Dow Jones futures: Stocks pared weekly losses Friday. AI winners Super Micro and Nvidia are forging bases. Elon Musk teased a Tesla robotaxi.

Dow Jones Futures: Market Constructive As AI Winners Nvidia, Super Micro Forge New Bases | Investor's Business Daily

Nvidia is sharing its newfound wealth by investing in other artificial intelligence (AI) stocks.

Nvidia Just Bought 5 Artificial Intelligence (AI) Stocks, and 1 Is Up 142% Already @themotleyfool #stocks $SOUN $NVDA

The stock is up 100% this year after announcing a partnership with Nvidia.

Should You Buy Nvidia Favorite SoundHound AI Stock? @themotleyfool #stocks $SOUN $GOOGL $AAPL $HMC $AMZN $NVDA $GOOG

&quot;The higher we go this year, the more we&#x27;re going to fall next year,&quot; said Gil Luria, an analyst at DA Davidson.

'They're not going to keep buying this': 3 Wall Street analysts explain why Nvidia's best days are behind it

“I did a little crazy thing,” Nvidia cofounder Curtis Priem said. “And I wish I&#x27;d kept a little bit more.”

Meet the off-the-grid Nvidia cofounder | Fortune

Navima

Naval Warfare.—Mechanical Appliances.—Music

Now A days, React Native is one of the leading app development choice when it comes to cross platform apps development and at we love it like anything, but that is not enough for a Hard core developer to satisfy his desire of doing something out of this world, Isn’t it? usually now a days is the default choice when it comes to high quality UI/IX, We thought why not create something cool with the latest of Material Design and React Native and that lead us to this concept made to reality. Mindinventory Google’s Material Design To write code of this beautiful Tabbar was not that damn simple as it may seem at the first glance. We tried various paths to develop it and finally we did it which can work the same as designed. Let’s Look At The Development Story Of This Vision. As usual what coders does, we did the same to place this idea in an app, we searched various plugin which could provide us the expected results, but was all in vain and there was no plugin available which could work, well that was a blessing in disguise, as a result, we developed a plugin for this custom tab design which is open source right now and you can implement it in your designs in quick, easy and simple steps. The first way, we tried to do it with the React Native components, but it didn’t work as expected and the results were not exactly the same as designed. So, we came up with another technical idea to built it and let’s see what it was. Meanwhile you can also check the animation on and . Dribbble Behance We decide to work on the idea of creating a complete Tabbar using SVG components. And followings are the elements which we put together to build this Tabbar. Hollow Semi Circular Tabbar Circular Tabbar Item Tabbar Item Image Animation in the Tabbar and circular tab item while switching. So, the next challenge we did face was, React native doesn’t support SVG, so we need to get what could support the SVG and we found react-native-svg library, which was a point of emergence to get this technical idea work. We created an SVG for Circular Tabbar Item and Hollow Semi Circular Tabbar. import

Circle,

Path

} from 'react-native-svg';

<Svg version="1.1"

<AnimatedPath fill="#f0f0f0"

.3c17.2,0,31,14.4,30,31.6c0.2,2.7-0.3,5.5-0.3,8.2c0,71.2,58.1,129.6,129.4,130c72.1,0.3,130.6-58,130.6-130c0-2.7-0.1-5.4-0.2-8.1C${this.state.pathY}.7,74.5,${this.state.pathA}.5,60,${this.state.pathB}.7,60H1062c16.6,0,30,13.4,30,30v94c0,42-34,76-76,76H76c42,0-76-34-76-76V90C0,73.4,13.4,60,30,60z` }/>

<AnimatedCircle ref={ ref => this._myCircle = ref } fill="#f0f0f0"

</Svg> Next was the key point that make tab bar attractive i.e. animation while switching between two tabs. Well, where the default failed to work, we have to be the creator and have to create a customised one, as here the default animation didn’t work as transition animation fails for the Tabbar due to the hollow curvature and an animation along with the curvature in Tabbar we have to build custom SVG working with the dynamic path to support an animation and curvature. Following is the code responsible for curvature transition along with circular tab item. <AnimatedPath fill="#f0f0f0"

.3c17.2,0,31,14.4,30,31.6c-0.2,2.7-0.3,5.5-0.3,8.2c0,71.2,58.1,129.6,129.4,130c72.1,0.3,130.6-58,130.6-130c0-2.7-0.1-5.4-0.2-8.1C${this.state.pathY}.7,74.5,${this.state.pathA}.5,60,${this.state.pathB}.7,60H1062c16.6,0,30,13.4,30,30v94c0,42-34,76-76,76H76c-42,0-76-34-76-76V90C0,73.4,13.4,60,30,60z`}/> We created a circle as an animated component and the Tabbar as an animated path. const

const When user clicks on the tab to switch the animation works due to work of below written code. update(index) {

if(index == 0) {

this.setState ({

selectedIndex: index,

});

Animated.spring(this.state.pathD,{toValue:22,duration: 10, friction: 10

Animated.spring( this.state.circleRadius, { toValue: 211, friction: 10

}

else

this.setState({

selectedIndex: index,

});

Animated.spring(this.state.pathD,{toValue:691,duration:10,friction:10}).start();

Animated.spring( this.state.circleRadius, { toValue: 880, friction: 10

else

this.setState({selectedIndex: index,});

Animated.spring(this.state.pathD,{toValue:357,duration:10,friction:10}).start();

Animated.spring( this.state.circleRadius, { toValue: 546, friction: 10

}

} What makes an attractive look of the tab item and the Tabbar is done by styling it. TabBar.Item = TabBarItem;

const

container: {

flex: 1,

overflow: 'hidden',

},

content: {

flexDirection:"column",

zIndex: 0,

width: (Dimensions.get('window').width - 30),

marginBottom: '4%',

left: '4%',

right: '4%',

},

subContent: {

flexDirection: 'row',

marginLeft: 15,

marginRight: 15,

marginBottom: 10,

zIndex: 1,

position: 'absolute',

bottom: 5,

},

navItem: {

flex: 1,

paddingTop: 6,

paddingBottom: 6,

alignItems: 'center',

zIndex: 0,

},

navImage: {

width: 45,

height: 45,

},

circle: {

bottom: 18,

}

}); Setting the Tabbar default state at initial state. this.state = So, that was the code to developed in the plugin.You can use this plugin in your app and make it look great, let’s check how to use it. Implementation TabBar from "react-native-tab-bar-interaction";...render() { (<TabBar><TabBar.Itemicon={require('./tab1.png')}selectedIcon={require('./tab1_sel.png')}title="Tab1"screenBackgroundColor={{ backgroundColor: '#008080' }}><View> import return </View>  
    </TabBar.Item>  
    <TabBar.Item  
     icon={require('./tab2.png')}  
     selectedIcon={require('./tab2\_sel.png')}  
     title="Tab2"  
     screenBackgroundColor={{ backgroundColor: '#F08080' }}  
 >  
    <View>  

    </View>  
    </TabBar.Item>  
    <TabBar.Item  
     icon={require('./tab3.png')}  
     selectedIcon={require('./tab3\_sel.png')}  
     title="Tab3"  
     screenBackgroundColor={{ backgroundColor: '#485d72' }}  
 >  
     <View>  

     </View>  
     </TabBar.Item>  
     </TabBar>); } After installation done as per the guideline provided on , you need to import “react-native-tab-bar-interaction” to add the Tabbar in your app. Github import Add Tabbar Item: <TabBar.Itemicon={require('./tab1.png')}selectedIcon={require('./tab1_sel.png')}title="Tab1"screenBackgroundColor={{ backgroundColor: '#008080' }}> <View>/*tabbar body content here*/</View> * Your app must contain 3 tabs. 1. Add unselected icon in this line. icon={require('./tab1.png')} 2. Add selected icon in this line. selectedIcon={require('./tab1_sel.png')} 3. Add tab title in this line. title="Tab1”mind 4. Add Tab Screen Background color in this line. screenBackgroundColor={{ backgroundColor: '#008080' }} Check full source code of React Native Tabbar on . Github _Tabbar Component For React-Native. Contribute to Mindinventory/react-native-tabbar-interaction development by creating…_github.com Mindinventory/react-native-tabbar-interaction Originally published at www.mindinventory.com on August 29, 2018.

How We Created Tabbar Plugin with React Native

We see that multiple industries are working hard to accelerate the adoption of Artificial Intelligence, without having the proper security measures in place. It is important to know that AI is not some invincible new technology, but rather, a technology extremely vulnerable to cyber threats just like many others that came before it. The motivations for attacking AI are what you would expect. They range from financial gain to manipulating public opinion to gaining competitive advantage. While industries are gaining the benefit of increased efficiency and innovation thanks to AI, there is still the concerning reality that expanding the use of AI causes a significant increase in security risks. Risks Related to the Use of AI Like with any other life-changing technology, artificial intelligence is a double-edged sword. Although it’s already starting to have a massively positive impact on our lives and workflows, it also has tremendous potential to cause serious harm, especially if used carelessly or with overt malicious intent. There are plenty of ways in which adversaries - such as criminals, terrorists, cyber threat actors, foul-playing competitors, and repressive nation-states can utilize AI to their advantage. There are also numerous obscure risks related to the legitimate use of this technology. Privacy is also an issue when it comes to the information we share with AI-based tools. Data leakage can cause significant legal issues for businesses and institutions. In addition, because of code generation tools, vulnerabilities could be introduced into the software - intentionally, by poisoning the datasets, or unintentionally, by training the models on already vulnerable code. All this is on top of copyright violations and various ethical and societal concerns. Generative AI is especially vulnerable to abuse. It can be: a) manipulated to give biased, inaccurate, or harmful information b) used to create harmful content, such as malware, phishing, and propaganda c) used to develop deepfake images, audio and video d) leveraged by any malicious activity to provide access to dangerous or illegal information. There’s a lot of conversation about the safe and ethical use of AI-powered tools, however the security and safety of AI systems themselves are still often overlooked. It’s vital to remember that, like with any other ubiquitous technology, AI-based solutions can be abused by attackers, resulting in disruption, financial loss, reputational harm, or even risk to human health and life. There are, broadly, three types of attacks targeting AI: Adversarial machine learning attacks - Attacks against AI algorithms, aimed to alter AI’s behavior, evade AI-based detection, or steal the underlying technology.
Generative AI system attacks - Attacks against AI’s filters and restrictions, intended to generate content deemed harmful or illegal.
Supply chain attacks - Attacks against ML artifacts and platforms, with the intention of arbitrary code execution and delivery of traditional malware. Securing Your AI for Better Data Security Understanding and implementing extensive security measures for AI is no longer a choice. It’s a necessity. Too much is at risk for organizations, government, and society at large. Security must maintain pace with AI to allow innovation to flourish. That is why it is imperative to safeguard your most valuable assets, from development to operation and everything in between. Discovery and Asset Management First, identify where AI is already used in your organization. What applications has your organization already purchased that use AI or have AI-enabled features? Second, evaluate what AI may be under development by your organization. Third, understand what pretrained models from public repositories may already be in use. Risk Assessment and Threat Modelling First, conduct a benefit assessment to identify the potential negative consequences associated with the AI system if those models were to be compromised in any way. Second, perform threat modeling to understand the potential vulnerabilities and attack vectors that could be exploited by malicious actors to complete your understanding of your organization’s AI risk exposure. Data Security and Privacy Go beyond the typical implementation of encryption, access controls, and secure data storage practices to protect your AI model data. Evaluate and implement security solutions that are purpose-built to provide runtime protection for AI models. Model Robustness and Validation Regularly assess the robustness of AI models against adversarial attacks. This involves pen-testing the models’ response to various attacks such as intentionally manipulated inputs. Next, implement model validation techniques to ensure the AI system behaves predictably and reliably in real-world scenarios. This will help minimize the risk of unintended consequences. Continuous Monitoring and Incident Response Implement continuous monitoring mechanisms to detect anomalies and potential security incidents in real time for your AI. Require your vendors to utilize AI in their solutions to alert you to attacks that could compromise your data or business processes. Develop a robust AI incident response plan to quickly and effectively address security breaches or anomalies. Regularly test and update the incident response plan to adapt to evolving AI threats. Conclusion The security landscape as well as AI technology are dynamically and rapidly changing. It’s crucial to stay informed about emerging threats and best practices. Regularly update and refine your AI-specific security program to address new challenges and vulnerabilities. Responsible and ethical AI frameworks in many cases fall short of ensuring models are secure before they go into production, as well as after an AI system is in use. Always ask yourself the following questions 1) What am I doing to secure my organization’s use of AI? 2) Is it enough? 3) How do I know? Only by answering these questions with data-driven, intellectual honesty, can you maintain the integrity of your security role and keep your organization secure. We see that multiple industries are working hard to accelerate the adoption of Artificial Intelligence , without having the proper security measures in place. It is important to know that AI is not some invincible new technology, but rather, a technology extremely vulnerable to cyber threats just like many others that came before it. The motivations for attacking AI are what you would expect. They range from financial gain to manipulating public opinion to gaining competitive advantage. While industries are gaining the benefit of increased efficiency and innovation thanks to AI, there is still the concerning reality that expanding the use of AI causes a significant increase in security risks. Artificial Intelligence Risks Related to the Use of AI Like with any other life-changing technology, artificial intelligence is a double-edged sword. Although it’s already starting to have a massively positive impact on our lives and workflows, it also has tremendous potential to cause serious harm, especially if used carelessly or with overt malicious intent. There are plenty of ways in which adversaries - such as criminals, terrorists, cyber threat actors, foul-playing competitors, and repressive nation-states can utilize AI to their advantage. There are also numerous obscure risks related to the legitimate use of this technology. Privacy is also an issue when it comes to the information we share with AI-based tools. Data leakage can cause significant legal issues for businesses and institutions. In addition, because of code generation tools, vulnerabilities could be introduced into the software - intentionally, by poisoning the datasets, or unintentionally, by training the models on already vulnerable code. All this is on top of copyright violations and various ethical and societal concerns. Generative AI is especially vulnerable to abuse. Generative AI It can be: It can be: a) manipulated to give biased, inaccurate, or harmful information b) used to create harmful content, such as malware, phishing, and propaganda c) used to develop deepfake images, audio and video d) leveraged by any malicious activity to provide access to dangerous or illegal information. There’s a lot of conversation about the safe and ethical use of AI-powered tools, however the security and safety of AI systems themselves are still often overlooked. It’s vital to remember that, like with any other ubiquitous technology, AI-based solutions can be abused by attackers, resulting in disruption, financial loss, reputational harm, or even risk to human health and life. There are, broadly, three types of attacks targeting AI: There are, broadly, three types of attacks targeting AI: Adversarial machine learning attacks - Attacks against AI algorithms, aimed to alter AI’s behavior, evade AI-based detection, or steal the underlying technology. Generative AI system attacks - Attacks against AI’s filters and restrictions, intended to generate content deemed harmful or illegal. Supply chain attacks - Attacks against ML artifacts and platforms, with the intention of arbitrary code execution and delivery of traditional malware. Adversarial machine learning attacks - Attacks against AI algorithms, aimed to alter AI’s behavior, evade AI-based detection, or steal the underlying technology. Generative AI system attacks - Attacks against AI’s filters and restrictions, intended to generate content deemed harmful or illegal. Supply chain attacks - Attacks against ML artifacts and platforms, with the intention of arbitrary code execution and delivery of traditional malware. Securing Your AI for Better Data Security Understanding and implementing extensive security measures for AI is no longer a choice. It’s a necessity. Too much is at risk for organizations, government, and society at large. Security must maintain pace with AI to allow innovation to flourish. That is why it is imperative to safeguard your most valuable assets, from development to operation and everything in between. Discovery and Asset Management First, identify where AI is already used in your organization. What applications has your organization already purchased that use AI or have AI-enabled features? Second, evaluate what AI may be under development by your organization. Third, understand what pretrained models from public repositories may already be in use. Risk Assessment and Threat Modelling First, conduct a benefit assessment to identify the potential negative consequences associated with the AI system if those models were to be compromised in any way. Second, perform threat modeling to understand the potential vulnerabilities and attack vectors that could be exploited by malicious actors to complete your understanding of your organization’s AI risk exposure. Data Security and Privacy Go beyond the typical implementation of encryption, access controls, and secure data storage practices to protect your AI model data. Evaluate and implement security solutions that are purpose-built to provide runtime protection for AI models. Model Robustness and Validation Regularly assess the robustness of AI models against adversarial attacks. This involves pen-testing the models’ response to various attacks such as intentionally manipulated inputs. Next, implement model validation techniques to ensure the AI system behaves predictably and reliably in real-world scenarios. This will help minimize the risk of unintended consequences. Continuous Monitoring and Incident Response Implement continuous monitoring mechanisms to detect anomalies and potential security incidents in real time for your AI. Require your vendors to utilize AI in their solutions to alert you to attacks that could compromise your data or business processes. Develop a robust AI incident response plan to quickly and effectively address security breaches or anomalies. Regularly test and update the incident response plan to adapt to evolving AI threats. Conclusion The security landscape as well as AI technology are dynamically and rapidly changing. It’s crucial to stay informed about emerging threats and best practices. Regularly update and refine your AI-specific security program to address new challenges and vulnerabilities. Responsible and ethical AI frameworks in many cases fall short of ensuring models are secure before they go into production, as well as after an AI system is in use. Always ask yourself the following questions 1) What am I doing to secure my organization’s use of AI? 2) Is it enough? 3) How do I know? Only by answering these questions with data-driven, intellectual honesty, can you maintain the integrity of your security role and keep your organization secure.

Navigating Data Security Risks in the Age of Artificial Intelligence

Digital Stewardship: Navigating the Information Security Career Landscape Amid Rising Online Abuse

This paper is under CC 4.0 license. available on arxiv Authors: (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 5 THE UNDISCOVERED COUNTRY AI copilots will transform how we search. Tasks are central to people’s lives and more support is needed for complex tasks in search settings. Some limited support for these tasks already exists in search engines, but copilots will expand the task frontier to make more tasks actionable and address the “last mile” in search interaction: task completion [58]. Moving forward, search providers should invest in “better together” experiences that utilize copilots plus traditional search, make these joint experiences more seamless for searchers, and add more support for their use in practice, e.g., help people to quickly understand copilot capabilities and potential and/or recommend the best modality for the current task or task stage. This includes experiences where both modalities are offered separately and can be selected by searchers and those where there is unification and the selection happens automatically based on the query and the conversation context. The foundation models that power copilots have other search-related applications, e.g., for generating and applying intent taxonomies [43] or for evaluation [19]. We must retain a continued focus on human-AI cooperation, where searchers stay in control while the degree of system support increases as needed [44], and on AI safety. Searchers need to be able to trust copilots in general but also be able to verify their answers with minimal effort. Overall, the future is bright for IR, and AI research in general, with the advent of generative AI and the copilots that build upon it. Copilots will help augment and empower searchers in their information seeking journeys. Computer science researchers and practitioners should embrace this new era of assistive agents and engage across the full spectrum of exciting practical and scientific opportunities, both within information seeking as we focused on here, and onwards into other important domains such as personal productivity [5] and scientific discovery [22]. REFERENCES [1] Eugene Agichtein, Ryen W White, Susan T Dumais, and Paul N Bennet. 2012. Search, interrupted: understanding and predicting search task continuation. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 315-324. [2] Marcia J Bates. 1990. Where should the person stop and the information search interface start? Information Processing & Management 26, 5 (1990), 575–591. [3] Nicholas J Belkin. 1980. Anomalous states of knowledge as a basis for information retrieval. Canadian journal of information science 5, 1 (1980), 133–143. [4] Paul N Bennett, Ryen W White, Wei Chu, Susan T Dumais, Peter Bailey, Fedor Borisyuk, and Xiaoyuan Cui. 2012. Modeling the impact of short-and long-term behavior on search personalization. In Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 185–194. [5] Christian Bird, Denae Ford, Thomas Zimmermann, Nicole Forsgren, Eirini Kalliamvakou, Travis Lowdermilk, and Idan Gazit. 2022. Taking Flight with Copilot: Early insights and opportunities of AI-powered pair-programming tools. Queue 20, 6 (2022), 35–57. [6] Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al. 2021. On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258 (2021). [7] Lucas Bourtoule, Varun Chandrasekaran, Christopher A Choquette-Choo, Hengrui Jia, Adelin Travers, Baiwu Zhang, David Lie, and Nicolas Papernot. 2021. Machine unlearning. In 2021 IEEE Symposium on Security and Privacy (SP). IEEE, 141–159. [8] Andrei Broder. 2002. A taxonomy of web search. In ACM Sigir forum, Vol. 36. ACM New York, NY, USA, 3–10. [9] Andrei Z Broder and Preston McAfee. 2023. Delphic Costs and Benefits in Web Search: A utilitarian and historical analysis. arXiv preprint arXiv:2308.07525 (2023). [10] Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv preprint arXiv:2303.12712 (2023). [11] Katriina Byström and Kalervo Järvelin. 1995. Task complexity affects information seeking and use. Information processing & management 31, 2 (1995), 191–213. [12] Robert Capra and Jaime Arguello. 2023. How does AI chat change search behaviors? arXiv preprint arXiv:2307.03826 (2023). [13] Wei-Lin Chiang, Zhuohan Li, Zi Lin, Ying Sheng, Zhanghao Wu, Hao Zhang, Lianmin Zheng, Siyuan Zhuang, Yonghao Zhuang, Joseph E Gonzalez, et al. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https://vicuna. lmsys. org (accessed 14 April 2023) (2023). [14] Antonia Creswell and Murray Shanahan. 2022. Faithful reasoning using large language models. arXiv preprint arXiv:2208.14271 (2022). [15] Brenda Dervin. 1998. Sense-making theory and practice: An overview of user interests in knowledge seeking and use. Journal of knowledge management 2, 2 (1998), 36–46. [16] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018). [17] Karl Duncker and Lynne S Lees. 1945. On problem-solving. Psychological monographs 58, 5 (1945), i. [18] Brad Everman, Trevor Villwock, Dayuan Chen, Noe Soto, Oliver Zhang, and Ziliang Zong. 2023. Evaluating the Carbon Impact of Large Language Models at the Inference Stage. In 2023 IEEE International Performance, Computing, and Communications Conference (IPCCC). IEEE, 150–157. [19] Guglielmo Faggioli, Laura Dietz, Charles LA Clarke, Gianluca Demartini, Matthias Hagen, Claudia Hauff, Noriko Kando, Evangelos Kanoulas, Martin Potthast, Benno Stein, et al. 2023. Perspectives on Large Language Models for Relevance Judgment. In Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval. 39–50. [20] Jianfeng Gao, Chenyan Xiong, Paul Bennett, and Nick Craswell. 2023. Neural Approaches to Conversational Information Retrieval. Vol. 44. Springer Nature. [21] Ahmed Hassan Awadallah, Ryen W White, Patrick Pantel, Susan T Dumais, and Yi-Min Wang. 2014. Supporting complex search tasks. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management. 829–838. [22] Tom Hope, Doug Downey, Daniel S Weld, Oren Etzioni, and Eric Horvitz. 2023. A computational inflection for scientific discovery. Commun. ACM 66, 8 (2023), 62–73. [23] Peter Ingwersen and Kalervo Järvelin. 2005. The turn: Integration of information seeking and retrieval in context. Vol. 18. Springer Science & Business Media. [24] Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung. 2023. Survey of hallucination in natural language generation. Comput. Surveys 55, 12 (2023), 1–38. [25] Thorsten Joachims. 2002. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. 133–142. [26] Jeonghyun Kim. 2006. Task difficulty as a predictor and indicator of web searching interaction. In CHI’06 extended abstracts on human factors in computing systems. 959–964. [27] David R Krathwohl. 2002. A revision of Bloom’s taxonomy: An overview. Theory into practice 41, 4 (2002), 212–218. [28] Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–9474. [29] Yuelin Li and Nicholas J Belkin. 2008. A faceted approach to conceptualizing tasks in information seeking. Information processing & management 44, 6 (2008), 1822–1837. [30] Yuanchun Li and Oriana Riva. 2021. Glider: A reinforcement learning approach to extract UI scripts from websites. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1420–1430. [31] Paul Pu Liang, Chiyu Wu, Louis-Philippe Morency, and Ruslan Salakhutdinov. 2021. Towards understanding and mitigating social biases in language models. In International Conference on Machine Learning. PMLR, 6565–6576. [32] Gary Marchionini. 2006. Exploratory search: from finding to understanding. Commun. ACM 49, 4 (2006), 41–46. [33] James Mayfield, Eugene Yang, Dawn Lawrie, Samuel Barham, Orion Weller, Marc Mason, Suraj Nair, and Scott Miller. 2023. Synthetic Cross-language Information Retrieval Training Data. arXiv preprint arXiv:2305.00331 (2023). [34] Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, Sahaj Agarwal, Hamid Palangi, and Ahmed Awadallah. 2023. Orca: Progressive learning from complex explanation traces of gpt-4. arXiv preprint arXiv:2306.02707 (2023). [35] Marc Najork. 2023. Generative Information Retrieval. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1–1. [36] Alexandra Olteanu, Jean Garcia-Gathright, Maarten de Rijke, Michael D Ekstrand, Adam Roegiest, Aldo Lipani, Alex Beutel, Alexandra Olteanu, Ana Lucic, AnaAndreea Stoica, et al. 2021. FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. In ACM SIGIR Forum, Vol. 53. ACM New York, NY, USA, 20–43. [37] Soo Young Rieh, Kevyn Collins-Thompson, Preben Hansen, and Hye-Jung Lee. 2016. Towards searching as a learning process: A review of current perspectives and future directions. Journal of Information Science 42, 1 (2016), 19–34. [38] Shawon Sarkar and Chirag Shah. 2021. An integrated model of task, information needs, sources and uncertainty to design task-aware search systems. In Proceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval. 83–92. [39] Reijo Savolainen. 2012. Expectancy-value beliefs and information needs as motivators for task-based information seeking. Journal of Documentation 68, 4 (2012), 492–511. [40] Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom. 2023. Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761 (2023). [41] Chirag Shah. 2023. Generative AI and the Future of Information Access. In Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (Birmingham, United Kingdom) (CIKM ’23). Association for Computing Machinery, New York, NY, USA, 3. https://doi.org/10.1145/3583780.3615317 [42] Chirag Shah, Ryen White, Paul Thomas, Bhaskar Mitra, Shawon Sarkar, and Nicholas Belkin. 2023. Taking search to task. In Proceedings of the 2023 Conference on Human Information Interaction and Retrieval. 1–13. [43] Chirag Shah, Ryen W White, Reid Andersen, Georg Buscher, Scott Counts, Sarkar Snigdha Sarathi Das, Ali Montazer, Sathish Manivannan, Jennifer Neville, Xiaochuan Ni, et al. 2023. Using Large Language Models to Generate, Validate, and Apply User Intent Taxonomies. arXiv preprint arXiv:2309.13063 (2023). [44] Ben Shneiderman. 2022. Human-centered AI. Oxford University Press. [45] Adish Singla, Ryen White, and Jeff Huang. 2010. Studying trailfinding algorithms for enhanced web search. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval. 443–450. [46] Jaime Teevan, Kevyn Collins-Thompson, Ryen W White, and Susan Dumais. 2014. Slow search. Commun. ACM 57, 8 (2014), 36–38. [47] Jaime Teevan, Susan T Dumais, and Eric Horvitz. 2005. Personalizing search via automated analysis of interests and activities. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. 449–456. [48] Jaime Teevan, Meredith Ringel Morris, and Steve Bush. 2009. Discovering and using groups to improve personalized search. In Proceedings of the second acm international conference on web search and data mining. 15–24. [49] Maartje ter Hoeve, Robert Sim, Elnaz Nouri, Adam Fourney, Maarten de Rijke, and Ryen W White. 2020. Conversations with documents: An exploration of document-centered assistance. In Proceedings of the 2020 Conference on Human Information Interaction and Retrieval. 43–52. [50] Paul Thomas, Seth Spielman, Nick Craswell, and Bhaskar Mitra. 2023. Large language models can accurately predict searcher preferences. arXiv preprint arXiv:2309.10621 (2023). [51] Randall H Trigg. 1988. Guided tours and tabletops: Tools for communicating in a hypertext environment. ACM Transactions on Information Systems (TOIS) 6, 4 (1988), 398–414. [52] Sarah K Tyler and Jaime Teevan. 2010. Large scale query log analysis of re-finding. In Proceedings of the third ACM international conference on Web search and data mining. 191–200. [53] Pertti Vakkari. 2001. A theory of the task-based information retrieval process: A summary and generalisation of a longitudinal study. Journal of documentation 57, 1 (2001), 44–60. [54] Pertti Vakkari. 2016. Searching as learning: A systematization based on literature. Journal of Information Science 42, 1 (2016), 7–18. [55] Nicholas Vincent. 2022. The Paradox of Reuse, Language Models Edition.https://nmvg.mataroa.blog/blog/the-paradox-of-reuse-language-modelsedition/. Accessed: 2023-09-12. [56] Yu Wang, Xiao Huang, and Ryen W White. 2013. Characterizing and supporting cross-device search tasks. In Proceedings of the sixth ACM international conference on Web search and data mining. 707–716. [57] Ryen W White. 2016. Interactions with search systems. Cambridge University Press. [58] Ryen W White. 2018. Opportunities and challenges in search interaction. Commun. ACM 61,12 (2018), 36–38. [59] Ryen W White. 2018. Skill discovery in virtual assistants. Commun. ACM 61, 11 (2018), 106–113. [60] Ryen W White. 2022. Intelligent futures in task assistance. Commun. ACM 65, 11 (2022), 35–39. [61] Ryen W. White. 2023. Tasks, Copilots, and the Future of Search. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (Taipei, Taiwan) (SIGIR ’23). Association for Computing Machinery, New York, NY, USA, 5–6. https://doi.org/10.1145/3539618.3593069 [62] Ryen W White, Mikhail Bilenko, and Silviu Cucerzan. 2007. Studying the use of popular destinations to enhance web search interaction. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. 159–166. [63] Ryen W White, Wei Chu, Ahmed Hassan, Xiaodong He, Yang Song, and Hongning Wang. 2013. Enhancing personalized search by mining and modeling task behavior. In Proceedings of the 22nd international conference on World Wide Web. 1411–1420. [64] Ryen W White, Adam Fourney, Allen Herring, Paul N Bennett, Nirupama Chandrasekaran, Robert Sim, Elnaz Nouri, and Mark J Encarnación. 2019. Multi-device digital assistance. Commun. ACM 62, 10 (2019), 28–31. [65] Ryen W White, Ian Ruthven, and Joemon M Jose. 2005. A study of factors affecting the utility of implicit relevance feedback. In Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. 35–42. [66] Ryen W White, Ian Ruthven, Joemon M Jose, and CJ Van Rijsbergen. 2005. Evaluating implicit feedback models using searcher simulations. ACM Transactions on Information Systems (TOIS) 23, 3 (2005), 325–361. [67] Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang, and Chi Wang. 2023. AutoGen: Enabling nextgen LLM applications via multi-agent conversation framework. arXiv preprint arXiv:2308.08155 (2023). [68] Iris Xie. 2008. Interactive information retrieval in digital environments. IGI global. [69] Jinyun Yan, Wei Chu, and Ryen W White. 2014. Cohort modeling for enhanced personalized search. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. 505–514. [70] Da Yu, Saurabh Naik, Arturs Backurs, Sivakanth Gopi, Huseyin A Inan, Gautam Kamath, Janardhan Kulkarni, Yin Tat Lee, Andre Manoel, Lukas Wutschitz, et al. 2021. Differentially private fine-tuning of language models. arXiv preprint arXiv:2110.06500 (2021). [71] Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, and Gord Lueck. 2020. Generating clarifying questions for information retrieval. In Proceedings of the web conference 2020. 418–428. [72] Jieyu Zhang, Ranjay Krishna, Ahmed H Awadallah, and Chi Wang. 2023. EcoAssistant: Using LLM Assistant More Affordably and Accurately. arXiv preprint arXiv:2310.03046 (2023). [73] Yi Zhang, Sujay Kumar Jauhar, Julia Kiseleva, Ryen White, and Dan Roth. 2021. Learning to decompose and organize complex tasks. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2726–2735. [74] Wenhao Zhu, Hongyi Liu, Qingxiu Dong, Jingjing Xu, Lingpeng Kong, Jiajun Chen, Lei Li, and Shujian Huang. 2023. Multilingual machine translation with large language models: Empirical results and analysis. arXiv preprint arXiv:2304.04675 (2023). [75] Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving. 2019. Fine-tuning language models from human preferences. arXiv preprint arXiv:1909.08593 (2019).

Navigating Complex Search Tasks with AI Copilots: The Undiscovered Country and References

Navigating Complex Search Tasks with AI Copilots: Opportunities

This paper is under CC 4.0 license. available on arxiv : Authors (1) Ryen W. White, Microsoft Research, Redmond, WA, USA. Table of Links Abstract and Taking Search to task AI Copilots Challenges Opportunities The Undiscovered Country and References 3 CHALLENGES Despite the promise of copilots, there are significant challenges that should be acknowledged and we must find ways to overcome. Those include issues with the copilot output shown in response to searcher requests, the impacts that the copilots can have on searchers, and shifts in the degree of agency that humans have in the search process that result from the introduction of copilots. • : Searchers rely a lot on the answers from copilots,but those answers can be erroneous or non-sensical. So-called“hallucination” is a well-studied problem in foundation models [24]. Copilots can hallucinate for many reasons. One of the main reasons being gaps in the training data. RAG, discussed earlier, is a way to help address this by ensuring that the copilot has access to up-to-date, relevant information at inference time to help ground its responses. Injection of knowledge from other external sources, such as knowledge graphs and Wikipedia, can also help improve the accuracy of copilot responses. An issue related to copilots surfacing misinformation is toxicity (i.e., offensive or harmful content), which can also be present in the copilot output, and must be mitigated before answers are shown to searchers. Hallucinations • : Biases in the training data, e.g., social biases and stereotypes [31], affect the output of foundation models and hence the answers provided by copilots. Synthesis of content from different sources can amplify biases in this data. As with hallucinations, this is a well-studied problem [6]. Copilots are also subject to biases from learning from their own or other AI generated content (via feedback loops); biased historical sequences lead to biased downstream models. Copilots may also amplify existing cognitive biases, such as confirmation bias, by favoring responses that are aligned with searchers’ existing beliefs and values, and by providing responses that keep searchers engaged with the copilot, regardless of the ramifications for the searcher. Biases Learning may be affected/interrupted by the use of AI copilots since they remove the need for searchers to engage as fully with the search system and the information retrieved. Learning is already a core part of the search process [32, 37, 54]. Both exploratory search and search as learning involve considerable time and effort in finding and examining relevant content. While this could be viewed as a cost, this deep exposure to content also helps people learn. As mentioned earlier, copilot users can ask richer questions (allowing them to specify their tasks and goals more fully) but they then receive synthesized answers generated by the copilot, creating fewer, new, or simply different learning opportunities for humans that must be understood. • Human learning: Supporting search requires considering the degree of searcher involvement in the search process, which varies depending on the search task [2]. Copilots enable more strategic, • Human control: higher-order actions (higher up the “task tree” from Figure 1 than typical interactions with search systems). It is clear that searchers want control over the search process. They want to know what information is/not being included and why. This helps them understand and trust system output. As things stand, copilot users delegate full control of answer generation to the AI, but the rest is mixed, i.e., less control of search mechanics (queries, etc.) but more control of task specifications (via natural language and dialog). There is more than just a basic tension between automation and control. In reality, it is not a zero sum game. Designers of copilots need to ensure human control while increasing automation [44]. New frameworks for task completion are moving in this direction. For example, AutoGen [67], uses multiple specialized assistive AI copilots that engage with humans and with each other directly to help complete complex tasks, with humans staying informed and in control throughout. Overall, these are just a few of the challenges that affect the viability of copilots. There are other challenges, such as deeply ingrained search habits that may be a barrier to the adoption of new search functionality, despite the clear benefits to searchers.

Navigating Complex Search Tasks with AI Copilots: Challenges

Authors:
(1) Rui Cao, Singapore Management University;
(2) Ming Shan Hee, Singapore University of Design and Technology;
(3) Adriel Kuek, DSO National Laboratories;
(4) Wen-Haw Chong, Singapore Management University;
(5) Roy Ka-Wei Lee, Singapore University of Design and Technology
(6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Related Work Preliminary Proposed Method Experiment Conclusion and References Appendix 2 RELATED WORK Memes, typically intended to be humorous or sarcastic, are increasingly being exploited for the proliferation of hateful content, leading to the challenging task of online hateful meme detection [5, 12, 27]. To combat the spread of hateful memes, one line of work regards the hateful meme detection as a multimodal classification task. Researchers have applied pre-trained vision language models (PVLMs) and fine-tune them based on meme detection data [20, 26, 34, 37]. To improve performance, some have tried model ensembling [20, 26, 34]. Another line of work considers combining pre-trained models (e.g., BERT [4] and CLIP [29]) with task specific model architectures and tunes them end-to-end [13, 14, 28]. Recently, authors in [2] have tried converting all meme information into text and prompting language models to better leverage the contextual background knowledge present in language models. This approach achieves the state-of-the-art results on two hateful meme detection benchmarks. However, it adopts a generic method for describing the image through image captioning, often ignoring important factors necessary for hateful meme detection. In this work, we seek to address this issue through probe-based captioning by prompting pre-trained vision-language models with hateful content-centric questions in a zero-shot VQA manner. This paper is available on arxiv under CC 4.0 license. Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Authors: Authors: (1) Rui Cao, Singapore Management University; (2) Ming Shan Hee, Singapore University of Design and Technology; (3) Adriel Kuek, DSO National Laboratories; (4) Wen-Haw Chong, Singapore Management University; (5) Roy Ka-Wei Lee, Singapore University of Design and Technology (6) Jing Jiang, Singapore Management University. Table of Links Abstract and Introduction Abstract and Introduction Related Work Related Work Preliminary Preliminary Proposed Method Proposed Method Experiment Experiment Conclusion and References Conclusion and References Appendix Appendix 2 RELATED WORK Memes , typically intended to be humorous or sarcastic, are increasingly being exploited for the proliferation of hateful content, leading to the challenging task of online hateful meme detection [5, 12, 27]. To combat the spread of hateful memes, one line of work regards the hateful meme detection as a multimodal classification task. Researchers have applied pre-trained vision language models (PVLMs) and fine-tune them based on meme detection data [20, 26, 34, 37]. To improve performance, some have tried model ensembling [20, 26, 34]. Another line of work considers combining pre-trained models (e.g., BERT [4] and CLIP [29]) with task specific model architectures and tunes them end-to-end [13, 14, 28]. Recently, authors in [2] have tried converting all meme information into text and prompting language models to better leverage the contextual background knowledge present in language models. This approach achieves the state-of-the-art results on two hateful meme detection benchmarks. However, it adopts a generic method for describing the image through image captioning, often ignoring important factors necessary for hateful meme detection. In this work, we seek to address this issue through probe-based captioning by prompting pre-trained vision-language models with hateful content-centric questions in a zero-shot VQA manner. Memes This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

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