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Anthropomorphic Machine Learning:How to get Fast,Interpretable Deep Learning

December 11, 2019 @ 6:30 pm - 8:30 pm PST

Title: Towards Anthropomorphic Machine Learning: How to get Fast, Interpretable Deep Learning

Speaker: Prof Plamen Angelov, PhD, DSc, FIEEE from School of Computing and Communications, Lancaster University, UK and Vice President-International Neural Network Society.

Date: Wednesday, December 11, 2019. 5:30pm to 8:30pm Pacific Time.

Venue: Intel Santa Clara, SC9 auditorium (2200 Mission College Blvd, Santa Clara, CA 95054)


  • 6:30 – 7:00 PM Networking & Refreshments
  • 7:00 – 8:00 PM Talk
  • 8:00 – 8:30 PM Q&A

Organized by: IEEE Computational Intelligence Society Chapter of Silicon Valley (SCV)

Event co-sponsored by : IEEE Computer Society (CS) chapter of Silicon ValleyIEEE Solid-State Circuits Society chapter of Silicon Valley and ValleyML.ai

Admission Fee:

Open to all to attend (Online registration is needed. If you did not register, seating is not guaranteed.)

  • IEEE members – free
  • non-IEEE members – free. You do not need to be an IEEE member to attend!


Machine Learning (ML) and AI justifiably attract the attention and interest not only of the wider scientific community and industry, but also society and policy makers. Recent developments in this area range from accurately recognizing images and speech to beating the best players in games like Chess, Go and Jeopardy. In such well-structured problems, the ML and AI algorithms were able to surpass the human performance, acting autonomously. These breakthroughs in performance were made possible due to the dramatic increase of computational power and the amount and ubiquity of the data available. This data-rich environment, however, led to the temptation to shortcut from raw data to the solutions without getting a deep insight and understanding of the underlying dependencies and causalities between the factors and the internal model structure. Even the most powerful (in terms of accuracy) algorithms such as deep learning (DL) can give a wrong output, which may be fatal. Recently, a crash by a driverless Uber car was reported raising issues such as responsibility and the lack of transparency, which could help analyze the cause and prevent future crashes. Due to the opaque and cumbersome model structure used by DL, some authors started to talk about a dystopian “black box” society. Having the true potential to revolutionize industries and the way we live, the recent breakthroughs in ML and AI also raised many new questions and issues. These are related primarily to their transparency, explainability, fairness, bias and their heavy dependence on large quantities of labeled training data.

Despite the success in this area, the way computers learn is still principally different from the way people acquire new knowledge, recognize objects and make decisions. Children during their sensory-motor development stage (first two years of a child’s life) imitate observed activities and are able to learn from one or few examples in “one-shot learning”. People do not need a huge amount of annotated data. They learn by example, using similarities to previously acquired prototypes, not by using parametric analytical models. They can explain and pass aggregated knowledge to other humans. They predict based on rules they formulate using prototypes and examples.

Current ML approaches are focused primarily on accuracy and overlook explainability, the semantic meaning of the internal model representation and its link with the problem domain. They also overlook the efforts to collect and label training data and rely on assumptions about the data distribution that are often not satisfied. For example, the widely used assumption that the validation data has the same distribution as that of the training data is usually not satisfied in reality and is the main reason for poor performance. The typical assumption for classification, that all validation data come from the same classes as the training data, may also be incorrect. It does not consider scenarios in which new classes appear. For example, if a driverless car is confronted with a scene that was never used in the training data or if a new type of malware or attack appears in a cybersecurity domain. In such scenarios, the best existing approach of transfer learning will require a heavy and long process of training with huge amounts of labeled data. While driving in real time, the car will be helpless. In the cybersecurity area it is not possible to pre-train for all possible attacks and viruses. Therefore, the ability to detect the unseen and unexpected and start learning this new class/es in real time with no or very little supervision is critically important and is something that no currently existing classifier can offer. Another big problem with the currently existing ML algorithms is that they ignore the semantic meaning, explainability and reasoning aspects of the solutions they propose. The challenge is to fill this gap between high level of accuracy and the semantically meaningful solutions.

The most efficient algorithms that have fueled interest towards ML and AI recently are also computationally very hungry – they require specific hardware accelerators such as GPU, huge amounts of labeled data and time. They produce parameterized models with hundreds of millions of coefficients, which are also impossible to interpret or be manipulated by a human. Once trained, such models are inflexible to new knowledge. They cannot dynamically evolve their internal structure to start recognizing new classes. They are good only for what they were originally trained for. They also lack robustness, formal guarantees about their behavior and explanatory and normative transparency. This makes problematic use of such algorithms in high stake complex problems such as aviation, health, bailing from jail, etc. where the clear rationale for a particular decision is very important and the errors are very costly.

All these challenges and identified gaps require a dramatic paradigm shift and a radical new approach. In this talk I will present such a new approach towards the next generation of computationally lean ML and AI algorithms that can learn in real-time using normal CPUs on computers, laptops, smartphones or even be implemented on chip that will change dramatically the way these new technologies are being applied. It will open a huge market of truly intelligent devices that can learn lifelong, improve their performance and adapt them to the user’s demands. I call this approach anthropomorphic, because it shares similar characteristics to the way people learn, aggregate, articulate and exchange knowledge. It focuses on addressing the open research challenge of developing highly efficient, accurate ML algorithms and AI models that are transparent, interpretable, explainable and fair by design. Such systems are able to self-learn lifelong, and continuously improve without the need for complete re-training, can start learning from few training data samples, explore the data space, detect and learn from unseen data patterns, collaborate with humans or other such algorithms seamlessly.

Speaker Bio

Prof. Angelov (MEng 1989, PhD 1993, DSc 2015) is a Fellow of the IEEE, of the IET and of the HEA. His PhD supervisor, Dr. Dimitar P. Filev is now Member of the National Academy of Engineering, USA. Professor Angelov is Vice President of the International Neural Networks Society (INNS) for Conference and Governor of the Systems, Man and Cybernetics Society of the IEEE.

He has 30 years of professional experience in high level research and holds a Personal Chair in Intelligent Systems at Lancaster University, UK. He founded in 2010 the Intelligent Systems Research group which he led till 2014 when he founded the Data Science group at the School of Computing and Communications before going on sabbatical in 2017 and established LIRA (Lancaster Intelligent, Robotic and Autonomous systems) Research Centre (www.lancaster.ac.uk/lira ) which includes over 30 academics across different Faculties and Departments of the University. He is a founding member of the Data Science Institute and of the CyberSecurity Academic Centre of Excellence at Lancaster.

He has authored or co-authored 300 peer-reviewed publications in leading journals, peer-reviewed conference proceedings, 3 granted patents (+ 3 filed applications), 3 research monographs (by Wiley, 2012 and Springer, 2002 and 2018) cited 8800+ times with an h-index of 48 and i10-index of 156. His single most cited paper has 850 citations. He has an active research portfolio in the area of computational intelligence and machine learning and internationally recognized results into online and evolving learning and algorithms for knowledge extraction in the form of human-intelligible fuzzy rule-based systems.

Prof. Angelov leads numerous projects (including several multimillion ones) funded by UK research councils, EU, industry, UK MoD. His research was recognized by ‘The Engineer Innovation and Technology 2008 Special Award’ and ‘For outstanding Services’ (2013) by IEEE and INNS. He is also the founding co-Editor-in-Chief of Springer’s journal on Evolving Systems and Associate Editor of several leading international scientific journals, including IEEE Transactions on Fuzzy Systems (the IEEE Transactions with the highest impact factor) of the IEEE Transactions on Systems, Man and Cybernetics as well as of several other journals such as Applied Soft Computing, Fuzzy Sets and Systems, Soft Computing, etc. He gave over a dozen plenary and key note talks at high profile conferences. Prof. Angelov was General co-Chair of a number of high profile conferences including IJCNN2013, Dallas, TX; IJCNN2015, Killarney, Ireland; the inaugural INNS Conference on Big Data, San Francisco; the 2nd INNS Conference on Big Data, Thessaloniki, Greece and a series of annual IEEE Symposia on Evolving and Adaptive Intelligent Systems. Dr Angelov is the founding Chair of the Technical Committee on Evolving Intelligent Systems, SMC Society of the IEEE and was previously chairing the Standards Committee of the Computational Intelligent Society of the IEEE (2010-2012). He was also a member of International Program Committee of over 100 international conferences (primarily IEEE). More details can be found at www.lancs.ac.uk/staff/angelov

IEEE Santa Clara Valley Section CIS Chapter


Chair: Dr. Kiran Gunnam
Vice-Chair: Dr. Mehran Nekuii
Treasurer:  Ms. Priscilla Chen
Secretary: Mr. Shreyans Mulkutkar

Finance Committee Chair: Ms. Yllka Masada


IEEE SCV Computational Intelligence Society


Intel SC9 Auditorium

2250 Mission College Blvd,
Santa Clara, CA 95054 United States