Earlier this week, I found myself answering a question from a new colleague at Finning International that relates both to the research I do in the iSchool at the University of British Columbia, as well as the analytics, engineering & technology work that I lead at Finning. The questions were simple: 1) What is artificial intelligence? As I sat to reflect last evening, it dawned on me that taking time to craft a clear answer to these questions might be extremely beneficial for many. Analytics, data science, and predictive intelligence are hot topics in many communities and business areas. And yet, despite this interest, few folks I have talked to have a clear understanding of the history of the discipline; one, that frames much of the work currently going on within the space.

In this paper, we examine the problem of missing data in high-dimensional datasets by taking into consideration the Missing Completely at Random and Missing at Random mechanisms, as well as the Arbitrary missing pattern. Additionally, this paper employs a methodology based on Deep Learning and Swarm Intelligence algorithms in order to provide reliable estimates for missing data. The deep learning technique is used to extract features from the input data via an unsupervised learning approach by modeling the data distribution based on the input. This deep learning technique is then used as part of the objective function for the swarm intelligence technique in order to estimate the missing data after a supervised fine-tuning phase by minimizing an error function based on the interrelationship and correlation between features in the dataset. The investigated methodology in this paper therefore has longer running times, however, the promising potential outcomes justify the tradeoff. Also, basic knowledge of statistics is presumed.

I'm a programmer with no great mathematical background (2nd year university maths and stats, decades old and mostly forgotten) trying to teach myself about machine learning, and I found this book to be at exactly the right level for me. It's strongly oriented towards classifiers of one sort and another, and makes no claims to cover neural nets, genetic algorithms, genetic programming - but what it does cover it covers exceptionally clearly. I'd give it six stars out of five if it covered all aspects of machine learning, but I guess I can't have everything. In terms of writing style and comprehensibility this is probably one of the best textbooks I have ever read. I wish that it covered much much more, but what it does do it does remarkably well.

Data mining is everywhere, but its story starts many years before Moneyball and Edward Snowden. The following are major milestones and "firsts" in the history of data mining plus how it's evolved and blended with data science and big data. Data mining is the computational process of exploring and uncovering patterns in large data sets a.k.a. It is fundamental to data mining and probability, since it allows understanding of complex realities based on estimated probabilities. The goal of regression analysis is to estimate the relationships among variables, and the specific method they used in this case is the method of least squares.

I am currently building a genetic algorithm to tune n parameters where n will probably be in the range of 3 n 8 but could be up to 15. I would like my initial population N (let's say N 1000) to be evenly dispersed across the input space. When calculating the next generation I surmised that the most effective way to combine parents would be to calculate the centroid, on the surface of the hypersphere, between some m nearest-neighbour parents. The larger m is, the fewer new points we would add. The rest being calculated in a similar fashion but from random parents.

In the previous part of this series, I began discussing the field of advanced evolutionary artificial intelligence. AI has seen some stunning advancements in recent times, but we are still quite a while away from achieving the holy grail - general artificial intelligence. That is, an AI so developed that it could perform any cognitive task that a human can. To achieve this, we must look further than applying neural networks to specific tasks, we must look for algorithms that evolve and mutate to adapt to situations. What we're talking about are genetic algorithms; effectively the mathematical counterpart to Darwinian evolution.

"Mutation, it is the key to our evolution. It is how we have evolved from a single-celled organism into the dominant species on the planet. This process is slow, and normally taking thousands and thousands of years. Until few weeks back, it never occurred to me in so many years that above Darwinian quote from my all-time favourite sci-fi movie hints something about one of the most compelling theories in computer science I ever came across. Yes, I said – "Computer Science".

As someone so flummoxed by traffic I wrote a book about it, I have a near-clinical aversion to vehicular congestion. My global default strategy is to simply drive as little as possible, but there are times when I simply must put foot to gas pedal. Like many, I have become increasingly dependent on the Waze app, which, via each drivers' smartphone, turns an inchoate, undifferentiated mass of drivers into something resembling a collective form of networked intelligence. Waze, it occurred to me the other day while stuck in a bit of unexpected congestion (which had been duly flagged by at least 13 "Wazers"), is helping us turn into ants. Every time drivers travel down a path, Waze tracks their speed--information that can then be broadcast to every following driver.

Swarm intelligence has made some impressive predictions on horse racing this season, by correctly placing the top four Kentucky Derby finishers last month. This might affect bookies but what about horses? If an "artificial" intelligence can benefit betting, can it help keep a horse healthy and performing well? A French startup called Arionea thinks so. The company is betting big that artificial intelligence (AI) and the Internet of Things (IoT) will revolutionize the racetrack -- with the help of a new device dubbed the Equimetre.

With much current discussion of AI fixating on ethical implications--whether AI may eventually "outsmart" or harm us; how we can ensure that AI acts in our best interests--it's worth considering a new approach to AI that keeps humans in the loop: swarm intelligence. UNU, a software platform run by Unanimous A.I., brings groups of people together online to arrive at all kinds of real-time decisions and predictions, ranging from who will win March Madness to the top four horses at the Kentucky Derby. The system has proven remarkably effective at coming up with accurate answers. In fact, it has outperformed experts in a variety of contests--in the 2015 Oscar predictions, for instance, the swarm had a higher than 70% accuracy--New York Times critics, it should be noted, were right 55% of the time. SEE: How'artificial swarm intelligence' uses people to make better predictions than experts But, beyond accuracy, there is another advantage to using the swarm: according to new research, it makes more ethical decisions.