What Is Artificial Intelligence? Part 1/10
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A practical introduction to AI vs Data Mining and some example of AI as a services. Part 1 of AI class
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What Is Artificial Intelligence? Part 1/10
- 1. WELCOME TO: What Is ARTIFICIAL INTELLIGENCE?
- 3. Data Sources Evolution Source: www.microsoft.com
- 4. Data is out there and is free (Open data). It provides no competitive advantages. Finding patterns in data is the holy grail (the oil in a barrel!)
- 5. From Data Mining To AI
- 6. AI is Not Only for Data Mining and Forecasts It’s interface is based on ‘machine learning’ i.e. it learns and becomes better with use. This will be common with ALL products and will determine the competitive advantage of companies. Its a winner takes all game! Every product will have a ‘self learning’ interface/component and the product which learns best will win!
- 7. Computer Science 101Layers Computer Service 7. AI, ML High Computing Power, access to Multiple DB, new algorithms $1/hr (or you system $million) Predictions Azure ML, Clouders, DatBricks, etc IaaS 6. Cloud Computing Datacenter in the low cost area and cold. $.50/ service/ hour Web browser, Simultaneous use, shared economy, lower costs , access to open source data such as IoT (Microsoft Azure, AWS(Amazon Web Services), Google cloud 5. Applications (accounting, banking , retail sales) SAP, QuickBook, Word, excel SaaS (Software as a Service) 4. DATA ON HW DB(DataBase, Data Warehouse) Data store and retreive PaaS 3. Manager of Device components OS(Operating System) Windows, IoS, MacOS, Linux, ChromeOS orchestrator IaaS (VM)Vist ual Machine) 2.Hardware Device Laptop/Screen/Mouse/Monitor/M icrophone, Hard Drive (DELL, IBM, ASUS) Interaction 1.Network Cable, wifi Stream on 0-1
- 10. Data Mining Machine Learning/ AI Meaning Extracting knowledge from a large amount of data Introduce New Algorithm from data as well as past experiences/events History 1940s knowledge discovery in databases 1970s Samuel checker playing program (War Games Movie) Responsibility Get the rules from existing data Teach computers to learn the given rules Context Traditional DBs with structured data Un-structured no-sql data as well as algorithms Implementation Develop our own model where we use datamining techniques Use it in the decision tree, neural network Nature Human Interference Automated Applications Clustering Credit scoring, fraud detection
- 15. • The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases. --Fayyad et al., (1996) • Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable. Is Data Mining a misnomer? • Other names: knowledge extraction, pattern analysis, knowledge discovery, information harvesting, pattern searching, data dredging,… Data Mining
- 16. 1. What Is Machine Learning? Machine learning is the ability of a machine to vary the outcome of a situation or behavior based on knowledge or observation of events.
- 17. AI Modern Scenarios 2. Directed Knowledge where knowledge created elsewhere (by a central authority) will be used to modify edge behavior 1. Observed Knowledge which will modify behavior based on local learning (context) 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
- 18. Think about this like how the human brain learns from life experiences vs. from explicit instructions. The more data, the more effective the learning is. 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. Machine Learning
- 19. Microsoft Internal Applications Benchmark
- 22. AI Functional Components By: Prof. Kris Hammond, Northwestern University Practice: For A Banking AI BOT What Components Do You Need?
- 25. giuseppe@valueamplify.com WELCOME TO: ARTIFICIAL INTELLIGENCE APPLICATIONS FOR BUSINESS ANALYTICS
Editor's Notes
- 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
- https://pages.databricks.com/201906-US-WB-Getting-Data-Ready-for-Data-Science-TY-Replay.html