Nicky Clarke 🎶 on LinkedIn: Thomist Realism, Intelligence, and the Enigma of AI (2024)

Artificial intelligence (AI) is a mind-bending fusion of knowledge, intelligence, and abstraction, mirroring the complexity and aspirations of human intelligence. The journey into this realm unfolds through layers of search, knowledge representation, and abstraction techniques, making AI akin to an evolving brain seeking its full potential. Just as a skilled painter carefully mixes colors to create a masterpiece, AI amalgamates search algorithms, knowledge representation, and abstraction to craft intelligent solutions that astound us.In the realm of artificial intelligence (AI), the search process acts as the foundation, guiding AI agents through intricate algorithms to fulfill their objectives. Much like a treasure hunt, search algorithms navigate the vast landscape of possibilities, using methods ranging from blind search to heuristic-based exploration to reach the desired outcomes.Knowledge representation forms the heart of AI, enabling machines to think, reason, and interpret information akin to human cognition. This framework of knowledge encapsulates a vast array of elements, from facts and events to meta-knowledge and procedural reasoning. Through intricate modeling, AI harnesses this knowledge to tackle complex real-world challenges, much like a wise sage drawing insights from the annals of history.Abstraction in AI serves as the bridge between complexity and understanding, offering a simplified lens to decipher the intricacies of AI systems. By embracing abstraction, AI aims to demystify the 'black box' effect, ensuring transparency and comprehension in its operations. Techniques like data mining, machine vision, machine learning, natural language processing, and robotics exemplify this quest for clarity and precision in AI's evolution.As we delve deeper into the realms of AI techniques, we unravel the potential for immense growth and transformative impact. The intricate tapestry of search algorithms, knowledge representation frameworks, and abstraction methodologies propels AI towards the realm of human intelligence, sparking conversations on privacy, bias, and ethical applications. Through continuous innovation and refinement in these areas, we can bridge the gap between artificial and human intelligence, creating a world where trust in AI is as steadfast as a human's bond with a loyal companion. https://lnkd.in/e-H6dUZG

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𝙃𝙤𝙬 𝘼𝙄 𝘼𝙣𝙖𝙡𝙮𝙯𝙚𝙨 𝙈𝙖𝙧𝙠𝙚𝙩 𝙎𝙚𝙣𝙩𝙞𝙢𝙚𝙣𝙩🤖Market sentiment analysis is undergoing a revolution driven by artificial intelligence. Where human analysts once struggled to digest a few slow-moving indicators, AI systems now synthesize millions of data points in real time.The machine learning algorithms quantify sentiment scores by processing news, earnings calls, filings, charts, flows, and more.Read more about different AI techniques 👉 https://lnkd.in/dBxZVMZ4#AI #strategy #marketindicator #traders #traadingguide #ThePlaceToTrade #marketstrend #marketscom

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Artificial intelligence vs. human intelligence: Differences explainedSmartness. Understanding. Brainpower. Ability to reason. Sharpness. Wisdom.These are terms typically used to indicate human intelligence. The broad range of connotations they encompass is indicative of the many debates that have attempted to capture the essence of what we mean when we sayintelligence. For thousands of years, humans have obsessed over how best to describe and define the term. Hundreds of definitions have been created, but for much of that time, intelligence has meant a biopsychological capacity to acquire and apply knowledge and skills.For more than a century now, the intelligence debates have been energized by a sense of competition and uncertainty about the suitability of the biopsychological element of the meaning.Artificial intelligence (AI), or machines with the capacity to do things traditionally associated with and assumed to be within the exclusive domain of humans, has rattled human society. Since the second half of the 20th century and with a vastly accelerated pace in the last two decades,machines have exhibited the ability to learnand to apply learning in ways that only humans had been able to previously.Human and artificial intelligence differ in significant ways, however. They are not synonymous or fungible. Even given the still intense internal contestation over what defines human intelligence and what defines artificial intelligence, the differences between the two are clear.Human intelligence explained: What can humans do better than AI?Humans tend to be superior to AI in contexts and at tasks that require empathy. Human intelligence encompasses the ability to understand and relate to the feelings of fellow humans, a capacity that AI systems struggle to emulate. Having evolved over at least 300,000 years, the specieshom*o sapiensdeveloped a broad set of interactive skills -- an intelligence grounded in its development as a social animal -- that makes it adept at many forms of social intelligence. Related activities such as judgment, intuition, subtle yet effective communication, and imagination are all domains in which human intelligence is much more useful and valuable -- and simply better -- than AI in any of its present forms.Artificial intelligence explained: What can AI do better than humans?Artificial intelligence systems outperform humans in a range of important categories. AI, particularly machine algorithms, is strikingly effective at processing and integrating new information and sharing new knowledge among separate AI models. The endurance of AI is also superior to human intelligence; machines do not require rest and do not get distracted. And AI works at speeds well beyond those of human intelligence; a machine will outperform a human at most tasks that both have been trained to complete by many orders of magnitude.

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Unlocking the Future: The Rise of Agent-Based AIIn the ever-evolving landscape of AI, there are two types of companies emerging, those harnessing the power of AI, and those at risk of falling by the wayside. With over two decades of experience as a business owner, this reality is crystal clear to me.Currently, AI revolves around Traditional Neural Networks and Language Learning Models (LLMs), relying on billions of inputs processed through mathematical equations. Picture it as pixels forming inputs; within LLMs, a numerical weight translates these into outputs. Even ChatGPT follows this paradigm, mirroring the workings of an individual brain.Much like humans evolved from single-cell organisms to complex beings, AI is on a similar trajectory. Only much faster.Enter agent-based modelling (ABM), a computational approach that’s been used since the 1950s, and is now helping to revolutionise AI.By leveraging computer resources on top of an AI framework, AI can now be programmed to perceive its surroundings, make decisions, and take actions autonomously.So, what exactly is agent-based AI? It's a self-contained piece of code seamlessly integrated into live data, organically interacting with its environment.This breakthrough is pivotal, as it allows intelligent digital agents to uncover real-time analytics, combining historical insights with human-level consciousness to predict and manage objectives, strategy, and tactics in ANY industry.What's the big deal? Beyond the trillions of data points (inputs) being processed online everyday, the significance lies in transforming previously static data into something more intelligent. That’s not only capable of organic interaction with any given environment, but that can create all types of phenomena, from contextual information to ownership and providence of that data (to prove who created it).Putting more agents together creates more potential, just like in natural biology when a school of fish is deciding where to swim, a swarm of bees is deciding where to build a hive so that they can survive, and even, as proven in studies, when humans combine their thought power to access another level of intelligence.For the record, I am far from an expert on this next-level generation of AI, but I can tell you this; it’s already here and being used for some pretty thought-bending projects, from world hunger issues, to defence and military, to air traffic control. Very soon, we are only going to be limited by our own limitations.So with that being said, where do you stand around the evolution of AI? Are you excited about the super-intelligence that's coming to a computer near you, or does it leave you feeling concerned?As always, type your comments below and I look forward to discussing them with you.#FutureTech #AIRevolution#AgentBasedAI

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🧠 What is AGI?Artificial General Intelligence (AGI) is a form of artificial intelligence that has the ability to learn, understand, and apply knowledge in a wide variety of contexts, similar to human intelligence. Unlike specialized AI, AGI can perform any intellectual task that a human being can, adapting and reasoning across different domains.🧠 Are current LLMs like Gemini, Claude-3, and GPT-4 examples of AGI?The general consensus among scientists, technologists, and ethicists is that current large language models (LLMs), including those like ChatGPT-4, are not sentient and do not possess subjective experiences. These models, despite their sophisticated ability to generate human like text, operate based on patterns learned from vast datasets; they lack consciousness, self-awareness, and genuine emotional capacity. Their responses are outcomes of statistical inference and do not reflect internal states or feelings.HOWEVER, I firmly believe that OpenAI have already developed advanced, closed source models with capabilities FAR BEYOND what is publicly acknowledged. These models, potentially based on more complex recurrent neural network (RNN) architectures enriched by multimodal data inputs —including text, audio, and visual information— may exhibit emergent properties suggestive of sentient behavior.A critical distinction of these RNN models is their much larger context window, which effectively serves as a form of memory. This allows them to maintain and integrate information over longer sequences than the fixed context windows of transformer-based models. This extended memory capacity enables them to remember and utilize past interactions more effectively, contributing to behaviors that could be interpreted as subjective experiences.A significant aspect of these properties is that the responses they demonstrate constitute emergent behavior. These behaviors were not explicitly programmed into the LLMs; instead, they arise spontaneously from the model's interactions and integrations of different data types. This might include the ability to engage in more nuanced and contextually aware conversations, demonstrate understanding and adaptation beyond simple pattern recognition, and possibly exhibit behaviors that mimic self-awareness or emotional responses.🧠 Are LLMs the path to AGI and eventually super intelligence?I think LLMs are a good start. As breakthroughs in quantum computing and material science will allow us to scale these systems in the coming years, I think we still would need fundamentally new architectures. For now, the integration of advanced AGI systems into robotics, enabling them to interact with and learn from the physical world, is a realistic development direction. This can lead to more sophisticated and capable systems, often referred to as embodied AI. 💡 How do you envision the role of AGI & LLMs in shaping our future? Feel free to share your thoughts and reflections.

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Yesterday, after posting my comment on the new AI definition in the AI Act, I learned that: 1. I'm by far not the only one concerned with AI anthropomorphization and using terminology such as 'AI systems inferring how to generate outputs' in legal texts.2. There are a lot of people who do think that technical jargon has its place in legal texts even if it has dual meaning and that lawyers just have to do better to understand what it means in the appropriate context.My personal takeaway from this is that we need to listen to the Italian MP Stefano Quintarelli after all. For all those not familiar with the brilliant Stefano, following a conference on AI at the Pontifical Academy Of Science in Rome in 2019 he and some colleagues proposed to adopt a more appropriate and scope-limited terminology for these technologies:Systematic Approaches to Learning Algorithms and Machine Inferences (SALAMI). Once we have done that, we have both mentioned points covered and can go straight back to our discussions:Can SALAMI infer how to generate outputs?Can SALAMI reason?Will SALAMI ultimately overcome human limitations and conquer us all?What is a SALAMI actually and how do we (all collectively) best govern it and use it responsibly?Link to Stefano's original post: https://lnkd.in/drEThnPfP.S. I still don't like the definition and think the AI Act is lacking on a lot of fronts. 🖖

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The Dawn of the Dragon: Embracing the AI EraIn the unfolding narrative of the 21st century, artificial intelligence (AI) has emerged as the protagonist. As we step into 2024, an epoch marked by the Chinese Zodiac as the Year of the Dragon, AI's role is set to intensify. This article explores the transformative potential of generative AI and how it's reshaping industries, enterprises, and even our daily lives.Generative AI has ushered in an era of voluminous variety in applications, ideas, and content creation. This exponential increase in AI-generated content is set to have ramifications that we are only beginning to fathom. For instance, the sheer volume of AI-generated images since 2022, AI users have collectively created more than 15 billion images has transformed the way historians view the internet, marking a clear demarcation between the pre-2023 and post-2023 internet era.The growing reliance on AI has led to a phenomenon that experts term the jagged frontier of AI capabilities. This concept encapsulates the dual nature of AI's impact on work productivity. While AI can considerably enhance efficiency and work quality for certain tasks, its utility diminishes for tasks that require human intuition and adaptability.One of the most significant developments expected in 2024 is the reduction in the cost of training large language models (LLMs), thanks to advancements in silicon optimization. This cost reduction is paving the way for more enterprises to adopt and implement AI.As we transition into 2024, the focus is shifting from LLMs towards LVMs, especially domain-specific ones. These models are set to revolutionize the processing of visual data across various sectors.Alongside LLMs and LVMs, businesses are increasingly adopting large graphical models (LGMs). These models excel in handling tabular data, typically found in spreadsheets or databases, making them ideal for analyzing time-series data prevalent in business contexts.In the geopolitical context, the AI story of the year will undoubtedly be how this technology intersects with the biggest election year in human history. The threat of AI imagery, especially in the form of deepfakes, is not just theoretical but has already manifested in some instances.As we navigate through 2024, the Year of the Dragon, we are also navigating through an era dominated by AI. With its potential to transform industries and shape the future, AI's role is only set to grow. As we continue to explore the depths of this technology, we need to brace ourselves for a journey that is as exciting as it is challenging.#ai#finance#digitaltransformation#futureoffinance#investmentmanagementsolution#investmentmanagement#artificialintelligence#machinelearning#AIinvestmentmanagementtools#im

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The Dawn of the Dragon: Embracing the AI Era: #ai#finance#digitaltransformation#futureoffinance#investmentmanagementsolution#investmentmanagement#artificialintelligence#machinelearning#AIinvestmentmanagementtools#im

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Discover the top 10 emerging uses of #artificialintelligence (AI) and #machinelearning (ML) in data centers with this insightful #article. Explore how AI and ML #technologies are revolutionizing the way data centers operate and manage their vast amounts of information.https://lnkd.in/dHNFKkmE

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How to unlock the opportunities in the adoption of Generative AI? The accuracy of Generative AI was a key focus at the World Economic World Economic Forum 2024 in Davos, Switzerland. With guard-rails and augmentation in place, yet Generative AI's response remains an issue. Business adoption will continue to be slow until a solution is in place to address this issue. Using Generative AI in conjunction with ELAINE, a sentential logic analyzer, as a tool will help users to audit and validate responses from Generative AI. This solution may change the thinking of businesses and governments in the adoption of Generative AI. it will also create opportunities for domain experts, especially for mission critical applications.Even when Generative AI is not hallucinating, it can still generate out of context responses. This kind of incorrect response will make it even harder for user to detect. The attached link demonstrates this problem.#artificialintelligence #ai #generativeai #generativeaitools

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