AI for organisations, with Daniel Hulme
London Futurists
Is There Virtually No AI in Production?
The challenge is how do you safely build systems that can adapt themselves. CTOs say, no way am I going to put a system in production that's going to make mistakes. Over the next decade, there's going to be a convergence of three digital twin scenarios: Digital simulation and your organization and AI.
This episode features Daniel Hulme, founder of Satalia and chief AI officer at WPP. What is AI good at today? And how can organisations increase the likelihood of deploying AI successfully?
02.55 What is AI good at today?
03.25 Deep learning isn’t yet being widely used in companies. Executives are wary of self-adapting systems
04.15 Six categories of AI deployment today
04.20 1. Automation. Using “if … then …” statements
04.50 2. Generative AI, like Dall-E
05.15 3. Humanisation, like DeepFake technology and natural language models
05.40 4. Machine learning to extract insights from data – finding correlations that humans could not
06.05 5. Complex decision making, aka operations research, or optimisation. “Companies don’t have ML problems, they have decision problems”
06.25 6. Augmenting humans physically or cognitively
06.50 Aren’t the tech giants using true AI systems in their operations?
07.15 A/B testing is a simple form of adaptation. Google A/B tested the colours of their logo
08 .00 Complex adaptive systems with many moving parts are much riskier. If they go wrong, huge damage can occur
08.30 CTOs demand consistency from operational systems, and can’t tolerate the mistakes that are essential to learning
09.25 Can’t the mistakes be made in simulated environments?
10.20 Elon Musk says simulating the world is not how to develop self-driving cars
10.45 Companies undergoing digital transformations are building ERPs, which are “glorified databases”
11.20 The idea is to develop digital twins, which enable them to ask “what if…” questions
11.30 The coming confluence of three digital twins: workflow, workforce, and administrative processes
12.18 Why don’t supermarkets offer digital twins to their customers? They’re coming
14.55 People often think that creating a data lake and adding a system like Tableau on top is deploying AI
15.15 Even if you give humans better insights they often don’t make better decisions
15.20 Data scientists are not equipped to address opportunities in all 6 of the categories listed earlier
15.40 Companies should start by identifying and then prioritising the frictions in their organisations
16.10 Some companies are taking on “tech debt” which they will have to unwind in five years
16.25 Why aren’t large process industry companies boasting about massive revenue improvements or cost savings?
17.00 To make those decisions you need the right data, and top optimisation skills. That’s unusual
17.55 Companies ask for “quick wins” but that is an oxymoron
18.10 We do see project ROIs of 200%, but most projects fail due to under-investment, or mis-understandings
19.00 Don’t start by just collecting data. The example of a low-cost airline which collected data about everything except rivals’ pricing
20.15 Humans usually do know where the signals are
22.25 Some of Daniel’s favourite AI projects
23.00 Tesco’s last-mile delivery system, which saves 20m delivery miles a year
24.00 Solving PwC’s consultant allocation problem radically improved many lives
25.10 In the next decade there will be a move away from pure ML towards ML+ optimisation
26.35 How these systems have been applied to Satalia
28.10 Daniel has thought a lot about how AI can enable companies to be very adaptable, and allocate decisions well
29.00 Satalia staff used to make recommendations for their own salaries, and their colleagues would make AI-weighted votes
29.30 The goal is to scale this approach not just
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