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#FLOWERS4U #flower #floralfriday #flowerpower #flowerpics #flowerphotography #blom #lule #ծաղիկ #gül #кветка #ফুল #цвете #flor #花 #cvijet #květina #blomst #bloem #lill #bulaklak #kukka #fleur #ყვავილების #Blume #λουλούδι #ફૂલ #flè #फूल #virág #blóm #bunga #bláth #fiore #花 #ಹೂವು #ផ្កា #꽃 #ດອກ #florem #zieds #gėlė #цвет #fjura #blomst #kwiat #floare #цветок #kvetina #cvet #maua #blomma #மலர் #పువ్వు #ดอกไม้ #çiçek #квітка #hoa #blodau #wâbigin #nature #aard #natyrë #բնություն #təbiət #natura #প্রকৃতি #priroda #природа #naturalesa #kinaiyahan #chikhalidwe #大自然 #příroda #natur #natuur #loodus #kalikasan #luonto #natureza #ბუნება #natur #φύση #પ્રકૃતિ #yanayi #प्रकृति #természet #eðli #alam #nádúr #natura #自然 #ಪ್ರಕೃತಿ #табиғат #ធម្មជាតិ #자연 #ລັກສະນະ #daba #pobūdis #природата #പ്രകൃതി #निसर्ग #သဘာဝ #प्रकृति #natură #природа #narava #naturaleza #табиат #இயல்பு #ప్రకృతి #ธรรมชาติ #doğa #tabiat #натура #flowerstagram #flowerlovers #bloomsofinstagram #florallove #flowerpower #flowerbeauty #flowerphotography #petalperfection #floraladdict #flowergarden #bloomingbeauty #flowermagic #floral_perfection #flowersmakemehappy #floweraddiction #flowerfields #floralobsession #floralwonder #flowerlife #floraldesign #floweroftheday #flowergram #flowerlove #floralfix #flowerheaven #flowerart #floralinspiration #flower_daily #floraldecor #flowertherapy #AI_MAIN #ArtificialIntelligence #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #BigData#Tech #Innovation #AIArtistry #ArtificialArt #MachineArt #AIgenerativeart #ArtificialVision #NeuralArt #Cyberart #SyntheticDesign #DigitalPainting #AlgorithmicPainting #GeneratedArt #RoboticDesign #AIinspiredart #ArtificialInspiration #DataVisualization #TechDesign #ArtificialExpression #ArtificialBeauty #ArtificialBrilliance #TheArtOfMachine #AIInnovationInArt #AiArt #ArtificialIntelligence #MachineLearning #DeepLearning #NeuralNetworks #ArtificialCreativity #DigitalArt #GenerativeArt #TechArt #CyberpunkArt #FuturisticArt #RoboticArt #AIgenerated #SyntheticArt #CodeArt #DataArt #AlgorithmicArt #ArtificialImagination #TheArtofAI #AIandArt #AIinArt #ArteDeInteligenciaArtificial #ArteDeAprendizajeAutomatico #ArteDeAprendizajeProfundo #ArteDeRedesNeuronales #ArteDeCreatividadArtificial #ArteDigital #ArteGenerativo #ArteTecnológico #ArteCyberpunk #ArteFuturista #ArteRobótico #ArteGeneradoPorIA #ArteSintético #ArteDeCódigo #ArteDeDatos #ArteAlgorítmico #ImaginaciónArtificial #ElArteDeLaIA #InnovaciónEnElArteDeIA #ArteEInteligenciaArtificial #ArteInnovadorDeIA #ArtificialIntelligenceArt #ArtIntelligenceArtificielle #MachineArt #ArtMachine #DeepLearningArt #ArtApprentissageProfond #NeuralNetworksArt #ArtReseauxNeurones #ArtCréativitéArtificielle #CréativitéNumérique #ArtGénératif #ArtTech #ArtCyberpunk #ArtFuturiste #ArtRobotique #ArtGénéréparIA #DesignSynthétique #ArtCode #ArtDeDonnées #ArtAlgorithmique #ImaginationArtificielle #ArtEtIA #KünstlicheIntelligenzKunst #KIkunst #MaschinellesLernenKunst #MLKunst #TiefesLernenKunst #DLKunst #NeuronaleNetzeKunst #NNKunst #KünstlicheKreativität #Digitalekunst #GenerativeKunst #TechKunst #CyberpunkKunst #Zukunftskunst #RoboterKunst #IAgenerierteKunst #SynthetischeKunst #CodeKunst #DatenKunst #AlgorithmenKunst #KünstlicheImagination #KunstUndKI #人工知能アート #機械学習アート #深層学習アート #ニューラルネットワークアート #人工クリエイティブアート #デジタルアート #ジェネレーティブアート #テクノロジーアート #サイバーパンクアート #未来アート #ロボットアート #人工知能生成アート #シンセティックアート #コードアート #データアート #アルゴリズムアート #人工想像アート #人工知能とアート #人工知能革新アート #ArteDeInteligenciaArtificial #ArteDeAprendizajeAutomatico #ArteDeAprendizajeProfundo #ArteDeRedesNeuronales #ArteDeCreatividadArtificial #ArteDigital #ArteGenerativo #ArteTecnológico #ArteCyberpunk #ArteFuturista #ArteRobótico #ArteGeneradoPorIA #ArteSintético #ArteDeCódigo #ArteDeDatos #ArteAlgorítmico #ImaginaciónArtificial #ElArteDeLaIA #InnovaciónEnElArteDeIA #ArteEInteligenciaArtificial #ArteInnovadorDeIA
Technical Session # 7 : Neural Network and Brain Modeling
The First Conference on Artificial General Intelligence (AGI-08) 1-3 March 2008
Recurrent Feedback Neuronal Networks: Classification and Inference Based on Network Structure by Tsvi Achler and Eyal Amir from Department of Computer Science, University of Illinois at Urbana Champaign
1. AI -> AGI,
2. [Recurrent Feedback Neuronal Networks] Avoids Combinatorial Complexity via Simple Connectivity,
The China-Brain Project: Building China's Artificial Brain Using An Evolved Neural Net Module Approach by Hugo de Garis , Tang Jian Yu, Huang Zhiyong, Bai Lu, Chen Cong, Chen Shuo, Guo Junfei, Tan Xianjin, Tian Hao, Tian Xiaohan, Wu Xianjian, Xiong Ye, Yu Xiangqian, Huang Di of The International School of Software at Wuhan University (This work is being funded in the future by Xiamen University, 2008-11)
3. Neural Net Accelerator Board for China's Artificial Brain,
4. Multi-module neural network evolution is a challenging new research field.,
How Might Probabilistic Reasoning Emerge from the Brain? Ben Goertzel and Cassio Pennachin of Novamente
5. How Might Probabilistic Reasoning Emerge from the Brain?,
Vector Symbolic Architectures: A New Building Material for Artificial General Intelligence by Simon Levy of Washington and Lee University and Ross Gayler
6. The Need for New Representational Principles
mosaic_NN_brain_agi08
The China-Brain Project: Building China's Artificial Brain Using An Evolved Neural Net Module Approach by Hugo de Garis , Tang Jian Yu, Huang Zhiyong, Bai Lu, Chen Cong, Chen Shuo, Guo Junfei, Tan Xianjin, Tian Hao, Tian Xiaohan, Wu Xianjian, Xiong Ye, Yu Xiangqian, Huang Di of The International School of Software at Wuhan University (This work is being funded in the future by Xiamen University, 2008-11)
Here is the basic idea:
The artificial brain will consist of up to 15,000 Neural network circuit modules that are evolved in a Celoxica Field-programmable gate array (FPGA) accelerator board. Each Neural network module is downloaded to a PC. Human brain architects specify the intereconnections between the Neural network modules to build an artificial brain (A-brain)
In Technical Session # 7: Neural Network and Brain Modeling
Session Chair: Randal Koene , Laboratory of Computational Neurophysiology, Center for Memory and Brain, Boston University at the The First Conference on Artificial General Intelligence (AGI-08)
This room is The Zone, at the FedEx Institute of Technology, University of Memphis. It was a very good venue for this conference.
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI -- to create intelligence as a whole, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. AGI is also called Strong AI in the AI community.
Another good reference is Artificial General Intelligence : A Gentle Introduction Pei Wang
I030308 024
Simon Levy presents Vector Symbolic Architectures: A New Building Material for Artificial General Intelligence by Simon Levy of Washington and Lee University and Ross Gayler
(I'd like to take a class from Simon Levy, he is a great teacher from what I could see. Sorry that the photo is poor of him here, but this particular slide mad me think and do quite a bit of homework to increased my understanding of this topic.)
The Need for New Representational Principles
(My notes on this, please feel free to add or clarify anything I have said, it is just my [poor] understanding.)
- Ecological Affordance introduced by James J. Gibson (1979), The Ecological Approach to Visual Perception (an interesting sidebar - this was about the last thing that Gibson published before his death in 1979 at age 75.) A number of his writings are on-line, published as the Purple Perils
- Distributed Connectionist Representations (PDP - which is Parallel Distributed Processing was the start of Connectionism I had colleagues who did research in this area in the early 1990s, at the time I was not as curious as I could have been, being wrapped up in some completely different projects. Now - I have begun to tie it together, funny how that works! The classic work was done by James McClelland, David Rumelhart and the PDP Research Group. They wrote a two volume book: Parallel Distributed Processing: Explorations in the Microstructure of Cognition in 1986)
Holographic Representations Dennis Gabor 1971 and Plate 2003
I probably went down a related area with Karl Pribram's Holonomic brain theory but I need to do some more research about this.
Fractals Attractors Dynamical Systems Tabor 2000 , Levy and Pollack 2001
For a good introduction see Dynamical & Evolutionary Machine Organization part of Jordan Pollack's research at Brandeis University.
In Technical Session # 7: Neural Network and Brain Modeling
Session Chair: Randal Koene , Laboratory of Computational Neurophysiology, Center for Memory and Brain, Boston University at the The First Conference on Artificial General Intelligence (AGI-08)
This room is The Zone, at the FedEx Institute of Technology, University of Memphis. It was a very good venue for this conference.
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI -- to create intelligence as a whole, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. AGI is also called Strong AI in the AI community.
Another good reference is Artificial General Intelligence : A Gentle Introduction Pei Wang
I030308 045
The First Conference on Artificial General Intelligence (AGI-08) 1-3 March 2008
This room is The Zone, at the FedEx Institute of Technology, University of Memphis.
1. FedEx Institute of Technology, University of Memphis, 2. Panel Discussion Technical Session I: Overview of AGI Research, 3. Speakers view at The First Conference on Artificial General Intelligence (AGI-08), 4. Panel for Architecture of AGI Systems, 5. Panel Technical Session # 3: Language and Cognition, 6. Audience Interaction with Panel Technical Session # 3: Language and Cognition, 7. Panel for Technical Session # 4 : Reasoning, 8. Audience Interaction Technical Session # 4 : Reasoning, 9. Panel for Technical Session # 5: Learning, 10. Asking a Question at AGI-08 Conference, 11. Panel for Technical Session # 6: Virtually Embodied AI, 12. Dave Baldwin asks a question, 13. Should there be an AGI organization?, 14. Matthew Ikle asking a tough question during Artificial General Intelligence (AGI) Renaissance Discussion, 15. Panel for Technical Session # 7: Neural Network and Brain Modeling
mosaic_panel_agi08_1
The China-Brain Project: Building China's Artificial Brain Using An Evolved Neural Net Module Approach by Hugo de Garis , Tang Jian Yu, Huang Zhiyong, Bai Lu, Chen Cong, Chen Shuo, Guo Junfei, Tan Xianjin, Tian Hao, Tian Xiaohan, Wu Xianjian, Xiong Ye, Yu Xiangqian, Huang Di of The International School of Software at Wuhan University (This work is being funded in the future by Xiamen University, 2008-11)
Summary:
Neuroscience , Artificial General Intelligence, Cognitive science and ethological (animal behavior) models are the source of inspiration for artificial brains . If enough groups start, a research community can be formed. Eventually, Moore's Law implies that multi-module neural networks be be necessary. Multi-module Neural network evolution is a challenging new research field.
In Technical Session # 7: Neural Network and Brain Modeling
Session Chair: Randal Koene , Laboratory of Computational Neurophysiology, Center for Memory and Brain, Boston University at the The First Conference on Artificial General Intelligence (AGI-08)
This room is The Zone, at the FedEx Institute of Technology, University of Memphis. It was a very good venue for this conference.
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI -- to create intelligence as a whole, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. AGI is also called Strong AI in the AI community.
Another good reference is Artificial General Intelligence : A Gentle Introduction Pei Wang
I030308 031
Tsvi Achler presents Recurrent Feedback Neuronal Networks: Classification and Inference Based on Network Structure by Tsvi Achler and Eyal Amir from Department of Computer Science, University of Illinois at Urbana Champaign
The main points in this introductory slide were that Artificial General Intelligence has an ability to generalize and that it is necessary to avoid combinatorial explosion.
In Technical Session # 7: Neural Network and Brain Modeling
Session Chair: Randal Koene , Laboratory of Computational Neurophysiology, Center for Memory and Brain, Boston University at the The First Conference on Artificial General Intelligence (AGI-08)
This room is The Zone, at the FedEx Institute of Technology, University of Memphis. It was a very good venue for this conference.
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI -- to create intelligence as a whole, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. AGI is also called Strong AI in the AI community.
Another good reference is Artificial General Intelligence : A Gentle Introduction Pei Wang
I030308 015
Panel for Technical Session # 7: Neural Network and Brain Modeling
Simon Levy , Ben Goertzel , Hugo de Garis, Tsvi Achler
Session Chair: Randal Koene , Laboratory of Computational Neurophysiology, Center for Memory and Brain, Boston University at the The First Conference on Artificial General Intelligence (AGI-08)
This room is The Zone, at the FedEx Institute of Technology, University of Memphis. It was a very good venue for this conference.
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI -- to create intelligence as a whole, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. AGI is also called Strong AI in the AI community.
Another good reference is Artificial General Intelligence : A Gentle Introduction Pei Wang
I030308 056
Tsvi Achler presents Recurrent Feedback Neuronal Networks: Classification and Inference Based on Network Structure by Tsvi Achler and Eyal Amir from Department of Computer Science, University of Illinois at Urbana Champaign
In Technical Session # 7: Neural Network and Brain Modeling
Session Chair: Randal Koene , Laboratory of Computational Neurophysiology, Center for Memory and Brain, Boston University at the The First Conference on Artificial General Intelligence (AGI-08)
This room is The Zone, at the FedEx Institute of Technology, University of Memphis. It was a very good venue for this conference.
Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI -- to create intelligence as a whole, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. AGI is also called Strong AI in the AI community.
Another good reference is Artificial General Intelligence : A Gentle Introduction Pei Wang
I030308 016