A general-purpose organic gel computer that learns by itself
To build energy minimized superstructures, self-assembling molecules explore astronomical options, colliding ∼10 ^9 molecules s ^−1 . Thus far, no computer has used it fully to optimize choices and execute advanced computational theories only by synthesizing supramolecules. To realize it, first, we...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IOP Publishing
2023-01-01
|
| Series: | Neuromorphic Computing and Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/2634-4386/ad0fec |
| _version_ | 1797403397810290688 |
|---|---|
| author | Pathik Sahoo Pushpendra Singh Komal Saxena Subrata Ghosh R P Singh Ryad Benosman Jonathan P Hill Tomonobu Nakayama Anirban Bandyopadhyay |
| author_facet | Pathik Sahoo Pushpendra Singh Komal Saxena Subrata Ghosh R P Singh Ryad Benosman Jonathan P Hill Tomonobu Nakayama Anirban Bandyopadhyay |
| author_sort | Pathik Sahoo |
| collection | DOAJ |
| description | To build energy minimized superstructures, self-assembling molecules explore astronomical options, colliding ∼10 ^9 molecules s ^−1 . Thus far, no computer has used it fully to optimize choices and execute advanced computational theories only by synthesizing supramolecules. To realize it, first, we remotely re-wrote the problem in a language that supramolecular synthesis comprehends. Then, all-chemical neural network synthesizes one helical nanowire for one periodic event. These nanowires self-assemble into gel fibers mapping intricate relations between periodic events in any-data-type, the output is read instantly from optical hologram. Problem-wise, self-assembling layers or neural network depth is optimized to chemically simulate theories discovering invariants for learning. Subsequently, synthesis alone solves classification, feature learning problems instantly with single shot training. Reusable gel begins general-purpose computing that would chemically invent suitable models for problem-specific unsupervised learning. Irrespective of complexity, keeping fixed computing time and power, gel promises a toxic-hardware-free world. One sentence summary: fractally coupled deep learning networks revisits Rosenblatt’s 1950s theorem on deep learning network. |
| first_indexed | 2024-03-09T02:38:58Z |
| format | Article |
| id | doaj.art-fd57a881e4cb436ea51501d133c3c0d0 |
| institution | Directory Open Access Journal |
| issn | 2634-4386 |
| language | English |
| last_indexed | 2024-03-09T02:38:58Z |
| publishDate | 2023-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Neuromorphic Computing and Engineering |
| spelling | doaj.art-fd57a881e4cb436ea51501d133c3c0d02023-12-06T07:54:56ZengIOP PublishingNeuromorphic Computing and Engineering2634-43862023-01-013404400710.1088/2634-4386/ad0fecA general-purpose organic gel computer that learns by itselfPathik Sahoo0Pushpendra Singh1https://orcid.org/0000-0002-7274-6683Komal Saxena2Subrata Ghosh3R P Singh4Ryad Benosman5https://orcid.org/0000-0003-0243-944XJonathan P Hill6Tomonobu Nakayama7https://orcid.org/0000-0001-9696-475XAnirban Bandyopadhyay8https://orcid.org/0000-0002-8823-4914International Center for Materials and Nanoarchitectronics (MANA), Research Center for Advanced Measurement and Characterization (RCAMC), National Institute for Materials Science , 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, JapanInternational Center for Materials and Nanoarchitectronics (MANA), Research Center for Advanced Measurement and Characterization (RCAMC), National Institute for Materials Science , 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, JapanInternational Center for Materials and Nanoarchitectronics (MANA), Research Center for Advanced Measurement and Characterization (RCAMC), National Institute for Materials Science , 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, JapanChemical Science and Technology Division, CSIR-North East Institute of Science and Technology, NEIST , Jorhat, Assam 785006, India; Academy of Scientific and Innovative Research (AcSIR), CSIR-NEIST Campus , Jorhat, Assam 785006, IndiaQuantum Science & Technology Laboratory, Physical Research Laboratory, Navrangpura , Ahmedabad, Gujarat 380009, IndiaUniversity of Pittsburgh School of Medicine; Biomedical Science Tower 3 , Pittsburgh, PA 15260, United States of America; CNRS, UMRS 968, UMR 7210, INSERM UMRI S 968, Institut de la Vision, Sorbonne Université (UPMC University Paris 06) , 75012 Paris, France; Robotics Institute, Carnegie Mellon University , Pittsburgh, PA 15213, United States of AmericaInternational Center for Materials and Nanoarchitectronics (MANA), Research Center for Advanced Measurement and Characterization (RCAMC), National Institute for Materials Science , 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, JapanInternational Center for Materials and Nanoarchitectronics (MANA), Research Center for Advanced Measurement and Characterization (RCAMC), National Institute for Materials Science , 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, JapanInternational Center for Materials and Nanoarchitectronics (MANA), Research Center for Advanced Measurement and Characterization (RCAMC), National Institute for Materials Science , 1-2-1 Sengen, Tsukuba, Ibaraki 3050047, JapanTo build energy minimized superstructures, self-assembling molecules explore astronomical options, colliding ∼10 ^9 molecules s ^−1 . Thus far, no computer has used it fully to optimize choices and execute advanced computational theories only by synthesizing supramolecules. To realize it, first, we remotely re-wrote the problem in a language that supramolecular synthesis comprehends. Then, all-chemical neural network synthesizes one helical nanowire for one periodic event. These nanowires self-assemble into gel fibers mapping intricate relations between periodic events in any-data-type, the output is read instantly from optical hologram. Problem-wise, self-assembling layers or neural network depth is optimized to chemically simulate theories discovering invariants for learning. Subsequently, synthesis alone solves classification, feature learning problems instantly with single shot training. Reusable gel begins general-purpose computing that would chemically invent suitable models for problem-specific unsupervised learning. Irrespective of complexity, keeping fixed computing time and power, gel promises a toxic-hardware-free world. One sentence summary: fractally coupled deep learning networks revisits Rosenblatt’s 1950s theorem on deep learning network.https://doi.org/10.1088/2634-4386/ad0fecorganic computerOptical vortexNon-algorithmic computinghelical nanowireSingle shot learningDeep learning network |
| spellingShingle | Pathik Sahoo Pushpendra Singh Komal Saxena Subrata Ghosh R P Singh Ryad Benosman Jonathan P Hill Tomonobu Nakayama Anirban Bandyopadhyay A general-purpose organic gel computer that learns by itself Neuromorphic Computing and Engineering organic computer Optical vortex Non-algorithmic computing helical nanowire Single shot learning Deep learning network |
| title | A general-purpose organic gel computer that learns by itself |
| title_full | A general-purpose organic gel computer that learns by itself |
| title_fullStr | A general-purpose organic gel computer that learns by itself |
| title_full_unstemmed | A general-purpose organic gel computer that learns by itself |
| title_short | A general-purpose organic gel computer that learns by itself |
| title_sort | general purpose organic gel computer that learns by itself |
| topic | organic computer Optical vortex Non-algorithmic computing helical nanowire Single shot learning Deep learning network |
| url | https://doi.org/10.1088/2634-4386/ad0fec |
| work_keys_str_mv | AT pathiksahoo ageneralpurposeorganicgelcomputerthatlearnsbyitself AT pushpendrasingh ageneralpurposeorganicgelcomputerthatlearnsbyitself AT komalsaxena ageneralpurposeorganicgelcomputerthatlearnsbyitself AT subrataghosh ageneralpurposeorganicgelcomputerthatlearnsbyitself AT rpsingh ageneralpurposeorganicgelcomputerthatlearnsbyitself AT ryadbenosman ageneralpurposeorganicgelcomputerthatlearnsbyitself AT jonathanphill ageneralpurposeorganicgelcomputerthatlearnsbyitself AT tomonobunakayama ageneralpurposeorganicgelcomputerthatlearnsbyitself AT anirbanbandyopadhyay ageneralpurposeorganicgelcomputerthatlearnsbyitself AT pathiksahoo generalpurposeorganicgelcomputerthatlearnsbyitself AT pushpendrasingh generalpurposeorganicgelcomputerthatlearnsbyitself AT komalsaxena generalpurposeorganicgelcomputerthatlearnsbyitself AT subrataghosh generalpurposeorganicgelcomputerthatlearnsbyitself AT rpsingh generalpurposeorganicgelcomputerthatlearnsbyitself AT ryadbenosman generalpurposeorganicgelcomputerthatlearnsbyitself AT jonathanphill generalpurposeorganicgelcomputerthatlearnsbyitself AT tomonobunakayama generalpurposeorganicgelcomputerthatlearnsbyitself AT anirbanbandyopadhyay generalpurposeorganicgelcomputerthatlearnsbyitself |