Watch Economic Pessimism Vanish Biden's Gain Against Trump  Bloomberg

Payment processing behemoth Stripe has renewed its crypto payments, starting with stablecoin USDC. After a long hiatus, Stripe will extend its crypto support on the Solana, Ethereum, and Polygon blockchains. The company dropped support for Bitcoin payments in 2018 due to high volatility. Unlike before, the payment startup will now directly integrate crypto payments instead […]

Green trades are rising in the meme coin sector for two reasons – the rise in interest of traders plus investors and the willingness of the community to explore their options after Bitcoin Halving to take profits home. The word Gree Trades, here, reflects the upward trend in the listed price of meme coins. This [...] The post Green trades are rising for meme coins PEPE, BONK, WIF, FLOKI, and MVP, is it a bull run? appeared first on Blockonomi.

PRESS RELEASE. Excitement is brewing in the heart of Asia as Hong Kong regulators pave the way for a new era of innovation with the recent approval of spot Bitcoin exchange-traded funds (ETFs). This groundbreaking development underscores Hong Kong’s commitment to becoming a regulated hub for Bitcoin. At the same time, the Bitcoin Conference is […]

Recent on-chain data has shown a transfer of 456 million DOGE tokens into crypto exchange Coinbase amidst a flurry of Dogecoin whale transactions across various exchanges in the past 24 hours. The price of DOGE has majorly traded below the $0.165 price level in the past seven days despite the price uptick among other meme coins. These recent events of whale transfers, however, appear to be leading to a bearish view towards DOGE. Transfers of this nature into a crypto exchange point to the whales getting ready to sell off their bags, which risks crashing the DOGE price.  Whales Move 456 Million DOGE To Crypto Exchange Whale Alerts, a crypto whale transaction tracker, has shown various instances of whale transactions in recent hours. Amidst these transactions is the transfer of 456.83 million DOGE tokens worth $68.7 million into popular crypto exchange Coinbase. Interestingly, the transfer of DOGE was made to Coinbase in two bouts of 228 million DOGE, each worth over $34 million. Related Reading: Renowned Economist Reveals What Will Happen If Bitcoin Can’t Hold $60,000 However, considering these transactions were sent in quick succession, the nature of their movement points to them being controlled by the same whale entity.  🚨 🚨 228,105,810 #DOGE (34,341,747 USD) transferred from unknown wallet to #Coinbasehttps://t.co/G8TGqtW5Ws — Whale Alert (@whale_alert) April 26, 2024 🚨 🚨 228,725,906 #DOGE (34,435,104 USD) transferred from unknown wallet to #Coinbasehttps://t.co/ACnqjOBCAO — Whale Alert (@whale_alert) April 26, 2024 A further look into the two whale addresses involved, “DFBx6m” and “DCTpBb”, showed that they no longer hold any tokens at the time of writing. This lends additional credibility to the idea that the transfers were made with the intention of selling off tokens. Notably, on-chain data shows that both addresses have been holding their DOGE tokens since last year, considering the last inflow of DOGE occurred on November 1, 2023. During this time, the price of Dogecoin was trading around $0.068. It is worth noting that DOGE has gone on an over 120% price increase since then, putting the holdings at a significant profit. What’s Next For Dogecoin? At the time of writing, DOGE is trading at $0.1511. The crypto is now on a correction path after failing to substantially break above $0.164 in the past week. The recent movement of nearly half a billion Dogecoin suggests major price volatility could be on the horizon. Furthermore, the recent DOGE rally seems to have stalled out as other altcoins and meme coins gain momentum. Related Reading: Bitcoin Bears Risk Losing $7.2 Billion If BTC Price Reaches This Level While DOGE is currently down by 0.64% in the past seven days, Shiba Inu, PEPE, Dogwifhat, and FLOKI are up by 13.8%, 47.5%, and 32.5% in the same timeframe.  The future of DOGE can be hard to predict. As with any meme coin, Dogecoin remains highly speculative. Its price continues to be volatile and heavily influenced by hype and social media. According to a crypto analyst, DOGE could continue to drop in the short term. Other analysts are bullish on DOGE, with analyst Ali Martinez even predicting a rally to the $1 price mark.  DOGE price falls below $0.15 | Source: DOGEUSDT on Tradingview.com Featured image from Bitcoinist, chart from Tradingview.com

If interest rates remain high, investors typically steer clear of “risk-on” assets like Bitcoin. AD. AD. Back in January, the Securities and Exchange ...

The cryptocurrency market is currently under renewed bearish pressure due to significant outflows recorded by the biggest Bitcoin exchange-traded ...

Key House Republican Would Back Stablecoin-Marijuana Combo Bill  Bloomberg

For instance, from this graph, we can conclude that given the same time to complete the test, older users typically perform slightly better than their younger counterparts. This can be attributed to differences in visual experience and color perception needs across various age groups. You're invited to participate in the study by using the Dehancer Color Test. The second part of this series will focus on the algorithm used to calculate the number of colors based on the data we have gathered.

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:::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 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. 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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. 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:::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 4 OPPORTUNITIES For some time, scholars have argued that the future of information access will involve personal search assistants with advanced capabilities, including natural language input, rich sensing, user/task/world models, and reactive and proactive experiences [57]. Technology is catching up with this vision. Opportunities going forward can be grouped into four areas: (1) Model innovation; (2) Next-generation experiences; (3) Measurement, and; (4) Broader implications. The opportunities are summarized in Figure 6. There are likely more such opportunities that are not listed here, but the long list shown in the figure is a reasonable starting point for the research community. 4.1 Model Innovation There are many opportunities to better model search situations and augment and adapt foundational models to better align with searchers’ tasks and goals, and provide more accurate answers. Copilots can leverage these model enhancements to improve the support that they provide for complex search tasks. \ 4.1.1 Task modeling. Opportunity: Build richer task models that more fully represent tasks and task contexts. This includes how we infer tasks (e.g., from textual content of search process, from usersystem interactions, from other situational and contextual information such as location, time, and application usage) and how we represent those tasks internally (e.g., as a hierarchy (Figure 1) or a more abstract representation (semantic vectors, graph embeddings, Markov models, and so on)). We also need to be able to estimate key task characteristics, such as task complexity, which, in one use, can help search systems route requests to the most appropriate modality. In addition, we need to find ways for copilots to collect more user/world knowledge, both in general and specifically related to the task at hand. A better understanding of the task context will help copilots more accurately model the tasks themselves. \ 4.1.2 Alignment. Opportunity: Develop methods to continuously align copilots to tasks/goals/values via feedback, e.g., conversation content as feedback (e.g., searchers expressing gratitude to the copilot in natural language) or explicit feedback on copilot answers via likes and dislikes. The performance of copilots that are missing alignment will remain fixed over time. Copilots need applicationaligned feedback loops to better understand searcher goals and tasks and use that feedback to continuously improve answer accuracy and relevance. Beyond research on fine-tuning foundation models from human feedback (e.g., likes/dislikes) [75], we can also build on learnings from research on implicit feedback in IR, including work on improving ranking algorithms via SERP clicks [25] and developing specialized interfaces to capture user feedback [65]. \ 4.1.3 Augmentation. Opportunity: Augment copilots with relevant external knowledge and enhanced tools and capabilities. As mentioned earlier, RAG is a common form of knowledge injection for foundation models. Relevance models are tuned to maximize user benefit, not for copilot consumption. We need to evaluate whether this difference is meaningful practically and if so, develop new ranking criteria that consider the intended consumer of the search results (human or machine). Despite their incredible capabilities, foundation models still have shortcomings that manifest in the copilots that use them. We need to understand these shortcomings through evaluation and find ways to leverage external skills/plugins to address them. Copilots must find and recommend skills per task demands [59], e.g., invoking Wolfram for computational assistance. We can also integrate tool use directly into tool-augmented models, e.g., Toolformer [40], that can teach themselves to use tools. Models of task context may also be incomplete and we should invest in ways to better ground copilot responses via context, e.g., richer sensing, context filtering, and dynamic prompting. \ 4.1.4 Grounding. Opportunity: Use grounding to reduce hallucinations, build searcher trust, and support content creators. It is in the interests of copilots, searchers, and content creators (and providers and advertisers) to consider the source of the data used in generating answers. Provenance is critical and copilots should provide links back to relevant sources (preferably with specific details/URLs not generalities/domains) to help build user trust, provide attribution for content creators, and drive engagement for content providers and advertisers. It also important for building trust and for supporting learning for copilots to practice faithful reasoning [14], and provide intepretable reasoning traces (e.g., explanations with chain-of-thought) associated with their answers. We should also think about how we integrate search within existing experiences (e.g., in other copilots) to ground answers in their context of use and in more places that people seek those answers. \ 4.1.5 Personalization. Opportunity: Develop personal copilots that understand searchers and their tasks, using personal data, privately and securely. Searchers bring their personal tasks to search systems and copilots will be no different. Here are some example personal prompts that describe the types of personal tasks that searchers might expect a copilot to handle: (1) Write an e-mail to my client in my personal style with a description of the quote in the attached doc. (2) Tell me what’s important for me to know about the company town hall that I missed? (3) Where should I go for lunch today? These tasks span creation, summarization, and recommendation and quickly illustrate the wide range of expectations that people may have from their personal copilots. As part of developing such personalized AI support, we need to: (1) Study foundation model capabilities, including their ability to identify task-relevant information in personal data and activity histories, and model user knowledge in the current task and topic, and (2) Develop core technologies, including infinite memory, using relevant long-term activity (in IR, there has been considerable research on relevant areas such as re-finding [52] and personalization [47]); context compression, to fit more context into finite token limits (e.g., using turn-by-turn summarization rather than raw conversational content); privacy, including mitigations such as differential privacy and federated learning, and research on machine unlearning [7] to intentionally forget irrelevant information over time, including sensitive information that the searcher may have explicitly asked to be removed from the foundation model. \ 4.1.6 Adaptation. Two main forms of adaptation that we consider here are model specialization and so-called adaptive computation. \ • Model specialization. Opportunity: Develop specialized foundation models for search tasks that are controllable and efficient. Large foundation models are generalists and have a wide capability surface. Specializing these models for specific tasks and applications discards useless knowledge, making the models more accurate and efficient for the task at hand. Recent advances in this area have yielded strong performance, e.g., the Orca-13B model [34] uses explanation-based tuning (where the model explains the steps used to achieve its output and those explanations are used to train a small language model) to outperform state-of-the-art models of a similar size such as Vicuna-13B [13]. Future work could explore guiding specialization via search data, including anonymized large-scale search logs, and as well as algorithmic advances in preference modeling and continual learning. \ • Adaptive computation. Opportunity: Develop methods to adaptively apply different models per task and application demands. Adaptive compute involves using multiple foundation models (e.g., GPT-4 and a specialized model) each with different inferencetime constraints, primarily around speed, capabilities, and cost, and learning which model to apply for a given task. The specialized model can backoff to one or more larger models as needed per task demands. The input can be the task plus the constraints of the application scenario under which the model must operate. Human feedback on the output can also be used to improve model performance over time [72]. \ These adaptation methods will yield more effective and more efficient AI capabilities that copilots can use to help searchers across a range of settings, including in offline settings (e.g., on-device only). 4.2 Next-Generation Experiences Advancing models is necessary but not sufficient given the central role that interaction plays in the search process [57]. There are many opportunities to develop new search experiences that capitalize on copilot capabilities while keeping searchers in control. \ 4.2.1 Search + Copilots. Opportunity: Develop experiences bridging the search and copilot (chat) modalities, offering explanations and suggestions. Given how entrenched and popular traditional search is, it is likely that some form of query-result interaction will remain a core part of how we find information online. Future, copilot-enhanced experiences may reflect a more seamless combination of the two modalities in a unified experience. Both Google and Bing are taking a step in that direction by unifying search results and copilot answers in a single interface. Explanations on what each modality and style (e.g., creative, balanced, and precise) are best for will help searchers make decisions about which modalities and settings to use and when. Modality recommendation given task is also worth exploring: simple tasks may only need traditional search, whereas complex tasks may need copilots. Related to this are opportunities around conversation style suggestion given the current task, e.g., fact-finding task or short reply (needs precision) and generating new content (needs creativity). Search providers could also consider offering a single point of entry and an automatic routing mechanism to direct requests to the correct modality given inferences about the underlying task (e.g., from Section 4.1.1) and the appropriateness of each of the modalities for that task. \ 4.2.2 Human Learning. Opportunity: Develop copilots that can detect learning tasks and support relevant learning activities. As mentioned earlier, copilots can remove or change human learning opportunities by their automated generation and provision of answers. Learning is a core outcome of information seeking [15, 32, 54]. We need to develop copilots that can detect learning and sensemaking tasks, and support relevant learning activities via copilot experiences that, for example, provide detailed explanations and reasoning, offer links to learning resources (e.g., instructional videos), enable deep engagement with task content (e.g., via relevant sources), and support specifying and attaining learning objectives. \ 4.2.3 Human Control. Opportunity: Better understand control and develop copilots with control while growing automation. Control is an essential aspect of searcher interaction with copilots. Copilots should consult humans to resolve or codify value tensions. Copilots should be in collaboration mode by default and must only take control with the permission of stakeholders. Experiences that provide searchers with more agency are critical, e.g., adjust specificity/diversity in copilot answers, leading to less generality and less repetition. As mentioned in Section 4.1.4, citations in answers are important. Humans need to be able to verify citation correctness in a lightweight way, ideally without leaving the user experience. We also need a set of user studies to understand the implications of less control of some aspects (e.g., answer generation), more control over other aspects (e.g., macrotask specification), and control over new aspects, such as conversation style and tone. \ 4.2.4 Completion. Opportunity: Copilots should help searchers complete tasks while keeping searchers in control. We need to both expand the task frontier by adding/discovering more capabilities of foundation models that can be surfaced through copilots and deepen task capabilities so that copilots can help searchers better complete more tasks. We can view skills and plugins as actuators of the digital world and we should help foundation models fully utilize them. We need to start simple (e.g., reservations), learn and iterate, and increase task complexity as model capabilities improve with time. The standard mode of engagement with copilots is reactive; searchers send requests and the copilots respond. Copilots can also take initiative, with permission, and provide updates (for standing tasks) and proactive suggestions to assist the searcher. Copilots can also help support task planning for complex tasks such as travel or events. AI can already help complete repetitive tasks, e.g., action transformers, trained on digital tools[8] or create and apply “tasklets” (user interface scripts) learned from websites [30]. \ Given the centrality of search interaction in the information seeking process, it is important to focus sufficient attention on interaction models and experiences in copilots. In doing so, we must also carefully consider the implications of critical decisions on issues that affect AI in general such as control and automation. 4.3 Measurement Another important direction is in measuring copilot performance, understanding copilot impact and capabilities, and tracking copilot evolution over time. Many of the challenges and opportunities in this area also affect the evaluation of foundation models in general (e.g., non-determinism, saturated benchmarks, inadequate metrics). \ 4.3.1 Evaluation. Opportunity: Identify and develop metrics for copilot evaluation, while considering important factors, and find applications of copilot components for IR evaluation. There are many options for copilot metrics, including feedback, engagement, precisionrecall, generation quality, answer accuracy, and so on. Given the task focus, metrics should likely target the task holistically (e.g., success, effort, satisfaction). In evaluating search copilots, it is also important to consider: (1) Repeatability: Non-determinism can make copilots difficult to evaluate/debug; (2) Interplay between search and copilots (switching, joint task success, etc.); (3) Longer term effects on user capabilities and productivity; (4) Task characteristics: Complexity, etc., and; (5) New benchmarks: Copilots affected by external data, grounding, queries, etc. There are also opportunities to consider applications of copilot components for IR evaluation. Foundation models can predict searcher preferences [50] and assist with relevance judgments [19], including generating explanations for judges. Also, foundation models can create powerful searcher simulations that can better mimic human behavior and values, and expand on early work on searcher simulations in IR [66]. \ 4.3.2 Understanding. Opportunity: Deeply understand copilot capabilities and copilot impact on searchers and on their tasks. We have only scratched the surface in understanding the copilots and their effects. A deeper understanding takes a few forms, including: (1) User understanding: Covering mental models of copilots and effects of bias (e.g., functional fixedness [17]) on how copilots are adopted and used in search settings. It also covers changes in search behavior and information seeking strategies, including measuring changes in effects across modalities, e.g., search versus copilots and search plus copilots. There are also opportunities in using foundation models to understand search interactions via user studies [12] and use foundation models to generate intent taxonomies and classify intents from log data [43]; (2) Task understanding: Covering the intents and tasks that copilots are used for and most effective for, and; (3) Copilot understanding: Covering the capabilities and limitations of copilots, e.g., similar to the recent “Sparks of AGI” paper on GPT-4 [10], which examined foundation model capabilities. \ Measuring copilot performance is essential in understanding their utility and improving their performance over time. Copilots do not exist in a vacuum and we must consider the broader implications of their deployment for complex tasks in search settings. 4.4 Broader Implications Copilots must function in a complex and dynamic world. There are several opportunities beyond advances in technology and in deepening our understanding of copilot performance and capabilities. \ 4.4.1 Responsibility. Opportunity: Understand factors affecting reliability, safety, fairness, and inclusion in copilot usage. The broad reach of search engines means that copilots have an obligation to act responsibly. Research is needed on ways to understand and improve answer accuracy via better grounding in more reliable data sources, develop guardrails, understand biases in foundation models, prompts, and the data used for grounding, and understand how well copilots work in different contexts, with different tasks, and with different people/cohorts. Red teaming, user testing, and feedback loops are all needed to determine emerging risks in copilots and the foundation models that underlie them. This also builds on existing work on responsible AI and responsible IR and FACTS-IR, which has studied biases and harms, and ways to mitigate them [36]. \ 4.4.2 Economics. Opportunity: Understand and expand the economic impact of copilots. This includes exploring new business models which copilots will create beyond information finding. Expanding the task frontier from information finding deeper into task completion (e.g., into creation and analysis) creates new business opportunity. It also unlocks new opportunities for advertising, including advertisements that are shown inline with dialog/answers and contextually relevant to the current conversation. There is also a need to more deeply understand the impact of copilots on content creation and search engine optimization. Content attribution is vital in such scenarios to ensure that content creators (and advertisers and publishers) can still generate returns. We should avoid the so-called “paradox of reuse” [55] where lower visits to online content leads to less content being created which in turn leads to worse models over time. Another important aspect of economics is the cost-benefit trade-off and is related to work on adaptation (Section 4.1.6). Large model inference is expensive and unnecessary for many applications. This cost will reduce with optimization, for which model specialization and adaptive computation can help. \ 4.4.3 Ubiquity. Opportunity: Copilot integrations to model and support complex search tasks. Copilots must co-exist with the other parts of the application ecosystem. Search copilots can be integrated into applications such as Web browsers (offering in-browser chat, editing assistance, summarization) and productivity applications (offering support in creating documents, emails, presentations, etc.). These copilots can capitalize on application context to do a better job of answering searcher requests. Copilots can also span surfaces/applications through integration with the operating system. This enables richer task modeling and complex task support, since such tasks often involve multiple applications. Critically, we must do this privately and securely to mitigate risks for copilot users. 4.5 Summary The directions highlighted in this section are just examples of the opportunities afforded by the emergence of generative AI and copilots in search settings. There are other areas for search providers to consider too, such as multilingual copilot experiences (i.e., foundation models are powerful and could help with language translation [33, 74]), copilot efficiency (i.e., large model inference is expensive and not sustainable at massive scale, so creative solutions are needed [72]), the carbon impact from running foundation models at scale to serve billions of answers for copilots [18], making copilots private by design [70], and government directives (e.g., the recent executive order from U.S. President Biden on AI safety and security[9]) and legislation, among many other opportunities. \ \ [8] https://www.adept.ai/blog/act-1 \ [9] https://www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/factsheet-president-biden-issues-executive-order-on-safe-secure-and-trustworthyartificial-intelligence/

:::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 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. \ • Hallucinations: 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. \ \ • Biases: 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. \ • Human learning: 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 control: 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, 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.

Getting your first 100 users isn't easy. Here's how I do it with Reddit, where most people don’t care about your products and hate advertising

Watch Brightline West Breaks Ground  Bloomberg

Zuckerberg's one-day drop in net worth is the 11th-largest ever related to a stock move among those in the Bloomberg Billionaires Index, with his ...

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The Bank of Russia and Rosfinmonitoring revealed the existence of a ‘know your crypto customer’ system pilot, that aims to link the fiat operations of crypto users with their blockchain actions. The pilot, which has been ongoing since 2023, involves five banks in Russia and is expected to run until April, but can be extended. […]

Map: Where Pro-Palestinian College Protests Are Happening Across US  Bloomberg

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Yen (JPY) Short Bets Hit a Record Ahead of BOJ Meeting  Bloomberg

Rep. Hill on Backing Stablecoin Cannabis Banking Bills  Bloomberg

The token paints a bearish picture despite increasing sentiment for accumulation.

The hard fork is aimed at speeding up the network and making it more cost-effective.

Biden and Trump Showing Their 2024 Campaign Vulnerabilities  Bloomberg

The Pantera Fund V is scheduled for launch in April 2025.

Two US Senators are writing a bipartisan letter to federal agencies about the potential use of crypto assets by child abusers. According to a new press release, Senator Elizabeth Warren, a Democrat from Massachusetts, and Senator Bill Cassidy, a Republican from Louisiana, say in the letter that digital assets are the “payment of choice” for […] The post Two US Senators Pen Open Letter to DOJ and DHS Concerning Crypto Use by Child Abusers appeared first on The Daily Hodl.

As the crypto industry navigates the waves of this bull run, projects like NEAR Protocol (NEAR) are edging forward with new partnerships and developments. NEAR’s remarkable performance has crypto analysts considering that the toke is getting underway for a massive surge. Related Reading: Is SUI Sinking? TVL Tanks As Crypto Price Fails To Keep Afloat Is NEAR Protocol A “Market Leader”? At the beginning of the week, crypto analyst World of Charts recognized a bullish flag pattern formed on NEAR’s monthly chart. According to the analyst, a successful breakout could be followed by a 60-65% bullish wave in the coming days. On Thursday, NEAR tested the $7.00 resistance level, reaching above the $7.50 mark before retracing as the day ended. NEAR breaking out of the bullish flag pattern on Thursday. Source: World of Charts Affirming his previous forecast, the analyst stated that if the token successfully holds above the breakout level, investors could expect the price to move towards $14-$15. Since then, the token has remained above the $7.00 mark, hovering between $7.3-$7.1. Another crypto analyst has been following NEAR’s performance this week similarly. According to Bluntz, the token “has been one of the strongest movers from the lows and will probably be one of the first to make fresh highs.” Moreover, he considers NEAR “one of the better performers” in the top 20 cryptocurrencies. Previously, the trader displayed a chart identifying an ABC zigzag pattern followed by a still-forming impulse wave pattern. NEAR's forming a impulse wave pattern according to the analyst. Source: Bluntz As NEAR broke out of the $7,00 resistance, the analyst reaffirmed his prediction for the token’s movements, considering it “a market leader right now.” Bluntz added that the token kept “plodding along making fresh highs while everything else has stalled out and continued accumulating.” Network Expansion And Price Surge The NEAR Protocol is a Layer-1 “user-friendly and carbon-neutral” blockchain focused on performance, security, and scalability. According to its team, the “blockchain for everyone” was built with “usability in mind.” NEAR’s total value locked (TVL) of $309 million makes it the 16th largest blockchain by this metric. Notably, the network has doubled its TVL since Q4 2023, when it sat in the 25th spot with $128 million. The protocol collaborates with other projects constantly to continue “expanding financial horizons.” Projects like NodeKit and TrueZK have recently integrated NEAR’s solution designed for Ethereum rollups, NEAR DA. Similarly, on Thursday, it announced its partnership with Colombian fintech Lulo X and Peersyst Technology “to redefine the parameters of digital finance.” These collaborations have been seemingly well-received by the NEAR community. Despite being down by 6.25% in the monthly time frame and 65% below its all-time high (ATH) of $20,44 set in January 2022, the blockchain’s token has shown a remarkable performance during this bull run. In the last three months, NEAR has soared over 146%. Moreover, the token’s daily trading volume has increased by 6.5% in the past day, with over $800 million traded. Likewise, its market capitalization has risen 5% during the same timeframe, making it the 17th biggest cryptocurrency by this metric. As of this writing, NEAR is trading at $7.2, representing a 7.3% jump in the last 24 hours and a 26% rise in the past week. Related Reading: Crypto Bull Run Set To Return Next Week, Predicts Arthur Hayes NEAR's performance in the one-week chart. Source: NEARUSDT on TradingView Featured Image from Unsplash.com, Chart from TradingView.com

Shiba Inu is preparing for an upcoming hard fork scheduled to launch on May 2nd, which will change its layer-2 blockchain, Shibarium. Leading up to this event, the team is introducing a range of new features to improve the user experience and support our community of innovators and developers. This isn’t just any upgrade; it […]

Bitcoin market inertia is dragging on, and a BTC price drop over the next fortnight would correspond to classic post-halving behavior.