Recommended
More Related Content
What's hot
What's hot (20)
Similar to AI Developments and Trends (OECD)
Similar to AI Developments and Trends (OECD) (20)
More from AnandSRao1962
More from AnandSRao1962 (8)
Recently uploaded
Recently uploaded (20)
AI Developments and Trends (OECD)
- 1. AI Developments & Trends How AI is automating transforming, and disrupting business Dr. Anand S. Rao, Global AI Lead, PwC
- 2. PwC Experience Center THE METHOD 2 TWO PATHS TO AI Enterprises are realizing the value from digitization to AI along two distinct but related paths—to enhance productivity, increase profits and enhance experience. Digitization Productivit y Experienc e Profit s Revenue s Data (Volume, Velocity, Variety, Veracity, Value) Artificial Intelligence Simplification Standardization Automation Cognification Analytics Productivity Experience ProfitsRevenues Automation Path Analytics Path Personalization
- 3. PwC Experience Center THE METHOD 3 WHAT IS AI? AI is the theory and development of systems that sense the environment, make decisions, and act that would normally require human intelligence. Hear See Speak Feel AI that can act… • Robotic process automation • Deep question & answering • Machine translation • Collaborative systems • Adaptive systems AI that can sense… • Natural language • Audio & speech • Machine vision • Navigation • Visualization AI that can think… • Knowledge & representation • Planning & scheduling • Reasoning • Machine Learning • Deep Learning Statistics Econometrics Optimization Complexity Theory Computer Science Game Theory FOUNDATION LAYER Understand Plan Assist Learn Digital Reactive Physical Creative
- 4. PwC 4 Adoption> AI is automating, transforming, and disrupting all industries Value> AI is projected to contribute significant value to global GDP Operationalize AI > AI is moving from proof-of- concepts to operational systems Responsible AI> Responsible use of AI is gaining increased attention AI as a National Competitive Advantage> Countries are developing AI strategies to gain national competitive advantage Investments> Dramatic rise in deal value & volume is reshaping AI
- 5. PwC “Just as electricity transformed almost everything 100 years ago, today I actually have a hard time thinking of an industry that I don’t think AI will transform in the next several years” Value> AI is projected to contribute significant value to global GDP Source: Sizing the Prize, PwC Report, 2017; Andrew Ng – Google Brain, Baidu, Stanford GlobalGDPupliftduetoAI ($intrillions) Global GDP Impact of AI through 2030 2030 IMPACT: $15.7T 60% 40%
- 6. PwC Adoption> AI is automating, transforming, and disrupting all industries “Many CEOs feel they need to bring AI into their organisation. There’s this fear factor that if you’re not on the AI bandwagon, then you’re going to lose out to competitors that are going to be eating your market, because they’re using technologies to make decisions faster and better than you. Daniel Hume – CEO, Satalia, AI Solution provider
- 7. PwC 7 AUTOMATE TRANSFORM DISRUPT Large Global Pharmaceutical company using NLP and Machine Learning to extract adverse drug interaction from multiple unstructured data sources Global airline built predictive aircraft maintenance system that reduced maintenance related costs from Delays & Cancellations Global auto manufacturer gamified their strategy to evaluate go-to-market scenarios for a new rideshare and autonomous vehicle business unit 96% accuracy $14-18M Savings 200K GTM scenarios $1+ Bn Acquisition 15% reduction in delays $25M Cost reduction
- 8. PwC “In fact, the business plans of the next 10,000 startups are easy to forecast: Take X and add AI. Find something that can be made better by adding online smartness to it.” Investments> Dramatic rise in deal value & volume is reshaping AI Source: CapIQ data for Artificial Intelligence Software transactions. Includes M&A, private placements and IPOs Note: Some transactions included did not have transaction value data available Kevin Kelly – Founding Editor of Wired
- 9. PwC Operationalize AI > AI is moving from proof-of- concepts to operational systems Operationalizing AI “Software developers are from Mars, data scientists are from Venus, and you need Machine Learning Ops on Earth to operationalize Artificial Intelligence”
- 10. PwC Responsible AI> Responsible use of AI is gaining increased attention “We need to be vigilant about how we design and train these machine-learning systems, or we will see ingrained forms of bias built into the artificial intelligence of the future.” Kate Crawford – Co-founder of AI Now Institute, NYU 63% Had no formal measure or policy for addressing ethical issues arising from AI 18% Believed that developer or person who approved the AI solution is accountable for the actions of the AI 83.7% Not very confident in their or their organization's ability to detect, then shut down a malfunctioning AI before any serious problems were caused 13.2% Could clearly articulate the cause if their organization’s AI made an incorrect decision or did something unexpected
- 11. PwC 11 Performance • Risk of errors • Risk of bias • Risk of opaqueness • Risk of performance instability Security • Adversarial attacks • Cyber intrusion risks • Privacy risks • Open source software risks Ethical • Lack of values risk • Value alignment risk Societal • Reputational risk • Autonomous weapons proliferation • Risk of intelligence divide Economic • Job displacement • Liability risk • Risk of “winner takes all” concentration of power BUSINESS-LEVEL RISKS NATIONAL-LEVEL RISKS Risks • Lack of human agency in AI supported processes • Inability to detect/control rogue AI Control
- 12. PwC 12 ETHICS & LEGAL Ensure AI development is in line with major local and global regulations, both enacted and emerging; and allow the business to evaluate the ethics of an AI system and how to operationalize ethics in the organization INTERPRETABILITY Enable human users to understand, appropriately trust, and effectively manage the emerging generation of AI BIAS & FAIRNESS Uncover bias in the underlying data and model development process and enable the business to understand what process may lead to unfairness ROBUSTNESS & SECURITY Assess the performance of AI over time to identify potential disruptions or challenges to long term performance GOVERNANCE Introduce enterprise-wide and end-to-end accountability for AI applications and consistency of operations to minimize risk and maximize ROI Ethical & Societal Performance & Security Control PwC’s Responsible AI toolkit helps companies develop AI that is fair, explainable, safe, robust, transparent, and accountable
- 13. PwC Evaluating different fairness definitions, detecting biases, and bias interventions need to be part of model design & development From how data is sourced, how models are built, to how models are used, biases can be embedded into a machine learning process. Bias Detection Bias Intervention Fairness Definition What are the different fairness definitions? Which one should we use for what purpose? How do we detect bias with respect to different decisions, protected attributes, definitions, and datasets? How do we correct algorithms for biases and measure tradeoff between accuracy and fairness?
- 14. PwC Fairness Definitions: There are over 30 mathematical definitions of fairness. Which do you choose? Fairness is not a ‘fuzzy’ concept – it is a social construct that can be defined mathematically in multiple ways Bias Detection Fairness Definition Bias Interven tion Interpre ting results Bias Detection Fairness Definition Bias Interven tion Interpre ting results Bias Detection Bias Intervention Fairness Definition Important considerations: It is required to define fairness in order to measure to bias in the system. Bias is a function of: ● The fairness definition selected ● The decision being made ● The protected attributes such as Race, Gender, Age, and the combination thereof ● The dataset and model output being measured Statistical Measures True Positives False Positives False Negatives True Negatives Positive Predictive Value False Discovery Rate False Omission Rate Negative Predictive Value True Positive Rate False Positive Rate False Negative Rate True Negative Rate Predicted & Actual Outcomes Predictive Parity False Positive Error Rate Balance False Negative Error Rate Balance Equal Opportunity Conditional Accuracy Overall Accuracy Treatment Equality Predicted Outcomes Statistical Parity Conditional Statistical Parity Predicted Probabilities Test Fairness Well-Calibration Balance for Positive Class Balance for Negative Class Similarity-based Measures Causal Discrimination Fairness Through Unawareness Fairness Through Awareness Causal Definitions Counterfactual Fairness No Unresolved Discrimination No Proxy Discrimination Fair Inference Continuous (Regression) Mean Residual Difference Net Compensation Fair Covariance
- 15. PwC Fairness is a spectrum; an organization must decide how fair a system will be, and to with respect to whom Accuracy Fairness Trade-Off There are many ways an AI can develop biases; for an organization to adopt AI, it must be certain the model is fair. However, there are many definitions of fairness, and it is not possible to be fair to both the individual and the group. Population Fairness E.g.: People of a state getting loan approvals without discrimination Individual Fairness E.g.: Similar individuals in terms of multiple demographic and behavioral attributes getting approvals without discrimination Large group fairness E.g.: People of the state belonging to a specific race getting approvals without discrimination Small group Fairness E.g.: Females in the state belonging to a specific race getting approvals without discrimination Fairness Scale
- 16. PwC AI as a National Competitive Advantage> Countries are developing AI strategies to gain national competitive advantage “Artificial intelligence is the future, not only for Russia, but for all humankind. It comes with colossal opportunities, but also threats that are difficult to predict. Whoever becomes the leader in this sphere will become the ruler of the world.” Vladimir Putin – Russian President
- 17. PwC
- 18. PwC PwC’s Digital Services Thank you. © 2020 PwC. All rights reserved. Not for further distribution without the permission of PwC. “PwC” refers to the network of member firms of PricewaterhouseCoopers International Limited (PwCIL), or, as the context requires, individual member firms of the PwC network. Each member firm is a separate legal entity and does not act as agent of PwCIL or any other member firm. PwCIL does not provide any services to clients. PwCIL is not responsible or liable for the acts or omissions of any of its member firms nor can it control the exercise of their professional judgment or bind them in any way. No member firm is responsible or liable for the acts or omissions of any other member firm nor can it control the exercise of another member firm’s professional judgment or bind another member firm or PwCIL in any way. Dr. Anand S. Rao Global AI Lead anand.s.rao@pwc.com @AnandSRao https://www.linkedin.com/in/anandsrao/