A Framework for Evaluating Early-Stage Human - of Marcus Hutter

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10. Goals for the Next Few Years The first year is devoted largely to tool building (e.g. choosing and coding the evolvable neural net model, testing its evolvability as a pattern detector, implementing the Parcone model in the FPGA boards, programming the motions of the NAO robot with the Choregraphe software, writing the IMSI code, etc). In the second year, the first artificial brain architectures will be created and implemented, with 10, 20, 50, 100, etc modules. The language component will be added to the brain. At regular intervals, demos will be built, to show off progress. We expect that each scaling up of the size of the artificial brain (i.e. each substantial increase in the number of modules in the brain) will raise new challenges that will have to be overcome by the creativity of the team’s BAs (brain architects). At the end of 4 years, it is hoped that the artificial brain will have 10,000s of modules. Observing a robot controlled by such an artificial brain should make a casual observer feel that the robot “has a brain behind it”. 11. Artificial Brain Research Policy in China The first author thinks that the artificial brain industry will be the world’s biggest by about 2030, because artificial brains will be needed to control the home robots that everyone will be prepared to spend big money on, if they become genuinely intelligent and hence useful (e.g. baby sitting the kids, taking the dog for a walk, cleaning the house, washing the dishes, reading stories, educating its owners etc). China has been catching up fast with the western countries for decades. The first author thinks that China should now aim to start leading the world (given its huge population, and its 3 times greater average economic growth rate compared to the US) by aiming to dominate the artificial brain industry. At the time of writing (Oct 2008), plans are afoot (with the support of the most powerful people in China in artificial intelligence) to attempt to persuade the Chinese Ministers of Education and of Science and Technology to undertake a long term strategy (over a 20 year time frame) to dominate the global artificial brain industry, by initially stimulating the establishment of artificial brain (and intelligence science) labs in universities across China (somewhat similar to our Artificial Brain Lab at 29 Xiamen), then awarding “key lab” status at both province and national levels to some of these labs, the creation of a Chinese National Artificial Brain Association, and especially, within a 5 to 10 year time frame, the establishment of a “CABA” (Chinese Artificial Brain Administration), which would be a government administration (similar to America’s NASA) that would employ thousands of engineers and scientists to build artificial brains for the Chinese artificial brain industry. Copying the human brain with its 100 billion neurons and quadrillion synapses will be an immense task requiring large numbers of Chinese “brain workers”. It is expected that other countries will quickly copy China’s lead, so that one will soon see national brain building projects in most of the leading high tech nations. References [1] Marvin MINSKY, “Society of Mind”, Simon & Schuster, 1988. [2 ] http://www.aldebaran-robotics.com/eng /index.php [3] Hugo de Garis, in book, Artificial General Intelligence 2008 : Proceedings of the First AGI Conference (Frontiers in Artificial Intelligence and Applications) (Frontiers in Artificial Intelligence and Applications) [4] Leon CHUA and Tamas ROSKA, “Cellular Neural Networks and Visual Computing, Foundations and Applications”, Cambridge University Press, 2002. Fig. 1 The NAO Robot Controlled by Our Artificial Brain
Population Chr NListPtr m Chrm is a A chromosome is a pointer to a pointer to NList population of chromosome s NeuronWt struct Hash Neuron ID Weight Bits Weight Value NList HashPtr A Hash is a hash table of pointers to a NeuronWt struct Fig. 2 Data Structures of the “Parcone” Neural Net Model Evolution Set Faces Test Set Faces 60*60 (pixel)*3 (RGB) = 10800 pixel values = 10800 input neurons to Parcone Fig. 3 Face Images for Face Detection Evolution of Parcone 30
Page 2 and 3: Ben Goertzel, Pascal Hitzler, Marcu
Page 4 and 5: Artificial General Intelligence Vol
Page 6: Preface Artificial General Intellig
Page 9 and 10: Organizing Committee Tsvi Achler U.
Page 11 and 12: A formal framework for the symbol g
Page 13 and 14: Importing Space-time Concepts Into
Page 15 and 16: one structure, everything else adap
Page 17 and 18: generalize is essential to understa
Page 19 and 20: First Application The above framewo
Page 21 and 22: problem solving, use of knowledge,
Page 23 and 24: Of particular note is the separatio
Page 25 and 26: Conclusions and Future Work While p
Page 27 and 28: Conceptual Spaces The conceptual ar
Page 29 and 30: perceptions and actions. The lingui
Page 31 and 32: means of the knowledge stored in th
Page 33 and 34: grams given some (syntactically res
Page 35 and 36: For example, let reverse([]) = [] r
Page 37 and 38: Igor2 produced solutions with auxil
Page 39 and 40: evolve neural net modules as quickl
Page 41: ain”, consisting of some dozen or
Page 45 and 46: and shaping, we feel that exploring
Page 47 and 48: Table 2 reviews the key capabilitie
Page 49 and 50: One way to achieve this goal would
Page 51 and 52: question. Our approach of staying a
Page 53 and 54: H0 δB(H0) δF(H0) H1 δB(H1) δF(H
Page 55 and 56: Discussion and future work Testing
Page 57 and 58: Types of Reasoning Corresponding Fo
Page 59 and 60: Integrating Different Types of Reas
Page 61 and 62: good overview of analogy models can
Page 63 and 64: ��
Page 65 and 66: � ��
Page 67 and 68: ��
Page 69 and 70: A Unified Framework for IP Conditio
Page 71 and 72: negative evidence when added to a r
Page 73 and 74: ing strategy. Due to GOLEM’s rand
Page 75 and 76: any state index, n∈IN the current
Page 77 and 78: is the best observation summary, wh
Page 79 and 80: to a code for the rewards only, whi
Page 81 and 82: have exponentially many states (2O(
Page 83 and 84: where ˆσ 2 =Loss( ˆw)/n. Given
Page 85 and 86: • The cost function can be improv
Page 87 and 88: decides to introduce inflation or d
Page 89 and 90: € € The diffusion matrix is the
Page 91 and 92: tuning may be done adaptively by te
Page 93 and 94:
Of course, Harnad’s argument is n
Page 95 and 96:
By exploring variations of Harnad
Page 97 and 98:
Discussion The representational sys
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the cognitive map is less of a map
Page 101 and 102:
models of cognition except in cases
Page 103 and 104:
7(b), we can see that the straight
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eaction times, error rates, or exac
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comparing behavior with and without
Page 109 and 110:
Doorenbos, R. B. 1994. Combining le
Page 111 and 112:
egion, we leave the type of object
Page 113 and 114:
extract features in support of reco
Page 115 and 116:
with a minimum score of -1.0. The
Page 117 and 118:
the NASA Human Error Modeling compa
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whether their models are capable of
Page 121 and 122:
Incorporating Planning and Reasonin
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see an unclaimed ten-dollar bill al
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swering user queries) and the state
Page 127 and 128:
Program Representation for General
Page 129 and 130:
distribution P corresponding to the
Page 131 and 132:
with all Ei replaced by the unbound
Page 133 and 134:
Consciousness in Human and Machine:
Page 135 and 136:
at the sensory receptors, is pre-pr
Page 137 and 138:
The second choice is to take a deta
Page 139 and 140:
Hebbian Constraint on the Resolutio
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The Case of Inputs that Change by T
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This route is relevant if the noise
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1. Oculus Info. Inc. Toronto, Ont.
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Our implemented Turing judge used a
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2.7; Human vs. JabberWacky t(157.6)
Page 151 and 152:
UnsupervisedSegmentationofAudioSpee
Page 153 and 154:
FinallyweusedthediscreteFourierTran
Page 155 and 156:
Secondly,thereexistsmorethanonelogi
Page 157 and 158:
Parsing PCFG within a General Proba
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known in advance, although many imp
Page 161 and 162:
Figure 2: Shows the underlying weig
Page 163 and 164:
Self-Programming: Operationalizing
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expressivity. More over, self-progr
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existence of a distance function ov
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Bootstrap Dialog: A Conversational
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elation is typically a verb. In the
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node RDF proposition N1 N2 N3 N4 N5
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Analytical Inductive Programming as
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Problem domain: puttable(x) PRE: cl
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eq Hanoi(0, Src, Aux, Dst, S) = mov
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Human and Machine Understanding Of
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case orderings t correspond to the
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in (1) received a different denotat
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Abstract This paper analyzes the di
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Having grounded meaning: In an inte
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to experience” can be argued to b
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Abstract Case-by-case Problem Solvi
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First, since NARS is designed in th
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typical response is to find such an
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What Is Artificial General Intellig
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Finally, the facts that both seem t
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differ. NARS uses Narsese, the fair
Page 205 and 206:
Integrating Action and Reasoning th
Page 207 and 208:
states, with the condition that the
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action model. The system is able to
Page 211 and 212:
Neuroscience and AI Share the Same
Page 213 and 214:
Relevance Based Planning: Why Its a
Page 215 and 216:
General Intelligence and Hypercompu
Page 217 and 218:
To appear, AGI-09 1 Stimulus proces
Page 219 and 220:
Distribution of Environments in For
Page 221 and 222:
The Importance of Being Neural-Symb
Page 223 and 224:
Improving the Believability of Non-
Page 225 and 226:
Understanding the Brain’s Emergen
Page 227 and 228:
Abstract The challenge of creating
Page 229 and 230:
Importing Space-time Concepts Into
Page 231 and 232:
HELEN: Using Brain Regions and Mech
Page 233 and 234:
Holistic Intelligence: Transversal
Page 235 and 236:
Achieving Artificial General Intell
Page 237:
Achler, Tsvi . . . . . . . . . . .
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