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Newton Howard is a American cognitive scientist and director of the Mind Machine Project at the Massachusetts Institute of Technology (MIT) and is currently employed as a Professor at the University of Oxford.

Dr. Newton Howard is an international executive and a national security advisor to several U.S. Government organizations. He received his Doctoral degree in Cognitive Informatics and Mathematics from La Sorbonne, France where he was also awarded the Habilitation a Diriger des Recherches for his leading work on the Physics of Cognition (PoC) and its applications to complex medical, economical and security equilibriums. While a graduate member of the Faculty of Mathematical Sciences at the University of Oxford, England, he proposed the Theory of Intention Awareness (IA), which made a significant impact on the design of command & control systems and information exchange systems at tactical operational and strategic levels.

Dr. Howard is also an author, a national security advisor and the founder/director of the Mind Machine Project at MIT and he founded Brain Sciences Foundation and serves as the Chairman. He founded and serves as the Chairman of the Board of the Center for Advanced Defense Studies, the leading Washington, D.C, National Security Group. He is also head of the Descartes Institute for Mathematical Methods in Behavioral Codification and Global Security, focusing on behavior models and codification to develop new approaches for counter-terrorism based on in-depth analysis.[1]

He has directed leading international cooperation programs on emerging trends in global security and information assurance. His leadership in this area directly stimulated the creation of national centers of excellence in medicine, psychiatry, and computation at multiple U.S. research institutions. His work has contributed to more than 30 U.S. patents and over 40 publications in the areas of cognitive and computational theory, and has garnered him several awards and honors including a nomination to the White House fellowship

His research focus areas include Memory, Trauma, Machine Learning, Comprehensive Brain Modeling, Natural Language Processing and Artificial Intelligence.

Biography

Dr. Newton Howard, Ph. D., HDR earned his B.A. in Behavioral and Military Science from Concordia University and an M.A. in Technology & Information Security from Eastern Michigan University, he continued his studies at the University of Paris- Sorbonne, where he received a doctorate in Cognitive Informatics and Mathematics.


He has established Brain Sciences Foundation as an organization to improve diagnostic methods for neurodegenerative disorders. Collaborating with a multitude of fellows from various institutions including Oxford University, Massachusetts Institute of Technology, La Sarbonne University. Their research initiatives aim to improving the quality of life for those suffering from neurological disorders.


Dr. Howard developed the Fundamental Code Unit (FCU) , which serves as a “code” used to develop a higher order and to provide a foundation for the Brain Code. The Fundamental Code Unit serves as a blueprint for conscious thought. The goal of the FCU-based strategy is to understand fundamental properties of brain information processing before the molecular and neurophysiological complexities. To begin it’s essential to understand that the Fundamental Code Unit can’t be simplified to a neuron yet the difference between our intellectual abilities lies in the combination of quantitative differences of these neurons and neural networks.


He developed the Mood State Indicator Algorithm (MSI) a system that can model and explain the mental processes involved in human speech and writing, to predict emotional states. In 2011, he collaborated with Matthieu Guidere to develop the Language Axiological Input/Output system.Cite error: The <ref> tag has too many names (see the help page).Cite error: The <ref> tag has too many names (see the help page)..


A practical application of Mood Sate Indicator (MSI) is the Language Axiological Input/Output (LXIO), developed by Newton Howard. LXIO is a system that detects mood states. The system is based on analysis of patients’ written or spoken discourse using a mind default axiology (MDA) database, which supplies a pre-determined mood value for each word. A patient’s mood state is then represented based on the sum of values generated by a given sentence or word string, accounting for each word’s context and grammatical function. A learning algorithm tracks patients’ word analysis histories in order to enhance the accuracy of the system’s outputs.


LXIO parses sentences into words that are processed through modules to evaluate cognitive states. Words are processed through time orientation, contextual-prediction and consequent modules. The significance of LXIO is its ability to incorporate conscious thought and its bodily expressions, linguistic or otherwise, into a uniform code schema. LXIO’s ability to diagnose brain conditions based on mood analyzed through speech lends credence to Dr. Howard’s view that the whole of cognition, including language, consists of a series of different structures arising from the same units.


The theory of iA {Howard 98, 2001, 2004} is founded in the systematic design and construction of naturalistic systems….IA theory is not only as a system design philosophy , but rather a possible model for explaining volition in human intelligence; a phenomena that is recursive under all biological solutions. One that informs intelligent action planning; for it is both the process and product of planning. IA becomes the intelligent architecture to which we can credit purposeful action .


Afiliations:


Selected works

  • Howard, N., Argamon, S. (Eds.) (2009). Computational Methods For Counterterrorism. Berlin: Springer-Verlag.
  • Howard, N. (2002) Theory of Intention Awareness in Tactical Military Intelligence: Reducing Un- certainty by Understanding the Cognitive Architecture of Intentions. Bloomington, Indiana: Author House First Books Library.
  • Howard, N. (2001) Seeking Peace in our Time: Global Defense System of Law. Bloomington, Indiana: Author House First Books Library.
  • Howard, N. (2001) The Challenges in Weapon Systems Design. Bloomington, Indiana: Author House First Books Library.
  • Howard, N. (1999) The Logic of Uncertainty and Situational Understanding. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
  • Howard, N. (1999) Multidimensional Time Understanding Combinatorial Manifold. Published by Center for Advanced Defense Studies (CADS)/Institute for the Mathematical Complexity & Cognition (MC) Centre de Recherche en Informatique, Université Paris Sorbonne
  • Neuman, Y., Howard, N., (2015). The Embodied Nature of Abstract Words: A Proposed Neuro-Mechanism. Neuroscience and Biobehavioral Reviews, in review.
  • Nave, O., Lehavi, Y., Ajadi, S., Howard, N. & Goldshtein, V. (2015). Analysis of Polydisperse Fuel Spray Flame. Springer Plus, in press.
  • Poria, S., Cambria, E., Hussain, A. & Howard, N. (2015). Extending Text based Sentiment Analysis to Multimodal Sentiment Analysis. Cognitive Computation, in press.
  • Poria, S., Cambria, E., Howard, N. (2015). Fusing Audio, Visual and Textual Clues for Big Social Data Analysis. Neurocomputing, in press.
  • Howard, N. (2015). The Case for Intention Awareness in Security Systems., Journal of Cyber-Security and Digital Forensics, 1(1), in press.
  • Cambria, E., White, B., Durrani, T., Howard, N. (2014) Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1)
  • Poria, S., Agarwal, Basant., Gelbukh, A., Hussain, A., Howard, N. (2014) Dependency-Based Semantic Parsing for Concept-Level Text Analysis. Computational Linguistics and Intelligent Text Processing. Lecture Notes in Computer Science, 8403, 113-127
  • Hussain, A., Cambria, E., Schuller, B., Howard, N. (2014). Affective Neural Networks and Cognitive Learning Systems for Big Data Analysis, Neural Networks, Special Issue, 58, 1-3.
  • Cambria, E., Howard, N., Song, Y. & Wang, H. (2014). Semantic Multidimeensional Scaling for Open Domain Sentiment Analysis. IEEE Intelligent Systems, 29 March/April.
  • Bermann, J., Langdon, P., Mayagoita, R. & Howard, N. (2014). Exploring the use of sensors to measure behavioral interactions: An experimental evaluation of using hand trajectories. PLoS ONE, 9, e88080.
  • Nave, O., Neuman, Y., Perlovsky, L. & Howard, N. (2014) How Much Information Should We Drop to Become Intelligent? Applied Mathematics and Computation, 245: 261-264.
  • Howard, N., Leisman, G. (2013). DIME (Diplomatic Information Military and Economic) Power) Effects Modeling System: Applications for the Modeling of the Brain. Journal of Functional Neurology, Rehabilitation and Ergonomics. 3:2-3.
  • Howard, N. (2013). Identifying and modeling language, brain and perception: A narrative approach. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2.
  • Poria, S., Gelbukh, A., Hussain, A., Bandyopadhyay, S. & Howard, N. (2013). Music Genre Classification: A Semi-supervised Approach. Pattern Recognition. Springer Berlin Heidelberg,
  • Poria, S., Gelbukh, A., Agarwal, B., Cambria, E. & Howard, N. (2013). Common Sense Knowledge Based Personality Recognition from Text. Advances in Soft Computing and Its Applications,. Lecture Notes in Computer Science, Springer, 8266:2013, 484-496.
  • Howard, N., Cambria, E. (2013). Development of a Diplomatic, Information, Military, Health, and Economic Effects Modeling System. International Journal of Privacy and Health Information 1:1, pp. 1-1.
  • Cambria, E., Mazzocco, T., Hussain, A., Howard, N. (2013). Sentic Neurons: A Biologically inspired cognitive architecture for affective common sense reasoning. In Procedia Computer Science, 00, 1-6.
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  • Neuman, Y., Assaf, D., Cohen, Y., Last, M., Argamon, S., Howard, N. & Frieder, O. (2013). Metaphor identification in large texts corpora. PLoS One, 8.
  • Howard, N., Bergmann, J. & Stein, J. (2013). Combined Modality of the Brain Code Approach for Early Detection and the Long-term Monitoring of Neurodegenerative Processes. Frontiers Special Issue INCF Course Imaging the Brain at Different Scales.
  • Bergmann, J., Graham, S., Howard, N. & Mcgregor, A. (2013). Comparison of Median Frequency Between Traditional and Functional Sensor Placements During Activity Monitoring. Measurement, 46, 2193-2200.
  • Howard, N., Stein, J. & Aziz, T. (2013). Early Detection of Parkinson's Disease from Speech and Movement Recordings. Oxford Parkinson's Disease Center Research Day 2013.
  • Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer’s Disease. Journal of Functional Neurology, Rehabilitation and Ergonomics, 2(1), 29-38
  • Bergmann, J. & Howard, N. (2012). Combining computational neuroscience and body sensor networks to investigate Alzheimer’s disease. BMC Neuroscience, 13(supp), 178.
  • Cambria, E., White, B., Durrani, T., & Howard, N. (2012). Computational Intelligence for Natural Language Processing. IEEE Computational Intelligence Magazine, 9(1), 19-63.
  • Howard, N. (2012). Brain Language: The Fundamental Code Unit. The Brain Sciences Journal, 1(1), 4-45.
  • Howard, N. (2012). Energy Paradox of the Brain. The Brain Sciences Journal, 1(1), 46-61.
  • Howard, N., Lieberman, H. (2012). Brain Space: Automated Brain Understanding and Machine Constructed Analytics in Neuroscience. Brain Sciences Journal, 1(1), 85-97.
  • Howard, N., Guidere, M. (2012). LXIO The Mood Detection Robopsych. The Brain Sciences Journal, 1(1), 98-109.
  • Howard, N., Kanareykin, S. (2012) Transcranial Ultrasound Application Methods: Low-frequency ultrasound as a treatment for brain dysfunction. The Brain Sciences Journal, 1(1), 110-124.
  • Dunn, J., Heredia, J. B. D., Burke, M., Gandy, L., Kanareykin, S., Kapah, O., Taylor, M., Hines, D., Freieder, O., Grossman, D., Howard, N., Koppel, M., Morris, S., Ortony, A. & Argamon, S. *Howard, N., Jehel, L. & Arnal, R. (2014). Towards a Differntial Diagnostic of PTSD Using Cognitive Computing Methods. International Conference on Cognitive Informatics and Cognitive Computing ICCI CC. London, UK: IEEE.
  • Howard, N., Bergmann, J. & Fahlstrom, R. (2013). Exploring the Relationship Between Everyday Speech and Motor Symptoms of Parkinson's Disease as Prerequisite Analysis for Tool Development. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
  • Howard, N. (2013). Approach Towards a Natural Language Analysis for Diagnosing Mood Disorders and Comorbid Conditions. Lecture Notes in Computer Science, MICAI, November 24-30, 2013, Mexico City, Mexico.
  • Howard, N. & Cambria, E. (2013). Intention awareness: improving upon situation awareness in human-centric environments. Human-centric Computing and Information Sciences, 3;9, 1-17.
  • Howard, N. (2013). The Twin Hypotheses: Brain Code and the Fundamental Code Unit: Towards Understanding the Computational Primitive Elements of Cortical Computing. Lecture Notes in Artificial Intelligence, MICAI, November 24-30, 2013, Mexico City, Mexico.
  • Howard, N., Pollock, R., Prinold, J., Sinha, J., Newham, D., Bergmann, J. (2013). Effect of Impairment on upper limb performance in an Ageing Population. The Human Computer Interaction International 2013 Conference (HCI) July 21-26, 2013, Las Vegas, Nevada.
  • Howard, N. (2013). Toward Understanding Analogical Mapping and Ideological Cataloguing in the Brain. Research Challenges in Information Science Series Conference (RCIS) May 29-31, 2013, Paris, France.
  • Last, M., Assaf, D., Neuman, Y., Cohen, Y., Argamon, S., Howard, N., Frieder, O., Koppel, M. (2013) Towards Metaphor Analysis for Natural Language Understanding. The 35th Annual Conference on Information Retrieval, March 24-27, 2013, Moscow, Russia.
  • Assaf, D., Howard, N., Neuman, Y., Last, M., Cohen, Y., Frieder, O., Argamon, S., Koppel (2013). Identifying -Noun Metaphors. 2013 IEEE Symposium Series on Computational Intelligence April 15-19, 2013, Singapore.
  • Bergmann, J., Howard, N. (2013). Design Considerations for a Wearable Sensor Network that Measures Accelerations during Water-Ski Jumping. IEEE Body Sensor Network Conference, May 6-9, 2013, Cambridge, Massachusetts.
  • Howard, N., Rao, D., Fahlstrom, R. & Stein, J. (2013). The Fundamental Code Unit: A Framework for Biomarker Analysis. . The 2nd Neurological Biomarkers Conference at the 2013 Biomarker Summit. San Francisco, California. .
  • Howard, N. & Bergmann, J. (2012). Combining Computational Neuroscience and Body Sensor Networks to Investigate Alzheimer's Disease. Organization for Computational Neurosciences, July 21-26, 2012, Atlanta/Decatur.
  • Howard, N. (2012). Intention Awareness in Human-Machine Interaction Sensemaking in Joint Cognitive Systems. International Conference on Information Sciences and Interaction Science, June 26-28, 2012, Jeju Island, Korea, 293-299.
  • Howard, N. Global Defense Policy System of Laws: Graph Theory Approach to Balance of Power Theory. European Intelligence and Security Informatics Conference (EISIC), 2011a. IEEE, 248-258.


See also


References

  1. ^ Howard, Newton. "http://web.mit.edu/nhmit/www/". MIT. Retrieved 2 September 2011. {{cite web}}: External link in |title= (help)
  • BSF*
  • Mind Machine Project Homepage: [[1]]
  • Newton Howard's MIT Homepage: [[2][
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