122 – Artificial Intelligence
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In this episode we talk to UT Austin’s Ray Mooney about artificial intelligence. We start out by providing an overview over the field in general. We discuss some historical aspects as well as some of its subfields. We then spend some time looking at Ray Mooney’s own specialties: machine learning and natural language processing. We conclude the episode with a brief conversation about IBM’s Watson, the computer than won the jeopardy game.
- Raymond J. Mooney
- UT Austin AI-Lab
- Natural language processing (WP)
- Machine learning (WP)
- Dartmouth Conferences (WP)
- Cognitive science (WP)
- Church–Turing thesis (WP)
- Turing machine (WP)
- Turing Machine (WP)
- Dualism (WP)
- Moore’s Law (WP)
- Ray Kurzweil (WP)
- John McCarthy (WP)
- Loebner Prize (WP)
- Solipsism (WP)
- The Penn Treebank Project
- Turing test (WP)
- Turing Test (WP)
- John Searle (WP)
- Dragon – Dragon NaturallySpeaking – Nuance
- Wolfram|Alpha: Computational Knowledge Engine
- IBM Watson
- Noam Chomsky (WP)
- Computer vision (WP)
- Expert system (WP)
- Knowledge representation and reasoning (WP)
- First-order logic (WP)
- Prolog (WP)
- Logical Inference (WP)
- Probabilistic logic (WP)
- Fuzzy logic (WP)
- Bayesian inference (WP)
- Bayes Theorem (WP)
- Robotics (WP)
- Embodied cognition (WP)
- Swarm intelligence (WP)
- Emergence (WP)
- Neuron (WP)
- Marvin Minsky (WP)
- Roger Penrose (WP)
- Linked List (WP)
- Herbert A. Simon (WP)
- Neural network (WP)
- Decision tree (WP)
- Support vector machine (WP)
- graph (WP)
- Induction (WP)
- Deduction (WP)
- Abductive reasoning (WP)
- Plan Recognition (WP)
- Modus ponens (WP)
- Program synthesis (WP)
- Declarative programming (WP)
- IBM deep blue (WP)
- Jeaopardy (WP)
- Watson (computer) (WP)
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As someone who already worked on some AI problems, this was still a very inspiring motivating podcast for me and also provides a very good overview over the field. I really appreciate it!
Nice to hear that it was interesting even for an “insider” :-)
Markus
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This was a fantastic episode, probably one of my top-ten favorites. You really seemed to interact well with the interviewee, much better than the previous Austin professors. I’m not sure why that was. In the talk, Prof. Mooney alluded to a recent undergraduate survey class that he taught. I wonder if professors who still teach survey classes are better interview subjects that those who only teach advanced classes?
Quite inspiring & fascinating overall. This ep was in my Favorite 3.
The interviewee was a rapid cannon fount of knowledge. He must be perfect for training our future AI comp sci & AI systems.
I really enjoyed all the Austin interviews so far. Keep them coming and thanks for your English recordings.
Markus, will you release any new Software Engineering Podcasts?
> You really seemed to interact well with the interviewee, much
> better than the previous Austin professors. I’m not sure why that was
that’s obvious … because I have some background in software and stuff,
but not in perception or Chaos.
> I wonder if professors who still teach survey classes…
at least they haven’t “lost track” of the basic stuff.
Markus
> I really enjoyed all the Austin interviews so far. Keep them coming …
this was the last one :-)
> Markus, will you release any new Software Engineering Podcasts?
There are currently no plans.
Markus
The interview mentioned the Turing Test: If a human can’t reliably distinguish a computer from a human, then the computer must be declared “intelligent”.
What worries me is that one day, there might be a “Reverse Turing Test”, where computers succeed in reliably distinguishing a computer from a human, and then the humans are declared “unintelligent”…
:-)
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I found this interview and the one on perception connected somehow. Seems the droids have silently taken the lead. Perhaps the uncertainty component will emerge through quantum computing.
I find most of the episodes quite well done, but this one was outstanding for me. Thanks!
Thank you :-)
Markus
Finished this episode about “Artificial Intelligence” yesterday!
Great interview and many inputs to think about! Highly Recommended!
Viele Dank für diese herausragende Episode! Hier ist die Mischung aus Überblick und Details zum Thema besonders gelungen!
Noch ein allgemeiner Vorschlag: Wie wäre es, wenn Ihr denn die Themen vorweg auf Eure Weg-Site stellt und den Hörern ein Möglichkeit gebt (ohne Garantie der Übernahme!) dazu Fragen und Anregungen zu hinterlassen? Das würde Euch vielleicht die Vorbereitung eines Themas erleichtern, denn man merkt den einzelnen Episoden natürlich an, wie viel Background beim Interviewer vorhanden ist. Das letzte jetzt bitte nicht als Kritik auffassen, denn wenn man den Background nicht merken würde wäre das Thema trivial oder der Interviewer ein Genie. Damit würdet Ihr (s.o. „ohne Garantie“) die Hoheit über das Thema ja nicht abgeben…
Was man auch machen könnte ist eine Abstimmung über relevante Themen. Ich z.B. fände eine Episode über computerbasierten (Aktien-)Handel interessant…
Grüße, Mik
Hi Mik,
ich stelle tatsächlich öfters die Themen auf Google+ und Facebook um Fragen “einzusammeln”. Ob ich es mache,
hängt so ein bischen von der Zeit/Energie ab die ich für eine Episode habe.
Aber klar, man merkt den Hintergrund immer. Ich merke teilweise auch meine Planlosigkeit beim Interview selbst :-)
Computerbasierter Aktienhandel ist eine super Idee. Hast Du da zufällig nen Kontakt?
Markus
Artificial Intelligence is normally known in the short form AI. Artificial Intelligence is actually an applied and basic topic of Computer Science. Mainly Artificial Intelligence is the intelligence of robots and machines. In the section of Artificial Intelligence, designing intelligent agent is one of the main and important tasks.
While I enjoyed the interview, I was struck by Prof Mooney’s focus on the various traditional AI techniques like knowledge representation systems, decision trees, probabilistic logic, neural networks, etc. These approaches all have a common underpinning — they seek to implement intelligent functionality via mathematical structures.
I’m really intrigued with Jeff Hawkins’ research which takes a different approach to understanding intelligence. His neuroscience based approach uses sparse data representation and hierarchical temporal memory to make predictions and associations.
Anyhow, thanks for the enjoyable interview.
Do you happen to know Jeff so you could maybe help me convince him to be a guest?
Markus
A very interesting episode.