Traditional Data+AI systems utilize data-driven techniques to optimize performance, but they rely heavily on human experts to orchestrate system pipelines, enabling them to adapt to changes in data, queries, tasks, and environments. For instance, while there are numerous data science tools available, developing a pipeline planning system to coordinate these tools remains challenging. This difficulty arises because existing Data+AI systems have limited capabilities in semantic understanding, reasoning, and planning. Fortunately, we have witnessed the success of large language models (LLMs) in enhancing semantic understanding, reasoning, and planning abilities. It is crucial to incorporate LLM techniques to revolutionize data systems for orchestrating Data+AI applications effectively. To achieve this, we propose the concept of a 'Data Agent' - a comprehensive architecture designed to orchestrate Data+AI ecosystems, which focuses on tackling data-related tasks by integrating knowledge comprehension, reasoning, and planning capabilities. We delve into the challenges involved in designing data agents, such as understanding data/queries/environments/tools, orchestrating pipelines/workflows, optimizing and executing pipelines, and fostering pipeline self-reflection. Furthermore, we present examples of data agent systems, including a data science agent, data analytics agents (such as unstructured data analytics agent, semantic structured data analytics agent, data lake analytics agent, and multi-modal data analytics agent), and a database administrator (DBA) agent. We also outline several open challenges associated with designing data agent systems.

Medb Corcoran, Ireland lead for Accenture Labs, talks about her career journey and why data skills will be critical for businesses going forward. As the Ireland lead for Accenture Labs, Medb Corcoran oversees a team of AI researchers that seeks to address critical business problems with tools such as machine learning, natural language processing and knowledge representation. This team is based at The Dock, Accenture's R&D and innovation centre in Dublin. Corcoran is also the company's global responsible AI lead for technology innovation, helping organisations integrate a data-driven assessment of algorithmic fairness into their processes. Here, she reflects on her career and why she believes workplaces of the future will rely on data skills like never before.

If you google "how to learn skill " you're probably going to find at least one online course, youtube tutorial, book, or article covering it well. Many of these resources will even be for free. When it comes to deciding where to learn a skill, there are many opinions. The people with these opinions haven't tried every single educational product (and are maybe even trying to sell you something), so it's hard to say what the best resource is. When it comes to picking the resource, I have no recommendation, other than to not stick with it if you don't like it.

Kaggle Learn bills itself as "Faster Data Science Education," a free repository of micro-courses covering an array of "[p]ractical data skills you can apply immediately." As I'm sure you are well aware, there are all sorts of free and low-cost data science education alternatives available via numerous online platforms. So why am I feeling it necessary to write about another data science learning resource? As I plan to embark on a fresh fall learning initiative -- once Those Lazy-Hazy-Crazy Days of Summer are out of my system -- I wanted to first find some concise review material for concepts I have previously learned and skills I have already acquired but which may have gone a bit rusty on me. To be clear, Kaggle Learn does not bill its micro-courses specifically as review material; however, I am so far finding that they fit this requirement for me rather well (though, admittedly, I'm still early in the process).

The embrace of artificial intelligence has come quickly in government. In May 2017, Congress established the bipartisan Congressional Artificial Intelligence Caucus, and members have since introduced numerous pieces of AI legislation. More recently, the administration launched the American AI Initiative through a February 2019 executive order, and the Department of Defense released its own strategy on how to incorporate AI into national security. As government use of AI evolves, agency leaders will look for pathways to capitalize on opportunities, and the workforce will need new technical and social skills to succeed in AI-augmented workplaces. A new report -- More Than Meets AI: Assessing the Impact of Artificial Intelligence on the Work of Government -- aims to assist in that effort.

If that were not challenging enough for chief information officers, by then IT executive pay at most companies will be tied to business results, rather than operational performance, and CIOs that haven't already shifted their firm's systems to digital tools are likely to fail, the report said. IDC's forecast is based on an analysis of staffing data at companies worldwide in a range of industries. For IDC, shifting to digital involves more than just moving a few key business applications to the cloud. It requires CIOs to reinvent IT "from top to bottom" by putting in place new digital platforms, while modernizing legacy systems and jettisoning those that are obsolete. To stay competitive, CIOs and the companies they work will need to quickly raise their game when it comes to internal skills development and retention strategies, IDC researchers said: "IT is competing for talent with all organizations, from start-ups to global enterprises, and extreme salary increases for critical roles may decimate IT workforce budgets."

According to the World Economic Forum, computing and mathematically-focused jobs are showing the strongest growth, at the expense of less quantitative roles. So whether it's to maximize the part we play in data-driven economic growth, or simply to ensure that we and our teams remain relevant and employable, we need to think about transitioning to a more data-skewed skillset. But which skills should you focus on? Can most of us expect to keep pace with this trend ourselves, or would we be better off retreating to shrinking areas of the economy, leaving data skills to the specialists? To help answer this question, we rebooted and adapted an approach we took to prioritizing Microsoft Excel skills according to the benefits and costs of acquiring them.

Do you want to improve your R skills? Here are my favorite R language resources for users at any level. If you're just starting out with R, I (not surprisingly) recommend my Computerworld Beginner's Guide to R. It's also available as a handy Beginner's R Guide PDF download. To build on those beginner skills, R for Data Science gives readers a firm grounding in basic aspects of data analysis, from import and cleaning to visualizing and modeling. Wickham is well known for his suite of R packages dubbed the "tidyverse," and this book is designed for those who want to use tidyverse packages such as dplyr and purrr.

Data sharing and data within digital literacy were among the subjects addressed by expert witnesses during the second House of Lords select committee hearing on artificial intelligence (AI). Every conference this year contains a dead human genius reincarnated as software system or a robot. Yes, there is a lot of hype, but there is real worth in AI and Machine Learning. Read our counseling on how to avoid adopting "black box" approach. You forgot to provide an Email Address.