Artificial Intelligence and Smart Assistants.pptx
- 1. ARTIFICIAL INTELLIGENCE and Smart Assistants BY: ANUJA PAWAR Student of Bsc.data science - "Revolutionizing Human-Computer Interaction"
- 2. 1.Introduction to AI 2.Importance and Impact of AI in Various Industries: 3.Types of Artificial Intelligence 4.Examples of AI applications 5.AI in Machine Learning 6.AI in NLP 7.AI in reinforcement learning (RL) 8.AI in Neural Network 9.Smart assistants 10.Examples of popular smart assistants 11.The interaction flow between users and smart assistants 12.Application of smart assistant 13.Benefits of Smart Assistants 14.Challenges and Limitations of Smart Assistants 15.CASE STUDY 16.Ethical Considerations 17.conclusion content
- 3. Artificial Intelligence (AI) refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, understanding natural language, and even interacting with the environment what is AI?
- 4. Although the terms artificial intelligence (AI) and machine learning are frequently used interchangeably, (machine learning is a subset of the larger category of AI. Artificial intelligence signifies computers' general ability to mimic athought while carrying out tasks in real- world environments Machine learning implies to the technologies and algorithms that allow systems to recognize patterns, make decisions, and improve themselves through experience and data. how does machine Learning Relate to Ai?
- 5. AI IN NLP Artificial Intelligence (AI) in Natural Language Processing (NLP) is a field that focuses on enabling computers to understand, interpret, and generate human language. It involves developing algorithms and models that can analyze text or speech data, extract meaningful information, and perform tasks such as language translation, sentiment analysis, and text summarization. AI- powered NLP systems have numerous applications across various industries, including virtual assistants, healthcare, customer service, and social media analysis. Despite advancements, challenges such as ambiguity in language understanding and ensuring fairness and transparency in NLP models persist. However, AI in NLP continues to evolve rapidly, driving innovations in communication and human-computer interaction.
- 6. AI in reinforcement learning Artificial Intelligence (AI) in reinforcement learning (RL) is a branch of AI focused on training agents to make sequential decisions in dynamic environments. Unlike supervised learning, where models learn from labeled data, RL agents learn by interacting with an environment and receiving feedback in the form of rewards or penalties for their actions. Through trial and error, RL algorithms aim to discover optimal strategies or policies to maximize cumulative rewards over time. AI in RL has applications in various domains, including robotics, autonomous vehicles, game playing, and resource management. Despite challenges such as exploration- exploitation trade-offs and sample inefficiency, RL continues to advance, driven by innovations in deep learning and algorithmic improvements.
- 7. AI IN NEURAL NETWORK •AI plays a central role in neural networks, a class of machine learning algorithms inspired by the structure and function of the human brain. Here's how AI is utilized in neural networks: •Learning Representations: AI algorithms, such as backpropagation, stochastic gradient descent (SGD), and optimization techniques like Adam and RMSprop, are used to train neural networks by adjusting the weights and biases of connections between neurons to minimize the error between predicted and actual outputs. Activation Functions: AI algorithms are used to design and select activation functions for neurons in neural networks, determining how inputs are transformed into outputs. Common activation functions include sigmoid, tanh, ReLU (Rectified Linear Unit), and softmax, each serving different
- 8. EXAMPLE AI can be used for various situations, but these are some examples of AI in our daily life. E-commerce Virtual Assistance Autonomous vehicles chatbots Recommendation systems Navigation apps Facial recognition Text editors
- 9. SofiA THE AI ROBOT Sophia is a realistic humanoid robot capable of displaying humanlike expressions and interacting with people. It's designed for research, education, and entertainment, and helps promote public discussion about AI ethics and the future of robotics.
- 10. WHAT PROBLEMS CAN AI SOLVE? As shown above, AI can solve a LOT of problems. Let's explore a few on the next slide! → dectecting spam → medical records → idea generation, finding data → self driving cars
- 11. USE OF AI (Advantages of ai) Image and facial recognition It can help make data safer and more secure. For example, face authentication can ensure that only the appropriate person has access to sensitive information that is intended specifically for them Medical diagnosis Provides more exact diagnoses, detects hidden patterns in imaging investigations, and predicts how patients will respond to specific medications. This leads to better treatment strategies, fewer clinical errors, and more accurate diagnosis. Customer service Customer service teams can get feedback from customers by using AI. For example, AIpowered information can provide agents with information on client intent, language, and sentiment so they are aware of how to approach an encounter. Recommendation systems AI content recommendations help people stay engaged and informed. For example, Virtual(Siri and Alexa.), Personalized content on streaming platforms, Apps that suggest best routes based on traffic.
- 12. what are the disadvantages of AI? • Lack of Transparency • Bias and Discrimination • Privacy Concerns • Ethical Dilemmas • Security Risks • Concentration of Power • Dependence on AI • Job Displacement → lying about using AI → assumtion based of incorrect information
- 13. Put People First People should use their own creativity, not copy off of AI! AI is just a tool for efficiency! Minimize unintended bias Consider data and privacy goals Ensure AI transparency
- 14. REsponsible Ai USe AI can help do repetitive work for humans, but humans should still be prioritized. Create a culture that utilizes creativity, empathy, and dexterity from humans and AI for increased efficiency Businesses should adopt strong security measures, limit access to sensitive data, and anonymize data whenever possible to secure data privacy with AI and ML technologies There needs to be fairness in AI which entails identifying and eliminating discrimination while also encouraging diversity and inclusion. This is can be done by using training models with equal representation Develop explainable AI that is visible across processes and functions to generate trust among employees and customers. Provide examinability, comprehension, and traceability.
- 16. Smart assistants, also known as virtual assistants or intelligent personal assistants, are software applications or platforms that utilize artificial intelligence (AI), natural language processing (NLP), and machine learning algorithms to provide users with personalized assistance, perform tasks, and retrieve information in response to voice commands or typed queries. what is Smart Assistant
- 17. Examples of popular smart assistants Siri (Apple): Siri is Apple's virtual assistant, available on iOS devices (iPhone, iPad, iPod Touch), macOS, watchOS, and HomePod. Users can interact with Siri using voice commands to perform various tasks such as sending messages, making calls, setting reminders, playing music. Google Assistant: Google Assistant is Google's virtual assistant available on Android devices, iOS devices, Google Home speakers, smart displays, and other third- party devices. It can perform tasks similar to Siri, as well as provide personalized recommendations, control smart home devices, manage schedules, and answer questions using Google's vast knowledge graph.
- 18. The interaction flow between users and smart assistants The interaction flow between users and smart assistants typically follows a sequence of steps that involve input from the user, processing by the smart assistant, and output or action taken by the assistant. Here's a general overview of how the interaction flow works: • Wake Word Activation: The interaction begins when the user triggers the smart assistant by saying a wake word or phrase. This wake word activates the assistant and signals it to start listening for the user's command. • Input/Input Recognition: Once the wake word is detected, the smart assistant listens to the user's input, which can be in the form of a voice command or a typed query. • Intent Recognition: After understanding the user's input, the smart assistant identifies the user's intent or the action the user wants to perform • Processing and Contextual Understanding: The smart assistant processes the user's request, taking into account contextual information such as the user's preferences, past interactions, location, and other relevant data
- 19. Application of smart assistant • Home Automation: Smart assistants can control smart home devices such as thermostats, lights, locks, cameras, and appliances. Users can use voice commands to adjust settings, turn devices on or off, or create automation routines. • Personal Organization: Smart assistants help users manage their schedules, set reminders, create to-do lists, and organize appointments. They can also provide weather forecasts, traffic updates, and travel information. • Entertainment: Users can use smart assistants to play music, podcasts, audiobooks, and radio stations.
- 20. CASE Study Here are a couple of case studies highlighting how organizations have leveraged smart assistants to achieve success: Domino's Pizza: •Background: Domino's Pizza, a global pizza delivery company, wanted to enhance customer experience and streamline the ordering process. •Solution: Domino's introduced its virtual assistant, Dom, which allows customers to place orders using natural language commands via various platforms, including the Domino's website, mobile app, and smart speakers. •Successes: Dom has simplified the ordering process, making it faster and more convenient for customers. By integrating with various channels, Dom enables seamless ordering experiences across different platforms. •Lessons Learned: Domino's success with Dom highlights the importance of understanding customer preferences and providing convenient, intuitive interfaces for interacting with smart assistants. Continuous iteration and improvement based on user feedback are crucial for optimizing smart assistant performance and enhancing customer satisfaction.
- 21. . CONCLUSION We conclude that if the machine could successfully pretend to be human to a knowledgeable observer then you certainly should consider it intelligent. AI systems are now in routine use in various field such as economics, medicine, engineering and the military, as well as being built into many common home computer software applications, traditional strategy games etc. In conclusion, the implementation of smart assistants presents both opportunities and challenges across various industries and use cases. Smart assistants leverage AI and natural language processing technologies to streamline tasks, enhance productivity, and improve user experiences. However, several considerations must be addressed to maximize the benefits of smart assistant technology
- 22. Thank you