Data Science

Companies looking to make good on the promise of machine learning for data analysis are turning to a somewhat unlikely old friend. Business intelligence systems, largely the domain for analyzing past performance, are being retrofitted with artificial intelligence to bring predictive features to their reporting capabilities. The Symphony Post Acute Network is one such organization. The health care company, which has 5,000 beds in 28 health care facilities in Illinois, Indiana and Wisconsin, wanted to use artificial intelligence and machine learning to improve care for up to 80,000 patients a year recovering from procedures like knee surgery, or receiving dialysis treatment. For example, buried deep in a patient's medical core could be an indication that a patient is particularly at risk for a dangerous fall and therefore requires extra precautions.

To say that AI is big data is to overstate things a bit. And yet, without big data, AI wouldn't be where it is today. In the last few decades, the two technologies have advanced in lock-step. Largely because without big data, however clever the AI programmers were, they couldn't get past the theoretical stage. Mainly, this is down to what big data is used for.

I have seen a glimpse of the future impact of artificial intelligence on corporate communications – and it is good. AI will bring a new level of trust to information, improve the way information is delivered (i.e., via augmented reality and virtual reality apps) and provide better insights and predictive analytics for decision making by corporate communications professionals. My exposure to artificial intelligence has primarily been in the trusted identity technology industry, where AI is starting to revolutionize the digitization of identity and access management, physical access control and cybersecurity, especially as a proactive approach to threat and fraud detection. The management of identities, either physical or digital, is changing rapidly, requiring new ways of thinking to add trust. Trust is an important topic for corporate communications, too.

I have seen a glimpse of the future impact of artificial intelligence on corporate communications – and it is good. AI will bring a new level of trust to information, improve the way information is delivered (i.e., via augmented reality and virtual reality apps) and provide better insights and predictive analytics for decision making by corporate communications professionals. My exposure to artificial intelligence has primarily been in the trusted identity technology industry, where AI is starting to revolutionize the digitization of identity and access management, physical access control and cybersecurity, especially as a proactive approach to threat and fraud detection. The management of identities, either physical or digital, is changing rapidly, requiring new ways of thinking to add trust. Trust is an important topic for corporate communications, too.

When businesses identify a problem which can be solved by machine learning, they brief the data analysts and scientists to create a predictive analytics solution. In many cases, the turnaround period for delivering a solution is quite long. Even for seasoned data scientists, switching machine learning models that can accurately forecast the outcomes is always challenging and time-consuming. The complex workflow involved in machine learning units have several stages. Some of the substantial measures include data acquisition, information mining, feature engineering, design selection, experimentation and prediction.

Bouncing from a video capture device to a DEC workstation to a Convex Supercomputer to a Cray, I felt like I had been thrown into a caldron of Chinese water torture. Sitting over my head was a joke happy birthday poster from Arthur C. Clarke's Space Odysseyseries featuring HAL 9000. I found it ironic that I was essentially acting like a highly-trained monkey, while a fictional AI stared down at me, laughing. Over the two years of that AI project, I easily spent 60% of my time just getting the data captured, cleaned, imported and in a place where it could be used by the training system. AI, as practitioners know, is the purest example of garbage in, garbage out.

If we set as an overall goal "How can we create and sustain a consistent and awesome customer experience across multiple channels & touch points?", None of them is easy to tackle, and the goal might seem elusive since customer expectations continue to rise. The power of AI, bots, Big Data, and analytics can now enable us to attack them and thereby increase sustainable revenues, realize higher margins, and sustain greater levels of customer loyalty. Let's consider one of these seven challenges, Reduce demand for support. Collect and act on NPS-powered customer feedback in real time to deliver amazing customer experiences at every brand touchpoint.

We reached out to expert analyst Sanj Bhayro, SVP EMEA Commercial Sales at Salesforce, to highlight the urgent need for SMEs to be primed for automation and build a future-proof IT infrastructure. Like me, you may have recently read that fintech provider iZettle has received €30 million in funding from the European Investment Bank (EIB) to increase its use of emerging technologies such as Artificial Intelligence (AI). This part of the EIB's strategy, one element of its Investment Plan for Europe, aims to provide funding to start-ups and SMEs that will help boost the region's tech economy. I'm particularly excited to see how this funding can help small businesses innovate and grow rapidly. Over the years I've worked with hundreds of small companies across Europe and, more often than not, I find they make tech investment decisions based on what they need right now, rather than what they will need in the future.

The MIT Statistics and Data Science Center (SDSC), a part of the Institute for Data, Systems, and Society (IDSS), announced two new academic programs today: the MicroMasters program in Statistics and Data Science, and the Interdisciplinary Doctoral Program in Statistics, both beginning in the fall. The MicroMasters program, currently under development by MIT faculty, will be offered online through edX. "Digital technologies are enabling us to bring MIT's data science curriculum to learners around the world regardless of their location or socioeconomic status," says Vice President for Open Learning Sanjay Sarma. The curriculum includes foundational knowledge of data science methods and tools, a deep dive into probability and statistics, and opportunities to learn, implement, and experiment with data analysis techniques and machine learning algorithms. "The demand for data scientists is growing rapidly," says Dean for Digital Learning Krishna Rajagopal.

Mining is all about profitability. But tracking that can prove tricky, particularly when you're relying on people on the ground to report on activities. It all comes down to big data: the more data you can collect, the more information you have at your disposal, and the more insight you have into the business. IOT technology is paving the way for mine optimisation initiatives, says Johan Pietersen, MD of Virtualscape Technologies. "Mines are looking for the ability to gather data for various purposes, including improvements in efficiency and safety.