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What Is Artificial Intelligence (AI)?

Artificial intelligence is the ability of a machine or computer to simulate human cognitive function. A progressive learning algorithm enables the machine to perform tasks that normally require human intelligence, including thinking, adapting, taking action and problem solving. An AI adjusts its behavior based on the datasets it processes, constantly evolving based on new information. Rapid iterative processing hardware and sophisticated algorithms are used to identify patterns within the data. Data processed by AI comes from multiple sources including the Internet of Things (IoT), historic records, social media, public and government sources, and scientific research. An AI can often perform tasks much faster than a human, which has led to major disruptions in the tech industry, among others.

The concept of artificial intelligence was first introduced in the mid-20th century. It became popular in everyday life through virtual assistants, chatbots, autonomous vehicles and smart home technology. The use of application programming interfaces, or APIs, is also boosting the prevalence of AI in modern technology. APIs make artificial intelligence infrastructure accessible to other applications by allowing developers to add AI functions to their applications.

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Types of AI

AI can be broken down into seven types based on their capability and functionality. Narrow AI, general AI and super AI are based on capability, while reactive machines, limited memory, theory of mind and self-awareness are based on functionality.

  1. Narrow AI: This form of artificial intelligence is designed to operate within a limited range of functions. It lacks the intelligence to complete tasks beyond this barrier that are not pre-defined.
    • A real-world example of narrow AI is virtual personal assistants, like Apple's Siri, Amazon's Alexa and Google Assistant, or an AI language model like ChatGPT.
  2. General AI: Also known as general artificial intelligence, this is a hypothetical type of AI that can perform any intellectual task that a human can perform. It can understand, learn, adapt and apply knowledge. This form has not yet been achieved by AI research.
  3. Super AI: Artificial superintelligence, or super AI, refers to a hypothetical AI that surpasses human intelligence in all aspects, including creativity, problem-solving, desires and emotions.
  4. Reactive machines: Reactive machines can use only real-time data to determine future actions. They ignore everything besides the present and cannot learn from past experiences.
    • Spam filters are a real-world example of reactive machine AI.
  5. Limited memory: Limited memory AI uses past data to make decisions. It stores knowledge but its memory is not infinite.
    • Self-driving cars use limited memory AI to observe other cars on the road.
  6. Theory of mind: Theory of mind AI is another hypothetical AI type that can recognize and react to human emotions.
  7. Self-awareness: Another hypothetical form of AI, self-awareness AI not only has the ability to perceive human emotions, but it can also understand itself internally enough to have emotions and beliefs.

AI Challenges

When using artificial intelligence tools, there are some challenges to keep in mind:

  • Data bias: It is possible for AI algorithms to inherit biases present in training data, leading to biased decision-making and outcomes.
  • Data availability: AI relies on data to provide accurate, reliable services to users. Ensuring data quality and dealing with data scarcity in certain domains can be a challenge.
  • Privacy: The data processed by AI can include personal and sensitive information, causing concerns about privacy. To alleviate worry, companies adopting an AI infrastructure can implement data anonymization or introduce private AI architecture.
  • Control: The integration of generative AI introduces risks of control over access to AI models, content ownership and IP contamination.
  • Data integrity: Many organizations have established policies restricting employee use of generative AI tools in the wake of employees accidentally leaking sensitive internal data onto public AI tools.
  • Cost: Training and managing generative AI models is expensive. It requires massive amounts of specialized compute power and high-speed networking with lots of memory.
  • Vendor lock-in: The talent needed to build, fine-tune and run AI models is in short supply. This means it may be difficult to pivot to new innovations as they emerge without being locked into any single platform or vendor.

AI Benefits

AI has massive potential across numerous domains to drive innovation, efficiency and improvements in quality of life. However, as AI continues to advance, ethical and societal consequences must also be considered when discussing benefits.

  • Automation: AI can automate repetitive and time-consuming tasks, allowing humans to focus on other aspects of their work. This increases productivity and efficiency.
  • Data analysis: AI can analyze large datasets quickly with heightened accuracy, providing valuable insights.
  • Reduction in error: A major benefit of artificial intelligence is its ability to negate human error. When programmed properly, the work of AI is more precise than the work of humans.
  • AI for good: AI can address complex global challenges, including climate change, disease outbreaks and food security, by analyzing and modeling data to inform policy decisions.

AI Use Cases

AI research and development continue to advance with the goal of creating more capable and versatile AI systems. AI has a wide array of use cases across multiple industries. In an era when organizations are rushing to supplement their products with generative AI, it’s important to first ensure that the features being built are intuitive and resonate with customers. Product leaders should consider what pain points AI should address, whether AI aligns with the organization’s core values, what data is needed for AI to work properly, and how it will impact the user experience.

Popular use cases for AI include:

  • Chatbots and virtual assistants use natural language processing AI to provide 24/7 customer support, answering questions and resolving issues promptly.
  • AI enables self-driving cars and autonomous drones, potentially reducing accidents and improving transportation efficiency. Autonomous vehicles use limited memory to make critical decisions on the road, collecting data on traffic lights and the position of other vehicles.
  • Manufacturing efficiency and precision is enhanced with AI-driven robots. They can perform tasks like quality control and material handling.
  • AI security systems can detect and respond to cyber threats in real time by analyzing network traffic, identifying anomalies and preventing attacks.
  • AI optimizes cloud resource allocation based on usage patterns, reducing costs and improving efficiency.

How VMware Can Help

AI research and development continue to advance with the goal of creating more capable and versatile AI systems. AI has a wide array of use cases across multiple industries. In an era when organizations are rushing to supplement their products with generative AI, it’s important to first ensure that the features being built are intuitive and resonate with customers. Product leaders should consider what pain points AI should address, whether AI aligns with the organization’s core values, what data is needed for AI to work properly, and how it will impact the user experience.

Popular use cases for AI include:

  • Chatbots and virtual assistants use natural language processing AI to provide 24/7 customer support, answering questions and resolving issues promptly.
  • AI enables self-driving cars and autonomous drones, potentially reducing accidents and improving transportation efficiency. Autonomous vehicles use limited memory to make critical decisions on the road, collecting data on traffic lights and the position of other vehicles.
  • Manufacturing efficiency and precision is enhanced with AI-driven robots. They can perform tasks like quality control and material handling.
  • AI security systems can detect and respond to cyber threats in real time by analyzing network traffic, identifying anomalies and preventing attacks.
  • AI optimizes cloud resource allocation based on usage patterns, reducing costs and improving efficiency.

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