session_lecture.pdf

Transcript

1. Industry 4.0 Main skill is the ability to up-skill! Tanisha R. Bhayani Associate AI Researcher @F(x) Data Labs Latest Trends and Innovation
2. Industry 4.0 Tanisha R. Bhayani
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4. AI Applications Tanisha R. Bhayani
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9. Early AI was mainly based on logic. You're trying to make computers that reason like people. The second route is from biology: You're trying to make computers that can perceive and act and adapt like animals. - Geoffrey Hinton Tanisha R. Bhayani
10. What is AI? Tanisha R. Bhayani
11. AI Research  Generative Models  Reinforcement Learning  Capsule Networks  Markov Models  Probabilistic Models  Combining Symbolic AI with Connectionist Models  Information Theory for Deep Neural Networks Tanisha R. Bhayani
12. AI Statistics  China and U.K. estimate that 26% and 10% of their GDPs respectively in 2030 will be sourced from AI-related activities and businesses. [1.]  As per the Global AI Talent Report 2018, India only has 386 of a total of 22,000 PhD educated researchers worldwide, and is ranked 10th globally[1.]  Looking at all the research publications from 2001 – 2016, only 14% of all publications have come from industry, with universities contributing 86% of all publications.  There are 1,038 (2.8%) Artificial Intelligence startups in India out of 36,784. [2.]  NASCCOM predicts that by 2022, a startling 46% of the Indian workforce will be engaged in entirely new jobs that do not exist today or jobs that have radically changed skill sets.[1.] Tanisha R. Bhayani
13. Steps (Knowledge and Qualities)  Good Algorithmic background  Good Mathematical background  Good coding skills  Great amount of patience  Knowledge of Virtual Machines Tanisha R. Bhayani
14. Tools, Technologies, and Libraries Data Gathering Python Parsing libraries - depends on the type of dataset. Data Cleaning Numpy, Pandas Data Visualization Jupyter Notebooks, Matplotlib, Seaborn, Plotly Feature Engineering Jupyter Notebook, Domain Knowledge, Some database knowledge, may also require additional data gathering. Training and Testing tensorflow, keras, scikit-learn Deploying Tensor flow serving API, Any cloud provider, Firebase, integrate into a web app, through REST APIs. - depends on the use case. Tanisha R. Bhayani
15. Learning Resources  Websites  https://www.coursera.org/  https://lagunita.stanford.edu/  https://ocw.mit.edu/courses/find-by-department/  https://www.edx.org  Books  ARTIFICIAL INTELLIGENCE Third Edition by Kevin Knight, Elaine Rich, B. Nair  Deep Learning book (https://www.deeplearningbook.org/)  Research Papers  https://arxiv.org/  http://www.arxiv-sanity.com/  https://scholar.google.com/  Other references:  https://github.com/josephmisiti/awesome-machine-learning  https://www.kaggle.com/  https://colah.github.io/ Tanisha R. Bhayani
16. Statistics Resources 1.https://niti.gov.in/writereaddata/files/document_pub lication/NationalStrategy-for-AI-Discussion-Paper.pd f 2.https://tracxn.com/explore/Artificial-Intelligence-Sta rtups-in-India/ Tanisha R. Bhayani
17. - Donald Knuth “Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered. We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Yet we should not pass up our opportunities in that critical 3%.” Tanisha R. Bhayani
18. Thank You! Tanisha R. Bhayani