Finale, wrap-up while dispatching all the bad guys in ascending order. Back to the definitions of ML circa mid–1980s, there were planners, schedulers, etc. Not just pattern recognition. One area which was all but forgotten by the popular dialog about AI is genetic programming. GPs evolve many generations of programs to solve a given problem. Back to what Barba described as speech acts, GPs determine actions in the linguistic domain. For example, serving as planners. I wrote a tutorial about running a GP as an example of implementing a Mesos framework.
Taking a cue from precursors such as Eurisko, the field of probabilistic programming is showing promise. Keep an eye open about Gamalon, among others. What if entire workflows could evolve and restructure themselves, based on available data?
Another interesting area is in fuzzy logic, which made a blip in the early 1990s, then dove for cover during the AI winter. I’m seeming more than blips reemerge. For background, check out Fuzzy Thinking by Bart Kosko.
Pulling these threads together, recently a gaming AI defeated a US Air Force expert combat pilot in simulated combat. As the pilot explained, It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment. It knew how to defeat the shot I was taking. It moved instantly between defensive and offensive actions as needed.
The system ran on a $500 PC, employing a “genetic-fuzzy” approach.
Lest these examples cast too much of a shadow on AI in literature and practice, here’s a recommended antidote, Adventures in Narrated Reality by Ross Goodwin. Money quote:
The fictional definitions it created for real words were also frequently entertaining. My favorite of these was its definition for “love” — although a prior version of the model had defined love as “past tense of leave,” which I found equally amusing.
Also check out the inexplicably entertaining short film Sunspring, authored by an AI named Benjamin. That leverages a recurrent neural network approach called long short-term memory (LSTM ) which takes abductive reasoning to new levels. While more of a hack though hugely entertaining, I’ve recently enjoyed playing with this gem from Machinamenta.
- Beyond the AI Winter by Paco Nathan
Taking a cue from precursors such as Eurisko, the field of probabilistic programming is showing promise. Keep an eye open about Gamalon, among others. What if entire workflows could evolve and restructure themselves, based on available data?
Another interesting area is in fuzzy logic, which made a blip in the early 1990s, then dove for cover during the AI winter. I’m seeming more than blips reemerge. For background, check out Fuzzy Thinking by Bart Kosko.
Pulling these threads together, recently a gaming AI defeated a US Air Force expert combat pilot in simulated combat. As the pilot explained, It seemed to be aware of my intentions and reacting instantly to my changes in flight and my missile deployment. It knew how to defeat the shot I was taking. It moved instantly between defensive and offensive actions as needed.
The system ran on a $500 PC, employing a “genetic-fuzzy” approach.
Lest these examples cast too much of a shadow on AI in literature and practice, here’s a recommended antidote, Adventures in Narrated Reality by Ross Goodwin. Money quote:
The fictional definitions it created for real words were also frequently entertaining. My favorite of these was its definition for “love” — although a prior version of the model had defined love as “past tense of leave,” which I found equally amusing.
Also check out the inexplicably entertaining short film Sunspring, authored by an AI named Benjamin. That leverages a recurrent neural network approach called long short-term memory (LSTM ) which takes abductive reasoning to new levels. While more of a hack though hugely entertaining, I’ve recently enjoyed playing with this gem from Machinamenta.
- Beyond the AI Winter by Paco Nathan
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