Technology Tales

From boardroom to code: More options for AI and Data Science education

27^th July 2025

The artificial intelligence revolution has created an unprecedented demand for education that spans from executive strategy to technical implementation. Modern professionals face the challenge of navigating a landscape where understanding AI's business implications proves as crucial as mastering its technical foundations. This comprehensive examination explores five distinguished programmes that collectively address this spectrum, offering pathways for business professionals, aspiring data scientists and technical specialists seeking advanced expertise.

Strategic Business Implementation Through Practical AI Tools

LinkedIn Learning's Applying Generative AI as a Business Professional programme represents the entry point for professionals seeking immediate workplace impact. This focused five-hour curriculum across six courses addresses the practical reality that most business professionals need functional AI literacy rather than technical mastery. The programme emphasises hands-on application of contemporary tools including ChatGPT, Claude and Microsoft Copilot, recognising that these platforms have become integral to modern professional workflows.

The curriculum's strength lies in its emphasis on prompt engineering techniques that yield immediate productivity gains. Participants learn to craft effective queries that consistently produce useful outputs, a skill that has rapidly evolved from novelty to necessity across industries. The programme extends beyond basic tool usage to include strategies for creating custom GPTs without programming knowledge, enabling professionals to develop solutions that address specific organisational challenges.

Communication enhancement represents another critical component, as the programme teaches participants to leverage AI for improving written correspondence, presentations and strategic communications. This practical focus acknowledges that AI's greatest business value often emerges through augmenting existing capabilities rather than replacing human expertise. The inclusion of critical thinking frameworks for AI-assisted decision-making ensures that participants develop sophisticated approaches to integrating artificial intelligence into complex business processes.

Academic Rigour Meets Strategic AI Governance

The University of Pennsylvania's AI for Business Specialisation on Coursera elevates business AI education to an academic level whilst maintaining practical relevance. This four-course programme, completed over approximately four weeks, addresses the strategic implementation challenges that organisations face when deploying AI technologies at scale. The curriculum's foundation in Big Data fundamentals provides essential context for understanding the data requirements that underpin successful AI initiatives.

The programme's exploration of machine learning applications in marketing and finance demonstrates how AI transforms traditional business functions. Participants examine customer journey optimisation techniques, fraud prevention methodologies and personalisation technologies that have become competitive necessities rather than optional enhancements. These applications receive thorough treatment that balances technical understanding with strategic implications, enabling participants to make informed decisions about AI investments and implementations.

Particularly valuable is the programme's emphasis on AI-driven people management practices, addressing how artificial intelligence reshapes human resources, talent development and organisational dynamics. This focus acknowledges that successful AI implementation requires more than technological competence; it demands sophisticated understanding of how these tools affect workplace relationships and employee development.

The specialisation's coverage of strategic AI governance frameworks proves especially relevant as organisations grapple with ethical deployment challenges. Participants develop comprehensive approaches to responsible AI implementation that address regulatory compliance, bias mitigation and stakeholder concerns. This academic treatment of AI ethics provides the foundational knowledge necessary for creating sustainable AI programmes that serve both business objectives and societal responsibilities.

Industry-Standard Professional Development

IBM's Data Science Professional Certificate represents a bridge between business understanding and technical proficiency, offering a comprehensive twelve-course programme designed for career transition. This four-month pathway requires no prior experience whilst building industry-ready capabilities that align with contemporary data science roles. The programme's strength lies in its integration of technical skill development with practical application, ensuring graduates possess both theoretical knowledge and hands-on competency.

The curriculum's progression from Python programming fundamentals through advanced machine learning techniques mirrors the learning journey that working data scientists experience. Participants gain proficiency with industry-standard tools including Jupyter notebooks, GitHub and Watson Studio, ensuring familiarity with the collaborative development environments that characterise modern data science practice. This tool proficiency proves essential for workplace integration, as contemporary data science roles require seamless collaboration across technical teams.

The programme's inclusion of generative AI applications reflects IBM's recognition that artificial intelligence has become integral to data science practice rather than a separate discipline. Participants learn to leverage AI tools for data analysis, visualisation and insight generation, developing capabilities that enhance productivity whilst maintaining analytical rigour. This integration prepares trainees for data science roles that increasingly incorporate AI-assisted workflows.

Real-world project development represents a crucial component, as participants build comprehensive portfolios that demonstrate practical proficiency to potential employers. These projects address authentic business challenges using genuine datasets, ensuring that participants can articulate their capabilities through concrete examples.

Advanced Technical Mastery Through Academic Excellence

Andrew Ng's Machine Learning Specialisation on Coursera establishes the technical foundation for advanced AI practice. This three-course programme, completed over approximately two months, provides comprehensive coverage of core machine learning concepts whilst emphasising practical implementation skills. Andrew Ng's reputation as an AI pioneer lends exceptional credibility to this curriculum, ensuring that participants receive instruction that reflects both academic rigour and industry best practices.

The specialisation's treatment of supervised learning encompasses linear and logistic regression, neural networks and decision trees, providing thorough grounding in the algorithms that underpin contemporary machine learning applications. Participants develop practical proficiency with Python, NumPy and scikit-learn, gaining hands-on experience with the tools that professional machine learning practitioners use daily. This implementation focus ensures that theoretical understanding translates into practical capability.

Unsupervised learning includes clustering algorithms, anomaly detection techniques and certain approaches in recommender systems, all of which contribute to powering modern digital experiences. The programme's exploration of reinforcement learning provides exposure to the techniques driving advances in autonomous systems and game-playing AI. This breadth ensures that participants understand the full spectrum of machine learning approaches, rather than developing narrow expertise in specific techniques.

Cutting-Edge Deep Learning Applications

Again available through Coursera, Andrew Ng's Deep Learning Specialisation extends technical education into the neural network architectures that drives contemporary AI. This five-course programme, spanning approximately three months, addresses the advanced techniques that enable computer vision, natural language processing and complex pattern recognition applications. The intermediate-level curriculum assumes foundational machine learning knowledge whilst building expertise in cutting-edge methodologies.

Convolutional neural network coverage provides comprehensive understanding of computer vision applications, from image classification through object detection and facial recognition. Participants develop practical skills with CNN architectures that power visual AI applications across industries. The programme's treatment of recurrent neural networks and LSTMs addresses sequence processing challenges in speech recognition, machine translation and time series analysis.

The specialisation's exploration of transformer architectures proves particularly relevant given their central role in large language models and natural language processing breakthroughs. Participants gain understanding of attention mechanisms, transfer learning techniques and the architectural innovations that enable modern AI capabilities. This coverage ensures they understand the technical foundations underlying contemporary AI advances.

Real-world application development represents a crucial component, as participants work on speech recognition systems, machine translation applications, image recognition tools and chatbot implementations. These projects utilise TensorFlow, a dominant framework for deep learning development, ensuring that graduates possess practical experience with production-ready tools.

Strategic Integration and Future Pathways

These five programmes collectively address the comprehensive skill requirements of the modern AI landscape, from strategic business implementation through advanced technical development. The progression from practical tool usage through academic business strategy to technical mastery reflects the reality that successful AI adoption requires capabilities across multiple domains. Organisations benefit most when business leaders understand AI's strategic implications, whilst technical teams possess sophisticated implementation capabilities.

The integration of business strategy with technical education acknowledges that artificial intelligence's transformative potential emerges through thoughtful application rather than technological sophistication alone. These programmes prepare professionals to contribute meaningfully to AI initiatives regardless of their specific role or technical background, ensuring that organisations can build comprehensive AI capabilities that serve both immediate needs and long-term strategic objectives.

From mathematical insights to practical applications: Two perspectives on AI

19^th April 2025

As AI continues to transform our technological landscape, two recent books offer distinct yet complementary perspectives on understanding and working with these powerful tools. Stephen Wolfram's technical deep dive and Ethan Mollick's practical guide approach the subject from different angles, but both provide valuable insights for navigating our AI-integrated future.

What is ChatGPT Doing?: Wolfram's Technical Lens

Stephen Wolfram's exploration of large language models is characteristically thorough and mathematically oriented. While dense in parts, his analysis reveals fascinating insights about both AI and human cognition.

Perhaps most intriguing is Wolfram's observation that generative AI unexpectedly teaches us about human language production. These systems, in modelling our linguistic patterns with such accuracy, hold up a mirror to our own cognitive processes, perhaps revealing structures and patterns we had not fully appreciated before.

Wolfram does not shy away from highlighting limitations, particularly regarding computational capabilities. As sophisticated as next-word prediction has become through multi-billion parameter neural networks, these systems fundamentally lack true mathematical reasoning. However, his proposal of integrating language models with computational tools like WolframAlpha presents an elegant solution, combining the conversational fluency of AI with precise computational power.

Co-intelligence: Mollick's Practical Framework

Ethan Mollick takes a decidedly more accessible approach in "Co-intelligence," offering accessible strategies for effective human-AI collaboration across various contexts. His framework includes several practical principles:

• Invite AI to the table as a collaborator rather than merely a tool
• Maintain human oversight and decision-making authority
• Communicate with AI systems as if they were people with specific roles
• Assume current AI represents the lowest capability level you will work with going forward

What makes Mollick's work particularly valuable is its contextual applications. Drawing from his background as a business professor, he methodically examines how these principles apply across different collaborative scenarios: from personal assistant to creative partner, coworker, tutor, coach, and beyond. With a technology, that, even now, retains some of the quality of a solution looking for a problem, these grounded suggestions act as a counterpoint to the torrent of hype that that deluges our working lives, especially if you frequent LinkedIn a lot as I am doing at this time while searching for new freelance work.

Complementary Perspectives

Though differing significantly in their technical depth and intended audience, both books contribute meaningfully to our understanding of AI. Wolfram's mathematical rigour provides theoretical grounding, while Mollick's practical frameworks offer immediate actionable insights. For general readers looking to productively integrate AI into their work and life, Mollick's accessible approach serves as an excellent entry point. Those seeking deeper technical understanding will find Wolfram's analysis challenging but rewarding.

As we navigate this rapidly evolving landscape, perspectives from both technical innovators and practical implementers will be essential in helping us maximise the benefits of AI while mitigating potential drawbacks. As ever, the hype outpaces the practical experiences, leaving us to suffer the marketing output while awaiting real experiences to be shared. It is the latter is more tangible and will allow us to make use of game-changing technical advances.

The critical differences between Generative AI, AI Agents, and Agentic Systems

9^th April 2025

The distinction between three key artificial intelligence concepts can be explained without technical jargon. Here then are the descriptions:

• Generative AI functions as a responsive assistant that creates content when prompted but lacks initiative, memory or goals. Examples include ChatGPT, Claude and GitHub Copilot.
• AI Agents represent a step forward, actively completing tasks by planning, using tools, interacting with APIs and working through processes independently with minimal supervision, similar to a junior colleague.
• Agentic AI represents the most sophisticated approach, possessing goals and memory while adapting to changing circumstances; it operates as a thinking system rather than a simple chatbot, capable of collaboration, self-improvement and autonomous operation.

This evolution marks a significant shift from building applications to designing autonomous workflows, with various frameworks currently being developed in this rapidly advancing field.

Claude Projects: Reusing your favourite AI prompts

28^th March 2025

Some things that I do with Anthropic Claude, I end up repeating. Generating titles for pieces of text or rewriting text to make it read better are activities that happen a lot. Others would include the generation of single word previews for a piece or creating a summary.

Python or R scripts come in handy for summarisation, either for a social media post or for introduction into other content. In fact, this is how I go much of the time. Nevertheless, I found another option: using Projects in the Claude web interface.

These allow you to store a prompt that you reuse a lot in the Project Knowledge panel. Otherwise, you need to supply a title and a description too. Once completed, you just add your text in there for the AI to do the rest. Title generation and text rewriting already are set up like this, and keywords could follow. It is a great way to reuse and refine prompts that you use a lot.

Automating writing using R and Claude

16^th April 2024

Automation of writing using AI has become prominent recently, especially since GPT came to everyone's notice. It is more than automation of proofreading but of producing the content itself, as Mark Hinkle and Luke Matthews can testify. Figuring out how to use Generative AI needs more than one line prompts, so knowing what multi-line ones will work is what is earning six digit annual salaries for some.

Recently, I gave this a go when writing a post that used content from a Reddit post thread. The first step was to extract the content from the thread, and I found that I could use R to do this. That meant installing the RedditExtractoR package using the following command:

install.packages("RedditExtractoR")

Then, I created a short script containing the following lines of code with placeholders added in place of the actual locations:

library("RedditExtractoR")

write.csv(get_thread_content("<URL for Reddit post thread>"), "<location of text file>")

The first line above calls the RedditExtractoR package for use so that its get_thread_content function could be used to scape the thread's textual content. This was then fed to write.csv for writing out a text file with content.

Once I had the text file, I could upload it to Anthropic's Claude for the next steps. Firstly, I got it to give me a summary of the thread discussion before I asked it to give me the suggested solutions to the issue. Impressively, it capably provided me with the latter.

Now armed with these answers, I set to crafting the post from them. Claude did not do all the work for me, but it certainly helped with the writing. This is something that I fancy exploring further, especially given how business computing is likely to proceed in the next few years.

OWASP Top 10 for Large Language Model Applications

21^st January 2024

OWASP stands for Open Web Application Security Project, and it is an online community dedicated to web application security. They are well known for their Top 10 Web Application Security Risks and late last year, they added a Top 10 for
Large Language Model (LLM) Applications.

Given that large language models made quite a splash last year, this was not before time. ChatGPT gained a lot of attention (OpenAI also has had DALL-E for generation of images for quite a while now), there are many others with Anthropic Claude and Perplexity also being mentioned more widely.

Figuring out what to do with any of these is not as easy as one might think. For someone more used to working with computer code, using natural language requests is quite a shift when you no longer have documentation that tells what can and what cannot be done. It is little wonder that prompt engineering has emerged as a way to deal with this.

Others have been plugging in LLM capability into chatbots and other applications, so security concerns have come to light, so far, I have not heard anything about a major security incident, but some are thinking already about how to deal with AI-suggested code that others already are using more and more.

Given all that, here is OWASP's summary of their Top 10 for LLM Applications. This is a subject that is sure to draw more and more interest with the increasing presence of artificial intelligence in our everyday working and no-working lives.

LLM01: Prompt Injection

This manipulates an LLM through crafty inputs, causing unintended actions by the LLM. Direct injections overwrite system prompts, while indirect ones manipulate inputs from external sources.

LLM02: Insecure Output Handling

This vulnerability occurs when an LLM output is accepted without scrutiny, exposing backend systems. Misuse may lead to severe consequences such as Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), Server-Side Request Forgery (SSRF), privilege escalation, or remote code execution.

LLM03: Training Data Poisoning

This occurs when LLM training data are tampered, introducing vulnerabilities or biases that compromise security, effectiveness, or ethical behaviour. Sources include Common Crawl, WebText, OpenWebText and books.

LLM04: Model Denial of Service

Attackers cause resource-heavy operations on LLMs, leading to service degradation or high costs. The vulnerability is magnified due to the resource-intensive nature of LLMs and the unpredictability of user inputs.

LLM05: Supply Chain Vulnerabilities

LLM application lifecycle can be compromised by vulnerable components or services, leading to security attacks. Using third-party datasets, pre-trained models, and plugins can add vulnerabilities.

LLM06: Sensitive Information Disclosure

LLMs may inadvertently reveal confidential data in its responses, leading to unauthorized data access, privacy violations, and security breaches. It’s crucial to implement data sanitization and strict user policies to mitigate this.

LLM07: Insecure Plugin Design

LLM plugins can have insecure inputs and insufficient access control. This lack of application control makes them easier to exploit and can result in consequences such as remote code execution.

LLM08: Excessive Agency

LLM-based systems may undertake actions leading to unintended consequences. The issue arises from excessive functionality, permissions, or autonomy granted to the LLM-based systems.

LLM09: Overreliance

Systems or people overly depending on LLMs without oversight may face misinformation, miscommunication, legal issues, and security vulnerabilities due to incorrect or inappropriate content generated by LLMs.

LLM10: Model Theft

This involves unauthorized access, copying, or exfiltration of proprietary LLM models. The impact includes economic losses, compromised competitive advantage, and potential access to sensitive information.