We’re tracking notable developments in the democratization of AI, open source supply chain attacks, brain-computer interfaces, and more.
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
As organizations embrace machine learning, the need for new deployment tools and strategies grows.
Adversarial images aren’t a problem—they’re an opportunity to explore new ways of interacting with AI.
We shouldn't ask our AI tools to be fair; instead, we should ask them to be less unfair and be willing to iterate until we see improvement.
We won’t get the chance to worry about artificial general intelligence if we don’t deal with the problems we have in the present.
From data quality to personalization, to customer acquisition and retention, and beyond, AI and ML will shape the customer experience of the future.
Programmers have built great tools for others. It’s time they built some for themselves.
More than anything else, O'Reilly's AI Conference was about making the leap to AI 2.0.
Balancing risk and reward is a necessary tension we'll need to understand as we continue our journey into the age of data.
Mapping the complex forces that are reshaping organizations and changing the employee/employer relationship.
Radar spots and explores emerging technology themes so organizations can succeed amid constant change.
Much like human speech, bird song learning is social; perhaps we'll discover machine learning is social, too.
Consent is the first step toward the ethical use of data, but it's not the last.
Our bad AI could be the best tool we have for understanding how to be better people.
HTTPS "everywhere" means everywhere—not just the login page, or the page where you accept donations. Everything.
General intelligence or creativity can only be properly imagined if we peel away the layers of abstractions.
We can build a future we want to live in, or we can build a nightmare. The choice is up to us.
Five framing guidelines to help you think about building data products.
Oaths have their value, but checklists will help put principles into practice.
“Human in the loop” software development will be a big part of the future.
Data scientists, data engineers, AI and ML developers, and other data professionals need to live ethical values, not just talk about them.
It’s easy to imagine an AI winning a game of Go, but can you imagine an AI wanting to play a game of Go?
We need to build organizations that are self-critical and avoid corporate self-deception.
When we finally find the best use cases for blockchains, they may look like nothing we would have expected.
Successful projects will think seriously about what blockchains mean, and how to use them effectively.
Don’t pigeonhole blockchain as a technology that’s primarily useful for finance.
Unpacking the complexity of blockchain, term by term.
Demanding and building a social network that serves us and enables free speech, rather than serving a business metric that amplifies noise, is the way to end the farce.
Our survey reveals how organizations are using tools, techniques, and training to apply AI through deep learning.
The web was never supposed to be a few walled gardens of concentrated content owned by a few major publishers; it was supposed to be a cacophony of different sites and voices.
In the software world, we’re often ignorant of the harms we do because we don’t understand what we’re working with.
Publishers need to take responsibility for code they run on my systems.
Thoughts on "We are the people they warned you about."
Scale changes the problems of privacy, security, and honesty in fundamental ways.
What you need know before committing to AI.
Understanding the impact and expanding influence of DevOps culture, and how to apply DevOps principles to make your digital operations more performant and productive.
It's time to stop cursing the network we have and build the network we want.
To succeed in digital transformation, businesses need to adopt tools that enable collaboration, sharing, and rapid deployment. Jupyter fits that bill.
A new role focused on creating data products and making data science work in production.
Nothing says machine learning can't outperform humans, but it's important to realize perfect machine learning doesn't, and won't, exist.
The tools of defensive computing, whether they involve mascara and face paint or random autonomous web browsing, belong to the harsh reality we've built.
Is it possible to imagine an AI that can compute ethics?
We need AI researchers who are actively trying to defeat AI systems and exposing their inadequacies.
A framework for thinking about AI.
Mike Loukides and Ben Lorica examine factors that have made AI a hot topic in recent years, today's successful AI systems, and where AI may be headed.
If there's anything humans should learn from AlphaGo, it's that our survival depends on constantly looking at the data.
The "sharing economy" has nothing at all to do with sharing.
A lot of young artists are building brand equity and audience, but fame doesn't equal money and you can't eat brand equity.
The crisis of reproducibility is an opportunity to get better at doing science.
The Programmer's Oath is missing one essential element: the customer.
I don't want barely distinguishable tools that are mediocre at everything; I want tools that do one thing and do it well.
Our fears of automation aren’t due to problems of artificial intelligence, but of human intelligence.
Corporate leadership is as much about building people as it is about developing product.
It's easy to talk about eliminating hierarchy; it's much harder to do it effectively.
Explore how data analysis will help us structure the business of health care more effectively around outcomes, and personalize medicine for each specific patient.
Empathy, communication, and collaboration across organizational boundaries.
How the IoT is revolutionizing not just consumer goods and gadgets, but manufacturing, design, engineering, medicine, government, business models, and the way we live our lives.
A look at what lies ahead in the disenchanted age of postmodern computing.
Uber has built a great service. Why do they feel the need to use dirty tricks to succeed?
The data that drives products is shifting from overt to covert.
The future belongs to the companies and people that turn data into products.