AI
we are now at a critical juncture where many of the systems we need to master are fiendishly complex, from climate change to macroeconomic issues to Alzheimer’s disease.
The problem is that these challenges are so complex that even the world’s top scientists, clinicians and engineers can struggle to master all the intricacies necessary to make the breakthroughs required. It has been said that Leonardo da Vinci was perhaps the last person to have lived who understood the entire breadth of knowledge of their age. Since then we’ve had to specialise, and today it takes a lifetime to completely master even a single field such as astrophysics or quantum mechanics.
The systems we now seek to understand are underpinned by a vast amount of data, usually highly dynamic, non-linear and with emergent properties that make it incredibly hard to find the structure and connections to reveal the insights hidden therein.
Kepler and Newton could write equations to describe the motion of planets and objects on Earth, but few of today’s problems can be reduced down to a simple set of elegant and compact formulae.
This is one of the greatest scientific challenges of our times.
The founding fathers of the modern computer age — Alan Turing, John von Neumann, Claude Shannon — all understood the central importance of information theory, and today we have come to realise that almost everything can either be thought of or expressed in this paradigm. This is most evident in bioinformatics, where the genome is effectively a gigantic information coding schema. I believe that, one day, information will come to be viewed as being as fundamental as energy and matter.
the AI techniques underpinning AlphaGo are general-purpose and could be applied to a wide range of other domains, especially those with clear objective functions that can be optimised, and environments that can be accurately simulated, allowing for efficient high-speed experimentation
In many ways I see AI as analogous to the Hubble telescope — a scientific tool that allows us to see farther and better understand the universe around us
The problem is that these challenges are so complex that even the world’s top scientists, clinicians and engineers can struggle to master all the intricacies necessary to make the breakthroughs required. It has been said that Leonardo da Vinci was perhaps the last person to have lived who understood the entire breadth of knowledge of their age. Since then we’ve had to specialise, and today it takes a lifetime to completely master even a single field such as astrophysics or quantum mechanics.
The systems we now seek to understand are underpinned by a vast amount of data, usually highly dynamic, non-linear and with emergent properties that make it incredibly hard to find the structure and connections to reveal the insights hidden therein.
Kepler and Newton could write equations to describe the motion of planets and objects on Earth, but few of today’s problems can be reduced down to a simple set of elegant and compact formulae.
This is one of the greatest scientific challenges of our times.
The founding fathers of the modern computer age — Alan Turing, John von Neumann, Claude Shannon — all understood the central importance of information theory, and today we have come to realise that almost everything can either be thought of or expressed in this paradigm. This is most evident in bioinformatics, where the genome is effectively a gigantic information coding schema. I believe that, one day, information will come to be viewed as being as fundamental as energy and matter.
the AI techniques underpinning AlphaGo are general-purpose and could be applied to a wide range of other domains, especially those with clear objective functions that can be optimised, and environments that can be accurately simulated, allowing for efficient high-speed experimentation
In many ways I see AI as analogous to the Hubble telescope — a scientific tool that allows us to see farther and better understand the universe around us
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