Data is required to develop forecasting models for use in Model Predictive Control (MPC) schemes in building energy systems. However, data usage incurs costs from both its collection and exploitation. Determining cost optimal data usage requires understanding of the forecast accuracy and resulting MPC operational performance it enables. This study investigates the performance of both simple and state-of-the-art machine learning prediction models for MPC in a multi-building energy system simulation using historic building energy data. The impact of data usage on forecast accuracy is quantified for the following data efficiency measures: reuse of prediction models, reduction of training data volumes, reduction of model data features, and online model training. A simple linear multi-layer perceptron model is shown to provide equivalent forecast accuracy to state-of-the-art models, with greater data efficiency and generalisability. The use of more than 2 years of training data for load prediction models provided no significant improvement in forecast accuracy. Forecast accuracy and data efficiency were improved simultaneously by using change-point analysis to screen training data. Reused models and those trained with 3 months of data had on average 10% higher error than baseline, indicating that deploying MPC systems without prior data collection may be economic.

From spiraling claim volumes to more stringent payer requirements and increasing reporting obligations, today's health practitioners face higher challenges than ever. As a result, diagnostics providers across the country are effectively looking for new revenue streams, improved payer relations, cost-cutting opportunities, and more predictable reimbursement rates. For example, high-quality and carefully designed data sets facilitate improved analytics, and the use of Artificial Intelligence (AI) can expedite the Revenue Cycle Management (RCM) process while also enhancing financial and operational performance. Council for Affordable Quality Healthcare, Inc. (CAQH) used data from medical plans representing nearly half of the US insured population to find that electronic prior authorizations adoption rose by just one percentage point to 13 percent from 2018 to 2019. For years, visionary healthcare providers have been using technology to improve care for people suffering from sleep problems, eye illness, cancer, and now, even COVID-19.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. The key to getting more value from industrial internet of things (IIoT) and IoT platforms is getting AI and machine learning (ML) workloads right. Despite the massive amount of IoT data captured, organizations are falling short of their enterprise performance management goals because AI and ML aren't scaling for the real-time challenges organizations face. If you solve the challenge of AI and ML workload scaling right from the start, IIoT and IoT platforms can deliver on the promise of improving operational performance. More organizations are pursuing edge AI-based initiatives to turn IoT's real-time production and process monitoring data into results faster.

With food at the core of the business, Glovo delivers any product within your city at any time of day. Our vision and ambition are not only to give everyone easy access to everything in their city, but it is also to offer our employees the job of their lives. A job where you'll be challenged and have the most fun working in through tech-enabled experiences. Your work-life opportunity: Glovo is looking for a Business Analyst for the Global Partner Operations team. You will join a team of project managers and fellow analysts aiming to deconstruct operational performance of our Partners and deploy creative solutions to ensure a great experience for Glovo customers.

The perpetual penetration of new-age technology is demanding a need for DevOps intelligence in the entire software development lifecycle. From development to delivery, product companies have transitioned their approach. Traditional waterfall has been replaced by agile, DevOps is superseded by DevSecOps. However, it is worth noting that the roles served by Agile and DevOps are complementary. By combining the collective efforts of Agile and DevOps to incorporate CI/CD, product companies are ensuring regular software updates throughout the year rather than having just one major release.

Similarly, an Indian travel start-up, RailYatri, has created an Estimated Arrival Time prediction algorithm using Machine Learning and statistical modelling techniques to predict the arrival time of trains. The system, trained on historical data, can provide customers with realistic estimated times for the arrival of their trains. According to Kapil Raizada, Cofounder of RailYatri, the method to predict the arrival time of trains in India had not changed over decades and was typically based on a distance by speed ratio for trains with some buffer time. RailYatri's Machine Learning algorithm takes into considerations other parameters ("ground realities") such as increasing traffic, rush, seasonality, etc, and adapts as it learns from subsequent inputs, making the predictions better with time. It uses clustering techniques to organise historical train runs into thousands of patterns where time series data attributes are similar.

There's been a lot of media attention in recent years about how artificial intelligence (AI) and machine learning (ML) are going to change the world--how they're going to create new and interesting applications in fields as diverse as education, law, health care and transportation. But if I had to bet on a use case where AI and ML will create a tangible, lasting impact, I'm putting my chips on DevOps. DevOps is all about automation of tasks. Its focus is on automating and monitoring every step of the software delivery process, ensuring that work gets done quickly and frequently. While it doesn't eliminate human tasks--far from it--it does encourage enterprises to set up repeatable processes that promote efficiency and reduce variability.

I think we can safely add "travel agent" to the list of anachronisms that have gradually receded from our collective consciousness. One needn't look far to see the humble travel agent's replacement: hundreds of websites and mobile applications from which one can research options, read reviews and make reservations. In the next few years, though, even those websites and apps may face the same fate as your neighborhood travel agent. Sure, you may find yourself back chatting with an "agent" again, but this time the "person" on the other end will be an AI-powered bot that understands your needs and preferences, can hunt down the best deal and place your reservations. We're entering an era where machines will help us make sense of the vast amounts of information and get things done for us -- bots, robots, assistants.

Before the internet, if you wanted to book a flight or hotel, you picked up the phone and called an agent. The internet changed all that. It gave you hundreds of websites and mobile applications to research options, read reviews and make reservations. In the next few years, you'll be picking up the phone and chatting with an agent again -- but this time, you'll be talking to an AI-powered bot that will understand your needs and preferences, hunt down the best deal and take care of your reservations. We're entering an era where machines will help us make sense of the vast amounts of information and get things done for us -- bots, robots, assistants.

Is it too soon to do reviews and forecasts for the New Year? I noticed another contributor to SA has offered a forecast for Palo Alto (NYSE:PANW) and its prospective performance for the coming year. Several brokerage analysts have offered their evaluation of the year ahead for IBM (NYSE:IBM). The old year is passing at a frightful pace, and it means that all valuation metrics need to be adjusted for the year ahead. One of the interesting things about IBM is that its headline metrics are not expected to change much in 2017, with earnings expected to rise and revenues expected to show marginal shrinkage, how much credence should investors place in the consensus? How should investors look at prospects for IBM's dividend? How should investors value a company without growth, or will there be growth in the future? Of course, I do not hold all the answers. But I think as 2017 approaches, it might be worthwhile for me to express my point of view about what to expect going forward. I think that IBM is most likely to miss and/or guide down for a variety of reasons that I outline in the foregoing article.