Demystifying AI, a look at the history, applications, and dangers of AI with Wassim Ibrahim

Executive Summary

This webinar provides a comprehensive overview of the intersection of AI and data management from a data consultant's perspective. Wassim Ibrahim covers the impact and adoption of artificial intelligence and machine learning, the role of AI in business and human intelligence, and the concept of AI through conversation.

He delves into the nature and evolution of artificial intelligence, understanding supervised and unsupervised machine learning models, and applications of supervised machine learning such as decision tree algorithm and Nearest-Neighbour (NN).

Additionally, the webinar discusses machine learning applications in social network analysis, deep learning, ethical implications, and governance of AI, the role of statistics and research skills in AI development, and the impact and limitations of generative AI. And, it concludes with a focus on data biases, AI governance, risk management, and the challenges and implications of implementing generative AI in organizations.

A Data Consultant's Perspective: The Intersection of AI and Data Management

Wassim Ibrahim expresses gratitude for the opportunity to contribute to the community and briefly introduces himself as a data consultant with expertise in data management, governance, and analytics. Wassim shares his passion for data and AI, mentioning relevant certifications and experience in the field.

Impact and Adoption of Artificial Intelligence and Machine Learning

Various aspects of AI and machine learning will be discussed, covering topics such as its impact, introduction, deep learning, generative AI applications, ethics, and organizational implications. Wassim presents statistics and insights from reports by McKinsey, highlighting the significant economic impact of generative AI, the rapid adoption of ChatGPT, and the increasing use of AI across multiple industries, particularly in the Gulf region. He emphasizes the potential for AI to create value and its widespread adoption, while also acknowledging the importance of considering ethics and risks associated with AI implementation.

Role of AI in Business and Human Intelligence

During the presentation, a chart is shared depicting how organizations are dealing with AI, based on a study by MIT. Companies were categorized as passives, experimenters, investigators, and pioneers, with pioneers being the most invested in AI. Wassim emphasizes the importance of investing in technology and people for successful AI adoption and transformation. The audience was prompted to scan a code using their mobile devices and share their thoughts on what makes a human intelligent. Responses included reasoning, learning, interpreting data, humility, genes, emotional intelligence, and morality, highlighting the diverse perspectives on human intelligence.

Concept of Artificial Intelligence through Conversation

The prompts appear to be incomplete thoughts for a discussion on various topics. Additionally, they touch on ideas related to intelligence, AI, and personal preferences such as morning routines and living locations. Wassim guides the audience through a series of questions and responses, to provoke contemplation or engagement.

Understanding the Nature of Machine Intelligence

Wassim discusses the concept of machine intelligence and contrasts it with human intelligence. He emphasizes that experience and learning make humans intelligent and suggests that for machines, big data and machine learning are the key factors. Wassim also highlights the significant investment and data required for machines to achieve a level of intelligence comparable to human generative abilities. Additionally, he aims to provide appreciation for human intelligence while demystifying the current stage of artificial intelligence, suggesting that it should not be overrated.

An Overview of the Evolution of Artificial Intelligence

The history of AI dates back to the 1940s, with its conceptual journey beginning even earlier with mathematical and statistical algorithms. Notable milestones include Alan Turing's invention of a smart machine during World War 2, the introduction of chatbots in the 1990s, and the emergence of virtual assistants such as Siri, IBM Watson, Alexa, and Sophia. The recent boom in AI can be attributed to advancements in accompanying technologies such as ERP systems, the internet, CRMs, big data, and cloud computing, which have enabled the processing and leveraging of vast amounts of data. This historical context helps to understand the current significance of AI and its potential applications.

Understanding the Stage of Artificial Intelligence: Wide and General Intelligence

Currently, the field of artificial intelligence (AI) is primarily focused on narrow AI, which involves tasks such as classifying data, fraud detection, and spam detection. While significant progress has been made in this area, the development of general AI, which aims to mimic complete human cognition, is still in its early stages.

The concept of artificial general intelligence, representing the complete cognition of machines, remains a distant goal, and the scenarios depicted in science fiction movies are far from becoming a reality. Therefore, while narrow AI has seen remarkable advancements, general AI is still a long way from realization.

Understanding the Relationship between AI, Machine Learning, and Deep Learning

During a discussion about AI, machine learning, and deep learning, participants are asked about the relationship between these concepts. After collecting responses, It is concluded that AI is the overarching discipline, with machine learning being a subset of AI, and deep learning being a subset of machine learning. Some participants expressed uncertainty about the distinctions, highlighting the need for clarity in understanding these terms, particularly in industries with limited technology adoption.

Understanding the Nature and Evolution of Artificial Intelligence

Wassim provides an overview of AI and its related disciplines. He explains that AI encompasses machine learning and deep learning, with machine learning being the practical application of AI and deep learning involving more complex tasks using neural networks. Wassim also highlights the historical connection between statistics and AI, with an example of using statistical modelling to predict a son's height based on the father's height. Additionally, he emphasizes the growing demand for statistical skills and the wide-ranging applications of AI in fields such as medicine and healthcare.

Understanding Supervised and Unsupervised Machine Learning Models

Machine learning involves two main types of models: supervised and unsupervised learning. In supervised learning, the data is labelled, and the machine learns from this labelled data in order to make predictions or classifications when new data is introduced. On the other hand, unsupervised learning deals with unlabelled data, and the machine's task is to identify patterns within this data.

Naive Bayes is an example of a statistical methodology used in supervised learning, where labelled training data is employed to categorize or segregate information, such as sentiment analysis of tweets. This process enables the machine to recognize and classify sentiments in new, unlabelled data based on the patterns it has learned.

Applications of Supervised Machine Learning: Decision Tree Algorithm and K-Nearest-Neighbour

Supervised machine learning encompasses various algorithms such as decision trees, linear regression, and K-Nearest-Neighbour, all of which are utilized in different scenarios. Decision trees are used for prediction and classification, as illustrated by the famous Titanic survival prediction competition on Kaggle.

Linear regression is employed for making numerical predictions based on historical data, while logistic regression is suitable for binary decision-making tasks. Additionally, K-Nearest-Neighbour is used for classifying new items based on similarities with existing items. These algorithms offer diverse approaches to supervised machine learning, each with its own specific applications and benefits.

Understanding Data Analysis and Recommendation Systems in Amazon

Unsupervised learning involves analyzing data without the use of labelled information. For example, Amazon uses unsupervised learning to cluster customers based on purchasing habits, enabling targeted marketing. Association rules are utilized in unsupervised learning to identify relationships between variables, such as predicting which items customers are likely to buy together.

Dimensionality reduction helps find hidden features within existing features and combines them into one. Applications of unsupervised learning also include spam detection, where user feedback provides labelled data for the algorithm to link to various email attributes and automatically filter out spam. Additionally, unsupervised learning powers recommendation systems on platforms like Amazon, using clustering and association rules to suggest products based on user behaviour.

Machine Learning Applications in Social Network Analysis and Deep Learning

Machine learning has various applications, including social network analysis (SNA), as demonstrated by a real image of the SNA of the 9/11 terrorist attack. This image displays the connections among the investigated suspects, aiding investigators in identifying focal points and key suspects. Moving on from machine learning, the discussion transitions to deep learning, with a request for a brief pause before delving into further topics due to time constraints.

An Introduction to Artificial Neural Networks and Deep Learning Applications

Wassim explains the concept of artificial neural networks and their applications in deep learning. He uses the example of using a neural network to predict whether an interview should be offered to an applicant based on various criteria such as GPA, years at school, major, and extracurricular activities.

Wassim also discusses the human aspect of neural networks, the role of hidden layers, and the distinction between discriminative and generative deep learning applications. He emphasizes the potential of mathematical formulas, models, and computational power in generating complex solutions, and highlights examples of generative AI applications such as text analytics and ChatGPT.

Ethical Implications and Use of Image Recognition Technology

The image recognition tests, commonly known as CAPTCHA, is touched on as Wassim asks the audience what they think its purpose is. He expresses annoyance with the test and doubts whether self-driving vehicles are ready if training is still on-going by means of CAPTCHA. Wassim highlights the need for extensive data to train models effectively.

The Risks and Limitations of Machine Learning Models and the Dangers of Deep Fakes

It's important to remember that while machine learning models can be powerful, they are not infallible. George Box, a British Statistician, once said, "All models are wrong, but some are useful." The financial crisis, especially in the US, serves as a poignant example of how these models failed to predict risks.

Watching the movie "The Big Short" provides a compelling illustration of this. Additionally, Wassim notes emerging risks associated with generative AI in the form of generative adversarial networks (GANs), which are used to create deep fakes. These technologies have the potential to produce highly convincing fake content, such as news and events, making it crucial for individuals to be cautious and discerning in the face of these new developments.

Ethical Implications and Governance of AI

Wassim highlights the important ethical considerations regarding the use of AI. He emphasizes the need to carefully consider what tasks AI should replace and how it should be used in judgmental scenarios, particularly in relation to prior offenders.

Wassim also stresses the significance of addressing bias in data when dealing with people's information, as biased data can significantly impact algorithm results and compliance with regulations. Real examples of biased AI systems, such as the US facial recognition system and Amazon's job application solution, were cited to illustrate the potential consequences of inadequate training data. The session concluded with a focus on the governance of AI and its implications for organizations, offering the option to delve deeper into the topic if time permitted.

The Role of Data in AI Transformation and Governance

It's important to recognise that big tech companies like Google, Facebook, IBM, and Microsoft have a significant advantage in AI due to their access to vast amounts of training data. Before embarking on an AI transformation, organisations should prioritise a data-first approach, ensuring a clear vision, business strategy, and data strategy. Many organisations still have a long way to go in adopting AI, and focusing on trustworthy, well-managed, and high-quality data is crucial for a successful transformation.

Additionally, the rising need for AI governance is evident through regulations such as the Saudi data and AI authority's guidelines and the European Union's AI Act. Key aspects of a successful AI transformation include prioritising ethical data control and data management to ensure the quality and trustworthiness of the data being used.

The Evolution of Business Strategies: From Digital Transformation to Data Governance

The global focus has shifted from digital transformation to data, making it essential for all companies to foster a data culture. By creating a data strategy and embracing technology, including people and capabilities, businesses can guide themselves from their current state to their desired future, bridging the gap with use cases.

Data governance is crucial for AI governance, with 70-80% of AI governance being about data governance, emphasising the importance of managing, integrity, security, and privacy of data. The cycle of problem plan data and deployment is essential for governing AI, ensuring ethical implementation and minimising bias for better results.

Value and Potential of AI in Various Contexts

During a discussion, the concept of AI governance and its potential use cases is emphasised, drawing from Calibra's depiction of an AI cycle. The importance of transparency in AI models and the constant need for improvement and monitoring are highlighted.

The focus is on the true value of AI, which extends to sustainable cities and communities, quality education, and affordable water sanitisation. Reference was made to Bernard Maher's data strategy book for further insights. Additionally, the distinction between "narrow AI" and "shallow AI" was briefly addressed, emphasising the need for deeper learning enabled by big data.

Global Discussion on AI Governance and Safety

Several governments worldwide are taking noteworthy steps to ensure the safe integration of AI applications into governance practices. For instance, Singapore has established an AI governance framework and launched Project Moon Shot to test AI applications securely. Additionally, Saudi Arabia's SDAIA published guidelines for AI and is actively working on data privacy laws. The European region also has stringent data protection laws in place.

Ministries in various countries, such as the Ministry of Health, are enforcing testing and certification requirements for AI applications to prevent potential issues in deployment. Overall, governments are actively working to control the deployment of AI within various industries to ensure safety and compliance.

The Role of Statistics and Research Skills in AI Development

When discussing the skills needed for a career in AI, it's important to consider including research skills in IT education. Understanding the role of statistics is also crucial for working with data, as it helps identify bias and ensure the data is representative.

Detecting and managing bias within data is now possible with the help of tools and mechanisms. An accessible resource for learning statistics is the book "Statistics without Tears," which offers a smooth introduction to statistical concepts without heavy mathematical content.

The Role of Statistics and AI

During a discussion about statistics and AI, concerns are expressed about the widespread excitement over machine learning techniques, emphasising the importance of understanding the complex frameworks involved. Additionally, the potential for misleading results and disappointment among clients due to insufficient understanding of AI and machine learning is highlighted.

These concerns are echoed, emphasising the need for high standards and awareness among non-technical users. The discussion underscores the importance of promoting a thorough understanding of the costs and benefits of using machine learning and AI, emphasising the need for high standards and awareness among non-technical users.

The Impact and Limitations of Generative AI

Wassim emphasises the significant impact of AI on the way people work and the financial implications. He highlights the need for businesses to adopt data transformation and technology while acknowledging the importance of addressing the ethical and risk considerations associated with AI.

Wassim also mentions the potential for generative AI to assist in data management and policy interpretation, envisioning a transformation in how organisations handle data. Overall, a recognition of both the transformative potential and the need for responsible implementation of generative AI.

Data Biases, AI Governance, and Risk Management

Concerns about the lack of transparency and control in the use of AI are discussed, particularly regarding data biases and the difficulty of explaining decisions made by deep learning models.

The conversation also touches on the need for governance frameworks and regulatory oversight to ensure responsible and accountable use of AI technology. Wassim expresses the view that organisations and regulatory bodies are in the early stages of addressing these issues and emphasises the importance of transparency and comprehensive governance frameworks in guiding the use of AI.

Challenges and Implications of Implementing Generative AI in Organizations

Wassim emphasises the importance of organisations utilising AI and generative AI for better data management while acknowledging the challenges and risks associated with these technologies, particularly regarding security breaches and policy enforcement.

He also highlights the complexity of governing and controlling AI applications and suggests starting with simple use cases and pilot proof of concepts to understand the implications and potential of AI. The discussion also touches on the evolving landscape of AI regulations and the need for further dialogue and collaboration in this space.

huge thank you to Wassim Ibrahim for sharing this thought-provoking discussion with DAMA Southern Africa ! We greatly appreciate it!

Please comment on this article if you wish to receive a link of the recording.

#damasouthafrica #dama #artificialintelligence #ai #datasciece #machinelearning #dmbok