Klover

Jul 31, 2024

Global Thought Leader in Cybersecurity and Emerging Tech getty AI has become commonplace in our daily lives. Now, AI can read organized and unstructured data, figure out what is wrong, and solve problems, sometimes without being told to do so. Artificial intelligence is evolving, and humans are increasingly meshing with artificial intelligence. To make machines that are like people in terms of behavior and intelligence, AI systems try to copy human traits and abilities and go faster and further than humans. Should we fear AI will go beyond the current human/machine/algorithm mesh and overtake us in the capacity and capability to work and eventually lead the world? Not if we follow a different path to AI agents. The Dawn of AI Agents Artificial intelligence concept. Cloud computing. Deep learning. getty According to the global consulting firm McKinsey & Company, we are entering a new era of generative AI. McKinsey believes that “we are beginning an evolution from knowledge-based, gen-AI-powered tools—say, chatbots that answer questions and generate content—to gen AI-enabled “agents” that use foundation models to execute complex, multistep workflows across a digital world. In short, the technology is moving from thought to action.” Why agents are the next frontier of generative AI | McKinsey Prioritizing and acting on data can help people make better decisions with these technologies, especially in bigger networks with lots of users and variables. Basic tasks that computers with AI are now being built to do include recognizing speech, learning, planning, and solving problems. MORE FOR YOU According to Dany Kitishian, a global tech pioneer, and founder of Klover.ai. “An AI agent, or intelligent agent, is a sophisticated software entity designed to perceive its environment, make informed decisions, and perform actions to achieve specific objectives. These agents are fundamental components of intelligent systems, leveraging sensors and actuators to interact with their surroundings. By integrating natural language processing and machine learning, AI agents can significantly enhance communication and user interactions, making them indispensable in modern technological landscapes. The flexibility and adaptability of AI agents enable them to operate autonomously, often outperforming traditional software in dynamic and complex environments.” See Klover.AI — Humanizing AI to help people make better decisions that improve their lives. – We are the pioneers of Artificial General Decision Making Kitshian stresses that such agents need not replace humans assumed by Artificial General Intelligence (AGI). He envisions a future where machines possess human-like cognitive abilities. In stark contrast to the pursuit of AGI, Klover.ai advocates for Artificial General Decision-Making (AGD), a technology designed to augment and enhance human decision-making processes. Klover.ai’s approach leverages similar underlying technologies as AGI but with a different goal: to enhance human capabilities rather than replace them. By focusing on improving decision-making processes, AGD aims to empower individuals, allowing them to achieve their full potential and make better-informed choices. The idea of agent-driven automation is an interesting pathway and has the potential to augment human capabilities rather than replace them. Leading AI and Emerging Technology thought leader Dr. David Bray, like Dany Kitshian, is a strong advocate of pursuing and prioritizing strategies that enhance human capabilities with AI tech. “When we talk about people-centered, it is really the idea that AI technology should amplify human strengths. It’s about providing and informing [systems] with data to allow people more opportunities in their work.” 5 steps to ‘people-centered’ artificial intelligence | MIT Sloan Making Advanced AI Agents Come To Life Across Industries Smart city power energy industry sustainable oil gas plant control IOT internet of thing ICT digital ... [+] technology futuristic, automation management smart digital technology security and database getty What does an AI agent-driven architecture look like and how is it implemented? Klover.ai achieves this by using a multi-agent architecture that involves deploying a network of AI agents that work together to perform complex tasks. Klover.ai uses ensembles of AI systems with multi-agent systems as a core that are created uniquely for each decision and persona. These agents can specialize in different areas, such as data analysis, decision-making, and task execution, and can coordinate their efforts to provide comprehensive solutions. This collaborative approach leverages the strengths of each agent, leading to more efficient and effective outcomes. The company envisions a future of 172 billion AI agents all interacting on behalf of people and corporations to enable better decision-making and lead to prosperity and security. Human training, and management, will be key for AI agents to thrive. Bill Bowers, former Chief of Architect and Engineering Branch of the CIA, and a Strategic Advisor to the Diversified Outlook Group, LLC. notes that “learning to effectively use AI agents is less about understanding the technology and more about becoming a first-line manager. Your primary focus shifts from doing to directing and developing new skills of vision casting, coaching, and correcting. The good news is that the AI agents are not offended when you get it wrong.” AI Agents; Assisting Dev Ops and Cybersecurity Big data. Information concept. 3D render getty It does start with the coding, algorithms, and development of the architectures. And that necessitates Dev Ops. Dennis Pollutro, Managing Partner of the Diversified Outlook Group, and a former CxO, Cyber & Tech SME, Inventor characterizes AI as more than just another tool in the DevOps toolkit. He says “It is the force multiplier we have been waiting for. With AI, we can take the expertise of our top performers and spread it across the entire pipeline. It is like having your best DevOps engineer's brain working 24/7, making decisions in milliseconds. This is not about replacing our teams – it is about supercharging them. We are finally able to scale our capabilities to match the crazy pace of innovation in this field, without burning out our people or breaking the bank.” Diversified Outlook Group – Expert Business Strategists For cybersecurity, AI can provide a faster means to detect and identify cyber threats. Intelligent agent algorithms can be used to keep an eye on network anomalies, spot emerging dangers without established signatures, and detect them. Additionally, agents can be used to correlate data from silos to evaluate network risks and vulnerabilities as well as comprehend the nature of attacks. Generative AI can also help with coding, and AI agents can reshape practical cybersecurity models. Smart algorithms will make it necessary to keep changing how risk is managed and how technology is used. Keeping business stability and cyber-resilience will require dealing with new threats that are getting more complicated over the next few years. Functional AI Agents Are already Operational AI chatbot usage and concepts getty We are already operating in a world filled with AI-enabled agents. Chatbots, voice assistants, and other messaging apps that use conversational AI to fully automate customer service and provide round-the-clock help are useful for a variety of industry sectors. There are a variety of categories of AI agents that we often overlook. This includes Utility-Based Agents (i.e., financial trading, smart grid), Goal-Based Agents (i.e., Roomba vacuum, or video games), Learning Agents (i.e., fraud detection, content recommendation), Robotic Agents (i.e., manufacturing assembly lines, agricultural, surgical), and Model-Based Reflex Agents (i.e., autonomous vehicles, home automation systems), among others. Please see the following resource of the many types of agents: Real-World Examples of AI Agents (botpress.com) The AI Agent Future