https://hackmd.io/@alexcasper/dita_04_12/ # r2l62q --- <style> .reveal { font-size: 32px; font-family: "Meta Sans", sans-serif; } </style> ### Data Information Technologies and Applications based on material by Lyn Rbinson adapted by Alex Cline --- • The module provides the technical background required to collect, structure, describe, find, interpret, understand, manage and share data effectively in the infosphere. It is designed to support information tasks and processes by exploring the ways in which information is handled, and by taking note of new media and communication technologies. • As a successful student, on completion of this course you will understand contemporary technologies as they relate to the information professions, and you will be able to explain, evaluate, plan for and engage technological solutions throughout your career --- The module will allow you to: • Critically examine and discuss the collection, storage, structuring, discovery, retrieval, sharing and understanding of data • Analyse and apply generic principles in a disciplinary context (LIS) • Develop a reflective and ethical approach to data, its use and management --- ### Aims • To consider definitions of AI and related concepts • To consider the history and development of AI • To critically examine current trends in AI to highlight the possibilities and the challenges • To foster a critical, reflective and ethical understanding of the relevance of AI to library and information science --- ### Concepts - Robots - AI - Machine Learning - Deep Learning - Neural Nets ---  • Nam June Paaik 1932-2006: Bakelite Robot 2002 (through DeepDream) --- # Robots ---  ---  ---  ---  [Delivery Robots in Cambridge](https://www.cambridgeshire.gov.uk/council/communities/food-delivery-robots-in-cambridgeshire) ---  [Mir200 at Helsinki Library](https://futurice.com/blog/the-little-robot-that-lived-at-the-library) ---  [Aurora at Temasek Polytechnic (Singapore)](https://www.senserbot.com/case-studies/temasek-polytechnic/) ---  [IBM - An Introduction to Deep Learning](https://developer.ibm.com/articles/an-introduction-to-deep-learning/) ---  ---  Rosenblatt, F. 1958 The Design of an Intelligent Automaton --- ```mermaid graph LR subgraph Perceptron S[Sigma]-->A[Activation Function] end I1[Input 1]-->|Weight|S I2[Input 2]-->|Weight|S I3[Input 3]-->|Weight|S ``` --- “Artificial intelligence is that activity devoted to making machines intelligent, and intelligence is that quality that enables an entity to function appropriately and with foresight in its environment.” [Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements (Cambridge, UK: Cambridge University Press, 2010)](https://ai100.stanford.edu/2016-report/section-i-what-artificial-intelligence/defining-ai) --- What the Near Future of Artificial Intelligence Could Be *Luciano Floridi* Published online: 19 March 2019 # Springer Nature B.V. 2019 AI: a Working Definition • AI has been defined in many ways. Today, it comprises several technoscientific branches, well summarised in Corea (Aug. 29, Corea 2018) in Fig. 1.  --- Altogether, AI paradigms still satisfy the classic definition provided by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon in their seminal “Proposal for the Dartmouth Summer Research Project on Artificial Intelligence”, the founding document and later event that established the new field of AI in 1955: *For the present purpose the artificial intelligence problem is taken to be that of making a machine behave in ways that would be called intelligent if a human were so behaving.* (Quotation from the 2006 re-issue in (McCarthy et al. 2006)) --- ### Machine Learning Arthur Lee Samuel (1901-1990) among the first to use the term machine learning. 1959 paper: Some Studies in Machine Learning Using the Game of Checkers. IBM Journal of Research and Development. Programming computers to learn from experience; large sets of input data for training, to help make predictions and classifications. ML is a subset of artificial intelligence; it is a technique for realizing AI. --- ### Supervised machine learning Algorithms may be fed data samples that are labelled with information, so that the system can later make predictions --- ### Unsupervised machine learning Unlabelled data is used, so that the system may discover hidden patterns through inference. Problems with bias, incorrect, or manipulated data --- ### Deep Learning Deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning. DL algorithms are roughly inspired by the information processing patterns found in the human brain. Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines. --- Almost everything you hear about artificial intelligence today is thanks to deep learning. This category of algorithms works by using statistics to find patterns in data, and it has proved immensely powerful in mimicking human skills such as our ability to see and hear. To a very narrow extent, it can even emulate our ability to reason. These capabilities power Google’s search, Facebook’s news feed, and Netflix’s recommendation engine — and are transforming industries like health care and education. [Hao, K., 2019. We analysed 16,625 papers to figure out where AI is headed next. MIT Technology Review.](https://www.technologyreview.com/s/612768/we-analyzed16625-papers-to-figure-out-where-ai-is-headed-next) ---  --- # Neural Networks --- ```mermaid graph LR subgraph hidden layer S1[A1] S2[A2] S3[A3] S4[A4] end X1[Input 1]-->|Weight|S1 X1-->|Weight|S2 X1-->|Weight|S3 X1-->|Weight|S4 X2[Input 2]-->|Weight|S2 X2-->|Weight|S1 X2-->|Weight|S3 X2-->|Weight|S4 X3[Input 3]-->|Weight|S3 X3-->|Weight|S2 X3-->|Weight|S1 X3-->|Weight|S4 S1-->Y1[Output 1] S2-->Y1 S3-->Y1 S4-->Y1 S1-->Y2[Output 2] S2-->Y2 S3-->Y2 S4-->Y2 ``` --- ```mermaid graph LR subgraph hidden layer 1 S1[A1-1] S2[A2-1] S3[A3-1] S4[A4-1] end subgraph hidden layer 2 T1[A1-2] T2[A2-2] T3[A3-2] T4[A4-2] end X1[Input 1]-->|Weight|S1 X1-->|Weight|S2 X1-->|Weight|S3 X1-->|Weight|S4 X2[Input 2]-->|Weight|S2 X2-->|Weight|S1 X2-->|Weight|S3 X2-->|Weight|S4 X3[Input 3]-->|Weight|S3 X3-->|Weight|S2 X3-->|Weight|S1 X3-->|Weight|S4 T1-->Y1[Output 1] T2-->Y1 T3-->Y1 T4-->Y1 T1-->Y2[Output 2] T2-->Y2 T3-->Y2 T4-->Y2 S1-->T1 S1-->T2 S1-->T3 S1-->T4 S2-->T1 S2-->T2 S2-->T3 S2-->T4 S3-->T1 S3-->T2 S3-->T3 S3-->T4 S4-->T1 S4-->T2 S4-->T3 S4-->T4 ``` --- ### Specific types of Deep Learning model --- # Convolutional Neural Network Feed forward model Good at image generation # Recurrent Neural Network Feeds outputs back into model Good for temporal data --- ## Transformer Used for translation, summarisation, and generative content.  --- ## Generative Adversarial Network Used for advanced image generation.  --- ### Autoencoder Used for image enhancement.  --- # What is the Relevance of AI to LIS? --- - Bots (interacting and scraping) - Cataloguing - Automatic indexing and classification - Data Mining - Question answering systems - Information retrieval - Recommender systems - Merging information for different sources and formats - Robots and intelligent systems in physical libraries