Ethical Artificial Intelligence
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This document discusses ethical issues related to artificial intelligence. It notes that nearly half of those polled oppose giving robots emotions or personalities. It also discusses using machine learning for credit scores, the lack of understanding of deep neural networks, reinforcement learning challenges like safe exploration and gaming reward functions. The document calls for ethically aligned design of AI through accountability, transparency, embedding human values, and allowing control over digital identities. However, it acknowledges that current guidelines are not possible given technology limitations.
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The document discusses several ethical issues related to artificial intelligence (AI) with reference to law. It discusses how AI systems can exhibit bias if the data used to train them is biased. It also discusses privacy concerns around how much personal data about individuals is collected. It provides steps that can be taken to address these issues, such as making sure data quality is good, using synthetic data, checking for bias, ensuring transparency and interpretability, including an ethical committee, rigorous testing, and keeping records. It discusses some examples of AI being used in the Indian legal system, like a machine translation tool for court documents and a research engine for judges. It concludes that while AI can help with administrative tasks, human judgment is still needed and more data
AI in Action: Real World Use Cases by Anitaraj
The presentation was made in “Web3 Fusion: Embracing AI and Beyond” is more than a conference; it's a journey into the heart of digital transformation.
The conference a provided a platform where the future of technology meets practical application. This three-day hybrid event, set in the heart of innovation, served as a gateway to the latest trends and transformative discussions in AI, Blockchain, IoT, AR/VR, and their collective impact on the information space.
AI-900 Slides.pptx
This document provides information about an AI certification course for the Microsoft Azure AI Fundamentals exam (AI-900). It outlines the intended audience, prerequisites, language, content included, exam details, and types of artificial intelligence. The course is intended for anyone interested in learning the basics of AI or clearing the AI-900 exam. It includes over 8 hours of video content, practice tests, quizzes and other study materials. Upon completion, students will receive a certificate and lifetime access to the course content.
Tru_Shiralkar_Gen AI Sec_ ISACA 2024.pdf
Generative AI's impact on creativity and productivity is undeniable. This presentation dives into real-world security and privacy risks, along with methods to address them. Can generative AI be used for cybersecurity? Let's explore!
AI basic.pptx
Artificial intelligence (AI) is a branch of computer science concerned with building intelligent machines that can perform tasks requiring human intelligence. AI is advancing rapidly through machine learning and deep learning techniques. Developers use AI to automate tasks and solve problems. AI systems can learn with or without human supervision. While strong AI that matches human intelligence does not yet exist, weak AI is used for applications like smart assistants, self-driving cars, and spam filters. The future of AI is uncertain but it has potential to transform many industries through automation and improved decision making. Challenges include the costs of development and potential job disruption.
AI.pptx
Artificial intelligence (AI) is a branch of computer science concerned with building intelligent machines that can perform tasks requiring human intelligence. AI is advancing rapidly through machine learning and deep learning techniques. Developers use AI to automate tasks and solve problems. AI systems can learn with or without human supervision. While strong AI that matches human intelligence does not yet exist, weak AI is used for applications like smart assistants, self-driving cars, and spam filters. The future of AI is uncertain but it has potential to transform many industries through automation and improved decision making. Challenges include the costs of development and potential job disruption.
What is artificial intelligence Definition, top 10 types and examples.pdf
What is artificial intelligence?
Although many definitions of artificial intelligence (AI) have emerged over the past few decades, John McCarthy provided the following definition in this 2004 paper (link is located outside ibm.com): MASU. Especially intelligent computer programs. It deals with the same task of using computers to understand human intelligence, but AI does not need to be limited to biologically observable methods.
Definition of artificial intelligence
Artificial intelligence is the imitation of human intelligence processes by machines, especially computer systems. Typical applications of AI include expert systems, natural language processing, speech recognition, and machine vision.
How does artificial intelligence (AI) work?
As the hype around AI grows, vendors are making efforts to promote how AI is used in their products and services. Often, what they call AI is just a component of technologies like machine learning. AI requires specialized hardware and software infrastructure to write and train machine learning algorithms. Although no single programming language is synonymous with AI, Python, R, Java, C++, and Julia have features that are popular among AI developers.
Generally, AI systems work by ingesting large amounts of labeled training data, analyzing correlations and patterns in the data, and using these patterns to predict future situations. This way, given examples of text, chatbots can learn to generate authentic-like conversations with people. Image recognition tools can also learn to recognize and describe objects in images by considering millions of examples. New and rapidly advancing generic AI technology allows you to create realistic text, images, music, and other media.
Artificial intelligence programming focuses on cognitive skills such as:
• Learn: This aspect of AI programming focuses on taking data and creating rules to turn it into actionable information. Rules, called algorithms, provide step-by-step instructions for computing devices to accomplish a particular task.
• Logic. This aspect of AI programming focuses on selecting the appropriate algorithm to achieve the desired result.
• Self-correction: This aspect of AI programming is designed to continuously improve the algorithms and provide the most accurate results possible.
• Creativity. This aspect of AI uses neural networks, rule-based systems, statistical methods, and other AI techniques to generate new images, new text, new music, and new ideas.
Differences between AI, machine learning and deep learning
AI, machine learning, and deep learning are common terms in enterprise IT, especially when companies use them interchangeably in marketing materials. But there are differences too. The term AI was coined in the 1950s and refers to the emulation of human intelligence by machines. A constantly changing set of capabilities is incorporated as new technologies are developed. Technologies falling under the umbrella of AI include machine learning and deep lea
ARTIFICIAL INTELLIGENCE.pptx
Artificial intelligence (AI) is the simulation of human intelligence by machines, especially computer systems. AI works by ingesting large amounts of labeled training data to analyze patterns and correlations in order to make predictions. The main types of AI are reactive machines (task-specific without memory), limited memory systems (can use past experiences), theory of mind systems (understand human emotions and intentions), and self-aware systems (have consciousness). AI is important because it can provide insights by analyzing large amounts of data faster than humans and in some cases perform tasks better. However, AI also has disadvantages such as being expensive, requiring expertise, and only knowing what it has been exposed to through data.
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More from Rudradeb Mitra
Architecting IoT with Machine Learning
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Successful adoption of Machine Learning
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Predictive Analytics to the rescue of IoT
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Predictive Analytics using Neural Networks
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Predictive analytics can be used to disrupt product development in two key ways:
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2. By analyzing attributes of users like driving behavior from a driver app and friends' networks, unsupervised neural networks can cluster users and infer new features for different groups, like incentives or gamification for improving driver safety. This was shown through a road trip tracking app case study.
3. The future of predictive analytics includes using self-organizing maps to predict bugs based on code dependencies and regions
Predictive Analytics
This document discusses predictive analytics and how it can be used to make important business predictions. It addresses why prediction is important, how predictions can be made using data, and what types of predictions are useful. Specifically, it notes that predictive analytics can help businesses understand customer equity, determine what customers are willing to pay, and inform marketing and sales decisions. Deep neural networks are presented as one technique for deriving insights from data to make predictions. Examples of using predictive analytics at Instacart and Zalando are also briefly described. The document concludes by emphasizing that predictive analytics is the future of business intelligence and businesses should focus on adding value for customers with their predictions.
Artificial Intelligence: Case studies (what can you build)
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Recently uploaded
Programming Foundation Models with DSPy - Meetup Slides
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
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For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
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Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
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Ethical Artificial Intelligence
- 2. Are you afraid of Judgement day?
- 3. Just under half of those polled opposed the idea of robots or programming being equipped with emotions or a personality
- 5. How it is relevant?
- 6. Baidu invested in ZestFinance - uses machine learning to transform complex data into credit scores. Risk increases if your friends also have similar habits.
- 7. Black-box components Software developers regularly use ‘black-box’ components, the functioning of which they often do not fully understand.
- 8. Nobody truly understands on a theoretical basis why deep neural networks provide good results
- 9. Constant Bag Of Words / Skip Gram
- 10. word2vec
- 11. ReInforcement learning - intuition. Trade prediction.
- 12. Concrete AI problems • Safe exploration - No catastrophic actions • “gaming” their reward functions • feedback is very expensive
- 13. Future AI systems may have the capacity to impact the world on the scale of the agricultural or industrial revolutions.
- 14. Ethically Aligned design • How can we assure that AI/AS are accountable? • How can we ensure that AI/AS are transparent? • How can we extend the benefits and minimize the risks of AI/AS technology being misused?
- 15. • Embed relevant human norms or values • Allows everyone access to tools to control over attributes that define his/her identity • AI systems should be designed so that they always are able to show the process which led to their actions (Government decision-making) Guidelines
- 16. None of the guidelines are possible now!
- 17. Source: CBS news
- 18. Thank You