I advise high-tech corporations, fintech investors, blockchain startups and charity groups, giving practical and scientifically-informed advice in three areas: (1) developing and re-engineering large software systems; (2) choosing and implementing machine learning algorithms; and on (3) technological forecasting.
For details of my Professional Record see separate page
The difference between theory and practice is greater in practice than in theory
Experience gained with —
See also:
Expertise offered in —
There are two ways of constructing a software design. One way is to make it so simple that there are obviously no deficiencies. The other way is to make it so complicated that there are no obvious deficiencies.
He who loves practice without theory is like the sailor who boards ship without a rudder and compass and never knows where he may cast.
The design of computing systems can only properly succeed if it is well grounded in theory, and the important concepts in a theory can only emerge through protracted exposure to application.
Experience gained with —
Expertise offered in —
Machine learning — a field of expertise since 1991 — is replacing humans in investment planning, fraud detection, personal assistants, recommender systems, speech and image recognition, and many other tasks. Early classifiers such as decision trees and bayesian nets were superseded by deep learning neural nets — multi-layered networks that gradually build an ‘ontological hierarchy’ them much more complex reasoning.
Genetic algorithms, instance-based learning, or neural nets? Programmers today can use machine learning libraries, each suitable for different tasks:
Our machine learning research started in 1990 in asking how humans classify everyday objects quickly and without any formal criteria and whether a learning algorithm can perform as well as a human. Since then the variety of available neural network types has exploded.
Experience gained with —
Expertise offered in —
Which investment algorithms will hedge funds use next decade? How will AI affect computer trading, now that almost all stocks are traded by algorithms? How will driverless cars affect transportation? Will AI displace 40% of jobs within 20 years? When will solar energy be cheaper than fossil fuel, if at all?
Making reliable technological forecasts seems to require a crystal ball.
Organizations spend staggering amounts of time and money trying to predict the future but no time or money measuring their accuracy or improving on their ability to do it
Who expected the World Wide Web and mobile phones to take the 1990s by storm? Or BigDog, cryptocurrencies, and computer trading overtaking stock exchanges less than a decade later? Disruptive technology emerges at a breakneck speed as paradigm shifts accelerate. As for experts, when they’re pushed to make accurate forecasts they perform worse on average than simply expecting the future to repeat the past, according to the Good Judgement Project.
The further backward you look, the further forward you can see
After beating the experts in predicting the oscars, college football, and the Stanley Cup, Unanimous AI predicted the results of the Kentucky Derby at odds of 540:1 using swarm intelligence which combines predictions from individual experts whose predictions were only 23% accurate using AI, thereby reaching impressive accuracy — 100% in this example. In many domains (such as energy and stock prices) predictions with high reliability (72% and above) can be made using ‘big data’, data mining, and predictive modelling techniques, ranging from simple statistical and regression methods to more sophisticated machine learning inference algorithms.
Compare for example the state of Enterprise 3D Printing in 2016 according to Gartner’s Hype Cycle (above), which is approaching the Plateau of Productivity, with Machine Learning which is at the Peak of Inflated Expectations. Both technologies are revolutionary. Unrealistic expectations do not prove that either technology is worthless.
The future is already here — it’s just not evenly distributed