| title | subtitle | venue | author | abstract | |||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Data Analytics Perspectives |
Machine Learning |
CSaP Annual Conference, Royal Society |
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In this talk we will firstly set the landscape of machine learning, artificial intelligence and data science by describing what characteristics they share, and how they differ. We'll then shift focus to the promise and challenges associated with both Data Science and Artficial Intelligence, with particular attention paid to the potential for a "data crisis" and challenges in "machine learning systems design".
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\include{talk-macros.tex} \include{_ml/includes/what-is-ml.md} \include{_ml/includes/data-science-vs-ai.md}
\subsection{Two Phenomena underpinned by ML}
- Data Science
- Artificial Intelligence
\subsection{Operating at Different Time Scales}
\includediagram{\diagramsDir/data-science/ai-time-frame}
\center{\coloryellow{data science} \colorcyan{artificial intelligence}}
\subsection{Data Science}
- New technologies historically led to new professions:
- Brunel (born 1806): Civil, mechanical, naval
- Tesla (born 1856): Electrical and power
- William Shockley (born 1910): Electronic
- Watts S. Humphrey (born 1927): Software
The major cause of the software crisis is that the machines have become several orders of magnitude more powerful! To put it quite bluntly: as long as there were no machines, programming was no problem at all; when we had a few weak computers, programming became a mild problem, and now we have gigantic computers, programming has become an equally gigantic problem.
Edsger Dijkstra, The Humble Programmer
The major cause of the data crisis is that machines have become more interconnected than ever before. Data access is therefore cheap, but data quality is often poor or personally sensitive. We need cheap high quality data and systems which protect individual privacy.
Me (born 1972)
\subsection{Artificial Intelligence}
\subsection{AI Bubble?}
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>If 1997 to 2001 was the dot com bubble, are we now in the dot ai bubble?
— Neil Lawrence (@lawrennd) June 28, 2017
\newslide{AI Bubble?}
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>It's not a bubble as long as it's not filled with hot air. AI is based on actual testable results and deployed in real life situations.
— visarga (@visarga) June 29, 2017
\newslide{AI Bubble?}
<script async src="//platform.twitter.com/widgets.js" charset="utf-8"></script>dot com was also based on real deployable technology. Boom vs bubble is driven by expectations.
— Neil Lawrence (@lawrennd) June 29, 2017
\newslide{Artificial Intelligence}
- Challenge of empathy.
"AlphaGo will replace accountants next"
BEIS Discussion Under Chatham House Rule
\newslide{Artificial Intelligence}
- Challenge of empathy.
"It doesn't even replace a human Go player!"
Thinks me
\subsection{Deploying ML in Real World: Machine Learning Systems Design}
- Internet of Things
- Major new challenge for systems designers.
- AI systems are currently fragile
- Example: Stuxnet
\newslide{Deploying ML in Real World: Machine Learning Systems Design}
- Internet of
Things - Major new challenge for systems designers.
- AI systems are currently fragile
- Example: Stuxnet
\newslide{Deploying ML in Real World: Machine Learning Systems Design}
- Internet of People
- Major new challenge for systems designers.
- AI systems are currently fragile
- Example: Stuxnet
\newslide{Machine Learning Systems Design}
\includepng{../diagrams/SteamEngine_Boulton&Watt_1784}{50%}
\subsection{Peppercorns}
- A new name for system failures which aren't bugs.
- Difference between finding a fly in your soup vs a peppercorn in your soup.
\thanks
\references