henry shevlin, leverhulme cfi, university of cambridge · status of ai: thriving, with many...

Henry Shevlin, Leverhulme CFI, University of Cambridge

▪ Status of AI: thriving, with many striking successes, especially from deep learning.

▪ Challenges to overcome; Moore’s Law slowing.

▪ Important to manage expectations – two previous “AI winters” (1974-1980, 1987-1993). New fears.

Introduction: where we are, where we’re going

Introduction: where we are, where we’re going

▪ However, still grounds for optimism. Business and academia heavily invested, conditions right for a ‘forcing economy’ in AI.

▪ Datasets growing rapidly; 4.4 trillion GB in 2013, at least 18 trillion GB today.

▪ Awareness among companies of limitations of current paradigms, openness to interdisciplinary contributions.

▪ Key to recognize, though, that existing AI still lacks general intelligence.

General Intelligence

▪ Famous distinction by Isaiah Berlin betweenfoxes and hedgehogs.

▪ The fox knows lots of things, the hedgehog one big thing.

▪ Most AI is currently at the hedgehog stage. In other words, AI has specialized intelligence but not general intelligence.

▪ Compare: chess grand master and home cleaner.

General Intelligence

▪ We can distinguish three aspects of general intelligence

Robustness: resistant to interference.Flexibility: capable of learning/transferring skills.Autonomy: capable of extended independent operation.

▪ Note that a wide variety of animals across multiple phyla excel according to these benchmarks.

▪ Suggests that the key to general AI may lie with copying nature’s tricks.

General Intelligence (I) – Robustness

▪ Animals can accomplish feeding, mating, navigating, etc. in a wide range of environments/conditions.

▪ The Western honey bee, for example, inhabits every continent except Antarctica, and has adapted foraging strategies for different habitats.

▪ Birds (e.g. great snipe) manage very long migrations (>5500km) and make smart stopovers to reflect remaining distance.

▪ In short: even complex animal behavior is reliable.


▪ By contrast, the performance of current artificial systems is generally extremely brittle.

▪ Rare to see animals lose control of basic motor functions, fall over, get stuck in loops, ‘glitch out’, etc.

▪ These problems ubiquitous in artificial systemsand robots (as any owner of a robot vacuumcleaner will know).


▪ Likewise, outside of tightly-regulated training environments, artificial systems are vulnerable to a snowball effect of small errors, and can often be tricked into making spectacular errors, e.g., adversarial examples.

‘Dog’ Filter ‘Ostrich’


▪ Key goal for future AI: eliminate simple failure modes, make systems reliable and resilient. This may require new leaps in understanding of biological intelligence.

▪ But what would a more robust AI look like? Imagine a system that ensures it doesn’t run out of power; doesn’t fall over going upstairs; can fix its own software glitches.

▪ However, perhaps harder to control – fewer natural ‘off switches’ or points of intervention. Control systems vital for malware or runaway AI.

General Intelligence (II) – Flexibility

▪ A second key aspect of general intelligence where machines fail and animal excel is flexibility.

▪ We are very flexible thinkers. Even animals can learn to do lots of different jobs (think of dogs with jobs).

▪ Lots of other demonstrations of flexible intelligencein animals – bumblebees engaging in social learning,forward planning in rats, ingenious tool use by birds…


▪ Most AIs limited to learning a single set of tasksdue to problems with transfer learning andcatastrophic forgetting – e.g., Frostbite challenge.

▪ Also severe problems with ‘one shot’ learning.


▪ Fast and flexible learning is currently a target of intense interest in AI research. So what would a more flexible AI look like?

▪ Imagine just one program that can do your taxes, order pizza, respond emails, diagnose your medical condition, and run a bath how you like it.

▪ Immediately raises worries about automation.

▪ However, also likely to shift how we think about AIs – no longer tools, but agents in the world like us capable of diverse tasks.

General Intelligence (III) – Autonomy

▪ A final key aspect of general intelligence is autonomy – the ability to manage one’s needs and goals without external support.

▪ Autonomy requires the ability to keep track of priorities, model the environment, and exploit resources, as well as perhaps self-replicate.

▪ Almost all animals manage this – if they didn’t, they wouldn’t be alive. Even hive/swarm creatures are ‘collectively autonomous’.


▪ By contrast, we’re decades away from robotic systems that can provide their own power, repair damage, or self-replicate.

▪ Even on the more limited challenge of ‘functional autonomy’ (e.g., driverless cars), there have been major obstacles (including fatal crashes).

▪ Autopilots only work reliably in predictable conditions – and the real world environments that humans and animals inhabit are anything but predictable.


▪ Nonetheless, truly autonomous systems would be a gamechanger – from truly effective autonomous vehicles and drones to space probes and rovers capable of repairing damage and using environmental resources.

▪ However, also a threat – autonomous systems are by definition those that can operate without human inputs, raising worries about loss of control.

▪ Consider computer viruses, arguably a simple autonomous system already in existence (but also imagine an autonomous ‘digital immune system’).

Conclusion

▪ AI has surpassed humans in many ways: maths, logic, go and Jeopardy.

▪ However, there are many areas where they still fall short of the performance of simple animals – robustness, flexibility, and autonomy.

▪ While we can go a long way with existing systems (e.g., medical diagnosis), next great leap in AI will require us to meet these challenges.

▪ Luckily we know it can be done, and cognitive science can show us the way – we just need to learn from nature.

Thanks for your attention! For more, see:

Shevlin, H., Vold, K., Crosby, M., &

Halina, M. (2019). The limits of

machine intelligence.

EMBO Reports, e49177.

https://doi.org/10.15252/embr.20

1949177

henry shevlin, leverhulme cfi, university of cambridge · status of ai: thriving, with many...

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