The Rise of AI and the World of Machine Learning Peter J Bentley - - PowerPoint PPT Presentation

Peter J Bentley

The Rise of AI

and the World of Machine Learning

What is AI?

Robots? Magic software? Terrifying Super Intelligences? Machine Learning?

This is a maze-solving machine that is capable of solving a

maze by trial-and-error means, of remembering the solution, and also of forgetting it in case the situation changes and the solution is no longer

applicable. …Now I would like to show you one further feature of the machine. I will change the maze so that the solution the machine found no longer works. By moving the partitions in a suitable way, I can obtain a rather interesting effect. In the previous maze the proper solution starting from Square A led to Square B, then to C, and on to the goal. By changing the partitions I have forced the machine at Square C to go to a new square, Square D, and from there back to the original square, A. When it arrives at A, it remembers that the old solution said to go to B and so it goes around the circle A, B, C, D, A, B, C, D …. It has established a vicious circle, or a singing condition.

• A neurosis
• Yes
• It can't do that when its mind is blank, but can do it after it has been conditioned?
• Yes, only after it has been conditioned. However, the machine has an

antineurotic circuit built in to proven just this sort of solution

• It doesn't have any way to recognise that it is "psycho" it just recognizes that it

has been going too long?

• Yes. As you see, it has now gone back to the exploring strategy.

1951

Presentation of a Maze-Solving Machine

Claude Shannon 1951

I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be

able to speak of machines thinking without expecting to be contradicted.

I believe further that no useful purpose is served by concealing these beliefs. The popular view that scientists proceed inexorably from well- established fact to well-established fact, never being influenced by any unproved conjecture, is quite mistaken. Provided it is made clear which are proved facts and which are conjectures, no harm can result. Conjectures are of great importance since they suggest useful lines of research.

1950

Computing Machinery and Intelligence

• A. M. Turing

1950

In analyzing the functioning of the contemplated device, certain classificatory distinctions suggest themselves immediately. First: Since the device is primarily a computer, it will have to perform the elementary

• perations of arithmetics most frequently. These are addition, multiplication and division.

It is therefore reasonable that it should contain specialized organs for just these operations... a central arithmetic part of the device will probably have to exist and this constitutes the first specific part: CA. Second: The logical control of the device, that is the proper sequencing of its operations can be most efficiently carried out by a central control organ... this constitutes the second specific part: CC. Third: Any device which is to carry out long and complicated sequences of operations (specifically of calculations) must have a considerable memory... this constitutes the third specific part: M. ...The three specific parts CA, CC and M correspond to the associative

neurons in the human nervous system.

It remains to discuss the equivalents of the sensory or afferent and

the motor or efferent neurons.

These are the input and the output organs of the device.

1945

First Draft of a Report on the EDVAC

John von Neumann 1945

AI has always been inspired by biology

From living systems we discover what might be possible. AI enables computers to do what they currently cannot. (Once computers can do it, it’s no longer AI)

What can’t we do with computers?

• We can classify and predict from data.
• It’s a fashionable area in business and research – we

develop new machine learning methods to solve significant problems in business.

• But predicting the behaviour of complex systems is still

tricky.

Can we predict the behaviour of developers?

Predicting behaviour of App Stores

App Store User App Developer builds and uploads downloaded by

Cyprus, Malaysia, Belarus, Ukraine, Colombia, Costa Rica, Indonesia, Vietnam, Sweden, Guatemala, Kazakhstan, Singapore, Chile, Puerto Rico, Thailand, Argentina, El Salvador, Peru, Philippines, Croatia, Ecuador, Greece, Norway, Panama, Paraguay, Romania, Austria, Belgium, Bolivia, Caribbean, Dominican Republic, Fiji, Ghana, Honduras, Ireland, Ivory Coast, Kyrgyzstan, Mauritius, Netherlands, Pakistan, Poland, Portugal, St. Vincent, Switzerland, Taiwan, T urkey, Uruguay, and Venezuela.

China Australia Japan Canada Mexico Russia USA Brazil France UK Spain Germany Italy India South Korea

N=508 N=299 N=245 N=430 N=260 N=261 N=278 N=344 N=258 N=514 N=278 N=255 N=271 N=232 N=215

!

(a) (b)

The spread of the excellent infectious app through the user network using (a) the Mass Exposure strategy, and (b) the Enhancing Mode of Transmission through New Apps Chart strategy. An epidemic curve for a good app resulting from Mass Exposure in an example run.

Can we predict the behaviour of cancer?

Cancer

Most conventional treatments are old. They are based on a reductionist approach. The effect of treatments on tumours is not properly understood.

Intestinal Crypts- Normal Case

Dynamical tracing at genetic scale

Day 1 Day 20

Intestinal Crypts- Cancer Case

Can we predict the behaviour of irrational, unpredictable people?

Human Collaboration

0.97 0.975 0.98 0.985 0.99 0.995 1 E I E I E I E I 0% 5% * 10% * 20% * 0.97 0.975 0.98 0.985 0.99 0.995 1 S N S N S N S N 0% * 5% * 10% 20% * 0.99 0.992 0.994 0.996 0.998 1 T F T F T F T F 0% * 5% * 10% 20% 0.99 0.992 0.994 0.996 0.998 1 J P J P J P J P 0% 5% 10% 20%

Can we make predictions on graph- structured data?

• Identification of role, dependence, and influence in social networks.
• Predicting outcomes of corporate merges, social acceptance, impact
• f economic legislation, and (of course) who your next social media

friend should be!

• Enriching isolated models with appropriate background from

knowledge graphs, i.e. bringing ‘common sense’ to machine learning.

• Predicting chemical properties such as the effectiveness of a drug against

a given condition, as well as prediction of new chemicals given a set of desired properties.

• Predicting structural information for amino acids, proteins, and enzymes.

• Fraud

analysis/identification

• Network analysis and

cyber security

• Logistics optimisations
• Training data enrichment

Smart Connectivity

AI will always be inspired by natural systems

What did brains evolve to do? Predict the unpredictable.

The future of AI

Our next challenge is moving beyond simple Machine Learning We shall predict the behaviour of complex systems. To achieve this, we use Machine Learning that resembles the brain.

For more details of these and other agent based models:

Lim, S L and Bentley, P. J. (2018) Coping with Uncertainty: Modelling Personality when Collaborating on Noisy Problems. Proc. of The Sixteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 2018). 23-27 July 2018. Tokyo, Japan. Williams, A. C de C, Gallagher, E., Rei Fidalgo, A., Bentley, P. J. (2016) Pain expressiveness and altruistic behavior: an exploration using agent-based

• modeling. In PAIN 157(3) (The Journal of the International Association for the Study of Pain), Wolters Kluwer, ISSN: 0304-3959.

Lim, S. L., Bentley, P. J., and Ishikawa, F. (2015). The Effects of Developer Dynamics on Fitness in an Evolutionary Ecosystem Model of the App Store. IEEE Transactions on Evolutionary Computation (TEVC) vol PP issue 39 Lim, S. L., Bentley, P. J., Kanakam, N., Ishikawa, F. and Honiden, S. (2015). Investigating Country Differences in Mobile App User Behavior and Challenges for Software Engineering. IEEE Transactions on Software Engineering (TSE) vol 41 issue 1, pp 40-64. Lim, S L and Bentley, P J (2013) Investigating App Store Ranking Algorithms using a Simulation of Mobile App Ecosystems. 13th IEEE Congress on Evolutionary Computation (IEEE CEC). Lim, S. L. and Bentley, P. J. (2012) How to be a Successful App Developer: Lessons from the Simulation of an App Ecosystem. In Proc of Genetic and Evolutionary Computation Conference (GECCO 2012). ACM Pub. Philadelphia, USA. July 7-11 2012 Araujo, A., Rübben, A., Bentley, P. J. and Basanta, D. (2018) Testing Three Hypotheses of the Contribution of Geometry and Migration Dynamics to Intestine Crypt Evolution. Proc. of the 2018 Conference on Artificial Life (ALIFE 2018). 23-27 July 2018. Tokyo, Japan. Araujo, A., Baum, B. and Bentley, P. J. (2013) The Role of Chromosome Missegregation in Cancer Development: A Theoretical Approach Using Agent- Based Modelling. PLoS ONE 8(8): e72206. doi:10.1371/journal.pone.0072206 Araujo, A., Bentley, P. J. and Baum, B. (2010) Modelling the Role of Aneuploidy in Tumour Evolution. In Proc of 12th International Conference on the Synthesis and Simulation of Living Systems. Odense, Denmark, 19-23 August 2010. MIT Press. Bentley, P. J. (2009) The Game of Funding: Modelling Peer Review for Research Grants. In Proc of the Genetic and Evolutionary Computation Conference (GECCO 2009). Companion proceedings (EcoMASS Workshop) July 8-12, 2009. 2597-2602 Bentley, P. J. (2009) Methods for Improving Simulations of Biological Systems: Systemic Computation and Fractal Proteins. In Special Issue on Synthetic Biology, J R Soc Interface 2009 6:S451-S466; doi:10.1098/rsif.2008.0505.focus.

Thank you

Peter J Bentley

p.bentley@braintree.com p.bentley@cs.ucl.ac.uk