Imagine a complex plant or machinery being equipped with all kinds of sensors to monitor and control its performance and to predict potential points of failure. Such plants can range from an oil rig out in the ocean to an automated production line. Or such complex plants can be human beings, perhaps millions of them, who are being monitored with a variety of devices in a hospital or at home. Although we can use some standard models to monitor and compare performance of these physical systems, it would make more sense to either rebuild these models from scratch or adjust them to individual situations. This would be similar to what we do in economics.

In the following essay, Tom Davenport, the President's Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business and independent senior advisor to Deloitte Analytics, discusses why marketing, which is highly quantitative, targeted and tied to business outcomes, will likely become highly automated by 2025. If I had to place bets on which business function would have the fewest humans and most automated systems by 2025, I'd pick marketing. This is ironic, of course, since marketing has long been known for its creative and artistic orientation. If Mad Men's fictional character Don Draper, were alive in 2025, he would probably have wished he had never seen such extensive use of analytics and automation in his beloved function. While marketing will continue to be responsible for promoting products and services and generating interested buyers for them, the function, by 2025, may become much more automated. This is based on simple extrapolation of the marketing automation activity happening today.

There is a lot of talk about artificial intelligence (AI) these days, especially since Google's AlphaGo beat a Go world champion. Companies like IBM are using this technology already in a number of products. For example on Bluemix developers can easily consume cognitive Watson services like speech or image recognition that use machine and deep learning under the cover. While these Watson services are very easy to use for developers, sometimes you want to use machine learning for other scenarios. Since this technology looks so promising and powerful I'm trying to learn machine learning.

They continue to target computer networks and damage their infrastructure. Now, a combined team from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and machine-learning startup PatternEx has developed a powerful artificial intelligence system called AI2 which works significantly better than any existing cyber-attack detection system. The system has been tested on 3.6 billion log lines or pieces of data that reveal major system activities triggered by millions of users over a period of three months. Researchers have found that new tool can detect cyber-attacks with 85% accuracy which is roughly three times better than the previous benchmark. Moreover, it reduces the number of'false positives' – an event wrongly identified as threat – by a factor of 5. Conventional security systems are either virtual machine-based or humanly operated but none of them has proven overwhelmingly successful at encountering cyber-attacks.

Visitors to Australia's federal parliament are often surprised by the robust verbal confrontation between the government and the opposition – technically known as questions without notice, more commonly as question time. A theatrical highpoint of every sitting day, question time is part intellectual cage fight, part kindergarten spat – and all psychological warfare. Political journalists watch the hour-long question time as drought-stricken farmers view the clouds. They look for signs, they read the climate. But what if you were interested in facts?

Gradescope, which has graded millions of exam questions, has made the grade itself. The company has raised a 2.6 million round of funding from Freestyle Capital, Bloomberg Beta, Reach Capital and the House Fund. Existing investor K9 Ventures also participated. Dave Samuel from Freestyle will be joining Manu Kumar from K9 Ventures on Gradescope's board. The company, started as a side project at the University of California Berkeley in 2012, makes a software that helps science and engineering professors and teaching assistants grade exam questions on handwritten tests.

AlphaGo caused a stir by defeating 18-time world champion Lee Sedol in Go, a game thought to be impenetrable by AI for another 10 years. AlphaGo's success is emblematic of a broader trend: An explosion of data and advances in algorithms have made technology smarter than ever before. Machines can now carry out tasks ranging from recommending movies to diagnosing cancer -- independently of, and in many cases better than, humans. In addition to executing well-defined tasks, technology is starting to address broader, more ambiguous problems. It's not implausible to imagine that one day a "strategist in a box" could autonomously develop and execute a business strategy.

IT and analytics managers struggling with all the data flooding into their organizations may find it hard to ignore the increased marketing push machine learning tools are getting from technology vendors. And for good reason: Running automated algorithms designed to learn on their own as they churn through large data sets can accelerate data mining and predictive analytics applications -- and give users information they might not get otherwise. But companies looking to take advantage of machine learning often face a substantial learning curve. For starters, a lot of big data infrastructure technologies -- Hadoop, the Spark processing engine and related open source software in particular -- typically underlie machine learning efforts. In many cases, that means building a suitable data processing and management architecture from scratch.

The final homework assignment for CS545 Machine Learning was to implement a K-means clustering algorithm to cluster and classify the OptDigits data. The raw data looks something like the figures to the left. So these instances are fields of 0's whereby some 0's have been flipped to be 1's such that the image is recognizable (to humans) as a handwritten digit. For the K-means classifier, we ran 2 different experiments. The first expeiment used 10 centroids (one per digit), the second used 30 centroids to see if it could find clusters where the handwritten digits were different enough to notice differences.

Niall Dunne is the Chief Sustainability Officer for BT, working with BT's Chief Executive, Chairman and executive management team to bring the company's purpose, to use the power of communications to make a better world, to life. Before joining BT in 2011, Niall was Managing Director in Europe, the Middle East and Africa (EMEA) at Saatchi & Saatchi. Prior to that, Dunne was an executive at Accenture, where he helped establish the company's climate change and sustainability practice. Dunne has written and spoken about the power of communications to tackle major social, environmental and economic problems. Niall was vice chair of the WEF's Global Agenda Council on Sustainable Consumption 2012-14 and joined the WEF Global Agenda Council on Climate Change in 2014.