Artificial Intelligence in 5-10 years time:
12 ways it could be very different from today
David Wood – @dw2 – London Futurists

AI in 2010
Classical AI – Expert systems
Rules painstakingly hand-crafted by human experts
Lots of ongoing progress: probabilistic reasoning…
Aware of neural networks, but sceptical of them
“Good old fashioned AI” (GOFAI)
Perceptrons (1969)
Marvin Minsky
and Seymour Papert
Deep Neural Networks
Machine Learning (ML)
Parameters automatically trained
Unexpectedly swift progress in:
Image recognition
Language translation
Speech recognition…
Breakthroughs enabled by:
Big Data (labelled)
Improved hardware (GPUs, TPUs…)
Improvements in algorithms
Numerous innovations in design
AI in 2015-2020

“Good
old-fashioned
neural networks”
AI in 2020
Changes enabled by supply
Supply of ideas
Supply of human resources
Supply of financial investment
Changes driven by demand
Huge profits to be made
Numerous industries interested
Algorithmic stock trading
Healthcare + Medicine
Gaming + Entertainment
Engineering + Science…
Control of the world at stake
Deep Neural Networks
Machine Learning (ML)
Parameters automatically trained
Unexpectedly swift progress in:
Image recognition
Language translation
Speech recognition…
Breakthroughs enabled by:
Big Data (labelled)
Improved hardware (GPUs, TPUs…)
Improvements in algorithms
Numerous innovations in design
AI in 2025-2030

1. Even bigger sets of data for ML to learn from
Data generated and labelled by another AI
Data cleaned by another AI before driving learning
Qualitative change arising from quantitative change

2. Transfer learning –
enabling learning from
small data
Like brain trained
by evolution to
quickly learn new info
Systems for
unsupervised
learning

3. Systems that can self-learn natural language
Start with small seed and it grows from there
Iterative growth of “common sense” knowledge

4. GANs: Generative Adversarial Networks
Networks in adversarial relationship to each other
Creativity generated via arms race

5. Algorithms inspired by evolution
Genetic algorithms…

6. Systems inspired by
new insights from
neuroscience
Like “neural
networks”
but with a
more accurate
correspondence
7. Neuromorphic computing
New ideas in hardware as well as software

8. Quantum computing
Existing algorithms running faster
Brand new algorithms possible

9. Affective computing
Artificial emotional intelligence
10. Sentient computing
Consciousness designed in?!
Real emotional intelligence?!

11. AI that understands not just
correlation but causation
Beyond surface pattern
recognition to deeper insights

12. Intelligence emerging from decentralised network
Simpler components combine to higher powers
As targeted by SingularityNET

Potential timescale for major breakthroughs
1. Bigger sets of data
2. Transfer learning
3. Self-learned common-sense
4. Generative Adversarial Networks
5. Evolutionary algorithms
6. Neuroscience algorithms
7. Neuromorphic computing
8. Quantum computing
9. Affective computing
10. Sentient computing
11. AI that understands causation
12. Emergence from decentralised network
My advice: keep an open mind
Beware dogmatic sceptics
Recall that “experts”, like other people,
can become trapped
into their current paradigm of thinking
A single breakthrough from the list
might open up many new insights
Like quantum mechanics in 1925/26
Or like deep neural networks in 2012
Prepare NOW for potential breakthroughs
And for potential bad consequences
Consider “Anticipatory governance”
Ensure sufficient research into the
potential issues arising