This article explores the potential impact machine coworkers like robots, low-code tools and plug-and-play automation systems are just beginning to have on jobs.

The EU is participating in an international data ethics process as it proposes new bills that would "allow consumers to sue companies for damages—if they can prove that a company’s AI harmed them." This could cause a stifling impact on innovation—but it also could be a major tool to prevent algorithmic bias and other downsides of poor AI.

When self-driving cars cause harm, who is responsible? This problem exploration looks into the ethics, data and complexity of manufacturing AI.

The text-processing engine GPT-3 (by OpenAI) learned the worst biases of humans amplified by the internet. See how quickly things went wrong to see the importance of future AIs growing up right.

Network effects—network size, quality and growth rate—are critical to track for exponential projects and predict future paradigm changes. How do companies actually do it? VC firm Andreessen Horowitz explains.

Get a nine-minute quick take on what NFTs are and why they could transform economies around content creators.

Uploading one's mind to a computer, also known as whole brain emulation or brain uploading, is a theoretical concept in transhumanism and futurism that proposes to transfer the entirety of a person's consciousness, memories, and personality into a digital substrate, such as a computer or a robotic body. What could go right? What could go wrong? Kurzgesagt's thinkers and animators help us conceive of what some see as nirvana and others see as insanity.

In their prescient TED talk "We Are All Cyborgs Now," Amber Case, a cyborg anthropologist, argues that integration of technology into our daily lives has made us all cyborgs. She defines a cyborg as an organic being that uses technology to extend its physical and mental capabilities, and believes that our smartphones, computers, and other devices have become integral parts of our identity.

As AI becomes integrated into society, there is growing concern about how these technologies may affect individual and human privacy, human rights, and societal values. But what about the rights of the machines? This rich visual journey explores various facets of the idea.

Nanotechnology is often heralded as the answer to scarcity. Nanotech could mean abundant food, shelter, water, and the like. Diamond Age explores how old mental models of hierarchy and scarcity could still shape a world of abundant resources like AI and nanotech—and how tech could be appropriated by the poor to turn the tables.

In A.I., we see what might happen if humanoid robots (androids) were to encounter a lost child in need of help. What would their initial programming guide them to do—and how might they evolve in response to the very human experiences they are all having?

What if your Siri, Google Assistant, or Alexa became sentient—and became your friend? What if you fell in love with them—and they with you? If they had the ability to become exponentially intelligent, and you didn't, what might happen? This film explores what happens when an everyday person and an AI develop feelings for each other.

I, Robot, loosely based on a classic Isaac Asimov sci-fi short story, asks the question of how we would investigate crimes committed by machines.Asimov's original story forwarded the idea of the 'three laws of robotics:'"First Law: A robot may not injure a human being or, through inaction, allow a human being to come to harm.Second Law: A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.Third Law: A robot must protect its own existence as long as such protection does not conflict with the First or Second Law."What problems do you see with those laws? How could harm 'sneak through the cracks?'

Are you a nerd for cyborg anthropology? Read a discussion of the main points of Donna Haraway's classic 'Cyborg Manifesto!' (Might be a little densely academic).

Many of us are digitally fluent in the basic types of AI in today's headlines about ChatGPT and DALL-E, but want to know "why now?" This piece by Haomiao Huang dives into the… not-too-deep end? of why these 'generative' AI models have reached an inflection point. Unpacking the recent history and major network effects of the underlying models, datasets, and computing power, it's a great read on the trends in the field and why certain breakthroughs all seem to be happening at once.

What if you were already a cyborg - a combination of human and machine? The Cyborg Manifesto explores the interlocking relationships between technology, power, and culture and is considered a fundamental text in futurist literature. (Note: dense academic text).

What would it mean if we could project a simulacrum of our dead loved ones? A new tech field is emerging, with major implications for how we process grief, retain generational knowledge, and ethically navigate our concept of those who have passed.

Technology shapes, and is shaped by us. Developing ethical frameworks for the use and development of technologies is critical in establishing futures that are equitable, and kind, and thwart fascism.

Eli Pariser, author of Filter Bubble, talks about the importance of being mindful of the incentives of commercial algorithms, which are biased towards attracting users to spend more and more time on platforms—but are not necessarily designed to have a balanced variety of viewpoints.

Dollarstreet is a project of the myth-busting data site Gapminder. The site makes wealth disparity clearer by posing a set of uniform questions (and photo prompts) for households around the world, rich and poor. Explore the site to unlearn some of your assumptions about what poverty (and wealth) look like in different contexts.

Are machines coming for you and your jobs? Distinguish between automation of the industrial era and now to better understand our trajectory and the future of human (and machine) work.

Apple (and its visionary Steve Jobs) used very intentional language to introduce their revolutionary new iPhone in 2007—bridging familiar and unfamiliar concepts by using a kind of 'horseless carriage' concept that led to powerful unlearnings about the limits of mobile tech.

A great TED talk showing how quantum computing works in terms accessible to those of use who aren't quantum physicists.

This crowdsourced list of ways to unlearn things is a great (and diverse) starting point to find everyday strategies to intentionally adjust your biases and counteract social media's 'filter bubble.'

Before you begin a journey to "unlearn racism" you must first learn about it's history and development as a concept and a tool of political oppression. This article explores these histories while also examining the mindsets and motivations why individuals and groups would take on this task.

In Doha, Qatar, at a TED conference sponsored largely by the Queen of Qatar, I saw this great talk delivered by expert statistician (and storyteller) Hans Rosling. He started with a provocative question—what is the relationship between fertility rates and religions? It was clear that nearly everyone in the audience thought they knew the answer. But did they?

The 2007 announcement by Steve Jobs of the original iPhone is a great example of a horseless carriage.He began by talking about how Apple was announcing three new products: a touch-screen music player, a mobile phone and an Internet communicator. Then he showed how this wasn’t three products but one.By doing this, he ensured that people understood the iPhone wasn’t just a phone but had all three of these capabilities.

We know things. But we don't always know how we know. In this whirlwind tour of surprising statistics, expert statisticians help us see how our personal experiences, education, and media consumption all result in our flawed understandings of the world—that we take to be truths.

Our bias towards action can be counter-productive if we are operating inside an outdated way of thinking.‍In a recently-published study in Nature, researchers found that humans almost always added components to solve problems instead of subtracting them. This might explain why humans often tend to add more activity to solve problems rather than subtract ineffective actions or ways of thinking.

The authors lay out four stages people pass through when learning any new skill. People are:1. Unconsciously unskilled 2. Consciously unskilled 3. Consciously skilled 4. Unconsciously skilled. It is the first and fourth stages where unlearning is vital. Our 'unconscious unskilled-ness' and also our 'unconscous skilled-ness' are both times when we are operating on autopilot, with data-sorting and decision-making happening out of our conscious view. This is where our biases and set ways of thinking are invisible to us.

Our bias towards action can be counter-productive if we are operating inside an outdated way of thinking.‍In a recently-published study in Nature, researchers found that humans almost always added components to solve problems instead of subtracting them. This might explain why humans often tend to add more activity to solve problems rather than subtract ineffective actions or ways of thinking.

Unlearning deeply embedded mental models is tough—but it can be done. Check out this video for a great example of how deeply ingrained mental models can be. You’re not going to get exponential results with a “bike” (mental model) that’s a little better and a little faster. You're going to have to learn how to ride a backwards bicycle. The good news is that it can be done, and it doesn't necessarily take eight months. It takes rewiring your automatic responses, which means going through the awkward and frustrating phase where you don’t feel like you're good at what you’re doing. In this stage, even 'knowing' what you need to do differently is not enough. As the narrator says, knowledge is not equal to understanding.

Walgreens created a prescription refill API to help providers and other stakeholders coordinate actions and data related to the refill process, with the aim of improving treatment plan adherence and reducing costs and errors.

Algorithmic decision-making in healthcare settings promises to provide better, more equitable and efficient care—but can only do so if we shift mindsets and provide good data into those systems. The qqual rights watchdog American Civil Liberties Union lays out the risks of both action and inaction.

Want to go to the river, but unsure if you'll be swarmed by a cloud of mosquitos? Fear not, friend—data scientists and the bug repellant brand Off have come together to provide a tool that predicts mosquito populations via machine algorithm and live weather data.

AR/VR technology can transform the practice of surgery and medical care. Fraunhofer's suite of software serve as machine coworkers to provide data-backed decision support about the best strategies for surgical interventions and risk reduction.

Is it the new Marvel movie with heroic doctors? No, it's real-world AR/VR technology giving surgeons superpowers.

Getting your lab tests done shouldn't be painful or frustrating. AccuVein is an augmented reality tool that uses near-infrared technology to help practitioners find a vein with ease.

Figs captured the attention of the healthcare industry by offering scrubs as a lifestyle brand with more in common with fashion than with stiff and scratchy uniforms. Figs see the value in self-expression and empower their customers to be "Awesome Humans" who take pride in their profession and appearance.

This "Dear Apple" video shows real users of the Apple Watch who have written to Apple to share how the device has changed their lives. Each user had a positive experience based on little data—the data about them as an individual. Watch it to experience what little data feels like versus the more generic strategies of big data.

Procedures live on even after they’ve been proved ineffective. It can lead to harms and wasted resources. This piece unpacks what it means to unlearn stuck ways of operating amongst professionals used to being the 'smartest ones in the room.'