Drugs are a cornerstone of medicine, but sometimes doctors make mistakes when prescribing them and patients don't take them properly. A new AI tool developed at Oxford University aims to tackle both those problems. DrugGPT offers a safety net for clinicians when they prescribe medicines and gives them information that may help their patients better understand why and how to take them. Doctors and other healthcare professionals who prescribe medicines will be able to get an instant second opinion by entering a patient's conditions into the chatbot. Prototype versions respond with a list of recommended drugs and flag up possible adverse effects and drug-drug interactions.

When the robots rise up, they won't take your life. They'll take your job, particularly those in fields primed for automation, like manufacturing, trucking, and customer service. Technologists, economists, and policymakers believe this future is all but inevitable, and say it's time to begin thinking seriously about how to ensure artificial intelligence advances humanity--and improves the economy, without leaving the middle class behind. Two economists who recently left Washington say the answer lies in ensuring the government provides enough of a safety net to help middle class Americans navigate the coming transition. Jason Furman and Gene Sperling--former chief economic advisors to President Obama--prefer to think of it as a bridge, not a net, that will help people reach the future.

Here comes the million-dollar question. Which worker is more dangerous to your business? On a high-stakes task, the answer could be Ronnie Reliable… but perhaps not for the first reason that comes to mind. In another article, I've pointed out that ultra-reliable workers can be dangerous when the decision-maker is deranged. They "just follow orders" even if those orders are terrible, so they can amplify incompetence (or malice).

When training a model, there is always a chance that something might fail unexpectedly. Proper checkpointing provides a safety net during failures that enables users to restore the state of the model and trainer from a checkpoint file. In Lightning, checkpointing is a core feature in the Trainer and is turned on by default to create a checkpoint after each epoch. But checkpointing provides more than just a safety net in case of failure. Often we care about keeping track of the "best" model weights encountered during the course of training, because in practice not every new epoch leads to an improved generalization error (unstable optimization, overfitting).

During his recent keynote address to the National Conservative Conference, Republican Sen. Josh Hawley of Missouri brought attention to the crisis of a marginalized and long-forgotten group: men. "Over the last 30 years and more, government policy has helped destroy the kind of economy that gave meaning to generations of men," he said, describing low wages and corporate consolidation brought on by globalization. The result, he said, is "more and more men are withdrawing into the enclaves of idleness, and pornography, and video games." Hawley's remarks were immediately met with derision, criticism, and exasperation: Here was another conversative--a presidential hopeful no less--hand-wringing over pornography, another traditionalist subscribing to outdated gender norms by saying "a man is a father, a man is a husband, a man is someone who takes responsibility," and another male politician cautioning that a supposed liberal attack on manhood was at the root of this rot. Here's the issue: Hawley is partially right.

Does this have to be the way? Artificial intelligence was supposed to boost productivity and create better futures in medicine, transportation, and workplaces. Instead, AI research and development has focused on only a few sectors, ones that are having a net negative impact for humanity, MIT economistDaron Acemogluargues in "Redesigning AI," a Boston Review book. "Our current trajectory automates work to an excessive degree while refusing to invest in human productivity; further advances will displace workers and fail to create new opportunities," Acemoglu writes. AI also threatens "democracy and individual freedoms," he writes.

Here comes the million-dollar question. Which worker is more dangerous to your business? On a high-stakes task, the answer could be Ronnie Reliable… but perhaps not for the first reason that comes to mind. In another article, I've pointed out that ultra-reliable workers can be dangerous when the decision-maker is deranged. They "just follow orders" even if those orders are terrible, so they can amplify incompetence (or malice).

Teamwork isn't just a human characteristic: Colonies of army ants will form living'scaffolding' to protect members from falling. The insects are blind and have no designated leader but, according to new research, they're able to use simple behavioral rules to develop these safety structures without the need for direct communication. Once a scaffold was built, worker ants were almost 100 percent protected from falling off steep inclines. Understanding how they design such complex structures could help engineers development self-healing materials and swarm robotics, researchers said. Army ants in Central American rainforests will build scaffolds out of their body to help them traverse steep terrain.

This blog post generated a lot of discussion on Hacker News -- many people have reached out to me giving more examples of the stagnation and more examples of projects avoiding it. Maybe I'll add to this article or maybe I'll write a new one, let's see what happens. In the meantime if you can't wait for me to stop staring at the ceiling and write something new, I'm pretty sure you'll enjoy my e-book robotoverlordmanual.com Academics think of themselves as trailblazers, explorers -- seekers of the truth. Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering.

Robots might be taking over the blue-collar jobs of less-educated Americans, but artificial intelligence (AI) is poised to shake up college-educated employees in higher paying jobs, leaving no worker immune to the impact of technology on the American workforce. "[AI] will be used more extensively by the most high-paid and many of the best-educated workers," says Mark Muro, a senior fellow at the Brookings Institute. "Automation has usually tended to affect lower-pay workers. AI is going to be highly prevalent in the middle class, white collar office. It was surprising to see how clearly that jumped out."