Last week I looked at Singular Value Decomposition unsupervised machine learning technique as part of a four-part series on data science concepts for beginners. Remember that unsupervised machine learning is data driven rather than task driven (supervised machine learning). Today we'll be staying in the dimension reduction part of unsupervised machine learning as shown in the Cheat-sheet below and will talk about principal component analysis or PCA. In a similar manner to SVD, PCA is trying to reduce the number of dimensions for data exploration. The PCA method is trying to maximize variance of the data to make a predictive model and converts a set of possibly correlated variables into a set of linearly uncorrelated variables.

Contrary to the bleak picture painted by critics, a new IDC study of more than 1,000 organisations worldwide shows that AI will be in the workplace "sooner than we think", and will have a positive impact on productivity, revenues, and job creation. From 2017 to 2021, the Salesforce-sponsored study predicts that AI-powered CRM activities will boost business revenue by $1.1 trillion, and create more than 800,000 direct jobs and 2 million indirect jobs globally, surpassing those lost to AI-driven automation. The business revenue boost will be led primarily by increased productivity and lowered expenses due to automation, which account for $121 billion and $265 billion of the $1.1 trillion sum, respectively, according to the study. Keith Block, vice chairman and COO at Salesforce, said the impact of AI for the CRM market will be "profound" in that it will enable "new levels of productivity". "The convergence of increased computing power, big data, and breakthroughs in machine learning have meant artificial intelligence is set to transform the lives of workers, especially those that are already using CRM technology, by helping them be more productive in their development of more meaningful connections with customers," added Robert Wickham, RVP of Innovation and Digital Transformation at Salesforce APAC.

A New Zealand made mobile app is being used by local dairy farmers to replace veterinarians. Available on the App Store, the Betty app uses machine learning algorithms to help dairy farmers diagnose sick cows in their herd. Farmers using the app are presented with series of questions, with the response combined with regional farm and weather data to produce a list of the most likely causes of disease in their animals. Betty's creator, Dr. Jonathan Wong, said the idea was born out of frustration, while he was working as a dairy veterinarian. "There are a lot of farmers out there who are reluctant to call a vet early, especially if a problem is perceived to be minor. With Betty we can help farmers decide whether or not their sick cow is an emergency and to take immediate action, or connect them with a local vet if need be."

Thinking about how and when you'll die might be morbid, but it has creeped into everyone's mind at some point. Online tools like The Death Clock provide a very unscientific, and entertaining, prediction of your demise, but researchers have figured out a way to estimate a person's lifespan with 69 percent accuracy. In a very small study of 48 participants, all of whom were at least 60 years old, scientists from the University of Adelaide in Australia analyzed photos of people's organs using artificial intelligence. They were able to predict who would die within five years with 69 percent accuracy, which is roughly the same as an oncologist's. Using deep learning, which involves inputting data into a computer system to help it make decisions, the researchers used radiological images because they provide undetectable clues, according to study co-author and epidemiologist Dr. Lyle Palmer, Ph.D, in a story on ResearchGate.

As artificial intelligence proliferates, companies and governments are aggregating enormous data sets to feed their AI initiatives. Although privacy is not a new concept in computing, the growth of aggregated data magnifies privacy challenges and leads to extreme ethical risks such as unintentionally building biased AI systems, among many others. Privacy and artificial intelligence are both complex topics. There are no easy or simple answers because solutions lie at the shifting and conflicted intersection of technology, commercial profit, public policy, and even individual and cultural attitudes. Given this complexity, I invited two brilliant people to share their thoughts in a CXOTALK conversation on privacy and AI.

Three years ago, Gartner predicted that by 2017, CMOs would spend more on IT than their counterpart CIOs. Fast-forward to 2015; we are more than halfway there and can already bear witness to the shift in focus to the Chief Marketing Officer as the advocate and purchaser of technology of the future. The role of the CMO has evolved from a traditional and tactical approach based on simple data capture and inefficient targeted campaigns to performance-led strategies based on rich data insights and measurement of business impact. However, is there too much technology out there for marketers to handle? And how best should companies balance the adoption of technology with human capital?

Summary: At least one instance of Real Time Predictive Model development in a streaming data problem has been shown to be more accurate than its batch counterpart. Whether this can be generalized is still an open question. It does challenge the assumption that Time-to-Insight can never be real time. A few months back I was making my way through the latest literature on "real time analytics" and "in stream analytics" and my blood pressure was rising. The cause was the developer-driven hyperbole that claimed that the creation of brand new insights using advanced analytics has become "real time".

Jobs are disappearing, incomes retreating, the precariat growing. Thousands of people risk their lives in stormy seas to flee wars, moribund economies and climate change on a daily basis. Traditional politicians continue to avoid publicly addressing the tsunami of unemployment, apparently baffled as to how to react to a historic transition: the automation of critical masses of labour once performed by humans. Five acronyms - AI, AR, VR, BC and UBI - promise to shape the developed world's future and solve the problems of the present. In the process, however, these innovations risk transforming the world around us, and upsetting humanity's very definition of itself.

Results so far obtained allow us to predict the RainTomorrow Yes/No variable. As a consequence, we are able so far to predict if tomorrow rainfall shall be above 1mm or not. In case of "at least moderate" rainfall, we would like to be as much reliable as possible in predicting {RainTomorrow "Yes"}. Since RainTomorrow "Yes" is perceived as the prediction of a potential threat of damages due to the rainfall, we have to alert Canberra's citizens properly. That translates in having a very good specificity, as explained in the presecution of the analysis. That is motivated by the fact that weather forecast comprises more than one prediction.

This paper introduces DeepBach, a graphical model aimed at modeling polyphonic music and specifically hymn-like pieces. We claim that, after being trained on the chorale harmonizations by Johann Sebastian Bach, our model is capable of generating highly convincing chorales in the style of Bach. DeepBach's strength comes from the use of pseudo-Gibbs sampling coupled with an adapted representation of musical data. This is in contrast with many automatic music composition approaches which tend to compose music sequentially. Our model is also steerable in the sense that a user can constrain the generation by imposing positional constraints such as notes, rhythms or cadences in the generated score. We also provide a plugin on top of the MuseScore music editor making the interaction with Deep-Bach easy to use.