1. Chapter 15. Cognitive Adequacy in Brain- Like Intelligence in Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans Cinarel, Ceyda Biointelligence Laboratory School of Computer Science and Engineering Seoul National University http://bi.snu.ac.kr

2. Contents Artificially Intelligent Systems What Is Brain-Like Intelligence Perception and Action Learning and Memory Focusing Motivation Neurobionics Cognitive Adequacy Discussion 2

3. Artificially Intelligent Systems “Machines will be capable, within twenty years, of doing any work a man can do” inadequate architectures and algorithms The von Neumann architecture One main processor is responsible for all aspects of computation While in human brain specialized areas process different aspects of information simultaneously Source: https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are#t-425849 © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 3 ? ? ? ?

4. What Is Brain-Like? We want to imitate information processing features of biological brains Psychophysics has gathered a wealth of data We can only draw conclusions about how knowledge is represented in the human brain Incomplete list of desired features Perception and Action Learning and Memory Focusing Motivation © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 4

5. Perception and Action Through evolution: Random mutations occurred and those who were suited to their environment survived Multiple sensory input and effector systems Can’t be separated Embodied artificial intelligence © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 5

6. Learning and Memory Ability to learn without much effort Intimately interwoven in the neural representation knowledge representation & learning processing & storage of information concept formation and category learning © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 6

7. Focusing Relevant subsets of the data automatically or voluntary Consolidation of memory happens when the information has high emotional salience, behavioral relevance, been repeated many times Planning Choose one of many alternative actions © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 7

8. Motivation Goal is to avoid pain and to increase reward Sub-goals can be adaptively changed depending on whether a drive is reduced or not. Shouldn’t run into dead ends by trying to stick to one strategy © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 8

9. Neurobionics Exact anatomical or physiological mechanisms are not known Copy the functional anatomy or physiology of the biological model © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 9 NeurobionicsNeuroprosthetics

10. Cognitive Adequacy Paradox of AI Lokendra Shastri and Venkat Ajjanagadde “AI systems that do not require more processing time to process hard problems compared to easy ones” Massive parallelism is implied by adequacy Artificial Neural Networks Logical theorems can be clustered into different classes of difficulty based on human performance © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 10

11. Chapter 15. Cognitive Adequacy in Brain- Like Intelligence in Brain-Like Intelligence, Sendhoff et al. Course: Robots Learning from Humans Seunghwan Cho Computing and Memory Architecture Laboratory School of Computer Science and Engineering Seoul National University http://cmalab.snu.ac.kr

12. Contents Cognitive Adequacy and ‘Brain-Like’ Intelligence Measure of Cognitive Adequacy Reaction Time Error Rates Perception Measures – visual illusion or ambiguity Limitations and Criticisms Discussion 12

13. Cognitive Adequacy and ‘Brain-Like’ Intelligence An AI system or algorithm performs cognitively adequate, if it reveals the same relative performance measures as a biological system solving the same task. adequateness implies massive parallelism physical identity (A = A, but A = a) vs. phonetic identity (A = A and A = a) Reaction time analysis leads to that at first, a visual representation is built up and only subsequently a more abstract phonetic representation is established.

14. Measure of Cognitive Adequacy cognitive adequacy behaviorally rather than physiologically We can use abundant psychophysical data accumulated so for. 14 Simpler Problems More Difficult Problems fasterslower An AI solves An AI shows less errorsmore errors And AI show similar illusory or ambiguous percepts as those in human perception

15. Measure of Cognitive Adequacy : Reaction Time Cognitively adequate algorithms take more time to process such conditions for which also humans need more processing time. Kanizsa square experiment: Kanizsa vs. non- Kanizsa; 3 objects vs. 4 objects Image comparison is not done as a whole but with operating separately for the two features reaction times attributed to mechanisms of high-level information processing not to low- level stimulus processing human information processing : top-down processes 15

16. Measure of Cognitive Adequacy : Error Rates For cognitively adequate algorithms are more likely to produce an error in conditions in which also humans are more likely to perform erroneously. Color recognition experiment: red, blue, green black red, blue, green, black, orange Three-colored disk experiment: even though the identical stimuli were used -> significantly different error rates between the two tasks human color processing: the processing of similarity can lead to more errors Because similar color is likely to produce similar response. This feature of processing of color similarity avoids errors due to changing illumination 16

17. Measure of Cognitive Adequacy : Perception Measures – visual illusion or ambiguity For cognitively adequate algorithms show similar illusory or ambiguous percepts as those in human perception The ambiguous Necker cube experiment: perception alternates every few seconds brain needs to be viewed as a dynamical system 17 Dynamical system interpretation of perception. Left: Multistable perception Right: In case of perceiving a less ambiguous visual scene the state trajectory approaches a limit cycle. Two alternative perceptions of it

18. Limitations and Criticisms cognitive adequacy is based on an assumption. “If the program’s input/output and timing behaviors match corresponding human behaviors, that is evidence that some of the program’s mechanisms could also be operating in humans.” Does Cognitive Adequacy Guarantee ’Brain-Like’ Intelligence? Criticism of Behavioral Tests Chinese room argument: 18

19. Discussion How human like does AI really need to be? What more can we add to this list of features? Can we guarantee that adequate machine intelligence is really brain like? What kind of motivation can an AI have? Where does creativity fit in this schema? © 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr 19