In recent years, as the sophistication and capabilities of large language models (LLMs) have grown, so have the tasks for which they're applicable, going beyond information extraction and synthesis15 to include analysis, content creation, and reasoning.8 Unsurprisingly, many researchers find them useful for research tasks, such as identifying relevant papers,19 synthesizing literature reviews,3 writing proposals,11 and analyzing data.31 They have also been found effective for investigative tasks, such as drug discovery.35 There is growing concern, however, that a large portion of this success hinges on prompt engineering, which is often an ad-hoc method to revise prompts being fed into an LLM to achieve a desired response or analysis.24 LLMs are increasingly being used in scientific research, but their application often involves ad-hoc decisions that can impact research quality.

With the ever increasing complexity of Industry 4.0 systems, plant energy management systems developed to improve energy sustainability become equally complex. Based on a Model-Based Systems Engineering analysis, this paper aims to provide a general approach to perform holistic development of an autonomous energy management system for manufacturing industries. This Energy Management System (EMS) will be capable of continuously improving its ability to assess, predict, and act, in order to improve by monitoring and controlling the energy sustainability of manufacturing systems. The approach was implemented with the System Modeling Language (SysML).

Artificial Intelligence-powered product recommendations and a more scientific approach to data has seen IKEA lift average order value (AOV) by 2% worldwide. Here Albert Bertlisson, head of engineering at Edge at IKEA Retail (Ingka Group) explains how the company did it. "At IKEA we have multiple places in our customer journey in various channels where different kinds of personalisation can deliver a superior customer experience," he says. "After a while in the broader'recommendations' team there was a decision to split the team to have one sub-team focused on product recommendations. The pandemic altered customer behaviour and needs as well. At that inflection point we decided to change our way of working and dive head-first into a more scientific approach to handle the operational complexities of delivering high quality product recommendations at scale. We deemed this necessary to improve our level of personalization and to have a holistic understanding of our customers."

When it comes to successful data science for SEO, nothing is more important than having the right team in place. Challenges in obtaining and ensuring the consistency of the data, as well as in your choice of machine learning models and in the associated analyses, all benefit from having team members with different skill sets collaborating to solve them. This article presents the three main types of teams, who is on them, and how they work. Let's open the floor with that loneliest of data science SEO professionals -- the team of one. The one-person team is often the reality in small and large structures alike.

What it takes for businesses to succeed has changed dramatically in the last decade, and the 2020s portend even more change. Many organizations will need to undergo fundamental changes to keep up with evolving technology and competition. But traditional transformation practices, generally grounded in subjective rules of thumb and one-size-fits-all programs, will not be sufficient. Our work with companies pursuing large-scale change suggests they will need a more scientific and evidence-based approach to change. Many companies today are undergoing transformation in order to apply AI and machine learning to product development, operations, marketing, and other operational aspects of the business.

As we enter 2019, it's more clear than ever that data isn't going to simply play a supporting role – not for any industry, and certainly not for recruitment, where it may well take center stage. Artificial intelligence, machine learning, scientific approaches to assessment, talent intelligence, and other areas are dependent on it, so to keep up, recruitment firms must have a wealth of actionable information to draw on. To do so effectively, they must appoint data-driven business leaders. Just as many firms have a C-Suite, modern recruitment firms should have a D-Suite – where analyzing information and using insight to improve key business processes are treated as urgently as sourcing candidates; pleasing clients while fulfilling other critical business functions. If you're running a recruitment firm, here are three reasons to appoint some data-driven leaders.

The surge of big data and challenge of confirmation bias, lead data scientists to seek a methodological approach to uncover hidden insights. In predictive analytics, they often turn to machine learning to save the day. Machine learning seems an ideal candidate to handle big data using training sets. It also enjoys a strong scientific scent by making data driven predictions. But is machine learning really bias free? And how can we leverage this tool more consciously?

Should we be afraid of artificial intelligence? For me, this is a simple question with an even simpler, two letter answer: no. But not everyone agrees – many people, including the late physicist Stephen Hawking, have raised concerns that the rise of powerful AI systems could spell the end for humanity. Clearly, your view on whether AI will take over the world will depend on whether you think it can develop intelligent behaviour surpassing that of humans – something referred to as "super intelligence". So let's take a look at how likely this is, and why there is much concern about the future of AI.

Every time a startup surpasses their goal, an angel gets its wings? Well not exactly, but it does receive an impressive amount of press coverage. The latest Kickstarter wonder kid is Ministry of Supply. Of their original goal, which was €58,560, they have, with the help of 1,013 backers, already reached €245,289 with 26 days still to go. The latest tech buzz has been about Ministry of Supply's functional and voice-controlled intelligent outerwear.