How to Use AI-Powered Coding

Programmers are going to have some fun with this one. Picture the start of a typical day as a developer, all the priorities you need to set, preparation for any briefs you’ll get, and then the contingency planning on top of it all. Of course, nowadays with tools like Chat GPT, Visual Studio IntelliCode, and CodeSpace among other resources, the programming process has improved lightyears from what we had 10 years ago. 

With that in mind, let me ask you this (if you are a programmer); how much easier is it to approach a day on the job now compared to when you started programming? Better yet, how much easier would it have been for you to learn to code with the tools available today?

Answers to these questions will vary. A lot of software developers will argue that you need to learn the principles of programming without the “shortcuts” first and then introduce them as you get better. Others will argue that modern programming tools have made it much easier when learning to code and made the process more enjoyable. There really is no right or wrong answer to these questions because it comes down to one thing; these tools are the future. 

The Basics of AI-Powered Coding

AI advancements in the software development space will only get better and make the process more streamlined. Among the many tools programmers are leveraging in 2023, AI-powered code is a top choice to enhance the efficiency and effectiveness of their coding process. 

For the first example, we’re going to be looking at OpenAI’s Codex. Codex is an AI code assembler that uses machine learning to translate natural language into code. Here are 3 examples of using it: 

1. You’re creating a feature that tracks sample inventory in a LIMS system:

You could say to Codex: “Develop a functionality that can record and track the inventory of samples in the laboratory”

Codex will then: 

• Design a plan for a database that will store the following information: sample ID, sample type, quantity, and location.

• Implement CRUD (Create, Read, Update, Delete) operations for managing the sample inventory.

• Develop a user interface that allows users to see and update sample inventory records as they go.

2. You’re creating a feature for insurance software that calculates premiums based on risk factors: 

You could say: “Build a component that calculates insurance premiums for different insurance policies based on factors such as age, location, and coverage type.”

Codex will then:

• Design a data model that can store policy details, consisting of customer information and risk factors.

• Develop algorithms that calculate insurance premiums based on the risk factors provided and the parameters of the given policy.

• Lastly, implement validation checks to ensure the premium calculations are appropriate.

3. Now you want a feature that automatically tracks budgeting and expenses for a FinTech platform:

You could say: “Create an interface that users can use to set budgets, track expenses, and generate reports on overall spending”

Codex will then: 

• Create the design for the interface where users can input budget categories, set spending limits, and track expenses.

• Develop algorithms that will categorize expenses based on predefined rules and transaction data automatically.

• Generate visual reports that users can use to understand their spending habits, see how well they adhered to their budget, and recognize trends in their expenses.

Codex is a great platform, but it’s not the only one. Platforms like Tabnine, Snyk, and IntelliCode all perform unique roles that help software developers perform at the next level— but the key is knowing how and when to utilize them.

What Tools to Use and When

As a software developer, your time down to the second is highly valuable, so anything that can streamline repetitive processes or reduce manual effort is a go. Software depends on context which means that the tools a developer uses will depend on the specific requirements of the project and the developer's preferences.

With that in mind, here are some ways the AI-powered coding tools we’ve mentioned can be used in different situations:

1. Project: You’re building a mobile app for a transportation company

Tabnine will be a great resource in this case to handle the following: 

• The coding process for the mobile app will be shortened thanks to Tabnine’s code completion ability.

• The system can give suggestions for implementing features such as map integration, user location tracking, and in-app notifications. 

• It will get the right APIs involved so that third-party transportation services can be integrated.

  2. Project: You need to design and build a Web App for an E-Commerce store

The Web App development process can become a lot more satisfying using IntelliCode because of the following: 

• Whether it’s HTML, JavaScript, CSS, Angular, React, Vue.js, or you name the web app development language or framework - IntelliCode can suggest relevant code completions based on the context using Deep Learning algorithms.

• Because IntelliCode is so great at detecting what is necessary to complete a code, its error-prevention ability is significant

  3. Project: Develop a Node.js-based REST API

Snyk will be great for the security aspect of your REST API. 

• It identifies any known vulnerabilities in the open-source libraries you're using with your API and then provides insights and actionable recommendations to mitigate these vulnerabilities.

• The good news is that this is not a one-and-done tool. Snyk can continue to monitor the system over time and keep identifying gaps to fill.

Why AI-Powered Code?

The advantages that come with properly utilizing AI coding tools are immeasurable. Software developers are performing at a new level in today’s world thanks to these advancements. This means that now is the time for a business to get on before you start getting dramatically outperformed. 

AI-Powered code relies on 5 key components which include; the data source, Natural Language Processing (NLP), code parsing, Machine Learning, and most importantly feedback and iteration from the software developer. The interaction aspect is what allows the AI models to refine their predictions and generate accurate, or more contextually appropriate coding suggestions. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

Introducing Computer Agents

Most people’s knowledge of AI stops at Chat GPT. The “GPT” part of which stands for “Generative Pretrained Transformer”. Didn’t know that? Good, let’s break it down:

Generative: The AI model's ability to generate new content rather than relying solely on pre-existing data

Pretrained: The system has been trained on a large dataset to learn patterns, structures, and relationships in language.

Transformer: The underlying neural network infrastructure that allows the model to process and generate text quickly and efficiently. 

To say the absolute least, OpenAI set a whole new bar and got an absurd head start once they introduced Chat GPT, which has placed OpenAI among the top 20 most visited websites in the world during the 6-month span the platform’s been out. But this success alone won’t sustain them forever, especially since it’s influenced rival companies and researchers who are working tirelessly to catch up by developing their own products.

The Other AI Systems on The Rise

When you give a tool to the public, it’s a certainty that they will do whatever they can to make it as beneficial to them as possible. This mentality has led to the creation of Computer Agents which essentially act as a computer’s personal assistant. In case you’ve never heard of a computer agent, we’ll break the concept down.

The entire premise for computer agents is to do one thing: automate the tasks required to reach goals. This advanced AI infrastructure goes beyond basic copywriting and idea generation. It’s more so tailored to cater to the needs of enterprise businesses.

There are 3 key types of agents: Deliberative, Hybrid, and Reactive. While all are unique in their processes and abilities, they share the common goal which is to complete tasks as quickly and efficiently as possible. But not in the same way that Chat GPT responds to your prompts, computer agents use APIs to work with applications and services to perform tasks on your behalf.

Here is a closer look at each one:

Deliberative Agents: Deliberative agents use advanced planning and decision-making algorithms to perform complex tasks. Ever heard someone say “Use your brain”? Well, these systems do just that. They take their databases of past experiences and use them to analyze problems and make informed choices based on the situation they’re presented with. While they may not have a brain, they generate a plan of action to achieve the intended goal. These helpers are great for tasks that require foresight and optimization.

Reactive Agents: These tools are the complete opposite of what we know about deliberative agents. As the name implies, the system responds to its environment, except without any clear understanding of its purpose. The agent's behaviour and the rules they follow are usually pre-determined and don’t depend on reasoning. It has no learning capacity and instead relies on inputs to trigger pre-programmed responses. For tasks that need quick responses to a predictive environment (like an assembly line), this tool can be a game changer.

Hybrid Agents: With hybrid agents, you get the best of both worlds. These are your self-driving cars, Siri on your iPhone, and even the robots used for manufacturing. These systems combine the strengths of both deliberative and reactive agents to form one process. They can reason and plan like deliberative agents but also react quickly to dynamic environments just like reactive agents. They’re that perfect balance between preplanned actions and on-the-fly adaptation, which makes them great for tasks that require flexibility. 

You might be wondering then, “What’s the point of deliberative and reactive when there’s a hybrid?” Each agent serves a unique purpose, and there are going to be times when a hybrid isn’t as suitable a choice. Here are 4 reasons why: 

1. Complexity: While hybrid agents can handle a wide variety of tasks with the conjoined capabilities of deliberative and reactive agents, the integration does add complexity to the system. In some cases, the simplicity and efficiency of reactive or deliberative agents are simply better for the task which as everyone in IT knows - tasks never need to be more complex. 

2. Application Requirements: Different applications have different requirements. Some tasks prioritize speed and real-time responsiveness, while others require careful planning and decision-making. Having that variety of agent types allows programmers to select the most suitable one based on the application’s specific needs.

3. Specialization: As we said before, reactive agents are best for applications where immediate reactions are crucial, such as emergency systems— especially cybersecurity. Deliberative agents, on the other hand, will cover the big-picture tasks. It’s just like a development team, members have their unique roles and abilities but all contribute to an end goal. 

4. Resource Constraints: Depending on the available computational resources, it may be more practical to use simple reactive or deliberative agents as opposed to a complex hybrid agent. This consideration becomes crucial when dealing with limited processing power or memory constraints in resource-constrained environments.

How it Helps Businesses

Artificial Intelligence, Machine Learning, Autonomous Agents, you name it— it’s cool, but how can people use it to help their business? These systems integrate to fit the custom requirements of your product or service. As the trends suggest, if a company is not trying to automate as much of their workflows as possible, they’re putting themselves at a major competitive disadvantage.

Why would a company not want to move toward automation? Among many reasons, here are the most common:

• A simple lack of awareness/underestimating new technology

• Resistance to change

• Security concerns

• The complexity of the process

• The investment

• Volatile business environment

Most of these concerns are to be expected since the process of digital transformation— especially with AI involved— can be a significant undertaking with no clear guarantees. However, this is what it takes for companies to survive in times of change.

If a business leverages AI, ML, and these autonomous agents with a clear strategy defined and aligns them with their unique goals, they can embrace automation and adapt to the evolving landscape. What we’re getting at here is that leveraging the capabilities of systems like deliberative, hybrid, and reactive agents, such as a GPT, can drive efficiency and innovation, especially in the long term.

Frameworks Behind Computer Agents

Integrating computer agents into a business’s internal system requires a structured approach. Because of this, there are several framework options:

1. Behavior Tree (BT) Framework: The Behavior Tree framework is used for designing reactive agents. It structures an agent's behaviour into a hierarchy of tasks and conditions. The agent then evaluates these tasks and conditions in real-time and makes decisions based on its immediate environment and the rules you’ve given it.

2. Belief-Desire-Intention (BDI) Model: The BDI model is a very popular framework used in designing deliberative agents. It focuses largely on the agent's beliefs about the world, its desires or goals, and its intentions to achieve those goals. This model puts a big emphasis on reasoning, decision-making, and planning capabilities.

3. Sense-Plan-Act (SPA) Architecture: SPA is a popular hybrid architecture used to create highly intelligent agents. Each step is part of a cycle that is sensing the environment, planning its actions, and executing those actions. This is considered the foundation of intelligence that every robot needs.

   The architecture enables agents to combine reactive behaviours with some higher-level deliberative processes. This makes responsiveness happen in real-time through the reactive component, while also providing the ability to reason, plan, and then make strategic decisions using the deliberative component. 

4. Multi-Agent Systems (MAS): Simply put, MAS frameworks involve multiple agents working together to achieve common goals. Using a MAS framework, agents can coordinate their actions and communicate to solve problems that would be impractical for a single agent to handle alone.

The Takeaway

With a solid infrastructure in place, businesses can get the most out of integrating computer agents into their workflows. Whether it's leveraging reactive agents for quick responses, deliberative agents for strategic decision-making, or hybrid agents for the best of both, businesses first need to think about how their goals align with their technology selection. 

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.

The KPIs Your Business Needs When Integrating Machine Learning

In the age of Artificial Intelligence (AI) and Machine Learning (ML), the race to integration is a real thing. As businesses continue to find ways they can leverage these technologies, the importance of quality performance cannot be overstated. For this reason, KPIs are essential…

The two most important assets of AI and ML are:

1. How they benefit the service delivery/experience to the end-user 

2. How they enhance back-end functionality 

These two components are what put these technologies on a pedestal to a point where, should they fail in the future when dependency peaks, it will be catastrophic.

Where KPIs Come In

So we know that KPIs were created for the very reason to measure and evaluate the performance and impact of digital systems, but we should also note that KPIs alone are not sufficient for ensuring your system's success. Again, we’re preaching the “drawing board” approach where businesses outline and focus on their unique goals because it’s not a one-size-fits-all situation.

Continuous improvement is the name of the game, and while KPIs cannot directly guarantee the success of your systems, they play a crucial role in monitoring and guiding your progress.

Machine Learning - User-Friendly Industry Disruptor

Of course, these systems are user-friendly, but when it comes to leveraging these tools from a business standpoint, certain things cannot be ignored when competing in an AI-hungry industry:

1. Machine Learning Takes on Your Objectives: 

The integration of Machine Learning initiatives is meant to simplify your current processes and enhance results. When you consult with a team of AI architects, they’re not going to show you an entirely new way to do your job; rather, they’re pinpointing the more efficient methodology for your current process.

For instance, let’s look at sales: a salesman typically has to make around 50 contacts before they get a single person who’s interested. Let’s say he does 50 cold emails per day and 50 cold calls, landing about 10 interested prospects per work week. 

If the company then implements AI and ML algorithms that can analyze data and recognize the behaviours and characteristics of successful clients, the system can generate lead scores or rankings that indicate the likelihood of a lead converting into a sale. The sales team can then automate lead scoring and prioritize their efforts based on the highest-ranked leads, instead of manually sifting through and wasting touchpoints with unqualified prospects.

2. Predictive Analytics and Pattern Recognition Are Your Bread & Butter

The healthcare sector is a great example of a well-rounded execution through AI and ML strategizing. Hospitals are extremely data-intensive in nature, and that data is highly valuable when it comes to things like patient care, operational efficiency, and medical research as a whole. 

Something really interesting that predictive analytics and pattern recognition have done to revolutionize this landscape already is early diagnosis. Basically, by identifying patterns in a patient's data, Machine Learning can outline potential risks with that patient at an early stage.

Now the healthcare sector is certainly something that deserves a much more thorough analysis, but this gives a sense of the standards that this aspect of your system needs to be performing at.

3. Agile Implementation

This has been a staple in software development for decades as a means to implement solutions as quickly and efficiently as possible. For a company just beginning its journey with AI and ML, this is a great method to familiarize yourself with these technologies and to determine how cohesive they are with your current processes.

Implementing Machine Learning systems and Artificial Intelligence is highly transformative in an organization and is a lot more comprehensive of a stage than planning. Agile is a great approach for this reason as it allows adaptation to happen gradually, and the system can be refined consistently as the needs of its users evolve.

So, we’ve highlighted some key factors that businesses need to keep in mind when preparing for AI and ML integration, but now we have to identify what specifically can ensure your systems are going to consistently perform up to standard.

10 KPIs for Machine Learning:

Evaluation Metrics

1. Accuracy: How accurate are the predictions made by the Machine Learning model compared to actual outcomes?

   • Accuracy is a crucial measure of the system's performance and its ability to make correct predictions. It provides an overall assessment of the model's effectiveness.

2. Recall: How well does the recall of the Machine Learning model evaluate the proportion of true positive predictions compared to all actual positive instances in the dataset?

   • Recall measures the ML model's ability to capture all relevant positive instances, avoiding false negatives which leads us to the next point.

3. Precision: How does the precision of the Machine Learning model assess the proportion of true positive predictions?

   • Precision is significant in cases where the cost of false positives is high. It evaluates the system's ability to make accurate positive predictions, minimizing false positives.

Performance Metrics:

1. F1 Score: Balances precision and recall, providing a single metric that represents the model's overall performance.

2. Mean Absolute Error (MAE): Measures the average difference between predicted and actual values, which will indicate the model's average prediction error rate.

3. Mean Squared Error (MSE): Computes the average squared difference between predicted and actual values, which will emphasize larger errors than MAE would. 

4. Root Mean Squared Error (RMSE): Takes the square root of MSE, then provides a metric on the original scale of the targeted variable.

5. R-squared (R2) Score: Indicates the proportion of the variance in the target variable that can be explained by the model's predictions.

Evaluation Techniques:

1. Precision-Recall Curve: This plots the trade-off between precision and recall at different prediction thresholds, and helps to set an optimal threshold for the ML model's performance.

2. Receiver Operating Characteristic (ROC) Curve: Illustrates the trade-off between the true positive rate and the false positive rate at various classification thresholds, which ultimately aids in the selection of an appropriate threshold.

Moving Forward

The next step with your system is to experiment and evaluate. Planning is crucial but it can’t provide you with results. Focus on your unique goals. Performance evaluations might involve running simulations or real market tests and then analyzing the data you collect to dictate which metrics best suit your system.

Whatever it looks like for you, consider the nature of your system, the industry you're operating in, and the outcomes you want to achieve.

Written By Ben Brown

—

ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.