Artificial Intelligence – Richard Callaby

Academic Honesty in the Age of Artificial Intelligence: A New Era for Universities

The rise of artificial intelligence (AI) is reshaping how we live, work, and learn. In education, tools like ChatGPT, Grammarly, and AI-driven writing assistants have opened up incredible opportunities for students to learn faster and work smarter. But they’ve also brought new challenges—especially when it comes to academic honesty. How do we navigate a world where students can ask an AI to write their essay or solve their problem set? And how can universities adapt to these changes while still encouraging integrity and learning?

These are big questions, and while there’s no one-size-fits-all answer, there are some clear steps universities can take to move forward.

How AI Is Changing the Game

Let’s be real: AI tools are everywhere, and they’re not going away. They can write essays, solve equations, generate code, and even create entire research papers. While these tools can make life easier, they also blur the line between “getting help” and “cheating.”

For example, if a student uses an AI tool to clean up their grammar, most people would see that as fair game. But what if they ask the AI to write the entire essay? Or to generate an answer without putting in much effort themselves? That’s where things get tricky.

To make matters more complicated, AI-generated content doesn’t look like traditional plagiarism. Instead of copying and pasting from an existing source, AI creates something entirely new—which makes it harder to detect and even harder to regulate.

What Can Universities Do About It?

This new reality calls for a fresh approach. Universities need to rethink how they define and enforce academic integrity while still preparing students to use AI responsibly. Here are a few ways they can tackle this:

Set Clear Guidelines
First and foremost, universities need to be crystal clear about what’s okay and what’s not when it comes to using AI. Are students allowed to use AI to help brainstorm ideas? To check their grammar? To write entire paragraphs? These boundaries need to be spelled out in policies that are easy for both students and faculty to understand.
Teach AI Literacy
If AI is going to be part of our everyday lives, students need to understand it. Universities can offer workshops or courses that teach students how AI works, what its limitations are, and how to use it ethically. The goal isn’t to ban AI but to help students use it responsibly—just like any other tool.
Rethink Assessments
Let’s face it: traditional assignments like essays and take-home tests are easy targets for AI misuse. To combat this, universities can design assessments that are harder for AI to handle. Think in-class essays, oral exams, or group projects. Even better, create assignments that require students to connect course material to their personal experiences or analyze real-world case studies. These types of tasks are harder for AI to fake and more meaningful for students.
Use AI to Fight AI
Interestingly, AI can also help universities maintain integrity. Tools like Turnitin are now being upgraded to detect AI-generated content. While these tools aren’t perfect, they’re a step in the right direction. Training faculty to use these technologies can make a big difference.
Collaborate, Don’t Punish
Instead of treating AI misuse like a crime, universities should focus on educating students about its ethical use. AI can be a powerful learning tool when used properly, and students need to understand that. Faculty can model responsible AI use by demonstrating how it can support—not replace—critical thinking and creativity.
Build a Culture of Integrity
Policies and tools can only go so far. What really matters is creating a culture where honesty and integrity are valued. This can be done through honor codes, open discussions about ethics, and mentoring programs where older students help younger ones navigate these challenges.

Moving Forward

Artificial intelligence isn’t the enemy—it’s a tool. Like any tool, it can be used well or poorly. Universities have a unique opportunity to embrace this shift, teaching students not just how to use AI but how to use it wisely.

By updating their policies, rethinking assessments, and fostering a culture of academic honesty, universities can ensure that AI becomes a force for good in education. The goal isn’t to resist change but to adapt to it in a way that upholds the values of integrity, learning, and critical thinking.

This is a big moment for education. If universities handle it right, they’ll prepare students to thrive in an AI-driven world—not just as users of the technology, but as ethical and innovative thinkers who know how to make it work for them.

Trying to adapt the new normal of Artificial Intelligence creeping into the software development field.

There are some pretty rapid developments in the field of software development with the advent of artificial intelligence. Adapting to these changes means you will have to try and change rapidly.

Below I have written a brief article on how you could adapt to these changes. Now, obviously, I am going through this as well so over time I may update this list on this website as I discover ways that others can adapt to this new reality.

Adapting to the adoption of artificial intelligence (AI) in fields like software development and information security requires a combination of upskilling, mindset shifts, and proactive engagement with emerging technologies. Here are some strategies for professionals in the technology field to adapt effectively:

Continuous Learning and Skill Development: Stay updated with the latest advancements in AI technologies and their applications in your field. This may involve enrolling in relevant courses, attending workshops, participating in online forums, or pursuing certifications in AI and machine learning.
Embrace Automation and Augmentation: Understand that AI is not here to replace human workers entirely but rather to augment their capabilities. Embrace automation tools and AI-powered platforms that can streamline repetitive tasks, freeing up time for more creative and strategic endeavors.
Collaborate with AI Systems: Instead of viewing AI as a threat, collaborate with AI systems to enhance productivity and efficiency. Learn how to leverage AI algorithms and tools to optimize software development processes, improve code quality, or strengthen cybersecurity measures.
Adopt AI-Driven Development Practices: Explore AI-driven development practices such as AI-assisted coding, which can help software developers write better code faster. Similarly, in information security, utilize AI-powered threat detection and response systems to bolster cybersecurity defenses.
Enhance Data Literacy: AI heavily relies on data, so improving your data literacy skills is essential. Understand how to collect, clean, analyze, and interpret data effectively to derive meaningful insights and make informed decisions.
Focus on Creativity and Problem-Solving: While AI can handle routine tasks, human creativity and problem-solving skills remain invaluable. Cultivate these skills to tackle complex challenges, innovate new solutions, and add unique value to your projects.
Ethical Considerations: As AI becomes more pervasive, it’s crucial to consider the ethical implications of its use. Stay informed about ethical guidelines and best practices for AI development and deployment, and advocate for responsible AI adoption within your organization.
Stay Agile and Adaptive: The technology landscape is constantly evolving, so cultivate an agile mindset and be prepared to adapt to new developments and trends in AI and related fields.
Networking and Collaboration: Engage with peers, industry experts, and AI enthusiasts through networking events, conferences, and online communities. Collaborate on AI projects, share knowledge, and learn from others’ experiences to accelerate your AI learning journey.
Stay Curious and Open-Minded: Approach AI adoption with curiosity and an open mind. Be willing to experiment with new technologies, learn from failures, and adapt your strategies based on feedback and evolving best practices.

By adopting these strategies, professionals in the technology field can effectively adapt to the increasing adoption of AI and position themselves for success in a rapidly evolving digital landscape.

Now, these are just some of the ideas that came to mind. They may seem obvious to many but implementing them in practice takes a lot of work. Hopefully, since you know these changes are coming you can start to develop a backup plan or other means of making a living. Remember, your job shouldn’t define who you are but rather what you can contribute to this world.

As a software developer you can solve problems and think rationally and logically, that means you should be valuable as an employee regardless of what happens. Eventually, software developers may become even more valuable than they are now as software development becomes highly specialized.

Potential Security Risks in the new Microsoft Co-Pilot. and how to mitigate them.

Microsoft just today released a new product called Co-Pilot in the Windows 11 operating system. As paranoid security researcher I couldn’t help but think of the potential security threats this could subject every single user to.

A project like Copilot, an AI companion, could potentially have several security vulnerabilities that bad actors might attempt to exploit. Here are some potential vulnerabilities and mitigation strategies:

Data Privacy and Leakage:

Vulnerability: Copilot may handle sensitive information about its users. If this data is not properly protected, it could be accessed by unauthorized parties.
Mitigation: Implement strong encryption for data in transit and at rest. Use secure authentication methods and access controls to ensure only authorized users can access sensitive data. Regularly audit and review data handling processes for compliance with privacy regulations.

Malicious Input and Attacks:

Vulnerability: Copilot may interact with users through text or voice. Bad actors might try to inject malicious code or trick the AI into providing sensitive information.
Mitigation: Implement robust input validation and sanitization to prevent code injection and other forms of malicious input. Employ Natural Language Processing (NLP) models for intent recognition and context-aware responses to detect and mitigate potentially harmful requests.

Phishing and Social Engineering:

Vulnerability: Bad actors may attempt to manipulate users by impersonating Copilot or providing misleading information.
Mitigation: Educate users about common phishing tactics and provide clear instructions on how to verify the identity of Copilot. Implement multi-factor authentication and employ techniques like CAPTCHAs to thwart automated attacks.

Denial-of-Service (DoS) Attacks:

Vulnerability: A high volume of requests or traffic could overwhelm the system, causing it to become unresponsive.
Mitigation: Implement rate limiting, load balancing, and caching mechanisms to handle spikes in traffic. Employ DDoS protection services and monitor for unusual activity patterns.

Model Exploitation:

Vulnerability: Adversaries may attempt to exploit vulnerabilities in the underlying machine learning models to manipulate or deceive the AI.
Mitigation: Continuously monitor for model performance and anomalies. Employ adversarial testing to identify and mitigate potential model vulnerabilities. Regularly update and retrain models to stay resilient against evolving threats.

Third-Party Integrations:

Vulnerability: Integrations with external services or APIs may introduce security risks if not properly vetted or maintained.
Mitigation: Thoroughly assess the security of third-party services and conduct regular security audits. Implement proper authentication and authorization mechanisms for external integrations.

Software Vulnerabilities:

Vulnerability: Copilot may rely on various software components and libraries, which could have their own vulnerabilities.
Mitigation: Keep all software dependencies up-to-date and regularly apply security patches. Conduct thorough code reviews and employ static code analysis tools to identify and address potential vulnerabilities.

User Education and Awareness:

Vulnerability: Users may inadvertently expose sensitive information or fall victim to scams if they are not adequately informed.
Mitigation: Provide clear instructions on best practices for using Copilot securely. Offer user training and awareness programs to educate them about potential risks and how to avoid them.

Regular security audits, penetration testing, and ongoing monitoring for suspicious activities are crucial aspects of maintaining the security of a project like Copilot. Additionally, having a dedicated incident response plan in case of a security breach is essential for timely and effective mitigation.

Of course, this is just a hypothetical breakdown of the potential risks of using Microsoft Co-Pilot. Now, during the presentation it was brought to light that Microsoft is attempting to mitigate some of these risks by using Passkeys and other measures.

Only time will tell how vulnerable Microsoft Co-Pilot will make us in the future. I believe technology can help us, but I believe it is better to be more self reliant and not dependent upon tools and gadgets.

Image Segmentation: A Project You Should Consider Adding to Your Portfolio

Image segmentation is a crucial task in computer vision that involves dividing an image into different segments to identify and extract meaningful information from it. If you are looking to create an image segmentation project for your portfolio, there are several considerations you must keep in mind to ensure that your project is both engaging and informative. In this article, we will take a closer look at these considerations and discuss how you can create an outstanding image segmentation project that will help you stand out to potential employers.

Identify the Problem

The first step in creating an image segmentation project is to identify the problem you want to solve. There are many use cases for image segmentation, such as medical imaging, object detection, and autonomous vehicles. Identifying a problem that aligns with your interests and expertise can help you create a more engaging project.

For example, if you are interested in medical imaging, you may choose to create an image segmentation project that identifies different structures in medical images, such as organs or tissues. Alternatively, if you are interested in autonomous vehicles, you may create an image segmentation project that identifies different objects on the road, such as pedestrians, cars, or traffic signs.

Collect and Prepare the Data

The next step in creating an image segmentation project is to collect and prepare the data. Image segmentation requires a large amount of data, so you should start by collecting a dataset that is relevant to the problem you want to solve. There are many publicly available datasets for image segmentation, such as the COCO dataset, Pascal VOC dataset, or the ImageNet dataset.

Once you have collected the data, you will need to preprocess it to ensure that it is in a suitable format for your model. This may involve resizing, cropping, or augmenting the images to improve their quality or to increase the diversity of your dataset. Preprocessing the data can be time-consuming, but it is an essential step in creating an accurate and robust image segmentation model.

Choose the Right Model

The choice of the model you use for image segmentation can greatly affect the accuracy and performance of your project. There are many different models available for image segmentation, such as U-Net, Mask R-CNN, or DeepLabv3.

When selecting a model, you should consider factors such as accuracy, speed, and ease of implementation. A more complex model may provide better accuracy, but it may also be slower and more difficult to implement. On the other hand, a simpler model may be faster and easier to implement, but it may sacrifice accuracy.

Train and Evaluate the Model

Once you have selected a model, you will need to train and evaluate it on your dataset. Training an image segmentation model can be a time-consuming process, and it may require a significant amount of computing resources. You should train your model on a powerful machine or using cloud-based services like AWS or Google Cloud.

To evaluate your model, you can use metrics such as accuracy, precision, recall, and F1 score. These metrics will help you assess the performance of your model and identify areas for improvement.

Visualize the Results

Visualizing the results of your image segmentation project can help you communicate your findings and showcase your skills to potential employers. There are many ways to visualize the results of an image segmentation model, such as using heatmaps, overlays, or color-coded images.

By visualizing the results of your project, you can demonstrate your ability to communicate complex information in a clear and concise manner. This can be a valuable skill for employers, particularly in fields such as data analysis, computer vision, and machine learning.

Creating an image segmentation project for your portfolio can be an excellent way to showcase your skills and expertise in computer vision and machine learning. By considering factors such as identifying the problem, collecting and preparing the data, choosing the right model,

training and evaluating the model, and visualizing the results, you can create a project that is both informative and engaging.

To stand out to potential employers with your image segmentation project, consider incorporating the following elements:

Innovative problem-solving: Demonstrate your ability to think creatively and develop novel solutions to challenging problems in image segmentation.
Strong technical skills: Showcase your proficiency in programming languages such as Python and frameworks such as TensorFlow or PyTorch, which are commonly used in computer vision and machine learning.
Attention to detail: Demonstrate your attention to detail by carefully preprocessing your data, selecting the right model, and thoroughly evaluating the performance of your project.
Clear communication: Communicate your findings and results clearly and concisely through visualizations, presentations, or technical reports. This can showcase your ability to effectively communicate complex technical concepts.

Overall, creating an image segmentation project for your portfolio can be a valuable experience that can help you develop your skills, showcase your expertise, and stand out to potential employers in the field of computer vision and machine learning. By following the steps outlined in this article and incorporating the key elements mentioned, you can create a project that is both impactful and informative.

Face Recognition: What to consider before adding this type of project to your portfolio

Face recognition is a popular area of computer vision that has gained significant traction in recent years. As a data science student, working on a face recognition project can be a valuable experience that can help you develop your skills and knowledge in machine learning, computer vision, and deep learning.

In this article, we will explore some face recognition projects that data science students can work on and provide tips on how to make them robust and noticeable to future employers.

Face Recognition using OpenCV and Haar Cascades:

One of the simplest face recognition projects you can work on is to build a face detection and recognition system using OpenCV and Haar Cascades. OpenCV is an open-source computer vision library that provides various functions and algorithms for image and video processing. Haar cascades are a popular method for object detection, including faces.

In this project, you can start by training a Haar cascade classifier to detect faces in an image or video. Once you have detected a face, you can extract its features and use them to recognize the person. You can train a machine learning algorithm such as a Support Vector Machine (SVM) or a K-Nearest Neighbors (KNN) classifier on a dataset of face images to recognize individuals.

To make your project robust and noticeable to future employers, you can consider the following:

Use a large and diverse dataset of face images to train your machine learning algorithm. The dataset should include people of different ages, genders, races, and facial expressions to ensure that your model can recognize a wide range of faces.
Use data augmentation techniques to increase the size of your dataset. Data augmentation involves applying transformations such as rotation, scaling, and flipping to your images to create new samples.
Use a validation set to tune the hyperparameters of your machine learning algorithm. Hyperparameters are parameters that are not learned during training and can significantly affect the performance of your model.
Use metrics such as accuracy, precision, and recall to evaluate the performance of your model. These metrics can help you identify areas where your model needs improvement.

Face Recognition using Deep Learning:

Another face recognition project that data science students can work on is building a deep learning model using Convolutional Neural Networks (CNNs). CNNs are a type of deep learning algorithm that is well-suited for image processing tasks, including face recognition.

In this project, you can start by building a CNN architecture that can learn features from face images. You can use a pre-trained CNN such as VGG, ResNet, or Inception as a starting point and fine-tune it on a face recognition dataset.

To make your project robust and noticeable to future employers, you can consider the following:

Use a large and diverse dataset of face images to train your CNN. The dataset should include people of different ages, genders, races, and facial expressions to ensure that your model can recognize a wide range of faces.
Use transfer learning to leverage the knowledge learned by a pre-trained CNN. Transfer learning involves using a pre-trained CNN as a feature extractor and training a classifier on top of it.
Use data augmentation techniques to increase the size of your dataset. Data augmentation involves applying transformations such as rotation, scaling, and flipping to your images to create new samples.
Use a validation set to tune the hyperparameters of your CNN. Hyperparameters are parameters that are not learned during training and can significantly affect the performance of your model.
Use metrics such as accuracy, precision, and recall to evaluate the performance of your model. These metrics can help you identify areas where your model needs improvement.

Face Recognition using Siamese Networks:

Using Siamese networks for face recognition involves training the network to learn a similarity metric between pairs of face images. Given a pair of face images, the Siamese network outputs a similarity score that indicates how similar the two faces are. This similarity score can then be used to recognize a person’s face.

To make your project robust and noticeable to future employers, you can consider the following:

Use a large and diverse dataset of face images to train your Siamese network. The dataset should include people of different ages, genders, races, and facial expressions to ensure that your model can recognize a wide range of faces.
Use data augmentation techniques to increase the size of your dataset. Data augmentation involves applying transformations such as rotation, scaling, and flipping to your images to create new samples.
Use a validation set to tune the hyperparameters of your Siamese network. Hyperparameters are parameters that are not learned during training and can significantly affect the performance of your model.
Use metrics such as accuracy, precision, and recall to evaluate the performance of your model. These metrics can help you identify areas where your model needs improvement.
Consider using a triplet loss function to train your Siamese network. A triplet loss function involves training the network to minimize the distance between an anchor face image and a positive face image (i.e., an image of the same person) while maximizing the distance between the anchor image and a negative face image (i.e., an image of a different person). This approach can help improve the accuracy of your face recognition system.

Conclusion:

In conclusion, working on face recognition projects can be a valuable experience for data science students. To make your project robust and noticeable to future employers, you should consider using large and diverse datasets, applying data augmentation techniques, tuning hyperparameters, using appropriate metrics for evaluation, and exploring different machine learning and deep learning algorithms. By following these best practices, you can develop a face recognition system that can accurately recognize people’s faces and demonstrate your skills and knowledge in computer vision and machine learning.

Object Classification: What to consider when adding this type of project to your portfolio.

Object classification is a popular project in the field of machine learning and computer vision. It involves training a model to recognize and classify different objects based on their features and attributes. Object classification can be used in a wide range of applications, including image and video recognition, autonomous vehicles, and robotics.

If you are interested in adding object classification as a project to your portfolio, there are several steps you can take to ensure your project is successful. Here are some best practices to follow:

Define the problem and gather data: Before you begin your project, it’s important to define the problem you are trying to solve. What kind of objects do you want to classify? What features are important for classification? Once you have a clear idea of the problem, you can begin gathering data to train your model. There are several datasets available online, such as ImageNet and COCO, which contain thousands of images of different objects that you can use for training.
Preprocess the data: Preprocessing the data involves cleaning, normalizing, and transforming the data so that it is ready for training. This step is crucial for ensuring the accuracy of your model. Some common preprocessing techniques include resizing images to a standard size, converting images to grayscale, and normalizing pixel values.
Select a model: There are several deep learning models that you can use for object classification, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). CNNs are particularly well-suited for image classification tasks, as they are designed to recognize patterns in visual data. When selecting a model, consider factors such as accuracy, speed, and ease of use.
Train the model: Training the model involves feeding it with the preprocessed data and adjusting the weights and biases of the model to minimize the error between the predicted output and the actual output. This is an iterative process that involves adjusting the parameters of the model until the desired level of accuracy is achieved. It’s important to monitor the training process and adjust the hyperparameters as needed to avoid overfitting or underfitting the model.
Test the model: Once the model is trained, it’s important to test it on a separate dataset to evaluate its performance. This involves feeding the model with images it has not seen before and comparing its predicted output with the actual output. This step helps you identify any issues with the model and refine its performance.
Deploy the model: After the model is tested and refined, you can deploy it to your application or website. This involves integrating the model into your codebase and providing a user interface for users to interact with the model. It’s important to monitor the model’s performance over time and update it as needed to ensure it continues to perform at a high level.

In summary, object classification is a challenging and rewarding project that can demonstrate your skills in machine learning and computer vision. By following these best practices, you can ensure your project is successful and adds value to your portfolio. Remember to define the problem, gather and preprocess data, select a model, train and test the model, and deploy the model to your application or website.

A List of Computer Vision Projects to Help You Learn About the Subject

Image classification: Build an image classifier that can distinguish between different types of objects, such as cars, bicycles, and people. This can be done using techniques such as convolutional neural networks (CNNs).
Object detection: Create a program that can detect objects within an image and draw bounding boxes around them. This can be done using techniques such as Haar cascades or deep learning-based models.
Face detection: Build a program that can detect faces within an image or a video stream. This can be done using techniques such as Haar cascades, HOG+SVM, or deep learning-based models.
Image segmentation: Create a program that can separate an image into different regions based on their visual properties, such as color or texture. This can be done using techniques such as k-means clustering, graph cuts, or deep learning-based models.
Image filtering: Implement different types of filters, such as blur, sharpen, edge detection, and noise reduction, to enhance or modify an image. This can be done using techniques such as convolution.
Optical character recognition (OCR): Build a program that can recognize text within an image and convert it into machine-readable text. This can be done using techniques such as Tesseract OCR.
Lane detection: Create a program that can detect the lanes on a road from a video stream. This can be done using techniques such as Hough transforms or deep learning-based models.
Object tracking: Build a program that can track objects across frames in a video stream. This can be done using techniques such as Kalman filters or particle filters.

These projects will give you hands-on experience with different computer vision techniques and algorithms, and help you develop a deeper understanding of the subject.