Cable Services Company Casa Sells Cloud Unit to Fund Bankruptcy  Bloomberg

On Friday, the cryptocurrency trading platform crypto.com unveiled a new advertisement set to debut at the forthcoming Lakers NBA playoff game in Los Angeles. Additionally, the ad was highlighted on the social media site X by the hip-hop icon Marshall Mathers, also known as Eminem. Marshall Mathers Amplifies Crypto.com Ad Ahead of NBA Playoff Showcase […]

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This appendix provides in-depth details on implementing hateful meme detection models, insights from ablation studies, visual comparisons of Pro-Cap and basic PromptHate, and results highlighting the impact of using answers from single probing questions, suggesting optimization directions for meme detection models.

This study explores using pre-trained vision-language models (PVLMs) for hateful meme detection without fine-tuning, showcasing BERT's superiority and introducing PromptHate with probe-captioning. Limitations include heuristic probing question usage, suggesting future directions for optimization and deeper interpretation using gradient-based approaches.

Pro-Cap introduces a novel approach to hateful meme detection by utilizing frozen Vision-Language Models (PVLMs) through probing-based captioning, enhancing computational efficiency and caption quality for accurate detection of hateful content in memes.

We thoroughly evaluate and analyze the probing-based Pro-Cap method for hateful meme detection. Discover its performance on different datasets, understand its impact through an ablation study and baseline comparison, and gain insights into its effectiveness and stability in detecting hateful content.

Our method uses a probing-based approach to gather critical image details essential for detecting hateful content in memes. We design VQA questions for this purpose, generate Pro-Cap as image captions, and integrate them with BERT-based and PromptHate models for comparison in hateful meme detection effectiveness.

This section defines the task of zero-shot visual question answering (VQA) and explores the use of pre-trained vision-language models (PVLMs) like BLIP-2, highlighting its Querying Transformer component for bridging the modality gap in cross-modal understanding.

Explore various methodologies in hateful meme detection, including fine-tuning PVLMs, model ensembling, and leveraging pre-trained models like BERT and CLIP. Learn how the probing-based captioning approach enhances contextual understanding for improved detection of hateful content in memes.

:::info This paper is available on arxiv under CC 4.0 license. 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 2 AI COPILOTS Copilots[6] are applications of modern AI (foundation models, etc.) to help people with complex cognitive tasks. Copilots have conversational user interfaces and their users engage with them via natural language, they are powered by foundation models such as GPT-4, they are extensible with skills/tools/plugins, and they are scoped to specialized domains or applications (including search). Copilots are designed to to keep humans at the center of the task completion process and augment human capabilities to help them complete a broader range of tasks in less time. \ The Microsoft copilot stack (Figure 2) contains four main layers: (1) The copilot frontend, covering the user experience and extensibility with plugins, enabling developers to provide additional visible tools to the copilot; (2) The orchestration layer that handles the internal information flows, prompting, grounding, executing the plugins and processing their responses, among other things; (3) Copilots leverage the power of large foundation models that can be provided to the developer as is, specialized to specific tasks, domains, or applications, or developers can bring their own models to use to power copilot functionality, and; (4) This all runs on top of massive scale AI infrastructure hosted in the cloud on Azure. Underpinning all of this is Microsoft’s strong commitment to responsible AI, which ensures that copilots are safe, secure, and transparent, via an iterative, layered approach with mitigations spanning the model, prompts, grounding, and the user experience. \ Examples of copilots include GitHub Copilot,[7] an AI pair programmer that has been shown to reduce developer effort, enable more task success, and significantly expedite task completion [5]. Copilots are also emerging in search. Popular Web search engines such as Bing and Google are adding copilot functionalities to their search engines in the form of conversational agents such as Bing Chat and Google BARD. In search, copilots can help searchers to tackle a broader range of tasks than information finding and go deeper than surface (SERP-level) interactions with content by synthesizing answers on the searcher’s behalf. They also enable searchers to communicate their intents and goals more directly. Returning to the task tree (Figure 1), the focus on engaging copilots via natural language interactions allows both searchers and systems to consider higher-level task representations (macrotasks, subtasks) in addition to the more granular actions (queries, result clicks, pagination, and so on) that searchers already perform when engaging with traditional search engines. \ 2.1 Copilots in Search Copilots and chat experiences are a complement not a replacement to traditional search engines. Search engines have been around for decades and serve a valuable purpose: providing near instantaneous access to answers and resources for a broad range of search requests. These existing and emerging modalities can and should work well together to help searchers tackle a wider range of tasks. The capabilities of copilots to better understand intentions and provide assistance beyond fact finding and basic learning/investigation will expand the task frontier, broadening the range of complex search tasks that can be completed, e.g., direct support for tasks requiring creative inspiration (Figure 3). This all moves us a step closer toward intelligent search systems that can help with all-task completion, covering the full universe of tasks for which people might need search support. One way to define the range of tasks that copilots can support is though Bloom’s taxonomy of learning objectives [27]. Creation is at the pinnacle of that taxonomy and we have only scratched the surface in creativity support with next-word prediction through transformer models [16]. We are already seeing expansions into modalities beyond text (images, video, audio, and so on) and could consider support for other creative tasks including planning, analysis, and invention. There are also many other layers in Bloom’s taxonomy (e.g., evaluation - help searchers make judgments and decisions, application - help searchers complete new tasks, understanding - explain ideas and concepts to accelerate learning) that could form future task frontiers. \ Beyond greater capabilities, the introduction of copilots into search will also change how people will engage with search systems. In copilots, the mode of interaction is primarily natural language input, with some recent expansion toward multimodal inputs and outputs via the introduction of diffusion models such as DALL·E. Copilots can generate/synthesize direct answers, with source attribution for provenance, to build trust with users, and to drive traffic back to content creators, which is important to incentivize further content creation that fuels future foundation models. The overall search interaction flow is also different between search engines and copilots. When using copilots, searchers do not need to decompose their goal into sub-goals or sub-queries, examine SERPs and landing pages, and aggregate/synthesize relevant knowledge from retrieved information. Figure 4 has a comparison of the information seeking processes in the two modalities. In copilots, the responsibility for generating answers is delegated by the searcher to the system, which is not without its challenges in terms of human agency and human learning, as we discuss later in this article. \ 2.2 Adding Copilots to Search Engines It is not practical nor necessary to deploy copilots for all search tasks. Foundation model inference is expensive at massive scale and search engine algorithms have been honed over decades to provide relevant results for a broad range of tasks (e.g., navigation, factfinding). Conversational interfaces are less familiar for searchers and it will take time for searchers to adapt to this way of searching. Traditional search engines are sufficient when searchers know exactly what they want. Copilots are helpful for more complex search tasks or in situations where searchers may be struggling to find relevant information. Task complexity can be estimated using aggregate metrics such as the amount of engagement with the search engine (e.g., number of query reformulations) for similar tasks historically. As generative AI appears in more applications and searchers better understand search copilot capabilities, the tasks that searchers bring to search copilots will likely evolve. \ \ We will also see a growth in search experiences that unify traditional search and copilots. In a step towards this, search engines such as Bing and Google are already integrating dynamic answers from foundation models into their SERPs for some informational queries. Also, in the Google copilot (BARD), search results are displayed immediately below the copilot responses, allowing searchers to easily engage with them as desired. In the Bing copilot (Bing Chat), the search and copilot experiences are more separated and searchers must select a specific modality based on their task and personal preferences. Bing also provides searchers with control over other aspects, such as conversation style and tone, although it is not clear that searchers are sufficiently familiar with AI copilots at this time to use these nuanced controls effectively. \ Search copilots such as Bing Chat use retrieval augmented generation (RAG) [28] to ground copilot responses via timely and relevant results. This has many advantages, including: (1) There is no need to retrain the massive foundation model over time; (2) Search results provide relevant and fresh information to foundation models, and; (3) It provides a provenance signal linking generated content with online sources. In response to a searcher prompt, the foundation model generates internal queries iteratively that are used to retrieve the results that form context for the copilot answers created using generative AI. Displaying these queries to searchers inline in the dialog (as in Bing Chat) creates greater transparency and helps build trust with searchers that the system is understanding their tasks and goals. The Bing orchestrator can also pull in relevant instant answers from the search engine such as weather, stock, sports, and so on, and display those in copilot responses instead of or in addition to the answers generated by the foundation model. Figure 5 shows the high-level search process from query (+ conversation context) to the answer, and the role of various key system components. \ Copilots also enable search engines to support more complex search tasks. Using search alone would require more searcher effort to examine search results and manually generate answers or insights (see recent work on the Delphic costs and benefits of search [9]). Of course, there are different perspectives on task complexity, e.g., the copilot perspective (denoting the amount of computation, requests, etc. required for the system to complete the task) and the searcher perspective (denoting the amount of manual effort required for the human searcher to generate an answer and complete a task). Table 1 considers the task complexity from these two different perspectives and (again drawing from Bloom’s taxonomy) provides some current, anecdotal examples of the types of tasks that both searcher and systems may find to be more or less complex. Assuming that foundational model costs will drop and sophistication will increase, we focus here on the task complexity for searchers. \ \ \ [6] Copilots is the Microsoft terminology for an AI-powered digital assistant designed to work alongside humans to augment their cognitive capabilities. \ [7] https://github.com/features/copilot

Programmable cryptography is still a new concept, but one that offers the chance to make very complicated problems much simpler.

Unveil the dynamic governance model of the dYdX Chain and how it empowers community involvement in shaping the future of decentralized finance.

PIECHAIN introduces a Kafka-based framework for blockchain interoperability, ensuring atomic cross-chain transactions and enabling practical applications like cross-chain auctions, with support for Ethereum, Hyperledger Fabric, and Quorum blockchains.

PostgreSQL's ascendancy as the go-to database standard is rooted in its adaptability, reliability, and extensive ecosystem. This article delves into the reasons behind its dominance, from tackling database complexity to empowering developers to build the future with confidence. Discover how PostgreSQL is revolutionizing software development and data management practices.

:::info This paper is available on arxiv under CC 4.0 license. 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 ABSTRACT As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully understand the task and serve relevant results [58]. The task motivates the search, creating the gap/problematic situation that searchers attempt to bridge/resolve and drives search behavior as they work through different task facets. Complex search tasks require more than support for rudimentary fact finding or re-finding. Research on methods to support complex tasks includes work on generating query and website suggestions [21, 62], personalizing and contextualizing search [4], and developing new search experiences, including those that span time and space [1, 64]. The recent emergence of generative artificial intelligence (AI) and the arrival of assistive agents, or copilots, based on this technology, has the potential to offer further assistance to searchers, especially those engaged in complex tasks [41, 61]. There are profound implications from these advances for the design of intelligent systems and for the future of search itself. This article, based on a keynote by the author at the 2023 ACM SIGIR Conference, explores these issues and charts a course toward new horizons in information access guided by AI copilots. \ ACM Reference Format: \ Ryen W. White. 2023. Navigating Complex Search Tasks with AI Copilots. Under review at REDACTED. October, 2023 1 TAKING SEARCH TO TASK Tasks are a critical part of people’s daily lives. The market for dedicated task applications that help people with their “to do” tasks is likely to grow significantly (effectively triple in size) over the next few years.[1] There are many examples of such applications that can help both individuals (e.g., Microsoft To Do, Google Tasks, Todoist) and teams (e.g., Asana, Trello, Monday.com) tackle their tasks more effectively. Over time, these systems will increasingly integrate AI to better help their users capture, manage, and complete their tasks [60]. In information access scenarios such as search, tasks play an important role in motivating searching via gaps in knowledge and problematic situations [3, 15]. AI can be central in these search scenarios, too, especially in assisting with complex search tasks. \ 1.1 Tasks in Search Tasks drive the search process. The IR and information science communities have long studied tasks in search [42] and many information seeking models consider the role of task directly [3, 15]. Prior research has explored the different stages of task execution (e.g., pre-focus, focus formation, post-focus) [53], task levels [39], task facets [29], tasks defined on intents (e.g., informational, transactional, and navigational [8]; well-defined or ill-defined [23]; lookup, learn, or investigate [32]), the hierarchical structure of tasks [68], the characteristics of tasks, and the attributes of task searcher interaction, e.g., task difficulty and, of course, a focus in this article, task complexity [11, 26]. \ As a useful framing device to help conceptualize tasks and develop system support for them, tasks can be represented as trees comprising macrotasks (high level goals), subtasks (specific components of those goals), and actions (specific steps taken by searchers toward the completion of those components) [42]. Figure 1 presents an example of a “task tree” for a task involving an upcoming vacation to Paris, France. Examples of macrotasks, subtasks, and actions are included. Moves around this tree correspond to different task applications such as task recognition (up), task decomposition (down), and task prediction (across). Only actions (e.g., queries, clicks, and so on) are directly observable to traditional search engines. However, with recent advances in search copilots (more fully supporting natural language interactions via language understanding and language generation), more aspects of macrotasks and subtasks are becoming visible to search systems and more fully understood by those systems. Challenges in working with tasks include how to represent them within search systems, how to observe more task-relevant activity and content to develop richer task models, and how to develop task-oriented interfaces that place tasks and their completion at the forefront of user engagement. Task complexity deserves a special focus in this article given the challenges that searchers can still face with complex tasks and the significant potential of AI to help searchers tackle complex tasks. 1.2 Complex Search Tasks Recent estimates suggest that half of all Web searches are not answered.[2] Many of those searches are connected to complex search tasks. These tasks are ill-defined and/or multi-step, span multiple queries, sessions, and/or devices, and require deep engagement with search engines (many queries, backtracking, branching, etc.) to complete them [21]. Complex tasks also often have many facets and cognitive dimensions, and are closely connected to searcher characteristics such as domain expertise and task familiarity [38, 58]. \ To date, there have been significant attempts to support complex search tasks via humans (e.g., librarians, subject matter experts) and search systems (both general Web search engines and those tailored to specific industry verticals or domains). The main technological progress so far has been in areas such as query suggestion and contextual search, with new experiences also being developed that utilize multiple devices, provide cross-session support, and enable conversational search. We are now also seeing emerging search-related technologies in the area of generative AI [35]. \ Before proceeding, let us dive into these different types of existing and emerging search support for complex tasks in more detail. \ • Suggestions, personalization, and contextualization: Researchers and practitioners have long developed and deployed support such as query suggestion and trail suggestion, e.g., [21, 45], including providing guided tours [51] and suggesting popular trail destinations [62] as ways to find relevant resources. This coincides with work on contextual search and personalized search, e.g., [4, 47, 63], where search systems can use data from the current searcher such as session activity, location, reading level, and so on, and the searcher’s long-term activity history, to provide more relevant results. Search engines may also use cohort activities to help with cold-start problems for new users and augment personal profiles for more established searchers [48, 69]. \ • Multi-device, cross-device, and cross-session: Devices have different capabilities and can be used in different settings. Multidevice experiences, e.g., [64], utilizing multiple devices simultaneously to better support complex tasks such as recipe preparation, auto repair, and home improvement that have been decomposed into steps manually or automatically [73]. Cross-device and cross-session support [1, 56] can help with ongoing/background searches for complex tasks that persist over space and time. For example, being able to predict task continuation can help with “slow search” applications that focus more on result quality than the near instantaneous retrieval of search results [46]. \ • Conversational experiences and generative AI: Natural language is an expressive and powerful means of communicating intentions and preferences with search systems. The introduction of clarification questions on search engine result pages (SERPs) [71], progress on conversational search [20], and even “conversations” with documents (where searchers can inquire about document content via natural language dialog) [49], enable these systems to engage more fully with searchers to better understand their tasks and goals. There are now many emerging opportunities to better understand and support more tasks via large-scale foundation models such as GPT-4[3] and DALL·E 3,[4] including offering conversational task assistance via chatbots such as ChatGPT.5 \ All of these advances, and others, have paved the way for the emergence of AI copilots, assistive agents that can help people tackle complex search tasks. \ \ \ [1] https://www.verifiedmarketresearch.com/product/task-management-softwaremarket/ \ [2] https://blogs.microsoft.com/blog/2023/02/07/reinventing-search-with-a-new-aipowered-microsoft-bing-and-edge-your-copilot-for-the-web/ \ [3] https://openai.com/gpt-4 \ [4] https://openai.com/dall-e-3 \ [5] https://openai.com/chatgpt

Despite the immense promise of LMs as task-neutral foundation models, initial endeavors to apply pre-trained LMs to downstream tasks encountered significant challenges.

After three years of planning, a US city is reportedly a few months away from kicking off a family-focused guaranteed income program. Pomona, California’s Flourishing Families is an 18-month, $2.25 million project that seeks to support young parents who are going through financial struggles, reports local news outlet the Daily Bulletin. First proposed in 2021, […] The post $2,250,000 To Be Handed Out With No Restrictions As US City Launches Latest Guaranteed Income Experiment appeared first on The Daily Hodl.

Bitcoin is not invincible, but was designed with resilience at its core. The eighth installment of “10 Steps to Self-Sovereignty” powered by Ledger.

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BHP's South Africa Snub an Indictment of ANC, Opposition Says  Bloomberg