What is the true heart of mental healthcare -- innovation or humanity? Can virtual therapy ever replicate the profound human bonds where healing arises? As artificial intelligence and immersive technologies promise expanded access, safeguards must ensure technologies remain supplementary tools guided by providers' wisdom. Implementation requires nuance balancing efficiency and empathy. If conscious of ethical risks, perhaps AI could restore humanity by automating tasks, giving providers more time to listen. Yet no algorithm can replicate the seat of dignity within. We must ask ourselves: What future has people at its core? One where AI thoughtfully plays a collaborative role? Or where pursuit of progress leaves vulnerability behind? This commentary argues for a balanced approach thoughtfully integrating technology while retaining care's irreplaceable human essence, at the heart of this profoundly human profession. Ultimately, by nurturing innovation and humanity together, perhaps we reach new heights of empathy previously unimaginable.

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Once, on a crisp cloudless morning in early fall, a machine learning engineer left her home to seek the answers that she could not find, even in the newly-optimized Google results. She closed her laptop, put on her backpack and hiking boots, and walked quietly out her door and past her mailbox, down a dusty path that led past a stream, until the houses around her gave way to broad fields full of ripening corn. She walked past farms where cows grazed peacefully underneath enormous data silos, until the rows of crops gave way to a smattering of graceful pines and oaks, and she found herself in a forest clearing, headed into the woods. She went deeper through the decision trees and finally stopped near a data stream around midday to have lunch and stretch her legs. The sun made its way through the sky and eventually, she walked further, out of the forest.

Implementing AI responsibly implies adopting AI in a manner that's ethical, transparent, and accountable as well as consistent with laws, regulations, norms, customer expectations, and organizational values. "Responsible AI" promises to guard against the use of biased data or algorithms, providing an assurance that automated decisions are justified and explainable. According to Boston Consulting Group (BCG), less than half of enterprises that achieve AI at scale have fully mature, responsible AI deployments. Organizations' AI programs commonly neglect the dimensions of fairness and equity, social and environmental impact, and human-AI cooperation, BCG analysts found. The Responsible AI Institute (RAI) is among the consultancies aiming to help companies realize the benefits of AI implemented thoughtfully.

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Deriving scientific insights from artificial intelligence methods requires adhering to best practices and moving beyond off-the-shelf approaches. Artificial intelligence (AI) methods have emerged as useful tools in many Earth science domains (e.g., climate models, weather prediction, hydrology, space weather, and solid Earth). AI methods are being used for tasks of prediction, anomaly detection, event classification, and onboard decision-making on satellites, and they could potentially provide high-speed alternatives for representing subgrid processes in climate models [Rasp et al., 2018; Brenowitz and Bretherton, 2019]. Although the use of AI methods has spiked dramatically in recent years, we caution that their use in Earth science should be approached with vigilance and accompanied by the development of best practices for their use. Without best practices, inappropriate use of these methods might lead to "bad science," which could create a general backlash in the Earth science community against the use of AI methods.

Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. Computer software teaches customer service agents how to be more compassionate, schools use machine learning to scan for weapons and mass shooters on campus, and doctors use AI to map the root cause of diseases. Sectors such as cybersecurity, online entertainment and retail use the tech in combination with wide swaths of customer data in revolutionary ways to streamline services. Though these applications may seem harmless, perhaps even helpful, the AI is only as good as the information fed into it, which can have serious implications. You might not realize it, but AI helps determine whether you qualify for a loan in some cases.

One Georgia school district plans to spend $16.5 million to install artificial intelligence-powered surveillance cameras in its roughly 100 buildings in coming years. Businesses across almost every industry deploy artificial intelligence to make jobs simpler for staff and tasks easier for consumers. Computer software teaches customer service agents how to be more compassionate, schools use machine learning to scan for weapons and mass shooters on campus, and doctors use AI to map the root cause of diseases. Sectors such as cybersecurity, online entertainment and retail use the tech in combination with wide swaths of customer data in revolutionary ways to streamline services. Though these applications may seem harmless, perhaps even helpful, the AI is only as good as the information fed into it, which can have serious implications.

A growing number of organizations, from the smallest startups to the largest enterprises, all often bring up the same problem in our conversations with them: Even though they want to drive operating costs down, they also need to execute on an increasing volume of work. Some companies respond by deploying more systems. Others try throwing more people at the problem, like third-party contractors. Neither solution is ultimately productive, and both often create operational bottlenecks. We're seeing new AI solutions pop up in various respective industries to tackle the growing issues bubbling up within highly complex working groups.

Traffic signals serve to regulate the worst bottlenecks in highly populated areas but are not always very effective. Researchers at Penn State are hoping to use deep reinforcement learning to improve traffic signal efficiency in urban areas, thanks to a one-year, $22,443 Penn State Institute for CyberScience Seed Grant. Urban traffic congestion currently costs the U.S. economy $160 billion in lost productivity and causes 3.1 billion gallons of wasted fuel and 56 billion pounds of harmful CO2 emissions, according to the 2015 Urban Mobility Scorecard. Vikash Gayah, associate professor of civil engineering, and Zhenhui "Jessie" Li, associate professor of information sciences and technology, aim to tackle this issue by first identifying machine learning algorithms that will provide results consistent with traditional (theoretical) solutions for simple scenerios, and then building upon those algorithms by introducing complexities that cannot be readily addressed through traditional means. "Typically, we would go out and do traffic counts for an hour at certain peak times of day and that would determine signal timings for the next year, but not every day looks like that hour, and so we get inefficiency," Gayah said.