How is your organization preparing for artificial intelligence (AI)? Ask this question of businesses investing in this field today, and the answer almost always comes down to "data"-- with leaders talking about "data preparations" or "data science talent acquisition." Related: Which Countries Are Ready for AI Adoption? While there would be no AI without data, enterprises that fail to ready the other side of the equation-- people-- don't just stunt their capacity for good AI, they risk sunk investment and jeopardize employee trust, brand backlash or worse. After all, people are the ones building, measuring, consuming and determining the success of AI in enterprise and consumer settings.

The long and confusing set of numbers that follow every WiFi name is finally disappearing. And it is being swapped for something far more simple. Anyone who has ever tried to buy a WiFi device has probably struggled with the arcane naming system that each of them is given. Each begins with 802.11, and then is followed by letters like "ac" – it's those letters that matter, but it can be hard to work out how and why. The codes actually refer to something very useful: different generations of WiFi technology, which bring with them upgrades and updates to things like the speed they can transmit data.

Helsinki University is offering a free online Artificial Intelligence course in English to anyone, anywhere. This free Artificial Intelligence (AI) online course is made for non-technical people so no special knowledge or skills are needed to take the course. AI is embedded in so many part of our lives - this course is meant to dispel any mystery around the technology that AI uses, the impact it has on our lives and how AI will develop in the coming years. Do you wonder what AI really means? Are you thinking about the kind of impact AI might have on your job or life?

This post is a token of appreciation for the amazing open source community of Data Science, to which I owe a lot of what I have learned. For last few months, I have been working on a side project of mine to develop machine learning application on streaming data. It was a great learning experience with numerous challenges and lots of learning, some of which I have tried to share in here. This post is focused on how to deploy machine learning models on streaming data and covers all 3 necessary areas of a successful production application: infrastructure, technology, and monitoring. The first step for any successful application is to determine the technology stack in which it should be written, on the basis of business requirements. As a general rule, when the amount of data is huge, use Spark.

We present a developmental framework based on a long-term memory and reasoning mechanisms (Vision Similarity and Bayesian Optimisation). This architecture allows a robot to optimize autonomously hyper-parameters that need to be tuned from any action and/or vision module, treated as a black-box. The learning can take advantage of past experiences (stored in the episodic and procedural memories) in order to warm-start the exploration using a set of hyper-parameters previously optimized from objects similar to the new unknown one (stored in a semantic memory). As example, the system has been used to optimized 9 continuous hyper-parameters of a professional software (Kamido) both in simulation and with a real robot (industrial robotic arm Fanuc) with a total of 13 different objects. The robot is able to find a good object-specific optimization in 68 (simulation) or 40 (real) trials. In simulation, we demonstrate the benefit of the transfer learning based on visual similarity, as opposed to an amnesic learning (i.e. learning from scratch all the time). Moreover, with the real robot, we show that the method consistently outperforms the manual optimization from an expert with less than 2 hours of training time to achieve more than 88% of success.

Headed to a university near you – Disruption. But not in the way you might expect. Most believe it will be the MOOCs (Massive Open Online Courses) that forever changes the landscape of higher education – but something much more close to all of us will be the demise. For years the college degree has been the path of success. The colleges said it was the path, and the culture followed suits pouring their hard earned (or borrowed) money into a college education for us and our children.

The benefits of artificial intelligence – speed, accuracy, and automation of mundane tasks – take on new importance as patients demand the same levels of service and expectations from the healthcare industry as other consumer services. In this keynote from the Health:Further conference, Trust Insights co-founder Christopher Penn shares how today's commercially-available, immediately-applicable AI and machine learning technologies will change the healthcare industry's future. Learn how techniques like driver analysis, time-series forecasting, natural language processing, and intelligent conversation will deepen healthcare's understanding of the voice of the patient, and learn what parts of healthcare are highly unlikely to be automated. Complete this short form to access the video, slides, and transcript.

If the math behind data science is an enigma, the Khan Academy is a great place for insight. There are courses for different levels, and Sal Khan's relaxed delivery will get you through even the most difficult concepts (I think I have a small crush on him after the hours I've spent listening to his narratives!).

Given the rise of smart electricity meters and the wide adoption of electricity generation technology like solar panels, there is a wealth of electricity usage data available. This data represents a multivariate time series of power-related variables, that in turn could be used to model and even forecast future electricity consumption. In this tutorial, you will discover a household power consumption dataset for multi-step time series forecasting and how to better understand the raw data using exploratory analysis. How to Load and Explore Household Electricity Usage Data Photo by Sheila Sund, some rights reserved. The Household Power Consumption dataset is a multivariate time series dataset that describes the electricity consumption for a single household over four years. The data was collected between December 2006 and November 2010 and observations of power consumption within the household were collected every minute. Active and reactive energy refer to the technical details of alternative current.

This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We consider a typical workflow and how probabilistic programming languages can help to automate this workflow, especially in the matching of models with inference methods. We focus on two properties of a model that are critical in this matching: its structure---the conditional dependencies between random variables---and its form---the precise mathematical definition of those dependencies. While the structure and form of a probabilistic model are often fixed a priori, it is a curiosity of probabilistic programming that they need not be, and may instead vary according to random choices made during program execution. We introduce a formal description of models expressed as programs, and discuss some of the ways in which probabilistic programming languages can reveal the structure and form of these, in order to tailor inference methods. We demonstrate the ideas with a new probabilistic programming language called Birch, with a multiple object tracking example.