Delve into the dynamic world of cryptocurrency exploring the top AI crypto coins with high potential for remarkable profitability after halving. Uncover expert insights, trends, and strategies to make informed investments in the crypto market.

King Charles to Return to Public Duties After Cancer Diagnosis  Bloomberg

Trump Lawyer Hints That Tabloid CEO Tried to Help Model's Career  Bloomberg

In the dynamic world of cryptocurrencies, investors are always on the lookout for promising altcoins that offer significant returns. Among these, Doge Uprising combines humor with solid blockchain technology, presenting a playful yet secure investment. Ethereum Classic remains a symbol of resilience in the volatile market, while Internet Computer proposes significant enhancements to the internet’s [...] The post 2024’s Most Promising Altcoins: BlockDAG Shines With 30,000x ROI, Outperforming Doge Uprising, Ethereum Classic, Internet Computer, & Stellar appeared first on Blockonomi.

BlackRock’s Bitcoin exchange-traded fund (ETF), IBIT, recently made headlines as its streak of consecutive inflows abruptly ended after an impressive run of 71 days. On Wednesday, IBIT failed to attract any fresh inflows, while total outflows hit a substantial $120 million. This comes in the wake of Grayscale’s GBTC witnessing massive outflows, bleeding over $130 […]

PRESS RELEASE. Solama, one of the most captivating Solana meme coin 2024 craze, is partnering up with two essential platforms for a crypto project, such as BingX and AssetDash, as BingX mentioned on their X (Twitter) account. As a result of this team-up, all $SOLAMA enthusiasts could benefit from extensive accessibility, catering to all investors […]

Ethereum withdraws from centralized exchanges have ramped up over the last week, suggesting a direction for investor sentiment during this time. Given the sheer volume of ETH withdrawn from these exchanges, it is prudent to try to understand what this could mean for the crypto’s price. 260,000 ETH Leaves Exchanges Amid the uncertainty that has plagued the crypto market, Ethereum investors are making moves to secure their positions for better price prospects. Pseudonymous crypto technical analyst Titan of Crypto took to X (formerly Twitter) to share what Ethereum investors are doing about their holdings right. Related Reading: Bitcoin Bears Risk Losing $7.2 Billion If BTC Price Reaches This Level The post revealed that these investors have been withdrawing large amounts of ETH from centralized exchanges. In the one week period that was tracked, the report found that a total of 260,000 ETH were withdrawn from exchanges, which was worth almost $800 million at the time. #Altcoins Crypto exchanges witnessed an outflow of over 260,000 #ETH equivalent to more than $781 million within the past 7 days. It’s time for #Ethereum shine. ✨🌕 pic.twitter.com/jT1aocjvbI — Titan of Crypto (@Washigorira) April 24, 2024 Now, exchange deposits and withdrawals are important for any cryptocurrency because it can often tell how investors are looking at that coin and what they are doing with their holdings. In the case of large deposits to centralized exchanges, it can be very bearish for the price because investors often deposit their coins in order to sell them as exchanges provide deep liquidity. In contrast, withdrawals from exchanges suggest that investors are not looking to sell their ETH. Rather, they are accumulating the coins to wait for better prices before selling. Naturally, this is bullish for the Ethereum price as a diminished selling pressure gives room for the price to recover. In this case, the withdrawals are bullish or the Ethereum price, as investors continue to accumulate. It also signals that investors are expecting a price breakout, and as the withdrawals ramp up, demand could surpass supply, leading to a surge in price. Ethereum Headwinds Still Negative Ethereum, while currently seeing some positive activity from investors, has still not turned completely bullish. For one, there has been a significant decline in its daily trading volume. According to data from Coinmarketcap, Ethereum’s trading volume is down approximately 20% in the last day. Related Reading: Renowned Economist Reveals What Will Happen If Bitcoin Can’t Hold $60,000 This decline in volume suggests a declining interest from investors to actually trade the coin. As such, its price may be negatively affected as attention begins to shift elsewhere, with investors looking for better prospects. Nevertheless, the cryptocurrency still looks bullish for the long term. Ethereum continues to closely mirror the price performance of Bitcoin, which is expected to go on a bull run following the successful completion of its fourth halving event. For now, Ethereum continues to struggle to hold above $3,100 with small gains of 0.18% in the last day. Over the last month, it has suffered multiple crashes, registering a 12.36% loss in the last 30 days. ETH price struggles to hold $3,100 support | Source: ETHUSD on Tradingview.com Featured image from Investopedia, chart from Tradingview.com

A lawyer with the crypto investment arm of venture capital giant Andreessen Horowitz (a16z) says web3 projects should not publicly sell tokens in the US to raise funds. In a new blog post, a16 Crypto general counsel Miles Jennings says that the U.S. Securities and Exchange Commission (SEC) has made it clear that Initial Coin […] The post Fundraising in US via Token Sales Is an ‘Own Goal’ That Should Be Avoided at All Costs: a16z Crypto Lawyer appeared first on The Daily Hodl.

New Investigation from China Banning Bitcoin! In China, which banned Bitcoin, an investigation was launched against Yao Qian, the architect of CBDC, ...

During the recent meme coin boom, a trader has reportedly made over $26 million in profits trading tokens on Solana.

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 […]

Unlike traditional blockchains, Hedera employs a unique consensus mechanism known as hashgraph. This technology enhances security and scales up ...

Blockchain innovations Gaming Governance on the Blockchain: Empowering Decentralized Autonomous Organizations (DAOs). Decentralized finance (DeFi) ...

Despite adverse weather, Dubai's inaugural Global AI and Blockchain Shows triumph, drawing thousands and launching new digital media ventures, ...

Scheduled to launch in April 2025, Pantera Fund V will function as an “all-in-one” fund, encompassing investments across various blockchain asset ...

Mexican Companies Are Lining Up to Sell Shares, Barclays Says  Bloomberg

Bull flags are historically associated with more upside momentum, but Bitcoin price could still use a catalyst before rallying to new highs.

Slim Shady was the latest celebrity to advertise “fortune favors the brave” for Crypto.com following Matt Damon’s ad spot in October 2021.

Amazon UK Workers Challenge Use of QR Codes for Quitting Union  Bloomberg

Commercial Real Estate Debt Is Back With 170% Jump in Sales  Bloomberg

CoinShares' latest digital asset fund manager survey finds that since the approval of the US spot bitcoin ETFs, bitcoin has retained the top spot, ...

Bitcoin rose 1.17% over the past 24 hours to $64,169.2 by 09:04 ET (13:04 GMT). The world's largest cryptocurrency was set for a muted weekly ...

УНН Economy ✎ The price of bitcoin remained stable at around $64000 due to depressed sentiment in the cryptocurrency market, which is linked to ...

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.