News from CES, developments in automation, cloud computing, and trends from China.
In this edition of the Radar column, we explore questions and challenges facing ops teams as they attempt to assimilate AI.
We note three big things that will shape technology in 2020, and we’re tracking notable developments in open standards and security.
In this edition of the Radar column, we look at how the tools and techniques of programming are poised to evolve.
It’s clear that AI can and will have a big influence on how we develop software.
We’re tracking notable developments in privacy, security, health, and more.
In this edition of the Radar column, we explore the limitations and possibilities of high-speed 5G connectivity.
We need to remember that creating fakes is an application, not a tool—and that malicious applications are not the whole story.
We’re tracking notable developments in 5G, devices, augmented reality, blockchain, and more.
Quantum computing’s potential is still far off, but quantum supremacy shows we’re on the right track
In this edition of the Radar column, we explore Google’s quantum supremacy milestone.
The struggle is not about free speech; it's about the right to pay attention and to think.
We’re tracking notable developments in open source activism, quantum computing, AR/VR, and more.
In this edition of the Radar column, we look at what’s possible when ML apps can work with minimal or inconsistent power supplies.
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.
Machines will need to make ethical decisions, and we will be responsible for those decisions.
Balancing risk and reward is a necessary tension we'll need to understand as we continue our journey into the age of data.
The toughest bias problems are often the ones you only think you’ve solved.
The internet itself is a changing context—we’re right to worry about data flows, but we also have to worry about the context changing even when data doesn’t flow.
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.
If we’re going to think about the ethics of data and how it’s used, then we have to take into account how data flows.
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.
We need a new model for how AI systems and humans interact.
It’s time to think about how the systems we build interact with our world and build systems that make our world a better place.
Use cases and tips to help businesses take full advantage of AI technology.
The ability to appeal may be the most important part of a fair system, and it's one that isn't often discussed in data circles.
Since AI's most amazing advances have been in playing games, it seems fitting that the creative challenge should involve creating games.
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.
An AI-first strategy will only work if it puts the user first.
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?
If behavioral authentication could be made to work, it could be a big part of our future.
It makes no sense at all for programming to be stuck on laptops, but that's where we are.
Machines learn what we teach them. If you don't want AI agents to shoot, don't give them guns.
We need AI researchers who are actively trying to defeat AI systems and exposing their inadequacies.
A framework for thinking about AI.
Is it possible for an AI to create revolutionary art?
If you look carefully at how humans learn, you see surprisingly little unsupervised learning.
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.
A lot can happen in biotechnology with plain old organisms.
Open source has victories, but there are battles that still need to be fought.
Whether our prejudices are overt or hidden, our artificial intelligentsia will reflect them.
Finding patterns isn't really a question about random processes; it's a question about the human brain.
Pete Warden’s instructions on building a deep learning classifier looked so simple, I had to try it myself.
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.
The revolution in automation is fueling biology at scale.
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.
We need to nurture our imaginations to fuel the biological revolution.
It's easy to talk about eliminating hierarchy; it's much harder to do it effectively.
AI scares us because it could be as inhuman as humans.
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.
What the future of science will look like if we’re bold enough to look beyond centuries-old models.
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.
Biological products have always seemed far off. BioFabricate showed that they're not.
Uber has built a great service. Why do they feel the need to use dirty tricks to succeed?
We need to understand our own intelligence is competition for our artificial, not-quite intelligences.
Is the unemployment problem about a lack of qualified applicants in the workforce?
An astonishing connection between web ops and medical care.
The data that drives products is shifting from overt to covert.
The future belongs to the companies and people that turn data into products.
Why companies are turning to specialized machine learning tools like MLflow.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.
These studies provide a foundation for discussing ethical issues so we can better integrate data ethics in real life.
We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.
The AI Conference in NY will feature tutorials, conference sessions, and executive briefings to help business leaders understand and plan for AI technologies.
It's time to rally in defense of the internet again.
Greg Brown's new book, Programming Beyond Practices, is a thoughtful exploration of how software gets developed.
Shared learning: It's what we do at O'Reilly, and it's what we’d like to share with you.
Building the next generation of leaders, for any size organization.
At Cultivate, we'll address the issues really facing management: how to deal with human problems.
Moving biology out of the lab will enable new startups, new business models, and entirely new economies.
Cultivate is O'Reilly's conference committed to training the people who will lead successful teams, now and in the future.
BioCoder 6: iGEM's first Giant Jamboree, an update from the #ScienceHack Hack-a-thon, the Open qPCR project, and more.