Artificial Intelligence for Beginners AIB

1. ARTIFICIAL INTELLIGENCE FOR BEGINNERS Artificial Intelligence for Beginners

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3. The father of AI, John McCarthy, describes AI as the science & engineering of building intelligent machines, especially intelligent computer programs. So, in AI, we try to create machines that can perform cognitive thinking, perceiving, learning, reasoning, and solving just like a human does. We can create software or devices with AI which can solve real-world problems with better accuracy and deftness than a human being, such as in medical sectors, traffic issues, etc. Google Assistant, Cortana, Siri, etc., are some virtual assistants that use the methodologies of AI. We can build robots & machines which can work in an environment otherwise harmful for human beings. To create expert systems: a system that exhibits intelligent behaviour- learn, reason, perceive, think, explain and advise its user. We wish to build systems that can learn and function intelligently. 1 Page 3

4. AI is a technology based on many disciplines such as computer science, biology, psychology, etc. The biggest challenge is to build a machine/software that can mimic human intelligence. The following list enlists some areas which contribute towards the building of an intelligent machine: 1 COMPUTER SCIENCE 2 6 PSYCHOLOGY SOCIOLOGY 3 5 8 NEURON SCIENCE BIOLOGY STATISTICS 7 4 PHILOSOPHY MATHEMATICS AI has made its place in many areas so far, such as gaming, where it can predict next moves with heuristic knowledge and healthcare, helping in diagnosis and monitoring. The entertainment industry also uses it to recommend to the user on OTT platforms, and the automotive industry, such as Tesla, have made a TeslaBot to serve as a virtual assistant and create self-driven cars. The best applications of AI in the year 2020 were: Google’s AI-powered predictions (Google maps) AI autopilot in commercial ?ights Spam ?lter on E-mail Facial recognition Plagiarism checkers Voice-to-text features S Search assistants Smartphone assistants Fraud protection 2 Page 4

5. High accuracy and fewer errors make them reliable. Speed in making rational decisions. Useful for works risky to humans such as in bomb-diffusion. Can work as a digital assistant in many areas such as education, ?nance, e-commerce, etc. C Can be used for public utilities such as in self-driven cars to make journey safe or in facial recognition to ensure security. High cost of the required hardware, software, and maintenance costs. A machine developed is trained or programmed, and so, we’ve not yet achieved a machine that can think innovatively. With AI taking over the work, humans have become too much dependent on machines. 3 Page 5

6. In 1956, AI took birth through a summer project by a group of Avant-Garde experts. John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon organised it. They described the purpose of the project to ?nd a way to know every feature of learning or any other feature of intelligence that can be described so precisely that it can simulate a machine. The proposals in the summits had automatic computers, neuron nets, self-improvement, etc. This had sown the idea of creating intelligent machines, and we had a new ?eld to research in which is Arti?cial Intelligence, and it has been growing ever since. When an AI is an expert in a particular task and performs better than a human, we call it a narrow AI. There is still a lot of exploring this form of AI. Example: Siri, Self-driven cars, spam ?lter, etc. When an AI attains cognitive abilities and matches human intelligence, the AI is in a general state. Example: recognition, analogy, hypothesis testing, etc. It is a hypothetical concept as of now as a machine that can outperform a human and attain self-awareness is not yet created. 4 Page 6

7. AI & machine learning are used interchangeably by many big companies, but they both differ in some ways. AI refers to training machines to perform tasks like humans, whereas AI machine learning is training the machine in learning. Deep Learning comes under machine learning. It means that a machine learns via different layers of data which determines the depth of a model. For example, Google LeNet uses 22 layers for image recognition. The learning is done via a neural network, an architecture where layers are stacked on top of one another. Some of the job pro?les in the ?eld of AI, along with their skill set, are as follows: You will be building & managing various ML projects, among other responsibilities. You can be an ML engineer if you have a background in data science or applied research. Additional skills required are a good understanding of multiple programming languages, knowledge of predictive models and know-how to leverage natural language processing while working with massive datasets. It is advisable to take a machine learning certi?cate course to become a machine learning engineer. As a data scientist, you’re expected to collect, analyse & interpret large datasets by making maximum use of machine learning. You’re also supposed to make algorithms that can collect & ?lter data for further interpretations. It’ll be bene?cial if your skillset includes Hive, Hadoop, python, SQL, Scala, etc., along with strong analytical and communication skill. A big data engineer/architect plays an important role in developing an ecosystem where the business systems can communicate & collate data with each other. The tasks include planning, designing, and developing a big data environment on various platforms such as Hadoop. The skillset should include experience in languages such as python, C++, etc., along with data mining, data visualisation & data migration. 5 Page 7

8. As a BI developer, you’ll be responsible for designing, modelling, and maintaining complex datasets in cloud-based data platforms. You’re expected to analyse these datasets to determine the latest business & market trends. The skillset should include data mining, SQL, BI technologies, and data warehouse designs, along with strong communication skills. Arti?cial Intelligence is a growing ?eld. There are six branches of AI – machine learning, neural networks, deep learning, computer vision, natural language processing & cognitive computing - and all show equal promise. The investments have been ever increasing since the past few years. The number of jobs in the area has increased by 119%. We are witnessing the greatest of advancements in AI machine learning. It has already started to change our world and is making its way in every ?eld. From healthcare to self-driven cars - show us a glimpse of AI’s capabilities. There are new emerging ?elds such as IoT, big data, etc. looking at a report by World Economic Forum, Forum, by the year 2022, 133 million new AI jobs will be created. It ensures that the future of AI is bright. LSET is among the leading educational institutions in the ?eld of AI. Our courses for AI are meticulously designed to train you with every requirement of the said ?eld. LSET has a speciality in machine learning certi?cate course, which will help you inculcate all the required skills and knowledge. We have an expe expert teaching faculty who will help you understand and learn all the machine learning concepts. You will learn multiple programming languages such as python, java, and Scala. You’ll be familiarised with the IDE tools such as Eclipse and IntelliJ. In the AI machine learning course, we’ve got data science and natural language p language processing as well. At LSET, we provide AI and machine learning courses and the opportunity to get real-life working experience with some companies and work alongside experts in the ?eld. AI machine learning course from LSET will give you leverage in your career in AI and machine lea learning. 6 Page 8

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