One of the disadvantages of machine learning as a discipline is the lack of reasonable confidence intervals on a given prediction. There are all kinds of reasons you might want such a thing, but I think machine learning and data science practitioners are so drunk with newfound powers, they forget where such a thing might be useful. If you're really confident, for example, that someone will click on an ad, you probably want to serve one that pays a nice click through rate. If you have some kind of gambling engine, you want to bet more money on the predictions you are more confident of. Or if you're diagnosing an illness in a patient, it would be awfully nice to be able to tell the patient how certain you are of the diagnosis and what the confidence in the prognosis is. There are various ad hoc ways that people do this sort of thing.

Machine learning, a kind of artificial intelligence that enables computers to learn without being explicitly programmed, will be a driving force in smartphone innovation in the coming year, according to Deloitte's newly released report on digital trends, Technology, Media & Telecommunications (TMT) Predictions. This year, over 300 million smartphones will be equipped with neural network machine-learning capabilities, the report predicts. Such functionality could enhance a range of functions, including image classification, navigation and speech recognition. The rise of machine learning on smartphones could spark a similar dynamic in the fast-growing arena of wearables. For mobile users in disconnected spaces such as underground or in a plane, machine learning could enable new capabilities, while those operating in a connected environment may see tasks performed more quickly, or with enhanced privacy.

Self-learning, machine learning, and AI are all buzzwords in the tech field today. They all represent the next generation in software development and management. In this brave new world, programmers will often set up the application -- and the software will do the rest. Driven by big data, deep learning systems, and consumer demand, you may be investing in self-learning programs sooner than you think. Self-learning, often referred to as machine learning, is a form of AI.

The latest Gartner Hype Cycle for Emerging Technologies illustrates how quickly technology innovations have the potential to redefine buyer, supplier and customer relationships for any business. Gartner added 16 new technologies to the Hype Cycle this year, including blockchain, machine learning, general purpose machine intelligence, smart workspace in addition to many others. Gartner identifies transparently immersive experiences, the perceptual smart machine age, and the platform revolution as three overarching trends that have the most potential to reshape business models, and provide enterprises with access to emerging markets and ecosystems. The Hype Cycle is based on an assessment of the market hype, maturity, business benefit and future direction of more than 2,000 technologies, grouped into 11 topic areas.

Let's say you are a highly-experienced professional with little to no experience in analytics. You find yourself paired with a data analyst or data scientist and are presented with some fancy modelling that makes about as much sense as quantum physics or Ancient Greek. You are clearly out of your depth but need to be able to decide whether you can back the findings and present it to senior management, or send it back for fine-tuning. You just need to be able to speak enough of their language to let them use their expertise to help you. The two common mistakes are to either entirely distrust the numbers or be possessed of a blind faith in analytics.

Artificial Intelligence (AI) leaders and experts in Enterprise Search and Knowledge Management solutions, RAVN Systems, is blending the world of tech and law with their latest AI endeavors: a self-service robot that promises big things for the legal industry. Through the use of a self-service AI portal, RAVN Systems' newest tech allows law firms to train an AI robot to perform any custom tasks necessary. The robot, known as RAVN Extract Direct, is a self-service version of the London company's older model RAVN Extract, which allowed users to use AI technology to automatically summarize, analyze, and extract key information from documents. The distinction of RAVN Extract Direct's self-service feature is integral, as it's what enables clients to be in complete control of the information obtained by the robot. RAVN's AI robot gives clients a more nuanced AI experience by allowing them to have complete control over the platform.

Artificial intelligence is everywhere, at times obscured and sometimes fully hidden. It lurks in the Facebook newsfeed algorithm that curates the news you see, it's being implemented in the programs of semi-autonomous vehicles that decide who lives in case of an accident, and it spectacularly beat the top Go champions in the world with its deep neural network technology. The applications of AI are evolving with increased sophistication, sparking considerable, complex questions related the social impact, governance, and ethics of its technology. These questions are particularly salient as accountability mechanisms for algorithms are yet in a nascent stage, where the balance of power is skewed towards industry giants who control these technologies. In this particular moment, the research, development, and deployment of AI is primarily taking place in the private sector, while governments around the world are increasingly contracting out their own use of these powerful technologies. In this context, the future role of universities emerges as one that is particularly meaningful when it comes to addressing these questions of social impact, ethics, and governance of AI.

When Facebook Messenger first proclaimed the future of commerce was messaging in April 2016, it provided companies with a viable distribution mechanism (to over 1 billion monthly active users and growing) but excluded an important element in the equation -- a discovery mechanism. Developers and brands who chose Kik as their distribution platform fared a bit better, as Kik's official Bot Shop launch allowed discoverability and curation within multiple categories, modeled after Apple's App Store. By August over 20,000 bots had been created on Kik's Bot Shop. At the time Facebook Messenger announced it was supporting payments in September, over 30,000 bots had been built on Messenger. Although that's a far cry from the 2 million-plus apps in both Apple and and Google's app stores, discoverability remained a hurdle for bot creators.

Traditional outbound marketing campaigns are far less effective at winning and retaining customers than they once were. To achieve sustainable growth in today's always-connected, real-time world, marketers must deliver continuous, customized, two-way, insight-driven interactions with customers on an individual level. Brands that understand this and put the right Systems in place to scale are creating competitive advantages that are very difficult for their competitors to replicate because it's not just about technology. Forrester Research refers to this as creating a "Contextual Marketing Engine". In their 2016 survey of 115 technology, marketing, and customer experience professionals, Forrester found that, across the board, organizations' investments revolve around implementing customer personalization initiatives, solving people's challenges, and assembling digital experience systems.

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. Over the past few weeks, we published a series of posts outlining expert opinions in data science, machine learning, artificial intelligence, and related fields. In an encore post of this series, we bring you the collected responses to an amalgam question -- including experts from all of the previous posts' fields -- while adding a second dimension this time around. I'd like to thank one of my researchers, Alekh Agarwal, for great input here. The way to increase the number of women in AI, ML and data science is two-fold. First, we must expand the definitions of the fields to include their interaction with the other sciences, including the biological and social sciences.