recommendations.

Furthermore, programming of unsupervised learning may detect contributing factors that are not envisioned by programmers beforehand. AI software developers may choose to have such factors identified by the algorithm, or may want the algorithm to automatically adjust the relative contribution of the factors in its output to refine itself. The former is an example of semi-supervised learning, as AI reveals structures that programmers do not foresee but allows them to act upon it. The latter is true unsupervised learning in that the algorithms adjust automatically to refine output autonomously. It is at this stage that the preferred learning format should be determined based on the needs for the AI application.

5. Feed initial test data and refine algorithms based on observed machine learning.

The AI business application should then be initiated with a soft alpha launch, with the initial feed of real data to observe functionality. AI software developers will then look for any discrepancy between observed behavior and the initial objectives for the AI application and debug and refine the machine learning algorithms accordingly.

6. Launch AI application to gather real information.

After the alpha launch, testing, and debugging, a beta launch of the AI application will be initiated to expose the AI application to a wider user base. As data acquisition accelerates, the functioning of the AI application will be understood more thoroughly and thus refinement of the machine learning algorithms will become more advanced and exacting.

7. Refine algorithms based on observed machine learning to improve AI outcomes.

Continuous refinement of the AI machine learning algorithms during active deployment of the software represents the last stage of artificial intelligence software application development strategy. This stage effectively covers all subsequent time in the AI application's lifecycle after the hard launch. When using a supervised learning strategy, human observers will classify results be degree of success to help refine the machine learning algorithm heuristics, or the relative weight of the particular input and output stages in artificial neural network layers. Reinforcement learning will allow for continual refinement in AI applications deployed with unsupervised learning strategies. In both cases, the refinement stage of AI development is where the impact of artificial intelligence on business is maximized. AI's capacity to expand knowledge, adjust functioning from acquired experience, and improve its predictions and judgments are all hallmarks of its unique capabilities to reproduce human-like intelligence, and this potential is revealed more strongly as the AI system has time to mature. This is especially true when one chooses an AI software development company that is dedicated to targeting its AI solutions for each individual customer and offers constant responsive support throughout deployment of the application.

About is an AI software development company that is committed to making custom artificial intelligence solutions that are affordable for the broader business-to-business sector. As a division of DDA, has a 25-year history of serving companies both small and large, and understands how to find the appropriate solutions for each company's goals, budget, and timeline. Having proven itself as a highly capable custom software development firm through the creation of custom eLearning applications, virtual medical simulations, medical diagnostic and research platforms, education and career research and guidance platforms, advanced eCommerce platforms, point-of-sale (POS) applications, and more, is well prepared to navigate the challenges of effectively handling big data to produce tangible results for clients and end-users alike. Explore this website to learn more about the impact artificial intelligence will have on business, or if you have a particular need in mind you think would be best met through the use of AI, contact for a free, no obligation consultation.

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