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Future of AI and Machine Learning

Neil D. Lawrence

Executive MBA Course, Judge Business School, University of Cambridge

Evolved Relationship with Information

Evolved Relationship

Evolved Relationship
bits/min
billions
2000
6
billion
calculations/s
~100
a billion
a billion
embodiment
20 minutes
5 billion years
15 trillion years

What is Machine Learning?

What is Machine Learning?

\[ \text{data} + \text{model} \stackrel{\text{compute}}{\rightarrow} \text{prediction}\]

  • data : observations, could be actively or passively acquired (meta-data).
  • model : assumptions, based on previous experience (other data! transfer learning etc), or beliefs about the regularities of the universe. Inductive bias.
  • prediction : an action to be taken or a categorization or a quality score.

What is Machine Learning?

\[\text{data} + \text{model} \stackrel{\text{compute}}{\rightarrow} \text{prediction}\]

  • To combine data with a model need:
  • a prediction function \(\mappingFunction (\cdot)\) includes our beliefs about the regularities of the universe
  • an objective function \(\errorFunction (\cdot)\) defines the cost of misprediction.

Machine Learning

  • Driver of two different domains:
    1. Data Science: arises from the fact that we now capture data by happenstance.
    2. Artificial Intelligence: emulation of human behaviour.
  • Connection: Internet of Things

Machine Learning

  • Driver of two different domains:
    1. Data Science: arises from the fact that we now capture data by happenstance.
    2. Artificial Intelligence: emulation of human behaviour.
  • Connection: Internet of Things

Machine Learning

  • Driver of two different domains:
    1. Data Science: arises from the fact that we now capture data by happenstance.
    2. Artificial Intelligence: emulation of human behaviour.
  • Connection: Internet of People

Convention for the Protection of Individuals with regard to Automatic Processing of Personal Data (1981/1/28)

What does Machine Learning do?

  • ML Automates through Data
    • Strongly related to statistics.
    • Field underpins revolution in data science and AI
  • With AI:
    • logic, robotics, computer vision, speech
  • With Data Science:
    • databases, data mining, statistics, visualization

Deep Learning

DeepFace

Outline of the DeepFace architecture. A front-end of a single convolution-pooling-convolution filtering on the rectified input, followed by three locally-connected layers and two fully-connected layers. Color illustrates feature maps produced at each layer. The net includes more than 120 million parameters, where more than 95% come from the local and fully connected.

Source: DeepFace (Taigman et al., 2014)

Deep Learning as Pinball

Five AI Myths

  1. AI will be the first wave of automation that adapts to us.
  2. Hearsay data has significant value.
  3. The big tech companies have the landscape all ‘sewn up’
  4. ‘data scientists’ will come and solve all problems.
  5. The normal rules of business don’t apply to AI.

Mythbusting

  • Area of good data:
    • Finance

Criteria for Success

  • Executive sponsorship (Office of CEO).
  • Technical Expertise (Open minded expert).
  • Financial buy in (CFO).
  • Assimilated knownledge (CTO).

Normal Organisational Rules Apply

  • AI is not magical pixie dust
  • Standard organisational instincts apply
  • Disruption requires agile thinking.
    • Don’t be the Grand Old Duke of York
    • Be Special Forces

Three AI Actions

  • Data grooming: Do the basics right.
    • Incentivisation for data quality
  • People training: At all levels
  • Case studies

Thanks!

References

Taigman, Y., Yang, M., Ranzato, M., Wolf, L., 2014. DeepFace: Closing the gap to human-level performance in face verification, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/CVPR.2014.220