AI in Business - Impact and Possibility

As artificial intelligence (AI) continues to advance and become more integrated into our daily lives, it has become increasingly important to consider the ethical implications of this technology. AI has the potential to transform many industries and improve our lives in numerous ways, but it also raises important ethical questions. In this presentation, the ethical concerns surrounding AI are explored and discussed, with a focus on the need for ethical guidelines to be developed for AI development and use. We will examine issues such as privacy, bias, transparency, accountability, and the impact on jobs and society as a whole. Through this exploration, we will consider the various perspectives on these issues and weigh the benefits and drawbacks of different ethical approaches to AI. We will also examine some of the current efforts being made to address these concerns, including the development of ethical frameworks and best practices. The most important goal of this presentation is to disseminate a deeper understanding of the ethical considerations surrounding AI and the need for ethical guidelines to ensure that this technology is developed and used in a way that benefits all of us while respecting our values and principles.

1. AI and machine learning, especially deep learning, will transform many industries by enabling tasks that humans can currently do in under a second to be automated. 2. For companies to successfully adopt AI, they need clear strategies around data, talent, and how to differentiate themselves since algorithms and open-source code are commodities. 3. In retail specifically, computer vision and video analytics powered by AI can provide critical insights into customer behavior and in-store operations.

1) The UAE has launched an ambitious AI strategy to boost GDP by 30% and government performance by adopting AI technologies. 2) The strategy aims to save 50% on annual government costs and ensure 90% resistance to financial crises by 2031 through widespread AI use. 3) The UAE sees great potential in AI to automate services, enhance data analytics, and transform government work - but also faces challenges in developing expertise and managing new technical requirements.

“AI is the new electricity” proclaims Andrew Ng, co-founder of Google Brain. Just as we need to know how to safely harness electricity, we also need to know how to securely employ AI to power our businesses. In some scenarios, the security of AI systems can impact human safety. On the flip side, AI can also be misused by cyber-adversaries and so we need to understand how to counter them. This talk will provide food for thought in 3 areas: Security of AI systems Use of AI in cybersecurity Malicious use of AI

The document discusses the need for AI governance frameworks to build trust in AI systems. It outlines key pillars of governance including governing bodies, roles and responsibilities, and standard operating procedures. It also discusses the importance of model documentation, validation and certification. Effective governance requires a risk-based approach and measures to minimize bias and ensure fairness, transparency and human-centric AI. Governance spans the full model lifecycle from data collection and preprocessing to model deployment and use.

My presentation about the future of responsible generative AI, at the Ecosystems 2030 event in A Coruna, Spain

A journey into the business world of artificial intelligence. Explore at a high-level ongoing business experiments in creating new value. * Review AI as a priority for value generation * Explore ongoing experimentation * Touch on how businesses are monetising AI * Understand the intent of adoption by industries * Discuss on the state of customer trust in AI Part 1 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com

The document summarizes a presentation given by Prof. Dr. David Asirvatham on AI and future jobs. The presentation discusses how AI will impact various jobs and industries in the coming years and decades. It notes that many existing jobs will be automated or replaced by machines, but that AI will also create new types of jobs and work. The presentation emphasizes that acquiring new technological skills will be important for workers to adapt and ensure they are not left behind as AI disruption occurs. It concludes that AI will significantly change how people live and work, with humans needing to work together with machines.

Artificial intelligence has many applications including in software development, manufacturing, healthcare, space exploration, games, agriculture, automobiles, and finance. Some key uses of AI include taking over automated routines to reduce software development time, enabling predictive analytics and preventative maintenance in manufacturing, aiding diagnosis and performing robotic surgeries in healthcare, defeating world champions at chess and Go for games, enabling precision farming and predictive analytics for agriculture, assessing risk and detecting fraud in finance, and developing autonomous vehicles and self-driving capabilities for automobiles. AI is also used by NASA for autonomous target selection and planetary discovery and by ISRO for its AI-powered humanoid robot.

The document discusses the evolution of artificial intelligence (AI) and its impact on workplaces. It outlines three phases of AI development: artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. Currently, AI is in the early stages of artificial general intelligence where machine and human intelligence are becoming more equal. The document then provides examples of useful AI tools for content writing, designing, and content creation/work that are augmenting human capabilities in the workplace. It concludes by anticipating greater integration of AI assistants like GitHub Copilot and Microsoft 365 Copilot in the future.

This document provides guidance on integrating AI into organizations. It recommends aligning AI projects with business drivers to create value, focusing on scaling human capabilities with assisted and augmented intelligence, and taking a portfolio approach to AI innovation around customer needs. It also stresses the importance of developing an effective operating model with the right outcomes, team, tools, and iterative process, as well as understanding the range of AI and machine learning efforts.

Artificial intelligence and semantic computing can assist the financial services industry in several ways: - Machine learning and neural networks can analyze large amounts of data to detect patterns and make predictions about customer behavior, risks, and opportunities. This includes predictive analytics, risk analysis, and personalized recommendations. - Natural language processing allows customers to interact with services using human language across different channels. It also enables analysis of unstructured data like text to gain insights. - Semantic computing uses ontologies and semantic queries to understand relationships and context in data from various sources, helping to integrate information more easily. - Together these tools could help with tasks like marketing and pricing optimization, fraud detection, faster claims processing, and more personalized

Discover how AI and Deep Learning are being applied to financial applications in capital market banking, consumer banking, and the insurance industry.

This is presentation gives insight into the prospects and challenges of locally developed software in the finance sector of Nigeria

In an increasingly data-centric world, a company which fails to leverage the power of AI-powered business intelligence tools often lag behind. Learn from these slides how these tools are affecting businesses today and why should you choose them.

How do we protect privacy of users when building large-scale AI based systems? How do we develop machine learned models and systems taking fairness, accountability, and transparency into account? With the ongoing explosive growth of AI/ML models and systems, these are some of the ethical, legal, and technical challenges encountered by researchers and practitioners alike. In this talk, we will first motivate the need for adopting a "fairness and privacy by design" approach when developing AI/ML models and systems for different consumer and enterprise applications. We will then focus on the application of fairness-aware machine learning and privacy-preserving data mining techniques in practice, by presenting case studies spanning different LinkedIn applications (such as fairness-aware talent search ranking, privacy-preserving analytics, and LinkedIn Salary privacy & security design), and conclude with the key takeaways and open challenges.

This document discusses AI and machine learning applications in the financial industry. It outlines three use cases: 1) automated credit risk assessment using machine learning-based credit scores, 2) FX forecasting and hedging using cashflow forecasting, FX market prediction, and hedging optimization, and 3) extracting financial information from text using natural language processing and text analytics. The document argues that AI will be highly disruptive in finance, similarly to how electricity, the color TV, and the internet disrupted previous industries, and that financial firms should view AI not just as a disruption but as an opportunity.

Practical session reviewing the next evolution of robotic process automation (RPA) and the expanded value it can deliver supported by artificial intelligence (AI) Review business interest in advancing RPA with AI Explore the complementary strengths and weaknesses of RPA & AI Present the future of RPA in the form of Intelligent Automation powered by AI Discuss how your business can implement such capabilities Part 5 of a 9 Part Research Series named "What matters in AI" published on https://www.andremuscat.com