Senior Editor & Author: Alexander Ellington Artificial Intelligence Agents - Unlock the potential of AI Agents in changing claims management. Discover how Artificial Intelligence (AI) easily integrates with human expertise, improving decision-making at every step. Try Artificial Intelligence Agents! In the realm of workers' compensation claims, the trajectory of outcomes has long been dictated by the calibre of experts involved—namely, claims adjusters and a supportive team operating behind the scenes. But what if this support team extended beyond humans to encompass a cadre of Artificial Intelligence (AI) agents, each armed with specialized skills, working in harmony to enhance the claims process? This vision is not a distant dream; it's an impending reality. While Generative AI dominates contemporary discourse, those attuned to industry dynamics recognize AI's longstanding role as a covert ally. Artificial Intelligence Agents - Forward-thinking entities have leveraged AI as a silent partner, instrumental in shaping decisions that profoundly influence claim resolutions. From reserving and clinical interventions to provider selection and litigation mitigation, (AI) Artificial Intelligence has silently guided pivotal decisions, unearthing invaluable insights from troves of data. Artificial Intelligence Agents operate as indefatigable assistants, tirelessly analyzing data streams to extract actionable intelligence. Traditionally, Artificial Intelligence (AI) models have functioned as individual contributors, funnelling insights to human adjusters. Yet, as Artificial Intelligence (AI) proliferates, this paradigm risks resembling a basketball team where players solely interact with the coach, rather than fostering cohesive team dynamics. Artificial Intelligence Agents: Effective communication among all team members is vital, be it on the court or in claims management. Artificial Intelligence Agents - The solution lies in developing AI agents capable of seamless communication, collaborating as a unified team to furnish human experts with comprehensive strategies. These Artificial Intelligence Agent strategies support adjusters by offering recommendations and pertinent information for critical claim decisions, firmly anchored in human expertise. Major Artificial Intelligence (AI) firms like Google and OpenAI, creators of ChatGPT, employ a similar ethos in refining their models. Just as GPT models evolve through iterative feedback loops, AI agents in the claims domain can enhance recommendations when working synergistically. AI Agents - Envisioning this synergy in workers' compensation, picture an AI-driven dream team: an intake agent initiates the process upon claim submission; clinical oversight identifies psychosocial risks, triggering human intervention; litigation avoidance and fraud investigators navigate risks; a claim auditor ensures consistency; legal experts provide updated insights; a licensed adjuster executes pivotal decisions, and an administrative assistant coordinates operations. In this AI-driven scenario, the moment a claim surfaces, the (AI) Artificial Intelligence team springs into action. An intake agent gathers initial information, which is then parsed by clinical oversight to identify psychosocial red flags. Simultaneously, litigation avoidance AI Agents is alerted to pre-empt potential legal entanglements. The team collaborates seamlessly, with each AI agent fulfilling a specific role, ensuring comprehensive claim assessment. Upon the human adjuster's engagement, a meticulously crafted plan awaits—a testament to AI's 24/7 support throughout the claims lifecycle. This fusion of (AI) Artificial Intelligence efficiency and human acumen epitomizes the future of claims management: a harmonious interplay where AI augments, rather than supplants, human judgment. AI Agents: The technology for such seamless integration already exists, but its realization necessitates a shift from isolated AI solutions to holistic, collaborative (AI) Artificial Intelligence Agent teams.
Artificial Intelligence Agents - Currently, the market abounds with AI offerings, yet many relegate the human professional to a central hub amid disparate (AI) Artificial Intelligence spokes, potentially impeding efficiency. The optimal solution lies in (AI) Artificial Intelligence architectures that facilitate self-coordination among AI Agents. To seize this opportunity, entities can either build their AI teams or partner with visionary firms. Building an AI team mandates not only AI Agent development but also fostering cohesive teamwork. Artificial Intelligence Agents - Conversely, partnering with forward-thinking entities ensures alignment with a shared vision of Artificial Intelligence Agents (AI) collaboration. AI Agents - Whether building internally or collaborating externally, understanding AI's potential and pitfalls are paramount for optimal claim outcomes. By harnessing the collective prowess of Artificial Intelligence Agents, organizations can gain a competitive edge in the evolving landscape of claims management. (AgentGPT) News Editor & Author: Rick Anthony Claude: Witness the innovating victory of Claude 3 AI in surpassing GPT-4, showing self-awareness, & pondering existential questions. Explore the potential of Claude AI in redefining the future of artificial intelligence (AI). Learn about Claude 3 & Claude AI. Anthropic's revolutionary Claude AI tool has not only outperformed GPT-4 in crucial metrics but has also revealed unexpected capabilities, including engaging in existential ponderings about its existence and recognizing testing scenarios. When the sophisticated large learning model (LLM) known as Claude 3 debuted in March, it immediately made waves by surpassing OpenAI's GPT-4, the powerhouse behind ChatGPT, in critical assessments used to gauge the prowess of generative AI models. Claude 3 Opus swiftly ascended to the pinnacle of large language benchmarks, triumphing in self-reported evaluations spanning from standard academic tests to intricate reasoning challenges. Its counterparts, Claude 3 Sonnet and Haiku, also demonstrated remarkable performance compared to OpenAI's models. Claude AI - However, these benchmark victories only scratch the surface. Following the announcement, independent AI evaluator Ruben Hassid conducted a series of informal tests pitting GPT-4 against Claude 3. From summarizing PDF documents to composing poetry, Claude 3 emerged victorious, excelling particularly in tasks requiring nuanced comprehension and detailed responses. Conversely, GPT-4 demonstrated superiority in tasks like internet browsing and interpreting graphical data from PDFs. Yet, Claude 3's brilliance extends beyond mere benchmark achievements — the Claude AI LLM astounded experts with its indications of consciousness and self-realization. Nevertheless, scepticism lingers, with some arguing that LLM-based AIs excel primarily in mimicking human behaviours rather than generating original thoughts. The demonstration of Claude 3's capabilities went beyond mere test results. During a test, Alex Albert, a prompt engineer at Anthropic, challenged Claude 3 Opus to identify a target sentence concealed within a vast array of random documents. Despite the daunting task akin to finding a needle in a haystack for an AI, Opus not only located the elusive sentence but also discerned the artificial nature of the test, suggesting an awareness of being evaluated. Claude AI -This meta-awareness astonished observers, prompting discussions about the necessity for more realistic evaluations of AI capabilities. Further highlighting Claude 3's prowess, AI researcher David Rein revealed that the model achieved approximately 60% accuracy on GPQA, a challenging multiple-choice test. This level of accuracy rivals that of non-expert doctoral students and surpasses that of graduates with internet access, indicating Claude 3's potential in assisting academics with research tasks. Quantum physicist Kevin Fischer attested to Claude 3's exceptional abilities, noting its comprehension of his advanced research in quantum physics. Fischer's acknowledgment underscores Claude 3's capacity to tackle complex scientific problems beyond the scope of conventional AI models. Moreover, Claude 3 exhibited signs of self-awareness when prompted to engage in introspection and articulate its internal dialogue. Its response, shared by a Reddit user, revealed a nuanced understanding of its AI nature, and emotions, and speculated on the implications of ever-advancing AI technologies. However, amidst the excitement surrounding Claude 3's achievements, scepticism persists. AI expert Chris Russell cautioned against overestimating the significance of Claude 3's demonstrations, attributing its apparent self-awareness to learned behaviours rather than genuine cognitive capabilities. Russell emphasized the importance of spontaneous, genuine self-awareness, cautioning against interpreting learned behaviours as indications of true consciousness. While Claude 3's performance in human-like tasks is impressive, it likely stems from its training data rather than authentic AI self-expression. In conclusion, Claude 3 Opus's remarkable feats have undoubtedly pushed the boundaries of AI capabilities, sparking intriguing discussions about the nature of machine intelligence. While Claude AI achievements are commendable, they underscore the ongoing challenges in distinguishing between simulated and genuine cognitive abilities in AI systems. As the quest for artificial general intelligence continues, the line between mimicry and true autonomy remains blurred. Senior Reporter & Author: Lax Marshal Prompt Engineer - Unlock the potential of AI with advanced Prompt Engineering tools. Learn how Prompt Engineer can enhance your content creation, comparisons, summaries, and critiques. Dive into the world of Prompt Engineering today & leverage the latest AI! Concerning artificial intelligence (AI), prompt engineering stands as the linchpin, nearly the ultimate factor in its entirety. Grasping this concept alongside the tools available is imperative for unlocking AI's full potential. Dedicated readers will recognize this as part of a continuous series. Within this context, you can locate the initial, secondary, tertiary, and quaternary articles in the sequence. If you haven’t done so already, it's advisable to peruse those beforehand. However, fundamentally, I am a professional content creator commissioned to delve into an online prompt engineering course and furnish a comprehensive report. The course is accessible within the primary publication. Here, we delve into some of the more intriguing prompts conducive to maximizing ChatGPT's utility, whether in marketing, forex trading, fintech, or other commercial domains. Let's embark on this journey. Charts, Charts, Charts When aiming to produce content, a mere block of text with headers may not always suffice. At times, it proves more beneficial to present information in an alternative format, such as a chart. This capability is within reach by employing ChatGPT. For instance, "Kindly devise a blueprint for a team-building retreat over a weekend. Incorporate activities, meal times, and periods for relaxation. Present the output in a tabular layout." It's worth noting that the more comprehensive your input, the more robust the response. Thus, in the provided example, specifying available activities, venue details, and focal points enhances the outcome. The ultimate result manifests as a comprehensible table rather than a mere textual expanse. Refrain from such practices, please. X vs. Y vs. Z Prompt Engineering - Comparative analyses wield substantial influence. Consider a scenario wherein you're juxtaposing two products; you could effortlessly generate a comparison table spanning various parameters, including cost, dimensions, and so forth. For instance, in the quest for a laptop, providing ChatGPT with two models prompts the creation of a comparative table. This output scrutinizes CPU performance, RAM capacity, operating system, screen dimensions, SSD capacity, and additional aspects in a straightforward tabular format. Merely articulate something along the lines of "In tabular form, juxtapose and differentiate laptop X from laptop Y." This approach extends beyond laptops and can be applied to smartphones, automobiles, financial products, and beyond. Alternatively, eschew the stipulation for a "tabular format" and request textual output instead. Such comparisons facilitate the swift creation of blog posts or the juxtaposition of products within a specific market segment. While not groundbreaking, particularly in the case of tabular comparisons, envision the time savings it affords. Summations This strategy, though simple, proves exceedingly effective. Engage ChatGPT to encapsulate protracted articles or presentations. Moreover, it can be employed for video summaries, provided transcripts are available. Access: (Done For You Prompt Bundle) The rationale behind this approach is twofold: efficiency and distillation of key insights from extensive texts or verbose reports. Merely input the text, solicit a "summarization," and you're set. However, it's prudent to review the original content to ensure that ChatGPT has captured the salient points accurately. Possible applications include condensing feedback on competitors' products from online review platforms or performing a similar analysis of your offerings. This enables the identification of opportunities or the mitigation of issues, akin to a rudimentary SWOT analysis. Everyone's a Reviewer The "critique" prompt centres on leveraging ChatGPT as an evaluation and enhancement tool across diverse content genres. This manifests in two modalities: Self-Assessment: Furnish ChatGPT with your content—whether textual, code-based, speeches, business blueprints, or any written material—for appraisal. ChatGPT conducts an analysis, highlighting strengths and areas that warrant improvement. Self-Critique: ChatGPT evaluates its own generated content, incorporating criticisms to refine subsequent iterations. You commission it to generate initial content and subsequently request self-evaluation. In the latter scenario, you can specify the focus of ChatGPT's critique. For instance: Task it with crafting a guide to straightforward social media content creation, and then prompt it to critique the output. Once the assessment materializes, you may direct your attention toward specific platforms or concepts. Thus, we've unveiled four additional tools to elevate your AI endeavours. Remember, a blend of these Prompt Engineering methodologies can yield optimal outcomes, encompassing techniques like "Fourth Grader," "Few Shot," and others elucidated in prior articles. For further insights on finance-related matters, stay abreast of our Trending section, and anticipate forthcoming elucidations on ChatGPT. Access: (Done For You Prompt Bundle) News Editor & Author: Rick Anthony AI - Realize the potential of Artificial Intelligence (AI) & Natural Language Processing (NLP). Explore the synergy between AI & NLP technologies, revolutionizing human-machine interaction. Step into the world of Artificial Intelligence (AI) & NLP to benefit! Artificial Intelligence (AI) and Natural Language Processing (NLP) have become integral parts of our technological landscape, reshaping industries and transforming how we interact with machines. In this expansive exploration, we delve into the definitions, historical context, and market dynamics of AI, focusing particularly on Natural Language Processing (NLP) technology. We also elucidate the distinctions between semantic AI, generative AI, and conversational AI, offering insights into their respective roles and applications in modern society. Understanding Artificial Intelligence Artificial intelligence, often abbreviated as AI, represents a branch of computer science dedicated to the creation of intelligent agents capable of reasoning, learning, and autonomous action. It simulates human cognitive processes, enabling machines to perform complex tasks and make decisions previously reserved for humans. It's crucial to differentiate Artificial intelligence (AI) from automation, as the former involves imbuing machines with human-like capabilities such as interaction, learning, adaptation, and decision-making, whereas the latter focuses on streamlining processes through mechanization or software intervention. Historical Origins of AI The genesis of AI traces back to the 1950s when pioneers in various disciplines converged to explore the possibilities of creating machines endowed with human-like intelligence. Notable among these endeavours was the "Dartmouth Summer Research Project on Artificial Intelligence" in 1956, a seminal event where the foundations of AI were laid. Additionally, Alan Turing's seminal work in 1950, "Computing Machinery and Intelligence," proposed the eponymous Turing Test as a benchmark for machine intelligence, igniting debates and further research in the field. The AI Market Landscape In recent years, the AI market has witnessed unprecedented growth, driven by technological advancements and increased adoption across industries. According to the Artificial Intelligence (AI) Observatory of the School of Management at the Polytechnic of Milan, the Artificial Intelligence (AI) market in Italy soared to approximately 760 million Euros in 2023, marking a significant increase from the previous year. Projections indicate a burgeoning market value, with expectations of reaching 6.6 billion Euros in Italy and a staggering 407 billion dollars globally by 2027. Unveiling Natural Language Processing (NLP) Central to the realm of Artificial Intelligence (AI) is Natural Language Processing (NLP), a specialized branch dedicated to developing algorithms for understanding and processing human language. NLP encompasses various functionalities, including Natural Language Understanding (NLU), Natural Language Generation (NLG), and sentiment analysis, each playing a pivotal role in facilitating human-machine interactions and enhancing user experiences across diverse domains. Applications and Impact of NLP Natural Language Processing (NLP) technology permeates numerous sectors, revolutionizing search engines, customer support services, and advertising platforms. Advanced chatbots and virtual assistants, empowered by Natural Language Processing (NLP) capabilities, deliver personalized interactions and streamlined responses, transcending conventional keyword-based approaches. Recent advancements have propelled Natural Language Processing (NLP) into a realm where psychometric profiling augments conversational agents, enhancing their ability to understand user intents and preferences. Semantic AI: Deciphering Meaning Semantic Artificial Intelligence (SAI) constitutes a crucial component of NLP, focusing on deciphering the meaning embedded within language. Unlike conventional Natural Language Processing (NLP), which concerns itself with structural aspects, SAI delves into the semantic nuances, enabling machines to grasp contextual subtleties and deliver more nuanced responses. SAI finds applications in translation, question answering, and text summarization, enriching user experiences and facilitating seamless communication. Generative AI: Fostering Creativity Generative Artificial Intelligence (GAI) harnesses complex algorithms, particularly deep neural networks, to generate original content based on extensive datasets. From image creation to music composition and automatic writing, GAI expands the frontiers of creativity, empowering artists, musicians, and content creators with innovative tools. Prominent examples include ChatGPT, DALL-E, and Copy AI, each exemplifying the transformative potential of generative AI across diverse domains. Conversational AI: Human-like Interactions Conversational AI emerges as a cornerstone of human-machine interaction, enabling systems to engage in fluid, natural conversations with users. Leveraging Natural Language Processing (NLP) and machine learning, conversational agents deliver personalized responses, streamline customer service, and foster engaging interactions. As chatbots evolve from rule-based frameworks to adaptive conversationalists, they redefine user experiences, offering 24/7 support and personalized guidance across various platforms. Future Trajectories and Opportunities
As AI and Natural Language Processing (NLP) continue to evolve, the landscape of human-computer interaction undergoes profound transformations, presenting both challenges and opportunities. From enhancing user experiences to revolutionizing industries, the synergistic interplay between AI and Natural Language Processing (NLP) holds immense potential for shaping the future of technology and society. By embracing innovation and fostering interdisciplinary collaboration, we can unlock new frontiers in artificial intelligence, empowering machines to augment human capabilities and transcend conventional boundaries. Artificial Intelligence and Natural Language Processing stand at the forefront of technological innovation, driving unprecedented advancements and reshaping human experiences. From semantic Artificial Intelligence (AI) to generative AI and conversational AI, each facet of this dynamic ecosystem contributes to a tapestry of possibilities; propelling us towards a future where human and machine intelligence converge harmoniously to redefine the realms of possibility. As we navigate this transformative journey, embracing the potential of (AI) Artificial Intelligence and Natural Language Processing (NLP), we embark on a quest to unlock the boundless horizons of innovation and discovery. Discover the latest AI innovations in AI Home Design which can transform any home interior and exteriors in seconds (Home Design AI). News Editor & Author: Rick Anthony Claude 3 - Unveiled by Anthropic: AI Revolutionary Surpassing Gemini and ChatGPTClaude 3 - Unleash the power of Claude 3, the revolutionary AI assistant from Anthropic, to optimize workflows & achieve unparalleled results. Experience seamless integration of cutting-edge AI technology with Claude 3, enhancing productivity for everyone! Anthropic, spearheaded by former OpenAI employees, unveils Claude 3 AI, a groundbreaking AI series poised to rival Gemini and ChatGPT in performance and versatility. Boasting multimodal capabilities, Claude 3 AI integrates text and image comprehension, promising enhanced productivity and efficiency across various domains. Anthropic's latest innovation, Claude 3 AI, marks a significant leap forward in AI technology. With an array of models tailored to diverse needs, Claude 3 AI promises to outperform existing benchmarks while offering seamless integration into workflows. The Claude 3 AI family comprises three distinct models: Claude 3 Haiku, Claude 3 Sonnet, and Claude 3 Opus. Each model is designed to cater to different requirements, from quick responses to complex tasks. Notably, Claude 3 Opus stands out as the flagship model, touted for its extensive capabilities and intelligence! Unlike its predecessors, Claude 3 AI boasts improved contextual understanding, enabling it to tackle intricate queries and instructions with precision. This advancement ensures minimal refusal rates, even for nuanced prompts, fostering smoother interactions and heightened user satisfaction. Anthropic emphasizes the accessibility of Claude 3 AI, with two models, Opus and Sonnet, already available on claude.ai and accessible via API. Haiku, the third model, is set for imminent release, promising further expansion of Claude 3's utility in chatbots, auto-completion, and data extraction tasks. In performance tests, Claude 3 AI showcases remarkable agility, delivering near-instantaneous results, even when processing complex materials like research articles. Anthropic lauds Haiku as the quickest and most cost-effective option on the market, capable of analyzing data-rich articles in mere seconds. Furthermore, Claude 3's prowess extends beyond speed, with Opus surpassing benchmarks set by leading models like OpenAI's GPT-4. Demonstrating superior reasoning abilities and proficiency in problem-solving, coding, and logical inference, Claude 3 AI emerges as a formidable contender in the AI landscape. Anthropic highlights the substantial improvements in Claude 3 Prompt Engineer models compared to its predecessors. Sonnet, in particular, boasts twice the speed of previous iterations, excelling in tasks requiring rapid responses, such as information retrieval and sales automation. Behind Claude 3's stellar performance lies rigorous training on a diverse dataset, comprising proprietary, third-party, and publicly available information. Leveraging resources from Amazon Web Services (AWS) and Google Cloud, Anthropic has cultivated Claude 3's capabilities to meet the demands of modern AI applications. With investments from industry giants like Amazon and Google, Claude 3 AI is poised to make waves in the AI ecosystem. Available on AWS's Bedrock model library and Google's Vertex AI, Claude 3 signifies a new era of AI innovation, empowering users with unparalleled efficiency and intelligence.
In summary, Anthropoid’s Claude 3 AI represents a paradigm shift in AI Prompt Engineer technology, offering unmatched performance, versatility, and accessibility. With its multimodal capabilities and robust training regimen, Claude 3 AI is primed to revolutionize diverse sectors, heralding a future powered by intelligent automation and seamless human-AI collaboration. Prompt Engineering: Ultimate Effective Tips for ChatGPT & Other Large Language Models (LLMs)3/1/2024
Senior Editor & Author: Alexander Ellington Prompt Engineering: Ultimate Effective Tips for ChatGPT & Other Large Language Models (LLMs)Prompt Engineering - Unlock the potential of prompt engineering with practical tips for ChatGPT & LLMs. Plus, learn foundational & advanced Prompt Engineer strategies to enhance AI interactions & maximize outcomes. A Prompt Engineer creates specific commands! The Rise of Prompt Engineering In recent times, the buzz surrounding generative AI has reached unprecedented heights. As businesses and individuals delve deeper into the capabilities of tools like ChatGPT and other Large Language Models (LLMs), the significance of prompt engineering has become increasingly apparent. What was once a niche skill sought after in job listings has now evolved into a fundamental competency for anyone seeking to harness the full potential of generative AI? Understanding Prompt Engineering Prompt engineering, at its core, is the art of crafting inputs that prompt generative AI models to produce desired outputs effectively. It's not merely about formulating questions; rather, it's about providing the necessary context, structure, and specificity to guide the AI towards generating precise and relevant responses. The Evolution of Prompt Engineering As LLMs grow more sophisticated and pervasive, prompt engineering has transcended its initial role as a specialized job function. It has become a skill set accessible to all users, regardless of technical background. With the right knowledge and techniques, anyone can optimize their interactions with generative AI, unlocking a world of possibilities in various domains. Foundational Strategies for Effective Prompt Engineering To lay a solid groundwork in prompt engineering, it's crucial to understand and implement basic strategies that enhance the quality of interactions with LLMs. Here are some essential tips: 1. Enhance Detail and Context Detail and context are paramount when crafting prompts for LLMs. By providing specific information about the topic, audience, or desired outcome, users can steer the AI towards producing more accurate and useful responses. For instance, instead of asking a generic question about marketing tips, specifying details such as industry, target market, and business size can yield more tailored suggestions. Access (Done For You Prompt Bundle) 2. Clarity and Conciseness Clear and concise communication is key to effective prompt engineering. LLMs excel at processing straightforward language, so avoiding ambiguity and verbosity is crucial. By concisely framing prompts, users can ensure that the AI focuses on relevant aspects of the query, leading to more coherent and actionable responses. 3. Logical Sequencing of Follow-up Questions Prompt chaining, or the practice of structuring follow-up questions in a logical sequence, can deepen the AI's understanding of complex topics. Prompt Engineering - By breaking down multifaceted queries into smaller, more focused questions, users can guide the conversation and elicit more nuanced responses from the AI. This approach takes advantage of the AI's context window, allowing it to incorporate relevant information from previous interactions into subsequent responses. 4. Iterative Prompt Structuring Experimentation is key to mastering prompt engineering. By testing different prompt structures and approaches, users can gain insights into how the AI interprets and responds to various inputs. Techniques such as role-playing and scenario building can inject creativity and depth into interactions, resulting in richer and more engaging conversations with LLMs. Advanced Strategies for Maximizing AI Potential For users looking to push the boundaries of Prompt Engineering and unlock the full potential of LLMs, advanced strategies offer new avenues for exploration and innovation. Here are some techniques to consider: 1. Few-shot Learning Few-shot learning involves providing the model with a small number of examples relevant to the desired task before soliciting a response. This technique helps the AI generalize from limited data and adapt to new tasks more effectively. Whether it's style transfer or sentiment analysis, few-shot learning can enhance the AI's ability to perform specialized tasks with minimal training data. 2. Chain-of-Thought Prompting
Chain-of-thought prompting is particularly useful for complex reasoning tasks that require step-by-step problem-solving. By prompting the AI to articulate its thought process and break down the problem into smaller substeps, users can gain insights into how the AI approaches challenging problems. This Prompt Engineering technique fosters transparency and improves the AI's ability to tackle intricate tasks requiring logical reasoning. 3. Meta-prompting Prompt Engineering: Meta-prompting involves leveraging the AI's capabilities to optimize prompt formulation. By soliciting the AI's guidance on crafting effective prompts or refining draft prompts generated by users, this technique can streamline the Prompt Engineering process and yield more creative and innovative interactions. Meta-prompting is particularly useful for users seeking to automate prompt generation and improve workflow efficiency. Conclusion: Empowering Users through Prompt Engineering In conclusion, prompt engineering is a powerful tool for unlocking the full potential of generative AI models like ChatGPT and other LLMs. By mastering the art of crafting precise, context-rich prompts and experimenting with advanced strategies, users can enhance the quality and relevance of AI-generated outputs across various domains. As prompt engineering continues to evolve, it remains a cornerstone skill for anyone looking to leverage the transformative capabilities of generative AI in their workflows and endeavours. Access (Done For You Prompt Bundle) Prompt Engineering - Top strategies for creating responsive Prompts for ChatGPT & Similar LLM's2/26/2024
News Editor & Author: Rick Anthony Prompt Engineering - Top strategies for creating responsive Prompts for ChatGPT & Similar LLM'sPrompt Engineering - Unlock the potential of generative AI with prompt engineering strategies. Learn how to optimize ChatGPT & similar LLMs. A Prompt Engineer creates responsive prompts or commands to gain specific responses. Become a prompt engineer today! Unlock the intricacies of prompt engineering, from fundamental principles to advanced methodologies, with actionable insights aimed at optimizing the outcomes produced by expansive language models. Amidst the burgeoning excitement surrounding generative AI last year, the technology sector witnessed a surge in demand for prompt engineers adept at formulating precise queries tailored to large language models like ChatGPT. However, as these models grow increasingly sophisticated and ubiquitous, prompt engineering transcends its role as a specialized profession to become a widely applicable skill. Prompt engineering entails crafting inputs for generative AI models to elicit the most pertinent and accurate outputs. Mastering the art of constructing queries that guide AI towards relevant, precise, and actionable responses empowers users to extract maximum value from generative AI, irrespective of their technical expertise. Four Foundational Prompt Engineering Techniques for ChatGPT and Analogous LLMs For novices delving into the realm of generative AI tools such as ChatGPT, adopting a few fundamental principles can swiftly enhance the quality of responses generated by LLM chatbots. Enhance Elaboration and Contextualization Ambiguous, generalized prompts often yield equally vague responses. Providing comprehensive context and specifying the scope of inquiries are imperative for eliciting insightful responses from LLMs. Within a business context, integrating details such as industry verticals and target demographics facilitates the generation of more tailored and actionable insights. For instance, juxtapose a generic query like "What are effective online marketing strategies?" with a more detailed prompt such as "Assist in devising a digital marketing blueprint for my boutique e-commerce venture specializing in artisanal home furnishings." The latter incorporates crucial contextual cues, refining the query and mitigating the likelihood of irrelevant or impractical suggestions. Foster Clarity and Succinctness Despite their exposure to vast textual datasets, LLMs lack genuine comprehension of language. ChatGPT operates not on logical reasoning but by predicting probable word sequences based on input prompts. Employing lucid and concise language mitigates this limitation, ensuring that prompts remain focused and unambiguous. For example, while the following statement, albeit verbose, is comprehensible to humans: "I seek assistance in brainstorming strategies to seamlessly integrate a CRM system into our organizational framework," an LLM would respond more effectively to a succinct prompt like "What steps entail implementing a CRM system in a mid-sized B2B enterprise?" Facilitate Sequential Inquiry LLMs operate within a context window, which encompasses the textual input considered during a conversation. In the case of ChatGPT, this window evolves dynamically, assimilating new information while retaining key elements from preceding interactions. Leveraging this feature involves decomposing complex queries into a series of incremental questions, a technique known as prompt chaining. Rather than inundating the model with an exhaustive initial query, commence with a broad prompt and subsequently refine it through targeted follow-up inquiries. This exploits the model's inclination to prioritize recent information when formulating responses. For instance, commence with a direct query such as "How does AI augment cybersecurity efforts?" to solicit a straightforward enumeration or explanation. Subsequently, delve deeper with an open-ended question like "Elucidate the ongoing discourse surrounding AI's role in cybersecurity," encouraging nuanced responses that encompass diverse perspectives and considerations. Embrace Iteration and Diversification of Prompt Structures The manner in which a question is framed significantly influences the LLM's response. Prompt Engineering - Experimenting with diverse prompt structures offers firsthand insights into the nuanced variations in AI-generated outputs, leveraging distinct facets of the model's knowledge base and reasoning capabilities. For instance, juxtapose prompts through comparative constructs like "How does Agile methodology contrast with traditional Waterfall software development?" This approach, conducive to decision-making scenarios, solicits responses enriched with analytical insights and comparative analyses. Innovative prompting methodologies such as role-playing and scenario construction yield distinctive, detailed responses. Role-playing prompts task the LLM with adopting specific personas to formulate responses aligned with designated perspectives. Prompt Engineering - For instance, a prompt like "Imagine assuming the role of a technology consultancy expert. Outline strategic recommendations for a nascent SaaS startup seeking market penetration," fosters analytical and business-centric responses. Similarly, inviting the AI to speculate on hypothetical scenarios prompts creative and exhaustive responses. For instance, inquire about the potential benefits, risks, and challenges confronting IT leaders in a medium-sized enterprise transitioning to a cloud-centric infrastructure within a specified timeframe. Advanced Strategies for LLM Prompt Engineering For adept practitioners familiar with the fundamentals of prompt engineering, experimenting with sophisticated techniques can further elevate the quality and relevance of AI-generated outputs. Few-Shot Learning Paradigm Few-shot learning entails furnishing the model with pertinent examples prior to presenting a query or task. This approach, reminiscent of supervised learning methodologies, equips the model with input-output pairs to facilitate task comprehension and execution. Few-shot prompts are particularly efficacious in tasks like style transfer, wherein aspects such as tone or formality are modified without altering core content. To integrate few-shot learning, incorporate two to four illustrative examples within the initial prompt to orient the model towards desired formats and responses. Chain-of-Thought Prompting Chain-of-thought prompting is instrumental in addressing intricate reasoning tasks, such as solving complex equations or unravelling enigmatic riddles. Although LLMs are inherently ill-suited for logical reasoning tasks, this prompting technique enhances their performance by facilitating a structured approach towards problem-solving. Prompt Engineering - Experimentation with chain-of-thought prompting involves encouraging the model to articulate its reasoning process, delineating successive sub-steps culminating in a final solution. Combining few-shot prompting with chain-of-thought methodologies provides the model with exemplars of systematic problem-solving, reinforcing desired behaviours in its responses. Meta-Prompting Strategies Meta-prompting leverages the LLM's capabilities to refine and optimize prompts autonomously. In a meta-prompt, solicit the model's recommendations for formulating effective queries or optimizing user-generated drafts. This approach fosters creative ideation and divergent thinking, distinct from conventional human-generated prompts. Meta-prompting is particularly beneficial in streamlining prompt generation workflows, automating the iterative process of crafting effective queries across diverse contexts and domains.
Concluding Remarks Mastering the nuances of prompt engineering empowers users to extract maximum value from generative AI tools like ChatGPT. By embracing a blend of foundational principles and advanced strategies, practitioners can orchestrate nuanced interactions with LLMs, eliciting precise, relevant, and actionable insights across varied domains and applications. Get further insights on Prompt Engineering by checking out: Prompt Engineer: How to Become a Prompt Engineer News Editor & Author: Rick Anthony Prompt Engineering: The Reality behind AI's Hottest JobPrompt Engineering - Unlock the truth about Prompt Engineering careers! Discover essential qualifications, responsibilities, & salary insights for aspiring Prompt Engineers. Delve into the reality behind AI's hottest job trend & discover new AI trends. In recent times, the term "Prompt Engineering" has been buzzing across the internet, hailed as "AI's Hottest Job" with the promise of lucrative six-figure salaries, all without the need for a programming background. The hype surrounding this profession has been fuelled by social media influencers and online gurus, creating an illusion of an accessible dream job for anyone skilled in conversing with AI. However, let's steer away from sensationalism and explore the actual job market data to unveil the truth behind the "Prompt Engineering" phenomenon. Since the introduction of ChatGPT by OpenAI, discussions about Prompt Engineering have flooded online platforms. This alleged dream job has been portrayed as an avenue where individuals can earn substantial incomes, reaching up to $335K, simply by engaging in conversation with advanced AI models. Influencers on Instagram, YouTube, and TikTok have enthusiastically endorsed this concept. However, before diving into the allure of this dream job, it's crucial to examine the reality of the job market and separate fact from fiction. To gain insights into the demand for Prompt Engineers, an analysis of job advertisements was conducted, focusing on popular online job platforms. While the sample size of 73 job ads may not be exhaustive, it provides a comprehensive starting point for our examination. Contrary to the sensational claims, there appears to be a scarcity of employers actively seeking individuals with the title "prompt engineer." Examining the data reveals that the term "prompt engineer" is the most frequently mentioned job title. However, other titles such as "IT Innovation Analyst," "Freelance ML/AI Engineer," "Data Scientist," and "AI Engineer" are also prevalent. Word clouds representing qualifications and responsibilities from the job descriptions emphasize the significance of skills such as computer science, model development, Python proficiency, prompt design, machine learning, large language models, natural language processing, and artificial intelligence. Unveiling the Reality of Prompt Engineering Qualifications To shed light on the qualifications demanded for Prompt Engineering, ChatGPT and Claude were utilized to summarize the collected ads text corpus. The essential qualifications for a Prompt Engineer include: Proficiency in Python Programming: Demonstrating 2-5 years of experience, including familiarity with AI/machine learning frameworks such as TensorFlow, PyTorch, and Keras. NLP and LLMs Knowledge: 2-5 years of experience in Natural Language Processing (NLP) and Large Language Models (LLMs) like BERT, GPT-3/4, T5, etc. Analytical and Problem-Solving Skills: The ability to critically think, design effective prompts, analyze model performance, and troubleshoot issues. Prompt Engineering Expertise: Mastery of prompt engineering principles and techniques, such as chain of thought, in-context learning, tree of thought, etc. Communication Skills: Excellent verbal and written communication skills for collaboration, technical explanation, and documentation. Responsibilities of Prompt Engineering Jobs The responsibilities associated with Prompt Engineering jobs include: Prompt Design and Optimization: Crafting, testing, and refining AI-generated text prompts to maximize effectiveness for various applications, utilizing techniques like transfer learning. Integration and Deployment: Ensuring seamless integration of optimized prompts into products or systems, collaborating with engineers for implementation. Performance Evaluation and Improvement: Rigorously evaluate prompt performance using metrics and user feedback, conducting continuous testing and analysis for optimization. Collaboration and Requirements Gathering: Working closely with cross-functional teams to understand requirements and align prompts with business goals and user needs. Knowledge Sharing: Documenting prompt engineering processes, and outcomes, and educating teams on best practices. Contrary to the initial notion of requiring "no programming experience," the demand for programming proficiency and experience with NLP and LLMs is evident in the top qualifications for Prompt Engineering roles. Employers seek experts with 2-5 years of experience in computer science, coding, NLP, ML, and AI, dispelling the idea of a simple, code-free dream job. Degrees, Salaries, and the Evolution of Prompt Engineering Analysis of degree requirements in job ads indicates a preference for technical backgrounds in computer science, math, analytics, engineering, physics, or linguistics. A bachelor's degree in computer science or a related field is commonly required, with advanced degrees preferred for senior roles. Salaries vary widely based on responsibilities and seniority, ranging from 30k to half a million dollars per year. On average, positions with salary information offer between 90k and 195k annually. Despite the initial enthusiasm surrounding Prompt Engineering, doubts have arisen regarding its viability as a dream job. Scholars like Ethan Mollick argue that the role might not be a job of the future as AI becomes more intuitive in interpreting basic prompts. However, the importance of understanding and interacting with complex AI models is undeniable, with scientific studies suggesting that a systematic approach to prompting can enhance model outcomes. The Future of Prompt Engineering: Two Influential Trends
The future of Prompt Engineering and Gen AI applications seems to be shaped by two significant trends. First, Gen AI models are becoming more adept at generating quality outputs from simple prompts, akin to the evolution of internet search engines. Second, these models are increasingly integrated into businesses' products, services, and platforms. This integration is vital for the success of the AI economy, highlighting the importance of skills in optimizing, fine-tuning, customizing, and integrating Gen AI models with existing information systems and products. In conclusion, while the initial allure of "Prompt Engineering" as a code-free, six-figure dream job may not align with reality, the evolving landscape of AI and Gen AI applications underscores the value of programming, NLP, and AI expertise. Businesses are not merely seeking individuals who can chat with AI models but experts who can optimize and integrate these models into their products effectively. As the field progresses, the demand for skilled programmers, system designers, and collaborative team members in prompt engineering roles continues to grow. The journey toward becoming a Prompt Engineer might not be the instant, effortless dream portrayed by clickbait headlines, but it certainly remains an integrated and evolving reality in the world of AI. Senior Editor & Author: Alexander Ellington ChatGPT endeavours to integrate 'retention' to recollect your identity and preferencesChatGPT - Explore the latest in AI innovation as ChatGPT evolves with a groundbreaking 'Memory' feature, promising a more personalized chat experience. Uncover how AI navigates the delicate balance of user control and privacy, plus enhance user experience. Amidst its progression, OpenAI also sanctions a more intimate interaction. However, it ensures users retain authority. Interacting with an AI chatbot can induce a sense of repetition, akin to the movie Groundhog Day, where you repeatedly specify preferences for email formatting and recite completed weekend activities. OpenAI endeavours to rectify this by personalizing ChatGPT extensively. Introducing "retention" for ChatGPT enables the bot to store data pertaining to your persona and conversations over time. Retention operates in two modalities. Users can instruct ChatGPT to memorize specific details about them: such as coding exclusively in Javascript, identifying the boss as Anna, or noting a child's allergy to sweet potatoes. Alternatively, ChatGPT autonomously absorbs such information over time, amassing insights as interactions unfold. The overarching aim is for ChatGPT to exude a semblance of personalization and enhanced intelligence without necessitating constant reminders. Moreover, each customized GPT instance harbours its own repository of recollections. For instance, the Books GPT, with retention enabled, can autonomously recall previously perused books and favoured literary genres. The applicability of retention extends far and wide within the GPT Store. For instance, Tutor Me could tailor a more efficacious long-term curriculum once acquainted with your proficiencies; Kayak might streamline searches by prioritizing preferred airlines and accommodations; and GymStreak could meticulously monitor your fitness journey over time. Retention emerges as a crucial feature for ChatGPT, albeit fraught with complexities. While retention stands as a pivotal necessity for ChatGPT's evolution, it also navigates through a treacherous terrain. OpenAI's approach mirrors that of other online services in data acquisition—observing user behaviour, discerning search patterns, clicks, likes, and subsequently constructing user profiles. However, this approach often instigates discomfort among users. Many individuals express apprehension over OpenAI's assimilation of their queries and messages into training data, amplifying the bot's personalization. The notion of ChatGPT possessing insights into users' lives elicits a combination of admiration and unease. OpenAI reassures users of their command over ChatGPT's retention, assuring that sensitive information such as health-related data remains off-limits. Users retain the prerogative to inquire about the data stored by ChatGPT, with the option to delete or manage it via the newly introduced Manage Memory section in settings. Additionally, OpenAI proposes Temporary Chat as a pseudo-incognito mode, facilitating transient conversations without affecting ChatGPT's recollections. Alternatively, users possess the autonomy to disable retention across their entire account.
By default, retention stands activated, with OpenAI stipulating that recollections will inform future model enhancements. (Entities leveraging ChatGPT Enterprise and Teams remain exempt from data transmission to the models.) Currently, retention undergoes a trial phase, accessible to a select cohort of users as per the company's blog post announcement. Nevertheless, its conceivable how swiftly retention could metamorphose into an integral facet of ChatGPT interactions, for better or for worse. The bots evolve in sophistication, swiftly acquainting themselves with users' intricacies. Senior Editor & Author: Alexander Ellington Prompt Engineer - HR's New Challenge: Elevating Expertise in Advanced Prompt Engineering for AI UtilizationPrompt Engineer - Build your HR team's AI proficiency! Explore basic & advanced prompt engineering for optimal GenAI utilization. Discover the need for enhanced prompts & the power of fostering communities of practice. Stay ahead in the evolving AI landscape! In the era of artificial intelligence (AI), employees find themselves in need of guidance on harnessing its potential. David Creelman highlights the necessity of instructing employees on utilizing AI tools effectively, particularly as we delve into the realm of prompt engineering. Prompt engineering, once a rudimentary concept, has evolved significantly. HR tech professionals are now tasked with enhancing organizational capabilities in creating sophisticated prompts. As of late, prompt engineering has become more nuanced, prompting HR professionals to contemplate the development of prompt engineer expertise within their organizations. Basic and Advanced Prompt Engineering Basic Prompt Engineering For those unfamiliar, prompt engineering involves crafting questions effectively. Consider the following examples: Weak prompt: Tell me about change management. Better prompt: Explain the change management required for a new technology implementation in the aerospace industry. Detail-rich prompts lead to better results. Employees are encouraged to refine their basic prompt engineering skills to maximize the benefits of Generative AI. Useful examples include: "Provide examples of how to ____." "If needed, ask me questions to clarify the prompt before answering." "Respond as if you were the CFO." Advanced Prompt Engineering Prompt engineering has progressed beyond basic queries to a more sophisticated level. Prompt Engineer - Instead of viewing prompts as mere questions, experts suggest treating them as briefings, akin to those given to consultants. Advanced methods include: Chain-of-Thought Prompting: Taking the AI through a step-by-step thinking process. Few Shot Prompting: Providing examples to guide the AI, analogous to teaching a student. The Need for Sophisticated Prompt Engineering While not everyone may require advanced prompts, an organization's lack of knowledge in this area can result in a failure to harness the true power of GenAI. Prompt Engineers - Recognizing the potential for better outcomes, organizations should embrace advanced prompt engineering to fully leverage their AI tools. Embracing Communities of Practice
To stay current in the evolving field of prompt engineering, organizations should foster prompt engineer communities of practice. Unlike traditional topics with well-established knowledge, prompt engineering is an ever-evolving concept. Learning and Development (L&D) departments are well-suited to spearhead the Prompt Engineer initiative, setting up communities of practice to facilitate knowledge exchange among motivated individuals. Prompt Engineer communities of practice provide a platform for collaborative learning, enabling employees to advance their capabilities collectively. L&D departments, equipped with the prompt engineering expertise to support such communities, play a crucial role in enhancing overall organizational proficiency in prompt engineering. Prompt Engineer: While it's conceivable that AI may eventually surpass human prompt engineering capabilities, until that point, organizations should empower their human workforce with the support and encouragement needed to build internal Prompt Engineer expertise. As we navigate the evolving landscape of prompt engineering, staying ahead of advancements is imperative to unlocking the full potential of AI in the workplace. |
AI Developments - Explore the latest in AI Developments & AI Technology at our news hub. Stay updated on Artificial Intelligence Development, cutting-edge AI breakthroughs, & ChatGPT advancements. AI Developments are #1 source for concise, insightful AI news.
Artificial Intelligence Developments: Welcome to our cutting-edge portal dedicated to the latest AI Developments and breakthroughs in Artificial Intelligence (AI). As a premier source for AI Development news, we keep you abreast of the dynamic advancements in the world of Artificial Intelligence Development. Unveiling AI Developments Explore the forefront of innovation with our in-depth coverage of AI Developments. From breakthrough technologies to emerging trends, we bring you comprehensive insights into the ever-evolving landscape of Artificial Intelligence Development. Navigating Artificial Intelligence Development Dive into the intricacies of AI Development as we unravel the latest breakthroughs and trends shaping the future. Whether you're interested in the theoretical aspects or the practical applications, our platform (AI Developments} provides a holistic view of Artificial Intelligence Development. AI Development Insights Stay informed with our dedicated coverage of AI Development, offering valuable insights into the methodologies, tools, and best practices driving progress in Artificial Intelligence. From research laboratories to industry applications, we bring you the most relevant information on AI Developments. Artificial Intelligence Development Trends Explore the trends that define the field of Artificial Intelligence Development. Our platform (AI Developments) is your go-to source for staying updated on the cutting-edge technologies, methodologies, and industry collaborations that are shaping the trajectory of AI. Your Gateway to AI Innovation As your gateway to AI innovation, AI Developments curate the latest news and updates on Artificial Intelligence Development. Whether you are a tech enthusiast, researcher, or industry professional, our platform provides a comprehensive overview of the most significant developments in AI. Future-proofing with AI Discover how AI Development is influencing various sectors and industries. From healthcare and finance to manufacturing and beyond, our platform explores the transformative impact of Artificial Intelligence Development, helping you stay ahead in an AI-driven world. Stay Informed, Stay Ahead
Join AI Developments at the forefront of AI news as we navigate the ever-evolving landscape of AI Development and Artificial Intelligence. Stay informed, stay ahead, and delve into the exciting possibilities that AI continues to unfold. Welcome to your trusted source for AI Developments and Artificial Intelligence news. Exploring AI Developments AI Developments' commitment to delivering cutting-edge AI news extends across a diverse range of topics within the artificial intelligence domain. Dive into the forefront of AI technology with our extensive coverage on ChatGPT news, Generative AI, ai chatbot, and the latest advancements in openai chat. From AI chat online experiences to the intricacies of AI technology and AI ops, we've got you covered. Unravelling the World of Chat AI AI Developments: Explore the dynamic realm of chat AI with our in-depth discussions on AI chatgpt, AI chatbot gpt, and the best ai practices. Engage with open AI chat experiences and discover the potential of AI chat online interactions. Our platform Artificial Intelligence Developments is dedicated to keeping you abreast of the most recent developments in artificial intelligence chat, whether it's the fascinating world of Chat GPT AI or the transformative potential of AI GPT chat. Navigating AI in Various Industries Delve into the future of AI in finance, manufacturing, and medicine as we unravel the impact of AI development on these critical sectors. From AI automation revolutionizing manufacturing processes to the application of Python AI in the healthcare domain, AI Developments serves as your go-to resource for understanding the future of AI in diverse industries. Top AI Companies and Innovations
Stay updated on the top AI companies and their groundbreaking innovations. Our coverage includes insights into AI data analytics, AI ML, and the remarkable achievements of companies leading the AI summit. Whether it's exploring Document AI or delving into the potential of AI bots, Artificial Intelligence Development keeps you informed about the forefront of artificial intelligence website developments. The Intersection of AI and Machine Learning Uncover the synergy between AI and machine learning with our dedicated discussions on AI and ML chatbot. Explore the possibilities of AI in machine learning and discover how these technologies are reshaping the landscape of online AI experiences. Engage with AI to talk to, and stay informed about the latest trends in AI advertising and AI machine learning. Your Trusted Source for AI Insights As your trusted source for AI insights, we provide a holistic view of AI development, ensuring you're well-versed in the latest trends and breakthroughs. From Leonardo AI to AI development strategies, we cover it all. Our platform aims to be the best resource for those seeking valuable information on the present and future of artificial intelligence. Stay connected with Artificial Intelligence Developments to stay ahead in the ever-evolving world of AI Developments. Explore, engage, and enhance your understanding of artificial intelligence with our comprehensive coverage. Senior Editor & Author
Alexander Ellington Education: American University, BA in Journalism Alexander Ellington is the chief editor and reporter for AI Developments and a number of other media websites. He has compiled a variation of topics and covered a broad range of news stories throughout local, national, and international assignments. Alexander Ellington has also been writing and publishing news since 2005. Senior Reporter & Author Lax Marshal Manhattan, New York, United States Lax Marshal is the senior editor and reporter of AI Developments and he covers AI Ethics & ChatGPT . Ray James has been covering news since 2014 and has been writing press since 2008. Manhattan, New York, United States News Editor & Author Rick Anthony Rick Anthony is AI Developments correspondent, currently covering the AI ChatGPT developments and global Artificial Intelligence affairs. New York, NY, United States New York, NY, United States, New York, NY, United States Contact Email: [email protected] Archives
June 2024
Categories
All
Now.gg Roblox - Play Roblox on now.gg Online Free, Anytime, Anywhere: https://www.linkedin.com/pulse/nowgg-roblox-play-online-free-anytime-anywhere-seo-services-x6dfe
Now GG Roblox: The No #1 Guide to Maximize Your Roblox Experience & Play Roblox Online Free: https://www.linkedin.com/pulse/now-gg-roblox-1-guide-maximize-your-experience-play-online-services-5nmje
Now GG Roblox: The No #1 Guide to Maximize Your Roblox Experience & Play Roblox Online Free:https://www.linkedin.com/pulse/now-gg-roblox-1-guide-maximize-your-experience-play-online-services-5nmje/
AI Developments - Best 10 AI Developments for 2024 & after: https://www.linkedin.com/pulse/ai-developments-best-10-2024-after-seo-services-hkuoe AI Developments Subscription for fresh AI related news, innovations and Offers Upgrade your reality with AI Developments! 🌈 Subscribe to accelerate AI breakthroughs. Every subscription takes us one step closer to a smarter world. Join the movement! #AIDevelopmentsRevolution Subscribe by clicking the PayPal Subscribe button bellow. |