Artificial Intelligence Masterclass for managers and business heads
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The document discusses the application of artificial intelligence and machine learning in various industries, highlighting their use in predictive analytics, recommendation systems, and fraud detection. It emphasizes the importance of data quality and understanding different machine learning models, as well as the challenges associated with deploying AI solutions. It concludes by noting the expansive opportunities for AI across sectors like healthcare and finance, while underscoring the need for business acumen and expert knowledge in selecting and tuning machine learning models.
![Neural network in Python + Tensorflow
model = keras.Sequential([
keras.layers.Flatten(input_shape=(28,28)),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer=tf.train.AdamOptimizer(),
loss='sparse_categorical_crossentropy',
metrics=[‘accuracy’])
model.fit(train_images,train_labels,epochs=5)](https://image.slidesharecdn.com/spotle-webinar-200503204159/85/Artificial-Intelligence-Masterclass-for-managers-and-business-heads-11-320.jpg)
Artificial Intelligence Masterclass for managers and business heads
- 1. Madhavan Mukund https://www.cmi.ac.in/~madhavan AI — From Lab to Marketplace
- 2. Typical situations • Extrapolate from historicaldata • From model test scores, predict board exam performance • Should we give this customer a loan? • Do these symptoms indicate H1N1? • Look for patterns in existing data • Readymade clothes — is S, M, L enough? Add XL? • Should we sell a combo plan with voice calls plusSMS? Supervised learning Unsupervised learning
- 3. Anomaly detection Credit card fraud • Monitorregular transactions • Location, amount, time, items purchased • Flag anomalies • Proactively block card • Customer dissatisfaction (false positives) vs loss due to fraud
- 4. Failure prediction Major printer manufacturer • Drum failure • Down time • Servicing cost — assign support team on the fly • Track diagnostic codes to predict failure • Advance warning, better customer experience • Schedule support visit efficiently, save cost
- 5. Recommendation systems Netflix • $1 millionprize to improve their in-house algorithm to recommend movies • Won by BellKor’s Pragmatic Chaos — two teams from Bell Core, merged • When DVD rental was the main business • Still relevant for online streaming? • Audience preferences drive content development
- 6. Use of ML is exploding • Online advertising • E-commerce recommendations • Screening applications • Fraud detection • Conversationbots • Smart buildings • … • Self driving cars
- 8. • Probabilistic models Mathematical models for ML • Regression • Support Vector Machines • Decision trees
- 9. Mathematical models for ML … • And, of course, neural networks
- 10. Programming for ML • Open source languages like R and Python • Built in libraries • Classical statisticaltests, time-series analysis, classification,clustering, … • ML models • User friendly IDEs
- 11. Neural network in Python + Tensorflow model = keras.Sequential([ keras.layers.Flatten(input_shape=(28,28)), keras.layers.Dense(128, activation=tf.nn.relu), keras.layers.Dense(10, activation=tf.nn.softmax) ]) model.compile(optimizer=tf.train.AdamOptimizer(), loss='sparse_categorical_crossentropy', metrics=[‘accuracy’]) model.fit(train_images,train_labels,epochs=5)
- 12. Below the hood … • Need some understanding of what lies beneath • Analogy:database systems • Move from customised data storage to RDBMS • Standard model • Declarative query language • Still need to understand table design, query optimisation
- 13. Deploying AI/ML
- 14. The challenge • When do we deploy AI? • Problem structure? • Domain? • If AI, what approach? • Supervised or unsupervised? • Which model?
- 15. Where AI can help • AI solutions are driven by data • No obvious“direct” algorithm • Labelled data • This bus route meets timing requirement • These test readings indicate disease • This picture shows a stress crack
- 16. All data is not born equal • Every business generates data • How can it improve the business? • How usable is the data? • Clean? Labelled? • Is the actual data available? • Privacy issues, silos • IT vs ML solution • IT — columns of table • ML — rows of table
- 17. Which ML approach to use? • Supervised or unsupervised? • Flag good vs bad • Segment data • Which model to use? • Is data categorical or continuous? • Static or time-varying, seasonal? • No magic formula • Familiaritywith different approaches • Experience
- 18. The Future of AI
- 19. Opportunities galore … • Health care • Drug development • Image diagnostics • IoT • Transportation logistics • Preventive maintenance • Smart buildings • Finance, entertainment, … • Look out for labelled data!
- 20. Summary
- 21. ML everywhere • Data driven solutions have wide applicability • Need business understanding • Numerous off-the-shelf tools • Python, R, Tensorflow • Deployment is easy • Choosing ML solution not easy • Variety of models • Need expertise to tune parameters
- 22. Thank you