Alec Coughlin’s Post

**Unpacking AI Algorithms: A Closer Look at the Mechanics Powering the Future** In today’s rapidly evolving digital landscape, artificial intelligence (AI) stands at the forefront of innovation. From healthcare to finance, AI-driven technologies are reshaping industries. However, beneath the surface of this revolution are complex algorithms — mathematical models that power AI systems. While the term "AI" is widely understood, the intricate workings of these algorithms often remain elusive. Understanding these mechanisms is critical for decision-makers and professionals aiming to leverage AI effectively. **Why AI Algorithms Matter** At the core of AI are algorithms that enable machines to perform tasks autonomously. These algorithms are what give AI the ability to "learn" from data, recognize patterns, draw conclusions, and even predict future trends. As industries become more reliant on AI, the need to grasp how AI achieves these outcomes grows. This knowledge is no longer reserved for data scientists alone. Executives, managers, and innovators alike must engage with an understanding of these processes to make informed decisions, ensure ethical applications, and optimize AI’s potential across various disciplines. **Essential AI Algorithm Models** While AI algorithms come in many shapes, the most important to be familiar with include: 1. **Supervised Learning Algorithms**: These require labeled data and help predict outcomes, useful for tasks such as fraud detection or sales forecasting. 2. **Unsupervised Learning Algorithms**: These discern hidden patterns or groupings within data that is unlabeled. 3. **Reinforcement Learning Algorithms**: These enable models to make decisions by interacting with environments and learning from feedback. 4. **Neural Networks and Deep Learning**: Mimicking the human brain's processing mechanisms, these are vital for complex problem-solving, especially within areas like image and speech recognition. Understanding these will not only allow professionals to gauge the potential impact of AI on their work but also highlight possible limitations or biases present within the models. **Why Professionals Should Stay Informed** As AI continues to evolve, professionals across all industries must be able to navigate its foundations. Mastering the basics of AI algorithms arms professionals with the insights necessary to ask the right questions, champion responsible AI use, and drive effective AI adoption that enhances business outcomes. AI is here to stay. So, whether you're a business leader, an entrepreneur, or simply an intrigued observer, now is the time to invest in understanding the mechanics behind the buzz. **Join the Conversation** What aspect of AI algorithms are you most curious about? Share your thoughts or experiences with AI’s impact on your industry in the comments below. Let's continue the discussion!

Generative AI is getting ahead of us in the game, we need to catch up and these are some great principles to educate our user base with

Exited to share my experience attending the 1 day workshop on AI tools with Be10x! I'm an enthusiast of new innovations in technology, so this workshop was a great way to go more deeply into the world of AI and explore those transformative tools that reshape industries across the world. This workshop let me explore a wide array of diversified AI tools and techniques, each of which finds an ever-increasing application in both business processes nowadays and personal productivity. The session focused on how AI is being adopted across various industries to enhance operations, optimize decision-making, and unlock new possibilities for growth. Here's a short overview of my key takeaways: AI Tools Applied to Solve Modern Challenges: This workshop introduced state-of-the-art AI tools for solving complex problems and performing tasks that, until recently, demanded much from human resources. Among the key areas we touched upon were NLP, computer vision, automation of business processes, and predictive analytics-each crucial in determining how companies make use of data and technology. Equally exciting was the understanding of how AI democratizes advanced technology. With easy user interfaces and smooth, seamless processes, even those little or uninitiated in coding can lever the capabilities of AI to resolve challenges in their respective organizations. This is the openness that allows small businesses, startups, or even nontechnical industries the full use of AI. Practical Use Cases and Applications: The fact that was the icing on the cake was actually practical applications being made with the use of AI tools. The Be10x platform talked about how companies from different sectors are already using AI to fine-tune workflows for a competitive advantage in practical ways. A few such practical use cases that continue to be at the forefront of operations include the following: Retailers are selling, with better customer experiences, using AI-powered recommendation engines. For manufacturing industries, AI integration has been done in predictive maintenance, which reduces downtime and enhances overall productivity. Being informed about such real-world applications provided further clarity on the immediate value AI can bring to organizations of any size in a variety of industries. Hands-on Experience with AI Tools: The most interesting thing about this workshop was the practical interaction with some of these AI tools. Be10x shed light on various tools, such as those which will give much-needed leverage to customer service and user Thank You Be10x: I want to extend my gratitude to Be10x for organizing this workshop and providing such a comprehensive learning experience. The workshop not only expanded my knowledge of AI tools but also inspired me to think about how I can leverage these tools to drive innovation and efficiency in my own work.

AI Index Report 2024: Stanford University's latest AI Index Report, offers a comprehensive overview of AI's evolving landscape. With a focus on technical advancements, impact on productivity, public perceptions, estimates on AI training costs, and responsible AI practices. There are 10 key trends identified in the report. Below is a summary of my top 5 based around people, cost and standards. The document is a treasure trove of stats and insights. Its meaty, but worth a read if you want to delve deeper on specific areas that resonate. People 1) AI beats humans on some tasks, but not on all: While AI surpasses humans in certain tasks like image classification and language understanding, it still lags behind in complex areas such as high-level mathematics and nuanced reasoning. Understanding the nuanced strengths and limitations of AI is crucial for leveraging its potential while acknowledging the irreplaceable role of human intuition in certain domains. 2) AI's impact on productivity and work quality: Studies indicate that integrating AI into workflows enhances productivity and improves the quality of work. Other studies caution without proper oversight, there's a risk of diminished performance or unintended consequences. Finding the right balance between innovation and caution is critical for maximising the benefits of AI. 3) Growing awareness and concerns: Surveys show a heightened awareness of AI's potential impact globally, accompanied by increased nervousness towards AI products and services, with a majority of Americans expressing more concern than excitement about AI. Cost 4) Soaring costs of cutting-edge AI: The training costs for state-of-the-art, foundational AI models have reached unprecedented levels, e.g. OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute. This highlights the substantial investment needed for AI advancement. In addition, we are seeing Industry outpace academia in AI research, with industry producing 51 notable machine learning models in 2023 compared to academia's 15. Collaborations between industry and academia are on the rise, driving innovation and pushing AI's boundaries. Interestingly, reviewed at the business level there is growing evidence of AI reducing cost and increasing revenue. A McKinsey survey reveals that 42% of surveyed organisations report cost reductions from implementing AI (including generative AI), and 59% report revenue increases.   Standards 5) Lack of standardisation in responsible AI reporting: Current research underscores the lack of standardised evaluations for responsible AI practices. Leading developers are testing their models against different responsible AI benchmarks, complicating risk assessments across different models. Addressing this gap is crucial for ensuring AI's ethical and responsible deployment. #AI #ArtificialIntelligence #TechnologyTrends #InnovationManagement #AIProductivity

How Does KAN  (Kolmogorov–Arnold Networks) Act As A Better Substitute For Multi-Layer Perceptrons (MLPs)? https://lnkd.in/dJeRgH8Z The Advantages of Kolmogorov–Arnold Networks (KAN) Over Multi-Layer Perceptrons (MLP) Introduction Kolmogorov–Arnold Networks (KANs) offer practical solutions in AI by acting as a better substitute for Multi-Layer Perceptrons (MLPs) due to their enhanced accuracy, faster scaling qualities, and increased interpretability. The KAN architecture overcomes the limitations present in traditional MLPs, making it a valuable innovation in deep learning. Key Features and Benefits of KANs KANs, inspired by the Kolmogorov–Arnold representation theorem, utilize learnable activation functions to replace conventional fixed activations, leading to improved accuracy and faster scaling qualities. Their interpretability enhances collaboration between the model and human users, thus providing better insights. Additionally, KANs demonstrate better accuracy in tasks such as partial differential equation (PDE) solving, making them more efficient in producing smaller computation graphs. Applications and Implications Through examples from physics and mathematics, KANs have proven to be valuable tools for scientists in rediscovering and understanding complex mathematical and physical laws, thereby contributing to scientific inquiry. By leveraging KANs, deep learning models can enhance the understanding of underlying data representations and model behaviors, ultimately leading to innovative breakthroughs in various fields. AI Integration and Practical Solutions Companies can benefit from AI by leveraging KANs to redefine their way of work. Utilizing AI solutions such as the AI Sales Bot from itinai.com/aisalesbot can automate customer engagement 24/7 and effectively manage interactions across all customer journey stages. Furthermore, KANs can help identify automation opportunities, define KPIs, select appropriate AI tools, and implement AI solutions gradually to ensure measurable impacts on business outcomes. Conclusion The practical and innovative potential of KANs as a substitute for MLPs opens up new possibilities for deep learning innovation. By addressing the constraints of traditional MLPs, KANs offer enhanced accuracy, faster scaling qualities, and increased interpretability, marking a significant advancement in AI solutions. Check out the Paper. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. Join our Telegram Channel, Discord Channel, and LinkedIn Group. If you like our work, you will love our newsletter. Don’t Forget to join our 41k+ ML SubReddit Don’t Forget to join our If you want to evolve your company with AI, stay competitive, use for your advantage How Does KAN (Kolmogorov–Arnold Networks) Act As A Better Substitute For Multi-Layer ...

How to Use Explainable AI Tools AI Pitfalls Digest Deep Dive into Feature Importance, Partial Dependence Plot, and Sub-population Analysis Photo by Artem Sapegin on Unsplash The AI community has introduced various concepts and tools to interpret AI model outcomes, including feature importance, partial dependence plots, and sub-population analysis. The Explainable AI (XAI) tools are crucial in building trust among end-users and regulators, identifying and mitigating bias, and improving overall model performance. They are built to answer the main question of all users: “Why did the model make a specific prediction for an instance or a group of instances?” While the XAI tools are invaluable in identifying bias and building trust, they are highly susceptible to misuse. Welcome to the SHAP documentation - SHAP latest documentation For instance, most feature importance methods assume that features are independent. As a result, including highly correlated features in the analysis can lead to unreliable outcomes. Moreover, different approaches for calculating the global importance of features, such as using the “mean absolute value” or the “max absolute value,” can lead to inconsistent results. Before reading this article, you should be familiar with feature importance, partial dependence plot, and sub-population analysis; otherwise, you may not benefit from this article. If you’re already familiar with these concepts, this article will introduce you to new scenarios you may not have encountered. These lessons are based on my experience delivering solutions to enterprise clients in the past few years. There are few reliable resources for Explainable AI on the web; the following book is the best resource that I found. Interpretable Machine Learning — How to use Feature Importance? Feature importance refers to a family of techniques used to determine the significance of individual features in contributing to the predictions made by a machine learning model. Imagine you’re trying to predict something, like the price of a house using features like the number of bedrooms, location, and size. Feature importance tells you which of these factors has the most impact on the house price. There are two different categories of feature importance tools: model-specific and model-agnostic. Model-specific tools are limited to specific model types, such as coefficient-based and tree-based models. For instance, the magnitude of the coefficients in linear models can be used as an indicator of feature importance. Larger coefficients suggest greater importance. The Gini impurity is a specific measure in the tree-based models that shows the importance of a feature. Model-agnostic tools include many techniques, such as Shapley values, LIME, and the widely used Permutation Feature Importance (PFI). Shapley values and LIME are local methods that are suitable for explaining individual predictions, while PFI is a global method that is used to assess the impo...

Unlocking the Potential of Open-Source Generative AI: Key Insights and Implications ... In a recent research paper, "Risks and Opportunities of Open-Source Generative AI," a diverse group of researchers delve into the transformative potential of open-source generative AI models. The paper argues that the benefits of these models outweigh the risks, promoting innovation, transparency, and accessibility in the field of AI. 👉 The Importance of Open-Source Generative AI The researchers highlight the crucial role of open-source AI models in driving innovation and democratizing access to cutting-edge technologies. By enabling a broader range of researchers and developers to contribute to and improve AI systems, open-source models foster diverse and inclusive technological advancements. This has far-reaching implications for various sectors, including education, healthcare, and the environment. 👉 A Three-Stage Framework for AI Development To better understand the evolution of AI capabilities and the associated risks and opportunities, the paper introduces a three-stage framework: 1. Near-term 2. Mid-term 3. Long-term This framework serves as a valuable tool for policymakers and developers to create informed strategies for AI governance and development, ensuring that the technology progresses in a responsible and beneficial manner. 👉 Risks and Opportunities in the Near to Mid-Term The paper provides a comprehensive analysis of the risks and opportunities of open-source generative AI in the near to mid-term, categorizing the impacts into four key areas: - Research, Innovation and Development - Safety and Security - Equity, Access and Usability - Broader Societal Aspects By understanding these impacts, stakeholders can effectively mitigate risks while maximizing the benefits of generative AI technologies. The researchers offer practical insights and recommendations for navigating this complex landscape. 👉 Long-Term Impacts and the Concept of AGI Looking ahead, the paper explores the potential long-term impacts of achieving Artificial General Intelligence (AGI) and the role of open-source models in this context. While acknowledging the speculative nature of AGI, the researchers emphasize the importance of technical alignment to ensure that AGI systems behave in ways that align with human values. Open-source AGI could democratize AI development, ensuring that powerful AI systems are developed transparently and ethically. 👉 Policy Recommendations and Best Practices To manage the risks associated with open-source generative AI, the paper provides actionable recommendations for policymakers and developers. These include: - Promoting training transparency - Conducting robust safety evaluations - Adopting responsible development practices By implementing these recommendations, we can create a regulatory environment that supports innovation while safeguarding against misuse and harm.

A good starting point to build a responsible and effective AI Policy for your organization.

For all of my fellow data protection/privacy folks practicing in real worlds-ville: Once again, you’re simply not going to find it a better purveyor of “cheat sheets” (and an excellent candidate for the next James Bond or Kingsman) then Oliver Patel, AIGP, CIPP/E So how do you practically use this? Well, I would suggest that you print this sheet out and have it with you the next time you meet either with your AI governance board, or people that you know are involved in AI governance. It’s important to have this, as obviously, there will be plenty of overlap between AI and personal data. You could effectively use this as a checklist of items to help flesh out, policies, procedures, etc. Also, let me point out that the most important word in the cheat sheet he has compiled is the word “before“. Addressing these items “before“, will help mitigate a lot of possible headaches “after“.

Generative AI Policy Principles Cheat Sheet Every company needs robust, actionable and accessible generative AI policies. Today, generative AI is at the fingertips of all employees. Whilst this is leading to leaps forward for efficiency, productivity and innovation, it also poses significant risks to organisations. In my latest AI Cheat Sheet, I provide a simplified breakdown of the 20 Key Policy Principles for Generative AI Use. You can use this Cheat Sheet as a guide when crafting your own internal generative AI usage policies. A recent survey by Littler found that less than half of corporates have developed generative AI use policies, so there remains lots of work to do. Below, to accompany my Cheat Sheet, I provide a non-exhaustive snapshot of generative AI use policies, which have been published by prominent organisations. These policies are primarily from organisations in government, education, research and the media. These industries are at the forefront of grappling with the ethical and legal challenges posed by generative AI. Good luck to all! Snapshot of Generative AI Use Policies Future of Privacy Forum ✅ Generative AI for Organisational Use: Internal Policy Considerations 🔗 https://lnkd.in/e5QjruYX UK Government ✅ Generative AI framework for HM Government 🔗 https://lnkd.in/e5BcPh8s UK Government Communication Service ✅ Generative AI policy 🔗 https://lnkd.in/evifib2Y. Harvard University ✅ Internal guidelines for the use of generative AI tools 🔗 https://lnkd.in/eJ3TcQpH UKRI ✅ Use of generative AI in applications 🔗 https://lnkd.in/e4TCPzEn Russell Group Universities ✅ Principles on the use of generative AI tools in education 🔗 https://lnkd.in/eTUJUrEz Capita ✅ Generative AI Policy 🔗 https://lnkd.in/eduvBjHH Sage ✅ AI policy 🔗 https://lnkd.in/ei-U_52D Columbia University ✅ Generative AI Policy 🔗 https://lnkd.in/eTTNScrT Government of Canada ✅ Guide on the use of generative AI 🔗 https://lnkd.in/e_2gAkRH University of Oxford ✅ Use of generative AI tools to support learning 🔗 https://lnkd.in/eeEktTFJ University of Cambridge ✅ The use of generative AI in coursework 🔗 https://lnkd.in/evrsD-gz BBC ✅ Guidance: The use of AI 🔗 https://lnkd.in/eCz9zpes Derbyshire County Council ✅ AI Policy 🔗 https://lnkd.in/eibcBBZH European Commission ✅ Guidelines on the responsible use of generative AI in research 🔗 https://lnkd.in/e4F8Nfvx