Larry Learner is a computer program designed to play the "last one loses" pencil game. Some argue Larry is intelligent because it plays the game well, while others argue it is not intelligent because it simply follows its programming without understanding. A key reason given for why Larry lacks intelligence is that its abilities are narrow, being able to only play this one game, while humans can learn many different things.
These myths are a simple reflection of my own experience and experiences in the industry. Ai and cognitive are popular these days, but as engineers, data scientists and IT people in general we should make sure not to overate or misuse.
The Mind as the Software of the Brain by Ned Block httpww.docxarnoldmeredith47041
"The Mind as the Software of the Brain" by Ned Block http://www.nyu.edu/gsas/dept/philo/faculty/block/papers/msb.html
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The Mind as the Software of the Brain
Ned Block
New York University
1. Machine Intelligence
2. Intelligence and Intentionality
3. Functionalism and the Language of Thought
4. Searle's Chinese Room Argument
Cognitive scientists often say that the mind is the software of the brain. This chapter is about what this
claim means.
1. Machine Intelligence
In this section, we will start with an influential attempt to define `intelligence', and then we will move to a
consideration of how human intelligence is to be investigated on the machine model. The last part of the
section will discuss the relation between the mental and the biological.
1.1 The Turing Test
One approach to the mind has been to avoid its mysteries by simply defining the mental in terms of the
behavioral. This approach has been popular among thinkers who fear that acknowledging mental states
that do not reduce to behavior would make psychology unscientific, because unreduced mental states are
not intersubjectively accessible in the manner of the entities of the hard sciences. "Behaviorism", as the
attempt to reduce the mental to the behavioral is called, has often been regarded as refuted, but it
periodically reappears in new forms.
Behaviorists don't define the mental in terms of just plain behavior, since after all something can be
intelligent even if it has never had the chance to exhibit its intelligence. Behaviorists define the mental not
in terms of behavior, but rather behavioral dispositions, the tendency to emit certain behaviors given
certain stimuli. It is important that the stimuli and the behavior be specified non-mentalistically. Thus,
intelligence could not be defined in terms of the disposition to give sensible responses to questions, since
that would be to define a mental notion in terms of another mental notion (indeed, a closely related one).
To see the difficulty of behavioristic analyses, one has to appreciate how mentalistic our ordinary
behavioral descriptions are. Consider, for example, throwing. A series of motions that constitute throwing
if produced by one mental cause might be a dance to get the ants off if produced by another.
An especially influential behaviorist definition of intelligence was put forward by A. M. Turing (1950).
Turing, one of the mathematicians who cracked the German code during World War II, formulated the
idea of the universal Turing machine, which contains, in mathematical form, the essence of the
"The Mind as the Software of the Brain" by Ned Block http://www.nyu.edu/gsas/dept/philo/faculty/block/papers/msb.html
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programmable digital computer. Turing wanted to define intelligence in a way that applied to both men
and machines, and indeed, to anything that is intelligent. His version of behaviorism formulates the issue
of wh.
Artificial Intelligence: Introduction, Typical Applications. State Space Search: Depth Bounded
DFS, Depth First Iterative Deepening. Heuristic Search: Heuristic Functions, Best First Search,
Hill Climbing, Variable Neighborhood Descent, Beam Search, Tabu Search. Optimal Search: A
*
algorithm, Iterative Deepening A*
, Recursive Best First Search, Pruning the CLOSED and OPEN
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Turing’s Computing Machinery and Intelligence.pdfMuizz Anibire
A. M. Turing (1950) Computing Machinery and Intelligence. Mind 49: 433-46
COMPUTING MACHINERY AND INTELLIGENCE
By A. M. Turing
1. The Imitation Game
I propose to consider the question, "Can machines think?" This should begin with
definitions of the meaning of the terms "machine" and "think." The definitions might be
framed so as to reflect so far as possible the normal use of the words, but this attitude is
dangerous, If the meaning of the words "machine" and "think" are to be found by
examining how they are commonly used it is difficult to escape the conclusion that the
meaning and the answer to the question, "Can machines think?" is to be sought in a
statistical survey such as a Gallup poll. But this is absurd. Instead of attempting such a
definition I shall replace the question by another, which is closely related to it and is
expressed in relatively unambiguous words.
LESSON 1: INTRODUCTION TO PYTHON, VARIABLES, DNA CODING, AI
Introduction to Python, how to download (Python 3), Create your own Chat Bot. Introducing variables, sequence, programs, Alan Turing and Artificial Intelligence. Big ideas to discuss: DNA Coding and Intelligent design. Create apps which include the use of random number and item generation. Suggested videos on ‘Introducing Python’ and History of Computing. Learn about Mathematical and comparison operators and the importance of indentation in Python. Includes a suggested videos, ‘Big ideas’ discussion, and HW/research projects section.
AnswerTuring TestCoined by computing pioneer Alan Turing in .pdfnareshsonyericcson
Answer:
Turing Test:
Coined by computing pioneer Alan Turing in 1950, the Turing test was designed to be a
rudimentary way of determining whether or not a computer counts as \"intelligent\".
The test, as Turing designed it, is carried out as a sort of imitation game. On one side of a
computer screen sits a human judge, whose job is to chat to some mysterious interlocutors on the
other side. Most of those interlocutors will be humans; one will be a chatbot, created for the sole
purpose of tricking the judge into thinking that it is the real human.
Turing Test Objections:
1.The Theological Objection:
Substance dualists believe that thinking is a function of a non-material, separately existing,
substance that somehow “combines” with the body to make a person. So the argument might go
making a body can never be sufficient to guarantee the presence of thought: in themselves,
digital computers are no different from any other merely material bodies in being utterly unable
to think. Moreover to introduce the “theological” element it might be further added that, where a
“soul” is suitably combined with a body, this is always the work of the divine creator of the
universe: it is entirely up to God whether or not a particular kind of body is imbued with a
thinking soul.
2.The ‘Heads in the Sand’ Objection:
If there were thinking machines, then various consequences would follow. First, we would lose
the best reasons that we have for thinking that we are superior to everything else in the universe
(since our cherished “reason” would no longer be something that we alone possess). Second, the
possibility that we might be “supplanted” by machines would become a genuine worry: if there
were thinking machines, then very likely there would be machines that could think much better
than we can. Third, the possibility that we might be “dominated” by machines would also
become a genuine worry: if there were thinking machines, who\'s to say that they would not take
over the universe, and either enslave or exterminate us.
3.Arguments from Various Disabilities:
Turing considers a list of things that some people have claimed machines will never be able to
do:
(1) be kind.
(2) be resourceful.
(3) be beautiful.
(4) be friendly.
(5) have initiative.
(6) have a sense of humor.
(7) tell right from wrong.
(8) make mistakes.
(9) fall in love.
(10) enjoy strawberries and cream.
(11) make someone fall in love with one.
(12) learn from experience.
(13) use words properly.
(14) be the subject of one\'s own thoughts.
(15) have as much diversity of behavior as a man; (16) do something really new.
4.Argument from Continuity of the Nervous System:
The human brain and nervous system is not much like a digital computer. In particular, there are
reasons for being skeptical of the claim that the brain is a discrete-state machine. Turing observes
that a small error in the information about the size of a nervous impulse impinging on a neuron
may make a large difference to the size of the o.
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Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
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