Why we do not want to build intelligent mobile applications
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1 Why we do not want to build intelligent mobile applications Szymon Bobek Institute of Applied Computer Science AGH University of Science and Technology Szymon Bobek (AGH-UST) Mobile Trends January / 37
2 Outline I 1 Introduction 2 General issues 3 Engineering issues 4 Social and philosophical issues 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
3 Presentation Outline 1 Introduction What is (Artificial) Intelligence What are CAS How to build CAS 2 General issues 3 Engineering issues 4 Social and philosophical issues 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
4 What is intelligence Szymon Bobek (AGH-UST) Mobile Trends January / 37
5 Can submarine swim? Artificial Intelligence A question Can machine think? is similar to a question Can submarine swim?. Artificial intelligence is just a simulation of real intelligence, hence it requires appropriate model. Every model requires constraints. The looser constraints, the more difficult the simulation. Szymon Bobek (AGH-UST) Mobile Trends January / 37
6 Outline 1 Introduction What is (Artificial) Intelligence What are CAS How to build CAS 2 General issues 3 Engineering issues Gathering context Modelling context Processing context 4 Social and philosophical issues Semantics vs. context Usability, intelligibility Privacy 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
7 In Theory Context Where you are, who you are with, what resources are nearby (Schillit) Any informaiton that can be used to characterize the situation of an entity (Dey) Individuality, activity, location, time, relations (Zimmerman) Set of variables that may be of interest for an agent and that influence its actions (Bolchini) Aware Artificial intelligence methods Systems Intelligent homes, intelligent cars, robotics Ambient intelligence, pervasive environments, ubiquitous computing Mobile computing (location aware mobile applicaitons) Intelligent software (contextual advertising, etc.) Szymon Bobek (AGH-UST) Mobile Trends January / 37
8 In Practice Szymon Bobek (AGH-UST) Mobile Trends January / 37
9 In Practice Szymon Bobek (AGH-UST) Mobile Trends January / 37
10 In Practice Szymon Bobek (AGH-UST) Mobile Trends January / 37
11 In Practice Szymon Bobek (AGH-UST) Mobile Trends January / 37
12 In Practice Szymon Bobek (AGH-UST) Mobile Trends January / 37
13 In Practice Szymon Bobek (AGH-UST) Mobile Trends January / 37
14 Context is not only a location Szymon Bobek (AGH-UST) Mobile Trends January / 37
15 Context is not only a location Szymon Bobek (AGH-UST) Mobile Trends January / 37
16 Outline 1 Introduction What is (Artificial) Intelligence What are CAS How to build CAS 2 General issues 3 Engineering issues Gathering context Modelling context Processing context 4 Social and philosophical issues Semantics vs. context Usability, intelligibility Privacy 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
17 Acquire, represent, use Collect Interpret, Represent, Model Process, Use Szymon Bobek (AGH-UST) Mobile Trends January / 37
18 Presentation Outline 1 Introduction 2 General issues 3 Engineering issues 4 Social and philosophical issues 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
19 Why we do not want to build intelligent mobile applications? Because However Szymon Bobek (AGH-UST) Mobile Trends January / 37
20 Why we do not want to build intelligent mobile applications? Because However Szymon Bobek (AGH-UST) Mobile Trends January / 37
21 Why we do not want to build intelligent mobile applications? Because However Szymon Bobek (AGH-UST) Mobile Trends January / 37
22 Why we do not want to build intelligent mobile applications? Because However Szymon Bobek (AGH-UST) Mobile Trends January / 37
23 Why we do not want to build intelligent mobile applications? Because However No tools Well No skills Not possible No point AGH Jakdojade.pl, Google now, Google car We create the point Szymon Bobek (AGH-UST) Mobile Trends January / 37
24 Presentation Outline 1 Introduction 2 General issues 3 Engineering issues Gathering context Modelling context Processing context 4 Social and philosophical issues 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
25 Android API and SensorManager Szymon Bobek (AGH-UST) Mobile Trends January / 37
26 AWARE All in one solution 24 context providers implemented Open source client and server solution Plug-ins philosophy (so far about 15 plug-ins) Service oriented architecture Szymon Bobek (AGH-UST) Mobile Trends January / 37
27 AWARE All in one solution 24 context providers implemented Open source client and server solution Plug-ins philosophy (so far about 15 plug-ins) Service oriented architecture Szymon Bobek (AGH-UST) Mobile Trends January / 37
28 Estimote and Gimbal Microlocation with beacons Based on Bluetooth Low Energy (BLE technology) Opposite to GPS it allows detecting device position within a building or a room Android and ios API Preorder for 99$ Szymon Bobek (AGH-UST) Mobile Trends January / 37
29 Estimote and Gimbal Microlocation with beacons Based on Bluetooth Low Energy (BLE technology) Opposite to GPS it allows detecting device position within a building or a room Android and ios API Preorder for 99$ Szymon Bobek (AGH-UST) Mobile Trends January / 37
30 It looks awesome, but... Szymon Bobek (AGH-UST) Mobile Trends January / 37
31 It looks awesome, but... Szymon Bobek (AGH-UST) Mobile Trends January / 37
32 Outline 1 Introduction What is (Artificial) Intelligence What are CAS How to build CAS 2 General issues 3 Engineering issues Gathering context Modelling context Processing context 4 Social and philosophical issues Semantics vs. context Usability, intelligibility Privacy 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
33 Why bother with models For the same reason we......put data into database,...use UML,...design and plan things. So we are able to......add structure to data...add semantics to meaningless data...enhance/allow/prepare data for processing...allow data exchange and system interoperability Szymon Bobek (AGH-UST) Mobile Trends January / 37
34 Available modelling approaches Szymon Bobek (AGH-UST) Mobile Trends January / 37
35 It looks awesome, but... Szymon Bobek (AGH-UST) Mobile Trends January / 37
36 It looks awesome, but... Szymon Bobek (AGH-UST) Mobile Trends January / 37
37 Outline 1 Introduction What is (Artificial) Intelligence What are CAS How to build CAS 2 General issues 3 Engineering issues Gathering context Modelling context Processing context 4 Social and philosophical issues Semantics vs. context Usability, intelligibility Privacy 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
38 Android API Geolocation Entering the geofence Entering and dwelling for some period of time Exiting the geofence ActivityRecognition The device is in a vehicle The device is on a bicycle The device is on a user who is walking or running. The device is still. The device angle relative to gravity changed significantly. Unable to detect the current activity. Szymon Bobek (AGH-UST) Mobile Trends January / 37
39 Machine Learning (Not) a rocket science BigData ;) Weka, Matlab, Python for rapid prototyping JavaML for development Examples Clustering - for discovering patterns, groups Probabilistic graphical models - for handling uncertainty, predicting Regression - for discovering trends, patterns Szymon Bobek (AGH-UST) Mobile Trends January / 37
40 AWARE Framework Framework support Sensing Processing (offline via Context Providers, or on the server side) Sharing and communicating (via MQTT messages) Binding with other applications (via Context Observers and Context Broadcasters) Szymon Bobek (AGH-UST) Mobile Trends January / 37
41 It looks awesome, but... Szymon Bobek (AGH-UST) Mobile Trends January / 37
42 It looks awesome, but... Szymon Bobek (AGH-UST) Mobile Trends January / 37
43 Presentation Outline 1 Introduction 2 General issues 3 Engineering issues 4 Social and philosophical issues Semantics vs. context Usability, intelligibility Privacy 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
44 Context vs. semantics Fall detection The context is that a person is laying on the floor The semantic explains what does it mean Szymon Bobek (AGH-UST) Mobile Trends January / 37
45 Context vs. semantics Fall detection The context is that a person is laying on the floor The semantic explains what does it mean Szymon Bobek (AGH-UST) Mobile Trends January / 37
46 Outline 1 Introduction What is (Artificial) Intelligence What are CAS How to build CAS 2 General issues 3 Engineering issues Gathering context Modelling context Processing context 4 Social and philosophical issues Semantics vs. context Usability, intelligibility Privacy 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
47 Should user understand how system works? Intelligibility Ability of the system to explain how it works. Capability of being understood. The Clippy Microsoft Agent has mostly been abandoned because it made erroneous suggestions with no explanation of why these suggestions were being made Amazon.com added a link under a user s recommendations: Why is this recommended for you? Intelligibility improves usability, however only when a system is certain its decisions Szymon Bobek (AGH-UST) Mobile Trends January / 37
48 Outline 1 Introduction What is (Artificial) Intelligence What are CAS How to build CAS 2 General issues 3 Engineering issues Gathering context Modelling context Processing context 4 Social and philosophical issues Semantics vs. context Usability, intelligibility Privacy 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
49 Mind the... user Locally or in a cloud People do not feel comfortable sharing their location, and other personal data. Cloud sounds good only to developers users prefer Dropbox, GoogleDrive. Processing large amounts of information locally costs energy Szymon Bobek (AGH-UST) Mobile Trends January / 37
50 Presentation Outline 1 Introduction 2 General issues 3 Engineering issues 4 Social and philosophical issues 5 Summary Szymon Bobek (AGH-UST) Mobile Trends January / 37
51 In one word Szymon Bobek (AGH-UST) Mobile Trends January / 37
52 In one word Szymon Bobek (AGH-UST) Mobile Trends January / 37
53 In one word Szymon Bobek (AGH-UST) Mobile Trends January / 37
54 In one word Szymon Bobek (AGH-UST) Mobile Trends January / 37
55 Challenges Under research Energy consumption Intelligibility and usability Processing context, adaptability Methodologies for building CAS, modelling techniques and procedures Szymon Bobek (AGH-UST) Mobile Trends January / 37
56 Thank you! Szymon Bobek Institute of Applied Computer Science AGH University of Science and Technology 17 January Szymon Bobek (AGH-UST) Mobile Trends January / 37
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