Lifeboat Foundation

For airliners, cargo ships, nuclear power plants and other critical technologies, strength and durability are essential. This is why many contain a remarkably strong and corrosion-resistant alloy called 17–4 precipitation hardening (PH) stainless steel. Now, for the first time ever, 17–4 PH steel can be consistently 3D-printed while retaining its favorable characteristics.

A team of researchers.

Artificial intelligence researchers are doubling down on the concept that we will see artificial general intelligence (AGI) — that’s AI that can accomplish anything humans can, and probably many we can’t — within our lifetimes.

Responding to a pessimistic op-ed published by TheNextWeb columnist Tristan Greene, Google DeepMind lead researcher Dr. Nando de Freitas boldly declared that “the game is over” and that as we scale AI, so too will we approach AGI.

Greene’s original column made the relatively mainstream case that, in spite of impressive advances in machine learning over the past few decades, there’s no way we’re gonna see human-level artificial intelligence within our lifetimes.

Chinese scientists are reported to have developed a fully-automated, pig-cloning robot, and the AI-powered bot is apparently way more efficient than humans.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Watch here.

Every time someone invents a new standard, the rest of the world is compelled to comply: VHS to DVD, DVD to Blu-ray, or Blu-ray to streaming. Change is inexorable, but it can also be glacially slow. Converting the tech geeks and conspicuous consumers is easy, but getting the long tail of ordinary users to embrace the “new paradigm” is fiendishly hard — and so, it is with Web3.

We’re living through a transitional time when the slow deprecation of one standard is being countered with the mainstreaming of another. To say that red pilling the masses on the wonders of Web3 has been tough would be an understatement, however. At this stage, it cannot even be said for certain that Web3 will replace its numerical predecessor — rather, it is more of an if. For Web2 to go the way of the VHS and fully transition to a new format — much needs to happen first.

So how should companies begin to think about ethical data management? What measures can they put in place to ensure that they are using consumer, patient, HR, facilities, and other forms of data appropriately across the value chain—from collection to analytics to insights?

We began to explore these questions by speaking with about a dozen global business leaders and data ethics experts. Through these conversations, we learned about some common data management traps that leaders and organizations can fall into, despite their best intentions. These traps include thinking that data ethics does not apply to your organization, that legal and compliance have data ethics covered, and that data scientists have all the answers—to say nothing of chasing short-term ROI at all costs and looking only at the data rather than their sources.

In this article, we explore these traps and suggest some potential ways to avoid them, such as adopting new standards for data management, rethinking governance models, and collaborating across disciplines and organizations. This list of potential challenges and remedies is not exhaustive; our research base was relatively small, and leaders could face many other obstacles, beyond our discussion here, to the ethical use of data. But what’s clear from our research is that data ethics needs both more and sustained attention from all members of the C-suite, including the CEO.

We directly experience 3 spatial and 1 temporal dimensions (3+1). But here’s an interesting observation made by Kaluza and Klein almost a century ago: If you write the equations of general relativity in (4+1) dimensions, but imagine one spatial di…

Einstein did it the hard way, but there’s a much easier approach to relativity.

A whole monkey brain is imaged at high resolution in 100 hours.