IoT Evolution EXPO: Machine Learning Introductory Certification. PART 1
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Prof. Giuseppe Mascarella discusses machine learning and its impact on the Internet of Things. Machine learning discovers patterns in data through data science algorithms rather than applying predefined logic. Intelligence is being incorporated into machine learning for IoT through directed knowledge, where externally created knowledge modifies edge behavior, and sensor fusion knowledge, which combines sensory data to produce more informative results than individual sources.
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IoT Evolution EXPO: Machine Learning Introductory Certification. PART 1
- 1. By: Prof. Giuseppe Mascarella giuseppe@valueamplify.com
- 2. • Contact us for 1 free consultation: giuseppe@valueamplify.com • Twitter: @giuseppeHighTec • Linkedin: www.linkedin.com/in/giuseppemascarella
- 4. : giuseppe@valueamplify.com 1. What is it that Machines are learning?
- 6. Finding patterns in data is the holy grail (the oil in a barrel!)
- 9. 1. What Is Machine Learning?
- 10. 2. How intelligence is being packed into the ML for IoT? 2. Directed Knowledge where knowledge created elsewhere (by a central authority) will be used to modify edge behavior 3. Sensor Fusion Knowledge the combining of sensory data and data delivery orchestration such that the resulting information is in some sense better than would be possible when these sources were used individually. See Kalman filter
- 11. Machine Learning is a branch of Computer Science that, instead of applying pre-defined logic to solve problems in explicit, imperative logic, applies data science algorithms to discover patterns implicit in the data.
- 15. Prof. Kris Hammond, Northwestern University http://ai.xprize.org/news/periodic- table-of- ai?imm_mid=0ec3b7&cmp=em-data- na-na-newsltr_ai_20170116
- 22. Tuesday Feb 7th 2. Machine Learning Impact on IoT (2:15 PM)
Editor's Notes
- The goal of the analysis is to contact these high risk individuals and take necessary actions such as providing special offers and discounts to prevent them from leaving the business. https://azure.microsoft.com/en-us/documentation/videos/harness-predictive-customer-churn-models-with-cortana-analytics-suite/
- The goal of the analysis is to contact these high risk individuals and take necessary actions such as providing special offers and discounts to prevent them from leaving the business. https://azure.microsoft.com/en-us/documentation/videos/harness-predictive-customer-churn-models-with-cortana-analytics-suite/
- Check if will prompt music or other services depending on status Guided menu “Press 1,2,3” vs alexa (did you mean x or Y)
- Designing with artificial intelligence The secret to getting people to engage with products and services is to make interaction as simple as possible. Remove friction and people will embrace your product. But simplicity isn’t the same as minimalism. The secret to getting people to engage with products and services is to make interaction as simple as possible. Remove friction and people will embrace your product. But simplicity isn’t the same as minimalism. For IoT devices, the interface may be as minimal as a few LEDs and a touchpad—and that kind of minimalism can feel obscure and confusing to users. What’s more, IoT devices often need to operate in concert to create delightful services, such as coordinating the levels of light and sound in a room. This simply increases complexity. Unless we come up with new ideas, the world is about to feel terribly broken. That’s why interfaces and services increasingly rely on artificial intelligence technologies. Algorithms make sense of contextual data, anticipate user needs, and accept more natural forms of input, like voice commands. Keeping the interface simple means the device has to become more intelligent. AI isn’t magic—it’s engineering. To develop compelling products, designers and product managers need to understand the constraints and possibilities of AI. They also need to develop new ways of working together so that the resulting products and services feel more… human. This session looks at how algorithms work, examines what they can and can’t do, and explores case studies and examples of how product teams have combined a deep understanding of people with clever design and smart algorithms to produce truly wonderful products. Decisions of what data to keep, ignore, and what to forward to a centralized authority will be required. Many of the kinetic devices will be used and application whose action can neither tolerate long latency nor risk the possibility that the connection with the centralized authority (“the cloud”) is not available. Their decisions must be made instantly with local information and knowledge. Most IoT endpoints will be limited in capabilities due to size, cost, and the power requirements and will need companion computing that is either embedded in the larger system or in a companion gateway. These gateways will primarily bridge between the local device communication domains and higher level network domains and will in most cases make behavioral decisions. As the industry matures, these gateways will also be responsible for allowing data to be exchanged between intended devices, and ensuring the information is protected. Network traffic patterns will be significantly impacted as more device-to-endpoint traffic will occur and more machine-to-machine communication will materialize, shifting from today’s patterns. However, these solutions will not be static, and their evolving behavior will need to vary depending on local characteristics, giving rise to more software-defined functions at both the edge and within the datacenter. Further, their numbers will be vast and their operation cannot require human intervention.
- Sensory fusion Sensor fusion is a term that covers a number of methods and algorithms, including: Central Limit Theorem, Kalman filter, Bayesian networks, Dempster-Shafer Example: http://www.camgian.com/ http://www.egburt.com/ Kalman is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe. You have enabled Media Viewer for all files Next time you click on a thumbnail on Wikipedia, Media Viewer will be used. You have disabled Media Viewer Next time you click on a thumbnail on Wikipedia, you will directly view all file details. Media Viewer is now disabled Enable Media Viewer? Enable this media viewing feature for all files by default. Learn more Enable Media ViewerCancel Disable Media Viewer? Skip this viewing feature for all files. You can enable it later through the file details page. Learn more Disable Media ViewerCancel More details The Kalman filter keeps track of the estimated state of the system and the variance or uncertainty of the estimate. The estimate is updated using a state transition model and measurements. denotes the estimate of the system's state at time step k before the k-th measurement yk has been taken into account; is the corresponding uncertainty
- The goal of the analysis is to contact these high risk individuals and take necessary actions such as providing special offers and discounts to prevent them from leaving the business. https://azure.microsoft.com/en-us/documentation/videos/harness-predictive-customer-churn-models-with-cortana-analytics-suite/