6CS4_AI_Unit-5 @zammers.pptx(for artificial intelligence)

Artificial intelligence

1. ARTIFICIAL INTELLIGENCE (6CS4-05) Unit V Introduction To Natural Language Processing By: Er. Shweta Saraswat

2. DIFFERENT ISSUE INVOLVED IN NLP

3. INTRODUCTION Artificial intelligence has become part of our everyday lives – Alexa and Siri, text and email autocorrect, customer service chatbots. They all use  machine learning algorithms  and  Natural Language Processing (NLP)  to process, “understand”, and respond to human language, both written and spoken.

4. Language Processing limitations and problems: Contextual words and phrases and homonyms Synonyms Irony and sarcasm Ambiguity Errors in text or speech Colloquialisms and slang Domain-specific language Low-resource languages Lack of research and development

5. Contextual words and phrases and homonyms The same words and phrases can have different meanings according the context of a sentence and many words – especially in English – have the exact same pronunciation but totally different meanings. For example: I  ran  to the store because we  ran  out of milk. Can I  run  something past you real quick? The house is looking really  run  down. These are easy for humans to understand because we read the context of the sentence and we understand all of the different definitions. And, while NLP language models may have learned all of the definitions, differentiating between them in context can present problems. Homonyms – two or more words that are pronounced the same but have different definitions – can be problematic for question answering and speech-to-text applications because they aren’t written in text form. Usage of  their  and  there , for example, is even a common problem for humans.

6. Synonyms Synonyms can lead to issues similar to contextual understanding because we use many different words to express the same idea. Furthermore, some of these words may convey exactly the same meaning, while some may be levels of complexity (small, little, tiny, minute) and different people use synonyms to denote slightly different meanings within their personal vocabulary. So, for building NLP systems, it’s important to include all of a word’s possible meanings and all possible synonyms. Text analysis models may still occasionally make mistakes, but the more relevant training data they receive, the better they will be able to understand synonyms.

7. Irony and sarcasm Irony and sarcasm present problems for machine learning models because they generally use words and phrases that, strictly by definition, may be positive or negative, but actually connote the opposite. Models can be trained with certain cues that frequently accompany ironic or sarcastic phrases, like “yeah right,” “whatever,” etc., and  word embeddings  (where words that have the same meaning have a similar representation), but it’s still a tricky process.

8. Ambiguity Ambiguity in NLP refers to sentences and phrases that potentially have two or more possible interpretations. Lexical ambiguity:  a word that could be used as a verb, noun, or adjective. Semantic ambiguity:  the interpretation of a sentence in context. For example:  I saw the boy on the beach with my binoculars.  This could mean that I saw a boy through my binoculars or the boy had my binoculars with him Syntactic ambiguity:  In the sentence above, this is what creates the confusion of meaning. The phrase  with my binoculars  could modify the verb, “saw,” or the noun, “boy.” Even for humans this sentence alone is difficult to interpret without the context of surrounding text. POS (part of speech) tagging is one NLP solution that can help solve the problem, somewhat.

9. Errors in text and speech Misspelled or misused words can create problems for text analysis. Autocorrect and grammar correction applications can handle common mistakes, but don’t always understand the writer’s intention. With spoken language, mispronunciations, different accents, stutters, etc., can be difficult for a machine to understand. However, as language databases grow and smart assistants are trained by their individual users, these issues can be minimized.

10. Colloquialisms and slang Informal phrases, expressions, idioms, and culture-specific lingo present a number of problems for NLP – especially for models intended for broad use. Because as formal language, colloquialisms may have no “dictionary definition” at all, and these expressions may even have different meanings in different geographic areas. Furthermore, cultural slang is constantly morphing and expanding, so new words pop up every day. This is where training and regularly updating custom models can be helpful, although it oftentimes requires quite a lot of data.

11. Domain-specific language Different businesses and industries often use very different language. An NLP processing model needed for healthcare, for example, would be very different than one used to process legal documents. These days, however, there are a number of analysis tools trained for specific fields, but extremely niche industries may need to build or train their own models.

12. Low-resource languages AI machine learning NLP applications have been largely built for the most common, widely used languages. And it’s downright amazing at how accurate translation systems have become. However, many languages, especially those spoken by people with less access to technology often go overlooked and under processed. For example, by some estimations, (depending on language vs. dialect) there are over  3,000 languages in Africa , alone. There simply isn’t very much data on many of these languages. However, new techniques, like  multilingual transformers  (using Google’s BERT “Bidirectional Encoder Representations from Transformers”) and  multilingual sentence embeddings  aim to identify and leverage universal similarities that exist between languages.

13. Lack of research and development Machine learning requires A LOT of data to function to its outer limits – billions of pieces of training data. The more data NLP models are trained on, the smarter they become. That said, data (and human language!) is only growing by the day, as are new machine learning techniques and custom algorithms. All of the problems above will require more research and new techniques in order to improve on them. Advanced practices like artificial neural networks and deep learning allow a multitude of NLP techniques, algorithms, and models to work progressively, much like the human mind does. As they grow and strengthen, we may have solutions to some of these challenges in the near future.

14. Conclusion While Natural Language Processing has its limitations, it still offers huge and wide-ranging benefits to any business. And with new techniques and new technology cropping up every day, many of these barriers will be broken through in the coming years. NLP machine learning can be put to work to analyze massive amounts of text in real time for previously unattainable insights.

15. NATURAL LANGUAGE PROCESSING

16. INTRODUCTION Natural Language Processing (NLP) refers to AI method of communicating with an intelligent systems using a natural language such as English. Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc. The field of NLP involves making computers to perform useful tasks with the natural languages humans use. The input and output of an NLP system can be − Speech Written Text

17. Components of NLP There are two components of NLP as given − Natural Language Understanding (NLU) Natural Language Generation (NLG)

18. Natural Language Understanding (NLU) Understanding involves the following tasks − Mapping the given input in natural language into useful representations. Analyzing different aspects of the language.

19. Natural Language Generation (NLG) It is the process of producing meaningful phrases and sentences in the form of natural language from some internal representation. It involves − Text planning  − It includes retrieving the relevant content from knowledge base. Sentence planning  − It includes choosing required words, forming meaningful phrases, setting tone of the sentence. Text Realization  − It is mapping sentence plan into sentence structure. The NLU is harder than NLG.

20. Difficulties in NLU NL has an extremely rich form and structure. It is very ambiguous. There can be different levels of ambiguity − Lexical ambiguity  − It is at very primitive level such as word-level. For example, treating the word “board” as noun or verb? Syntax Level ambiguity  − A sentence can be parsed in different ways. For example, “He lifted the beetle with red cap.” − Did he use cap to lift the beetle or he lifted a beetle that had red cap? Referential ambiguity  − Referring to something using pronouns. For example, Rima went to Gauri. She said, “I am tired.” − Exactly who is tired? One input can mean different meanings. Many inputs can mean the same thing.

21. NLP Terminology Phonology  − It is study of organizing sound systematically. Morphology  − It is a study of construction of words from primitive meaningful units. Morpheme  − It is primitive unit of meaning in a language. Syntax  − It refers to arranging words to make a sentence. It also involves determining the structural role of words in the sentence and in phrases. Semantics  − It is concerned with the meaning of words and how to combine words into meaningful phrases and sentences. Pragmatics  − It deals with using and understanding sentences in different situations and how the interpretation of the sentence is affected. Discourse  − It deals with how the immediately preceding sentence can affect the interpretation of the next sentence. World Knowledge  − It includes the general knowledge about the world.

22. Steps in NLP There are general five steps − Lexical Analysis Syntactic Analysis (Parsing) Semantic Analysis Discourse Integration Pragmatic Analysis

23. Ambiguity and Uncertainty in Language Ambiguity, generally used in natural language processing, can be referred as the ability of being understood in more than one way. In simple terms, we can say that ambiguity is the capability of being understood in more than one way. Natural language is very ambiguous. NLP has the following types of ambiguities −

24. Lexical Ambiguity The ambiguity of a single word is called lexical ambiguity. For example, treating the word  silver  as a noun, an adjective, or a verb. Syntactic Ambiguity This kind of ambiguity occurs when a sentence is parsed in different ways. For example, the sentence “The man saw the girl with the telescope”. It is ambiguous whether the man saw the girl carrying a telescope or he saw her through his telescope. Semantic Ambiguity This kind of ambiguity occurs when the meaning of the words themselves can be misinterpreted. In other words, semantic ambiguity happens when a sentence contains an ambiguous word or phrase. For example, the sentence “The car hit the pole while it was moving” is having semantic ambiguity because the interpretations can be “The car, while moving, hit the pole” and “The car hit the pole while the pole was moving”. Anaphoric Ambiguity This kind of ambiguity arises due to the use of anaphora entities in discourse. For example, the horse ran up the hill. It was very steep. It soon got tired. Here, the anaphoric reference of “it” in two situations cause ambiguity. Pragmatic ambiguity Such kind of ambiguity refers to the situation where the context of a phrase gives it multiple interpretations. In simple words, we can say that pragmatic ambiguity arises when the statement is not specific. For example, the sentence “I like you too” can have multiple interpretations like I like you (just like you like me), I like you (just like someone else dose).

25. NLP Phases Following diagram shows the phases or logical steps in natural language processing −

26. NLP Phases

27. NLP Phases Morphological Processing It is the first phase of NLP. The purpose of this phase is to break chunks of language input into sets of tokens corresponding to paragraphs, sentences and words. For example, a word like  “uneasy”  can be broken into two sub-word tokens as  “un-easy” . Syntax Analysis It is the second phase of NLP. The purpose of this phase is two folds: to check that a sentence is well formed or not and to break it up into a structure that shows the syntactic relationships between the different words. For example, the sentence like  “The school goes to the boy”  would be rejected by syntax analyzer or parser.

28. Semantic Analysis It is the third phase of NLP. The purpose of this phase is to draw exact meaning, or you can say dictionary meaning from the text. The text is checked for meaningfulness. For example, semantic analyzer would reject a sentence like “Hot ice-cream”. Pragmatic Analysis It is the fourth phase of NLP. Pragmatic analysis simply fits the actual objects/events, which exist in a given context with object references obtained during the last phase (semantic analysis). For example, the sentence “Put the banana in the basket on the shelf” can have two semantic interpretations and pragmatic analyzer will choose between these two possibilities.

29. EXPERT SYSTEM

30. INTRODUCTION The expert system is a part of AI, and the first ES was developed in the year 1970, which was the first successful approach of artificial intelligence. It solves the most complex issue as an expert by extracting the knowledge stored in its knowledge base. The system helps in decision making for compsex problems using  both facts and heuristics like a human expert . It is called so because it contains the expert knowledge of a specific domain and can solve any complex problem of that particular domain. These systems are designed for a specific domain, such as  medicine, science,  etc.

31. INTRODUCTION The performance of an expert system is based on the expert's knowledge stored in its knowledge base. The more knowledge stored in the KB, the more that system improves its performance. One of the common examples of an ES is a suggestion of spelling errors while typing in the Google search box.

32. What is an Expert System? An expert system is a computer program that is designed to solve complex problems and to provide decision-making ability like a human expert. It performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries.

33. block diagram that represents the working of an expert system:

34. Below are some popular examples of the Expert System: DENDRAL:  It was an artificial intelligence project that was made as a chemical analysis expert system. It was used in organic chemistry to detect unknown organic molecules with the help of their mass spectra and knowledge base of chemistry. MYCIN:  It was one of the earliest backward chaining expert systems that was designed to find the bacteria causing infections like bacteraemia and meningitis. It was also used for the recommendation of antibiotics and the diagnosis of blood clotting diseases. PXDES:  It is an expert system that is used to determine the type and level of lung cancer. To determine the disease, it takes a picture from the upper body, which looks like the shadow. This shadow identifies the type and degree of harm. CaDeT:  The CaDet expert system is a diagnostic support system that can detect cancer at early stages.

35. Characteristics of Expert System High Performance:  The expert system provides high performance for solving any type of complex problem of a specific domain with high efficiency and accuracy. Understandable:  It responds in a way that can be easily understandable by the user. It can take input in human language and provides the output in the same way. Reliable:  It is much reliable for generating an efficient and accurate output. Highly responsive:  ES provides the result for any complex query within a very short period of time.

36. Components of Expert System An expert system mainly consists of three components: User Interface Inference Engine Knowledge Base

37. Components of Expert System

38. Components of Expert System 1. User Interface With the help of a user interface, the expert system interacts with the user, takes queries as an input in a readable format, and passes it to the inference engine. After getting the response from the inference engine, it displays the output to the user. In other words,  it is an interface that helps a non-expert user to communicate with the expert system to find a solution .

39. Components of Expert System 2. Inference Engine(Rules of Engine) The inference engine is known as the brain of the expert system as it is the main processing unit of the system. It applies inference rules to the knowledge base to derive a conclusion or deduce new information. It helps in deriving an error-free solution of queries asked by the user. With the help of an inference engine, the system extracts the knowledge from the knowledge base. There are two types of inference engine: Deterministic Inference engine:  The conclusions drawn from this type of inference engine are assumed to be true. It is based on  facts  and  rules . Probabilistic Inference engine:  This type of inference engine contains uncertainty in conclusions, and based on the probability. Inference engine uses the below modes to derive the solutions: Forward Chaining:  It starts from the known facts and rules, and applies the inference rules to add their conclusion to the known facts. Backward Chaining:  It is a backward reasoning method that starts from the goal and works backward to prove the known facts.

40. Components of Expert System 3. Knowledge Base The knowledgebase is a type of storage that stores knowledge acquired from the different experts of the particular domain. It is considered as big storage of knowledge. The more the knowledge base, the more precise will be the Expert System. It is similar to a database that contains information and rules of a particular domain or subject. One can also view the knowledge base as collections of objects and their attributes. Such as a Lion is an object and its attributes are it is a mammal, it is not a domestic animal, etc.

41. Components of Knowledge Base Factual Knowledge:  The knowledge which is based on facts and accepted by knowledge engineers comes under factual knowledge. Heuristic Knowledge:  This knowledge is based on practice, the ability to guess, evaluation, and experiences. Knowledge Representation:  It is used to formalize the knowledge stored in the knowledge base using the If-else rules. Knowledge Acquisitions:  It is the process of extracting, organizing, and structuring the domain knowledge, specifying the rules to acquire the knowledge from various experts, and store that knowledge into the knowledge base.

42. Development of Expert System Here, we will explain the working of an expert system by taking an example of MYCIN ES. Below are some steps to build an MYCIN: Firstly, ES should be fed with expert knowledge. In the case of MYCIN, human experts specialized in the medical field of bacterial infection, provide information about the causes, symptoms, and other knowledge in that domain. The KB of the MYCIN is updated successfully. In order to test it, the doctor provides a new problem to it. The problem is to identify the presence of the bacteria by inputting the details of a patient, including the symptoms, current condition, and medical history. The ES will need a questionnaire to be filled by the patient to know the general information about the patient, such as gender, age, etc. Now the system has collected all the information, so it will find the solution for the problem by applying if-then rules using the inference engine and using the facts stored within the KB. In the end, it will provide a response to the patient by using the user interface.

43. Participants in the development of Expert System There are three primary participants in the building of Expert System: Expert:  The success of an ES much depends on the knowledge provided by human experts. These experts are those persons who are specialized in that specific domain. Knowledge Engineer:  Knowledge engineer is the person who gathers the knowledge from the domain experts and then codifies that knowledge to the system according to the formalism. End-User:  This is a particular person or a group of people who may not be experts, and working on the expert system needs the solution or advice for his queries, which are complex.

44. Why Expert System?

45. Before using any technology, we must have an idea about why to use that technology and hence the same for the ES. Although we have human experts in every field, then what is the need to develop a computer-based system. So below are the points that are describing the need of the ES: No memory Limitations:  It can store as much data as required and can memorize it at the time of its application. But for human experts, there are some limitations to memorize all things at every time. High Efficiency:  If the knowledge base is updated with the correct knowledge, then it provides a highly efficient output, which may not be possible for a human. Expertise in a domain:  There are lots of human experts in each domain, and they all have different skills, different experiences, and different skills, so it is not easy to get a final output for the query. But if we put the knowledge gained from human experts into the expert system, then it provides an efficient output by mixing all the facts and knowledge Not affected by emotions:  These systems are not affected by human emotions such as fatigue, anger, depression, anxiety, etc.. Hence the performance remains constant. High security:  These systems provide high security to resolve any query. Considers all the facts:  To respond to any query, it checks and considers all the available facts and provides the result accordingly. But it is possible that a human expert may not consider some facts due to any reason. Regular updates improve the performance:  If there is an issue in the result provided by the expert systems, we can improve the performance of the system by updating the knowledge base.

46. Capabilities of the Expert System Advising:  It is capable of advising the human being for the query of any domain from the particular ES. Provide decision-making capabilities:  It provides the capability of decision making in any domain, such as for making any financial decision, decisions in medical science, etc. Demonstrate a device:  It is capable of demonstrating any new products such as its features, specifications, how to use that product, etc. Problem-solving:  It has problem-solving capabilities. Explaining a problem:  It is also capable of providing a detailed description of an input problem. Interpreting the input:  It is capable of interpreting the input given by the user. Predicting results:  It can be used for the prediction of a result. Diagnosis:  An ES designed for the medical field is capable of diagnosing a disease without using multiple components as it already contains various inbuilt medical tools.

47. Advantages of Expert System These systems are highly reproducible. They can be used for risky places where the human presence is not safe. Error possibilities are less if the KB contains correct knowledge. The performance of these systems remains steady as it is not affected by emotions, tension, or fatigue. They provide a very high speed to respond to a particular query.

48. Limitations of Expert System The response of the expert system may get wrong if the knowledge base contains the wrong information. Like a human being, it cannot produce a creative output for different scenarios. Its maintenance and development costs are very high. Knowledge acquisition for designing is much difficult. For each domain, we require a specific ES, which is one of the big limitations. It cannot learn from itself and hence requires manual updates.

49. Applications of Expert System In designing and manufacturing domain It can be broadly used for designing and manufacturing physical devices such as camera lenses and automobiles. In the knowledge domain These systems are primarily used for publishing the relevant knowledge to the users. The two popular ES used for this domain is an advisor and a tax advisor. In the finance domain In the finance industries, it is used to detect any type of possible fraud, suspicious activity, and advise bankers that if they should provide loans for business or not. In the diagnosis and troubleshooting of devices In medical diagnosis, the ES system is used, and it was the first area where these systems were used. Planning and Scheduling The expert systems can also be used for planning and scheduling some particular tasks for achieving the goal of that task.

50. ROBOTICS

51. INTRODUCTION Robotics is a domain in artificial intelligence that deals with the study of creating intelligent and efficient robots.

52. What are Robots? Robots are the artificial agents acting in real world environment. Objective Robots are aimed at manipulating the objects by perceiving, picking, moving, modifying the physical properties of object, destroying it, or to have an effect thereby freeing manpower from doing repetitive functions without getting bored, distracted, or exhausted.

53. What is Robotics? Robotics is a branch of AI, which is composed of Electrical Engineering, Mechanical Engineering, and Computer Science for designing, construction, and application of robots. Aspects of Robotics The robots have  mechanical construction , form, or shape designed to accomplish a particular task. They have  electrical components  which power and control the machinery. They contain some level of  computer program  that determines what, when and how a robot does something.

54. Difference in Robot System and Other AI Program

55. Robot Locomotion Locomotion is the mechanism that makes a robot capable of moving in its environment. There are various types of locomotions − Legged Wheeled Combination of Legged and Wheeled Locomotion Tracked slip/skid

56. Legged Locomotion This type of locomotion consumes more power while demonstrating walk, jump, trot, hop, climb up or down, etc. It requires more number of motors to accomplish a movement. It is suited for rough as well as smooth terrain where irregular or too smooth surface makes it consume more power for a wheeled locomotion. It is little difficult to implement because of stability issues. It comes with the variety of one, two, four, and six legs. If a robot has multiple legs then leg coordination is necessary for locomotion.

57. Wheeled Locomotion It requires fewer number of motors to accomplish a movement. It is little easy to implement as there are less stability issues in case of more number of wheels. It is power efficient as compared to legged locomotion. Standard wheel  − Rotates around the wheel axle and around the contact Castor wheel  − Rotates around the wheel axle and the offset steering joint. Swedish 45 o  and Swedish 90 o  wheels  − Omni-wheel, rotates around the contact point, around the wheel axle, and around the rollers. Ball or spherical wheel  − Omnidirectional wheel, technically difficult to implement.

58. Slip/Skid Locomotion In this type, the vehicles use tracks as in a tank. The robot is steered by moving the tracks with different speeds in the same or opposite direction. It offers stability because of large contact area of track and ground.

59. Components of a Robot Robots are constructed with the following − Power Supply  − The robots are powered by batteries, solar power, hydraulic, or pneumatic power sources. Actuators  − They convert energy into movement. Electric motors (AC/DC)  − They are required for rotational movement. Pneumatic Air Muscles  − They contract almost 40% when air is sucked in them. Muscle Wires  − They contract by 5% when electric current is passed through them. Piezo Motors and Ultrasonic Motors  − Best for industrial robots. Sensors  − They provide knowledge of real time information on the task environment. Robots are equipped with vision sensors to be to compute the depth in the environment. A tactile sensor imitates the mechanical properties of touch receptors of human fingertips.

60. Computer Vision This is a technology of AI with which the robots can see. The computer vision plays vital role in the domains of safety, security, health, access, and entertainment. Computer vision automatically extracts, analyzes, and comprehends useful information from a single image or an array of images. This process involves development of algorithms to accomplish automatic visual comprehension.

61. Hardware of Computer Vision System This involves − Power supply Image acquisition device such as camera A processor A software A display device for monitoring the system Accessories such as camera stands, cables, and connectors

62. Tasks of Computer Vision OCR  − In the domain of computers, Optical Character Reader, a software to convert scanned documents into editable text, which accompanies a scanner. Face Detection  − Many state-of-the-art cameras come with this feature, which enables to read the face and take the picture of that perfect expression. It is used to let a user access the software on correct match. Object Recognition  − They are installed in supermarkets, cameras, high-end cars such as BMW, GM, and Volvo. Estimating Position  − It is estimating position of an object with respect to camera as in position of tumor in human’s body.

63. Applications of Robotics The robotics has been instrumental in the various domains such as − Industries  − Robots are used for handling material, cutting, welding, color coating, drilling, polishing, etc. Military  − Autonomous robots can reach inaccessible and hazardous zones during war. A robot named  Daksh , developed by Defense Research and Development Organization (DRDO), is in function to destroy life-threatening objects safely. Medicine  − The robots are capable of carrying out hundreds of clinical tests simultaneously, rehabilitating permanently disabled people, and performing complex surgeries such as brain tumors. Exploration  − The robot rock climbers used for space exploration, underwater drones used for ocean exploration are to name a few. Entertainment  − Disney’s engineers have created hundreds of robots for movie making.

64. THANK YOU