Merits and Demerits of AI -
- 1. Birla School Pilani Name - Prince Gajraj Class - X 'E' Topic - Merits and Demerits of AI Submitted To - Mr. Virendra Sir
- 2. How does AI work? • As the hype around AI has accelerated, vendors have been scrambling to promote how their products and services use it. Often, what they refer to as AI is simply a component of the technology, such as machine learning. AI requires a foundation of specialized hardware and software for writing and training machine learning algorithms.
- 3. What is Artificial Intelligence (AI) ? • Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision
- 4. Why is artificial intelligence important? • AI is important for its potential to change how we live, work and play. It has been effectively used in business to automate tasks done by humans, including customer service work, lead generation, fraud detection and quality control. In a number of areas, AI can perform tasks much better than humans.
- 5. What are the advantages and disadvantages of artificial intelligence? ADvantages Reduced time for data-heavy tasks. AI is widely used in data-heavy industries, including banking and securities, pharma and insurance, to reduce the time it takes to analyze big data sets. Financial services, for example, routinely use AI to process loan applications and detect fraud. Saves labor and increases productivity. An example here is the use of warehouse automation, which grew during the pandemic and is expected to increase with the integration of AI and machine learning. Delivers consistent results. The best AI translation tools deliver high levels of consistency, offering even small businesses the ability to reach customers in their native language. DISADVANTAGES Expensive. Requires deep technical expertise. Limited supply of qualified workers to build AI tools. Reflects the biases of its training data, at scale. Lack of ability to generalize from one task to another. Eliminates human jobs, increasing unemployment rates.
- 6. Differences between AI, machine learning and deep learning • Machine learning enables software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. This approach became vastly more effective with the rise of large data sets to train on. Deep learning, a subset of machine learning, is based on our understanding of how the brain is structured. Deep learning's use of artificial neural networks structure is the underpinning of recent advances in AI, including self-driving cars and ChatGPT.
- 7. AI programming focuses on cognitive skills that include the following Learning. This aspect of AI programming focuses on acquiring data and creating rules for how to turn it into actionable information. The rules, which are called algorithms, provide computing devices with step-by-step instructions for how to complete a specific task. Reasoning. This aspect of AI programming focuses on choosing the right algorithm to reach a desired outcome. Self-correction. This aspect of AI programming is designed to continually fine-tune algorithms and ensure they provide the most accurate results possible. Creativity. This aspect of AI uses neural networks, rules-based systems, statistical methods and other AI techniques to generate new images, new text, new music and new ideas.
- 8. Strong AI vs. weak AI • Weak AI, also known as narrow AI, is designed and trained to complete a specific task. Industrial robots and virtual personal assistants, such as Apple's Siri, use weak AI. • Strong AI, also known as artificial general intelligence (AGI), describes programming that can replicate the cognitive abilities of the human brain. When presented with an unfamiliar task, a strong AI system can use fuzzy logic to apply knowledge from one domain to another and find a solution autonomously. In theory, a strong AI program should be able to pass both a Turing test and the Chinese Room argument.
- 9. What are the 4 types of artificial intelligence? Type 1: Reactive machines. These AI systems have no memory and are task-specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on a chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones. Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self-driving cars are designed this way. Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams. Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.
- 10. Ethical use of artificial intelligence • While AI tools present a range of new functionality for businesses, the use of AI also raises ethical questions because, for better or worse, an AI system will reinforce what it has already learned. • This can be problematic because machine learning algorithms, which underpin many of the most advanced AI tools, are only as smart as the data they are given in training. Because a human being selects what data is used to train an AI program, the potential for machine learning bias is inherent and must be monitored closely.
- 11. What are examples of AI technology and how is it used today? • Automation. When paired with AI technologies, automation tools can expand the volume and types of tasks performed. An example is robotic process automation (RPA), a type of software that automates repetitive, rules-based data processing tasks traditionally done by humans. When combined with machine learning and emerging AI tools, RPA can automate bigger portions of enterprise jobs, enabling RPA's tactical bots to pass along intelligence from AI and respond to process changes. • Machine learning. This is the science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics. There are three types of machine learning algorithms:
- 12. AI governance and regulations • Despite potential risks, there are currently few regulations governing the use of AI tools, and where laws do exist, they typically pertain to AI indirectly. For example, as previously mentioned, U.S. Fair Lending regulations require financial institutions to explain credit decisions to potential customers. This limits the extent to which lenders can use deep learning algorithms, which by their nature are opaque and lack explainability.
- 13. What are the merits and demerits of artificial intelligence? • The advantages range from streamlining, saving time, eliminating biases, and automating repetitive tasks, just to name a few. The disadvantages are things like costly implementation, potential human job loss, and lack of emotion and creativity.
- 14. AI tools and services • AI tools and services are evolving at a rapid rate. Current innovations in AI tools and services can be traced to the 2012 AlexNet neural network that ushered in a new era of high- performance AI built on GPUs and large data sets. The key change was the ability to train neural networks on massive amounts of data across multiple GPU cores in parallel in a more scalable way.
- 15. What is the history of AI? • The concept of inanimate objects endowed with intelligence has been around since ancient times. The Greek god Hephaestus was depicted in myths as forging robot-like servants out of gold. Engineers in ancient Egypt built statues of gods animated by priests. Throughout the centuries, thinkers from Aristotle to the 13th century Spanish theologian Ramon Llull to René Descartes and Thomas Bayes used the tools and logic of their times to describe human thought processes as symbols, laying the foundation for AI concepts such as general knowledge representation.