Intro to artificial intelligence
- 2. Intro To Artificial Intelligence And Machine Learning What we will cover • Machine learning concepts • Its use in artificial intelligence development • Some prominent research's going in artificial intelligence • Demo of a simple artificial intelligence based on learning curve • Benefits and risk associated with artificial intelligence • Real world applications of artificial intelligence • Future Scope in artificial intelligence
- 3. INTRO ARTIFICIAL INTELLIGENCE :-It is the broader concept of machines being able to carry out tasks in a way that we would consider “smart” MACHINE LEARNING:- Machine Learning is a application of AI where we give machines access to the data and let them learn themselves.
- 4. Machine learning concepts Machine learning is the subfield of computer science that, according to Arthur Samuel, gives "computers the ability to learn without being explicitly programmed." Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "machine learning" in 1959 while at IBM. Machine learning is the concept where a machine uses large amount of data to make decisions. Arthur Samuel 4
- 5. Machine learning tasks are typically classified into three broad categories, depe a learning system.
- 6. Supervised learning: The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs.
- 7. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data)
- 8. Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). The program is provided feedback in terms of rewards and punishments as it navigates its problem space.
- 10. Decision tree learning:-Decision tree learning uses a decision tree as a predictive model, which maps observations about an item to conclusions about the item's target value. Decision tree Association rule learning:-Association rule learning is a method for discovering interesting relations between variables in large databases.
- 11. Artificial neural networks:- An artificial neural network (ANN) learning algorithm, usually called "neural network" (NN), It is a learning algorithm that is inspired by the structure and functional aspects of biological neural networks. Computations are structured in terms of an interconnected group of artificial neurons, processing information using a connectionist approach to computation.
- 12. Deep learning:- It consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition. DEEP LEARNING
- 13. Deep Learning
- 14. Clustering:-Cluster analysis is the assignment of a set of observations into subsets (called clusters) so that observations within the same cluster are similar according to some predesignated criterion or criteria, while observations drawn from different clusters are dissimilar. Different clustering techniques make different assumptions on the structure of the data, often defined by some similarity metric and evaluated for example by internal compactness (similarity between members of the same cluster) and separation between different clusters. Other methods are based on estimated density and graph connectivity. Clustering is a method of unsupervised learning, and a common technique for statistical data analysis. Clustering CLUSTERING ALGORITHM
- 15. Bayesian networks:-A Bayesian network, belief network or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independencies via a directed acyclic graph (DAG). A Bayesian network could represent the probabilistic relationships. The network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Decision curve
- 16. Application in artificial intelligence development Game Playing:-You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second. Speech Recognition:-In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
- 17. Understanding Natural Language:-Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either. The computer has to be provided with an understanding of the domain the text is about, and this is presently possible only for very limited domains. Computer Vision:-The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
- 18. Some prominent research's Autopilot in Cars, Aircraft , Military equipment's etc. Playing games like chess , checkers by mimicking human moves Google's Alpha Go AI defeats human in first game of Go contest IBMs Deep Blue defeats chess champion Garry Kasparov
- 19. Research Work The AI system was able to recreate the complex quantum experiment to create an extremely cold gas trapped in a laser beam, known as a Bose-Einstein condensate in less than an hour while we took 80+years to do the same! his intriguing phenomenon—sometimes called the fifth state of matter next to solid, liquid, gas and plasma—was predicted by Satyendra Nath Bose and Albert Einstein in the 1920s. But it took a long time to develop the necessary experimental techniques and find suitable materials to actually create it, which finally happened in 1995. Several years later in 2001 the work was recognised with the Nobel Prize in Physics. Bose-Einstein condensation Experiment
- 20. Demo of a simple artificial intelligence Demo of the AI Based on Bayes Theorem and Decision Curve.
- 21. Risk The AI is programmed to do something devastating The AI is programmed to do something beneficial, but it develops a destructive method for achieving its goal Elon Musk: regulate AI to combat 'existential threat' before it's too late
- 23. Real World Applications Speech recognition Home automation Virtual Personal Assistants Video Games Purchase Prediction Security Surveillance News Generation Smart Home Devices Cleaning and housekeeping Labour intensive work Used as interactive Toy Military Application
- 24. Future Scope in artificial intelligence • Everything is now becoming inter connected • Computing is becoming cheaper • Data is becoming the new oil • Machine learning is becoming the new combustion engine • AI Becomes Open Source (Googles Tensor Flow Libraries)
- 25. SPECIAL THANKS TO TEAM I.R.I.S FOR MAKING THIS POSSIBLE