Machine Learning And Business
Created by 马明
Content
- What is machine learning?
- What business value does ML provide?
- What the common challenges with ML?
What is machine learning?
- Data analysis technology
- Extracts knowledge without being explicitly programmed
- Require large amounts of data
Learning From Data
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
The Basics of Machine Learning Technology
- Input data: wide variety of data
- Learning:
Eager learning
Lazy learning
- Output data:
Predictive
Prescriptive
What the difference between Maching Learning and Statistics?
- Statistics is not Machine Learning
- The are interwinned and cannot be seprated
- ML is more a hybrid field than statistics
Some answer?
- Machine learning is essentially a form of applied statistics
- Machine learning is glorified statistics
- Machine learning is statistics scaled up to big data
AI, Data Mining, Machine Learning, What is what?
Machine Learning and Business
What impact will AI have on my organisation, and is our business model threatened by AI disruption?
Where should we target investment, and what kind of capabilities would enable us to perform better?
Forms of AI
- Automated intelligence
- Assisted intelligence
- Augmented intelligence
- Autonomous intelligence
From PwC
- AI could contribute up to $15.7 trillion1 to the global economy in 2030
- 6.6 trillion is likely to come from increased productivity and 9.1 trillion is likely to come from consumption-side effects
- Productivity gains from businesses automating processes (including use of robots and autonomous vehicles).
- Productivity gains from businesses augmenting their existing labour force with AI technologies (assisted and augmented intelligence).
- Increased consumer demand resulting from the availability of personalised and/or higher-quality AI-enhanced products and services.
Healthcare
Three areas with the biggest AI potential
- Supporting diagnosis in areas such as detecting small variations from the baseline in patients’ health data or comparison with similar patients
- Early identification of potential pandemics and tracking incidence of the disease to help prevent and contain its spread
- Imaging diagnostics (radiology, pathology)
Barriers to overcome:
Concerns over the privacy and protection of sensitive health data
More advanced applications may take time to reach their potential and gain acceptance from patients, healthcare providers and regulator
Timing
- Ready to go: Medical insurance and smarter scheduling (e.g. appointments and operations
- Medium-term potential: Data-driven diagnostics and virtual drug development
- Longer-term potential: Robot doctors carrying out diagnosis and treatment
High potential use case: Data-based diagnostic support
Financial Services
Three areas with the biggest AI potential
- Personalised financial planning
- Fraud detection and anti-money laundering
- Process automation – not just back office functions, but customer facing operations as well
Barriers to overcome:
Consumer trust and regulatory acceptance
Timing
- Ready to go: Robo-advice, automated insurance underwriting and robotic process automation in areas such as finance and compliance
- Medium-term potential: Optimised product design based on consumer sentiment and preferences
- Longer-term potential: Moving from anticipating what will happen and when in areas such as an insurable loss (predictive analytics) to proactively shaping the outcome (prescriptive analytics) in areas such as reduced accident rates or improved consumer outcomes
High potential use case: Personalised financial planning
Retail
Three areas with the biggest AI potential
- Personalised design and production
- Anticipating customer demand – for example, retailers are beginning to use deep learning to predict customers’ orders in advance.
- Inventory and delivery management
Barriers to overcome:
Adapting design and production to this more agile and tailored approach. Businesses also need to strengthen trust over data usage and protection
Timing
- Ready to go: Product recommendation base on preferences
- Medium-term potential: Fully customised products
- Longer-term potential: Products that anticipate demand from market signals
High potential use case: Personalised design and production
Automotive
Three areas with the biggest AI potential
- Autonomous fleets for ride sharing
- Semi-autonomous features such as driver assist
- Engine monitoring and predictive, autonomous maintenance.
Barriers to overcome:
Technology still needs development
Timing
- Ready to go: Automated driver assistance systems (e.g. parking assist, lane centring, adaptive cruise control etc.)
- Medium-term potential: On-demand parts manufacturing and maintenance
- Longer-term potential: Engine monitoring and predictive, autonomous maintenance
High potential use case: Autonomous fleets for ride sharing
Technology, communications and entertainment
Three areas with the biggest AI potential
- Media archiving and search – bringing together diffuse content for recommendation
- Customised content creation (marketing, film, music, etc.)
- Personalised marketing and advertising
Barriers to overcome:
Cutting through the noise when there is so much data, much of it unstructured
Timing
- Ready to go: Content recommendation for consumers
- Medium-term potential: Automated telemarketing capable of holding a real conversation with the customer
- Longer-term potential: Use-case specific and individualised AI-created content
High potential use case: Media archiving and search
Manufacturing
Three areas with the biggest AI potential
- Enhanced monitoring and auto-correction of manufacturing processes
- Supply chain and production optimisation
- On-demand production
Barriers to overcome:
Making the most of supply chain and production opportunities requires all parties to have the necessary technology and be ready to collaborate. Only the biggest and best-resourced suppliers and manufacturers are up to speed at present
Timing
- Ready to go: Greater automation of a large number of production processes
- Medium-term potential: Intelligent automation in areas ranging from supply chain optimisation to more predictive scheduling
- Longer-term potential: Using prescriptive analytics in product design – solving problems and shaping outcomes, rather than simply predicting and responding to demand in product design
High potential use case: Enhanced monitoring and auto-correction
Energy
Three areas with the biggest AI potential
- Smart metering – real-time information on energy usage, helping to reduce bills
- More efficient grid operation and storage
- Predictive infrastructure maintenance
Barriers to overcome:
Technological development and high investment requirements in some of the more advanced areas
Timing
- Ready to go: Smart metering.
- Medium-term potential: Optimised power management
- Longer-term potential: More efficient and consistent renewable energy supply in areas such as improved prediction and optimisation of wind power.
High potential use case: Smart meters
Transport and logistics
Three areas with the biggest AI potential
- Autonomous trucking and delivery
- Traffic control and reduced congestion
- Enhanced security
Barriers to overcome:
Technology for autonomous fleets is still in development
Timing
- Ready to go: Automated picking in warehouses
- Medium-term potential: Traffic control
- Longer-term potential: Autonomous trucking and delivery
High potential use case: Traffic control and reduced congestion
ML Can Deliver Value to Organizations
- Customer Relationship Management
- Failure Prediction for Preventive Maintenance
- Finance and Hedge Fund Portfolio Pricing
- State Government and Litigation Case Management
- Workforce Management
ML can benefit IT include
- Security Operations
- IT Call Centers
- DevOps
- Project Management
Stages of the Machine Learning Process
Life Cycle for Developing Learning Systems
Continuous ML Model and Control Framework
- Collaboration for heterogeneous teams and technologies
- Monitoring of ML models with statistical analysis capabilities
- Reusability of ML models for rapid development
- Interoperability between different analytic platforms and ML frameworks
Challenges related to the use of ML in business
- It requires considerable data and compute power
- It requires knowledgeable data science specialists or teams
- It adds complexity to the organization's data integration strategy
- The context of data often changes
- Algorithmic bias, privacy and ethical concerns may be overlooked
Success story
Healthcare Analytics: VeraData
Supply Chain Analytics: Shell
Tax and Audit Analytics
Government Analytics
Higher Education Analytics
Releasing AI potentials
- Work out what AI means for your business
- Prioritise your response
- Make sure you have the right talent and culture, as well as technology
- Build in appropriate governance and control
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Marvelous List
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Tabular Tables
| Item | Value | Quantity |
|---|---|---|
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| Lemonade | $2 | 18 |
| Bread | $3 | 2 |
Clever Quotes
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Much more
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