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What to expect from business intelligence in 2017

Major Growth

As it looks like Business Intelligence is going to be going from strength to strength in 2017. Organizations in a variety of global markets are planning major investment in their Business Intelligence strategies this year. Over the pond in the UK, over three quarters of small to medium sized enterprises are planning a major analytics or data project this year.

Where there is investment, there is research; and where there is research, there is innovation. This means that we can expect some exciting steps forward this year, as organizations stumble over themselves to stay on the cutting edge of the discipline.

Data Diversity Is the Order of the Day

In order to keep ahead of the curve, businesses in 2017 are turning their attentions to a variety of different sources from which to draw their data. After all, why limit your insight when we are practically adrift in a sea of data and understanding?

If you can find a way to connect it to an analytics platform, it is a data source. This means, businesses now have the technology to measure just about everything. Need qualitative data from customer reviews? No problem. Want customer behavior data from a physical product itself? It’s yours. Looking for information on which of your competitors your churned customers have moved on to? Right here.

The fact is, you cannot get too much data, and the greater variety of sources you have for that data, the more comprehensive the understanding you can gain from it. This is why datasets and sources will be becoming increasingly diverse in 2017.

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My Robot Made Me Buy It!

January 30, 2017 – by Joseph F. Coughlin

2017 might (finally) be the year of the robot, if we go by the public attention that robotics and AI have been receiving lately from the media, consumers, and retailers. The biggest newsmaker at the 2017 Consumer Electronics Show was Amazon’s AI personal assistant, Alexa – and these robotic helpers are rapidly appearing on kitchen counters, nightstands and desks in homes across the country. Ford Motor Company has announced that it will make Alexa mobile by making it a feature in our cars. Amazon, Apple, Google, Microsoft and emerging competitors will make these robots a ubiquitous feature in our lives in the near future. From playing our favorite tunes to turning on the lights to ordering cat food, these robotic assistants are filling wants and creating new needs we did not know we had. Service robots now scurry across our homes, vacuuming floors and cleaning gutters. Soon robotic cars will be taking us far and wide. As a researcher interested in consumer behavior and technology use across the lifespan, I’m especially fascinated with the use of social bots that coo and purr to keep older adults company or mitigate certain chronic conditions.

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Areas of AI & machine learning to watch closely

Distilling a generally-accepted definition of what qualifies as artificial intelligence (AI) has become a revived topic of debate in recent times. Some have rebranded AI as “cognitive computing” or “machine intelligence”, while others incorrectly interchange AI with “machine learning”. This is in part because AI is not one technology. It is in fact a broad field constituted of many disciplines, ranging from robotics to machine learning. The ultimate goal of AI, most of us affirm, is to build machines capable of performing tasks and cognitive functions that are otherwise only within the scope of human intelligence. In order to get there, machines must be able to learn these capabilities automatically instead of having each of them be explicitly programmed end-to-end.

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One Third of Americans Prefer a Software Robot Over a Human Boss

Digitization and automation are ever-growing topics in relation to the workplace.

A famous Oxford study on the future of employment from 2013 estimated that up to 47% of American jobs may be automated by 2035; a brand new McKinsey study shows that current technologies could automate 45 percent of job activities; and the business mantra goes that if you can digitize, you should digitize to gain a competitive advantage.

But how do we, as human beings, really feel about potentially working with or even for AIs, and what impact do we think they will have on our workplace?

A recent study conducted in the US, UK and Denmark explores people’s openness towards working with and for “unbiased computer programs”—defined as “a software robot that makes decisions or proposals for decisions based on data from HR, financial or market information. The software robot is unbiased, i.e. it is not affected by the personal, social and cultural bias that influence human decision making, but balances all input only based on the data.”

The study shows some surprising results in openness, and big geographical differences.

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Simple Analytics is Good for Business

A research paper by renowned consultancy Aberdeen Group reveals that “[data] complexity is often best answered with simplicity”. Several new surveys conducted by the group reveal some interesting findings with regards to the costs and benefits of using an integrated tool for data preparation, querying and visualization, as opposed to the “assembly line” approach of dividing these tasks between various proprietary DW, ETL and visualization tools.

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Circuits built from carbon nanotubes

The silicon semiconductor industry has chugged along for more than 50 years. Like a steamroller, it has trundled over bumps and holes, while defying repeated warnings that it was running out of fuel or was about to be overtaken by flashier competitors.

So we technologists are understandably reluctant to speculate about the end of silicon. And yet, speculate we must. After decades of steady improvements to the efficiency and speed of our computer chips, brought about by physically shrinking the dimensions of silicon transistors, we’ve reached a point where the massive effort to miniaturize those switches ekes out only very modest gains in performance. The steamroller still rolls, but it’s slowing down, and the maintenance and upkeep on it are fast becoming unsustainable.

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Reid Hoffman: A.I. Is Going to Change Everything About Managing Teams

Imagine a spider chart mapping a complex web of interactions, sentiments, and workflow within an office. What would your company look like?

When most of us think of artificial intelligence in the workplace, we imagine automated assembly lines of robots managed by an algorithm. LinkedIn’s Reid Hoffman has a different idea.

In an essay for MIT Sloan Management Review, Hoffman describes human applications for the technology. Among other things, it would help to use data science to improve the way we onboard new team members, organize workflow, and communicate about performance. Addressing the question of how technology will change management practices over the next five years, Hoffman explains how the use of a “knowledge graph” will become standard management practice.

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The Apollo 11 guidance computer code is now available on Github

The system responsible for the first moon landing is now readily available online, after an enterprising former NASA intern uploaded the Apollo Guidance Computer code to Github this week.

Although the code for the MIT-designed system has long been available to interested researchers, it’s never been quite this at hand. Quartz has an excellent, thorough breakdown, but the jokes and asides are a special point of interest.

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Preparing for the Future of A.I.

There is a lot of excitement about artificial intelligence (AI) and how to create computers capable of intelligent behavior. After years of steady but slow progress on making computers “smarter” at everyday tasks, a series of breakthroughs in the research community and industry have recently spurred momentum and investment in the development of this field.

Today’s AI is confined to narrow, specific tasks, and isn’t anything like the general, adaptable intelligence that humans exhibit. Despite this, AI’s influence on the world is growing. The rate of progress we have seen will have broad implications for fields ranging from healthcare to image- and voice-recognition. In healthcare, the President’s Precision Medicine Initiative and the Cancer Moonshot will rely on AI to find patterns in medical data and, ultimately, to help doctors diagnose diseases and suggest treatments to improve patient care and health outcomes.

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