Data science is a pillar of artificial intelligence (AI), which is transforming nearly every domain of human activity, from the social and physical sciences to engineering and medicine. While data-driven findings in AI offer unprecedented power to extract insights and guide decision-making, many are difficult or impossible to replicate. A key reason for this challenge is the uncertainty introduced by the many choices made throughout the data science life cycle (DSLC). Traditional statistical frameworks often fail to account for this uncertainty. The Predictability-Computability-Stability (PCS) framework for veridical (truthful) data science offers a principled approach to addressing this challenge throughout the DSLC. This paper presents an updated and streamlined PCS workflow, tailored for practitioners and enhanced with guided use of generative AI. We include a running example to display the PCS framework in action, and conduct a related case study which showcases the uncertainty in downstream predictions caused by judgment calls in the data cleaning stage.

The advent of foundation models (FMs) such as large language models (LLMs) has led to a cultural shift in data science, both in medicine and beyond. This shift involves moving away from specialized predictive models trained for specific, well-defined domain questions to generalist FMs pre-trained on vast amounts of unstructured data, which can then be adapted to various clinical tasks and questions. As a result, the standard data science workflow in medicine has been fundamentally altered; the foundation model lifecycle (FMLC) now includes distinct upstream and downstream processes, in which computational resources, model and data access, and decision-making power are distributed among multiple stakeholders. At their core, FMs are fundamentally statistical models, and this new workflow challenges the principles of Veridical Data Science (VDS), hindering the rigorous statistical analysis expected in transparent and scientifically reproducible data science practices. We critically examine the medical FMLC in light of the core principles of VDS: predictability, computability, and stability (PCS), and explain how it deviates from the standard data science workflow. Finally, we propose recommendations for a reimagined medical FMLC that expands and refines the PCS principles for VDS including considering the computational and accessibility constraints inherent to FMs.

Chris Winfield, founder of Understanding A.I., tells'Fox & Friends Weekend' host Will Cain about a study showing patients preferred medical answers from artificial intelligence over doctors. Artificial intelligence fails to match humans in judgment calls and is more prone to issue harsher penalties and punishments for rule breakers, according to a new study from MIT researchers. The finding could have real world implications if AI systems are used to predict the likelihood of a criminal reoffending, which could lead to longer jail sentences or setting bail at a higher price tag, the study said. Researchers at the Massachusetts university, as well as Canadian universities and nonprofits, studied machine-learning models and found that when AI is not trained properly, it makes more severe judgment calls than humans. Human participants then labeled the photos or text, with their responses used to train AI systems.

If you spend any time on the internet, you're likely now familiar with the gray-and-teal screenshots of AI-generated text. At first they were meant to illustrate ChatGPT's surprising competence at generating human-sounding prose, and then to demonstrate the occasionally unsettling answers that emerged once the general public could bombard it with prompts. OpenAI, the organization that is developing the tool, describes one of its biggest problems this way: "ChatGPT sometimes writes plausible-sounding but incorrect or nonsensical answers." In layman's terms, the chatbot makes stuff up. As similar services, such as Google's Bard, have rushed their tools into public testing, their screenshots have demonstrated the same capacity for fabricating people, historical events, research citations, and more, and for rendering those falsehoods in the same confident, tidy prose.

Digital marketers still have a job of course. But it is not going to be quite the same job, as artificial intelligence begins its "second act". Yes, AI is still good at compiling, sorting and categorizing massive amounts of data. Only now it's increasingly able to assist in creating content in ways it could not before. All you need to do is give an AI app a specific input.

MIT researchers have created a new system that automatically cleans "dirty data" -- the typos, duplicates, missing values, misspellings, and inconsistencies dreaded by data analysts, data engineers, and data scientists. The system, called PClean, is the latest in a series of domain-specific probabilistic programming languages written by researchers at the Probabilistic Computing Project that aim to simplify and automate the development of AI applications (others include one for 3D perception via inverse graphics and another for modeling time series and databases). According to surveys conducted by Anaconda and Figure Eight, data cleaning can take a quarter of a data scientist's time. Automating the task is challenging because different datasets require different types of cleaning, and common-sense judgment calls about objects in the world are often needed (e.g., which of several cities called "Beverly Hills" someone lives in). PClean provides generic common-sense models for these kinds of judgment calls that can be customized to specific databases and types of errors.

It is thought-provoking to read the pair of articles on 10 challenges in data science by Xuming He and Xihong Lin from a statistics perspective and Jeannette Wing from a computer science perspective. Unsurprisingly, there is a good overlap of important topics including multimodal and heterogenous data, data privacy, fairness and interpretability, and causal inference or reasoning. This overlap reflects and confirms the foundational and shared roles of statistics and computer science in data science, which is the merging of statistical and computing thinking in the context of solving domain problems. The challenges in both articles are presented as separate, not integrated, topics, and mostly decoupled from domain problems, possibly because of the mandate of "10 challenges." In my mind, the most exciting 10 challenges in data science are to solve 10 pressing real-world data problems with positive impacts. For example, how is data science going to help control covid-19 spread while allowing a healthy economy?

Are robots coming for your job? According to a Dell Technologies survey, 82% of leaders expect their employees and machines to work as "integrated teams". And many employees look forward to artificial intelligence (AI) that can help them do their job better. But not everybody has such a rosy outlook. In Australia, the report "Australia's Future Workforce" predicts about 40% of jobs could be lost to robotics, automation and artificial intelligence in the next 10-15 years.

Softbank has bet $300 million, with more to come, that robotic process automation (RPA) will be what brings artificial intelligence (AI) in the enterprise. Robotic Process Automation (or RPA) is one of the hottest areas in the enterprise technology sector these days, reaching $1.3 billion this year, says Gartner. According to the market research firm, RPA software revenue grew 63.1% in 2018 to $846 million, making it the fastest-growing segment of the global enterprise software market, with the top-five RPA vendors (UiPath, Automation Anywhere, Blue Prism, NICE, Pegasystems) controlling 47% of the market. North America continues to dominate the RPA software market, with a 51% share in 2018, followed by Western Europe, while Japan came in third, with adoption growth of 124% in 2018. "This shows that RPA software is appealing to organizations across the world, due to its quick deployment cycle times, compared with other options such as business process management platforms and business process outsourcing," said Fabrizio Biscotti, research vice president at Gartner.

What will it be like when machines make and execute decisions without any human intervention? Why would we make such systems and what are their implications for the future of human judgment and free will? Hundreds, if not thousands, of science fiction stories tell us it's a bad idea to build automated systems without "Human-in-the-loop" (HITL) processes for keeping them in check. In real life, the need for human intervention before executing an automated process is most obvious when it has serious, irreversible consequences: like killing a person with a drone. In high stakes situations like drone strikes, humans make the difficult judgment call before the weapon's deadly automation kicks in.