Radar trends to watch: May 2022
Developments in Web3, Security, Biology, and More
The General Purpose Pendulum
Are we swinging away from general-purpose CPUs?
Radar trends to watch: April 2022
Developments in Programming, Biology, Hardware, and More
AI Adoption in the Enterprise 2022
Epstein Barr and the Cause of Cause
If a cause rarely causes something, is it a cause?
Radar trends to watch: March 2022
Developments in AI, Blockchain, Education, and More
Intelligence and Comprehension
What does it mean to say a computer model “understands”?
The Human Web
Web3 needs to solve the problems of Web0
Radar trends to watch: February 2022
Developments in Web, Metaverse, Infrastructure, and More
Technology Trends for 2022
What O'Reilly Learning Platform Usage Tells Us About Where the Industry Is Headed
What Is Causal Inference?
An Introduction for Data Scientists
What’s ahead for AI, VR, NFTs, and more?
Here are some predictions for tech in 2022.
Radar trends to watch: January 2022
Developments in AI, IoT, Programming, and More
The Cloud in 2021: Adoption Continues
Radar trends to watch: December 2021
Developments in Programming, Quantum Computing, Cryptocurrency, and More
Radar trends to watch: November 2021
Developments in AI, Security, Quantum Computing, and More
The Quality of Auto-Generated Code
When Copilot writes your code, will you care whether it’s good or bad?
Radar trends to watch: October 2021
Developments in Security, Law, Quantum Computing, and More
2021 Data/AI Salary Survey
Radar trends to watch: September 2021
Trends in AI, robotics, social media, and more
Defending against ransomware is all about the basics
Authentication, Backups, Updates, and Least Privilege
Radar trends to watch: August 2021
Trends in Programming, Robotics, Security, and More
Thinking About Glue
The code that holds our systems together
Radar trends to watch: July 2021
Trends in AI, Ethics, Security, and More
Code as Infrastructure
The Next Critical Talent Shortage Won’t Be Fortran
Radar trends to watch: June 2021
Trends in AI, Security, Programming, and More
What happens when deepfakes become cheap?
Radar trends to watch: May 2021
Trends in AI, Security, Finance, and More
AI Adoption in the Enterprise 2021
NFTs: Owning Digital Art
Conspicuous consumption for the online world
Radar trends to watch: April 2021
Trends in AI, Social Media, Augmented Reality, and More
The Next Generation of AI
Is a new generation of AI systems arising from cross-fertilization between different AI disciplines?
Radar trends to watch: March 2021
Trends in AI, Ecology, Finance, and More
Product Management for AI
The Wrong Question
What questions should we be asking about the future of social media? “Free Speech” isn’t it.
Radar trends to watch: February 2021
Trends in AI, Programming, Quantum Computing, and More
Where Programming, Ops, AI, and the Cloud are Headed in 2021
Following O'Reilly online learning trends to see what's coming next.
Patterns give you a language for discussing solutions to problems.
Radar trends to watch: January 2021
Trends in AI, Security, Biology, and More
What is functional programming?
Be as functional as you want to be
Radar trends to watch: December 2020
Trends in AI, Robotics, Infrastructure, and more.
We need to learn how to effectively use multi-paradigm languages that support functional, object oriented, and procedural paradigms.
Radar trends to watch: November 2020
Trends in AI, Programming, Security, and more.
AI Product Management After Deployment
The AI product manager’s job isn’t over when the product is released. PMs need to remain engaged after deployment.
AI and Creativity
Creativity means making something new, not copying what exists already.
Radar trends to watch: October 2020
Pair Programming with AI
Radar trends to watch: September 2020
Trends in AI, COVID-19, Programming, and more.
An Agent of Change
A look into the Covid-19 pandemic's influence on how we think, spend, and manage our businesses.
The Least Liked Programming Languages
What are some of the least liked/most dreaded programming languages? Why are they dreaded, and are they being evaluated fairly?
Radar trends to watch: August 2020
Trends in COVID-19, AI, data, robotics, programming, VR, technology and society, and security.
Bringing an AI Product to Market
Previous articles have gone through the basics of AI product management. Here we get to the meat: how do you bring a product to market?
Power, Harms, and Data
Data is often biased. But that isn’t the real issue. Why is it biased? How do we build teams that are sensitive to that bias?
AI, Protests, and Justice
Microservices Adoption in 2020
Everyone’s talking about microservices. Who’s actually doing it?
Automated Coding and the Future of Programming
How automation is likely to change professional software development.
Radar trends to watch: July 2020
Trends in disruptions in COVID-19 and #BlackLivesMatter, AI, programming, social media, and cloud.
COVID-19 and Complex Systems
Machine Learning and the Production Gap
Radar trends to watch: June 2020
Trends in COVID-19, programming, machine learning & AI, payment systems, and networks.
The Business of Open Source
How do you build a business around open source when you’re competing with AWS and the like? Chef’s answer: double down on Open Source.
Practical Skills for The AI Product Manager
When models are everywhere
Radar trends to watch: May 2020
Trends in technology and Coronavirus, robotics, AI, programming, and the New Workplace.
Every time the cry for COBOL programmers has gone up, we’ve muddled through; this time, we should do something better.
How data privacy leader Apple found itself in a data ethics catastrophe
Companies that succeed will protect, fight for, and empower their users
Radar trends to watch: April 2020
Coronavirus, real time transcription, quantum computing, and regulating cryptography.
Governance and Discovery
What you need to know about product management for AI
A product manager for AI does everything a traditional PM does, and much more.
Radar trends to watch: March 2020
Disposable bluetooth stickers, Coronavirus impact, smart farming, and cybersecurity.
The death of Agile?
In this edition of the Radar column, we examine the big picture around Agile, and look at what it means and what it doesn't.
Radar trends to watch: February 2020
News from CES, developments in automation, cloud computing, and trends from China.
AI meets operations
In this edition of the Radar column, we explore questions and challenges facing ops teams as they attempt to assimilate AI.
Radar trends to watch: January 2020
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.
The road to Software 2.0
It’s clear that AI can and will have a big influence on how we develop software.
Radar trends to watch: December 2019
We’re tracking notable developments in privacy, security, health, and more.
A 5G future
In this edition of the Radar column, we explore the limitations and possibilities of high-speed 5G connectivity.
A world of deepfakes
We need to remember that creating fakes is an application, not a tool—and that malicious applications are not the whole story.
Radar trends to watch: November 2019
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.
Defusing propaganda feedback loops on the social web
The struggle is not about free speech; it's about the right to pay attention and to think.
Radar trends to watch: October 2019
We’re tracking notable developments in open source activism, quantum computing, AR/VR, and more.
TinyML: The challenges and opportunities of low-power ML applications
In this edition of the Radar column, we look at what’s possible when ML apps can work with minimal or inconsistent power supplies.
Radar trends to watch: September 2019
We’re tracking notable developments in the democratization of AI, open source supply chain attacks, brain-computer interfaces, and more.
How new tools in data and AI are being used in health care and medicine
An overview of applications of new tools for overcoming silos, and for creating and sharing high-quality data.
Machine learning requires a fundamentally different deployment approach
As organizations embrace machine learning, the need for new deployment tools and strategies grows.
Learning from adversaries
Adversarial images aren’t a problem—they’re an opportunity to explore new ways of interacting with AI.
The circle of fairness
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.
Maximizing paper clips
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.
How AI and machine learning are improving customer experience
From data quality to personalization, to customer acquisition and retention, and beyond, AI and ML will shape the customer experience of the future.
Toward the next generation of programming tools
Programmers have built great tools for others. It’s time they built some for themselves.
Looking Back on the O’Reilly Artificial Intelligence Conference
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.
Strata San Francisco, 2019: Opportunities and Risks
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.
Changing contexts and intents
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.
Future of the firm
Mapping the complex forces that are reshaping organizations and changing the employee/employer relationship.
What is O’Reilly Radar?
Radar spots and explores emerging technology themes so organizations can succeed amid constant change.
Reinforcement learning for the birds
Much like human speech, bird song learning is social; perhaps we'll discover machine learning is social, too.
Rethinking informed consent
Consent is the first step toward the ethical use of data, but it's not the last.
What we learn from AI’s biases
Our bad AI could be the best tool we have for understanding how to be better people.
The ethics of data flow
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.
A quick reminder on HTTPS everywhere
HTTPS "everywhere" means everywhere—not just the login page, or the page where you accept donations. Everything.
Why we need to think differently about AI
General intelligence or creativity can only be properly imagined if we peel away the layers of abstractions.
Data’s day of reckoning
We can build a future we want to live in, or we can build a nightmare. The choice is up to us.
The five Cs
Five framing guidelines to help you think about building data products.
Of oaths and checklists
Oaths have their value, but checklists will help put principles into practice.
What machine learning means for software development
“Human in the loop” software development will be a big part of the future.
Doing good data science
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?
Don’t let your ethical judgement go to sleep
We need to build organizations that are self-critical and avoid corporate self-deception.
Steering around blockchain hype
When we finally find the best use cases for blockchains, they may look like nothing we would have expected.
7 questions to ask before you launch an enterprise blockchain project
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.
What is a blockchain?
Unpacking the complexity of blockchain, term by term.
From USENET to Facebook: The second time as farce
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.
4 things business leaders should know as they explore AI and deep learning
Our survey reveals how organizations are using tools, techniques, and training to apply AI through deep learning.
It’s time to rebuild the web
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.
Do no harm
In the software world, we’re often ignorant of the harms we do because we don’t understand what we’re working with.
Why I won’t whitelist your site
Publishers need to take responsibility for code they run on my systems.
The working relationship between AIs and humans isn’t master/slave
We need a new model for how AI systems and humans interact.
Machine learning tools for fairness, at scale
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.
Tips for jumpstarting your journey to developing AI apps
Use cases and tips to help businesses take full advantage of AI technology.
The problem with building a “fair” system
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.
Can AI create a new game? A challenge.
Since AI's most amazing advances have been in playing games, it seems fitting that the creative challenge should involve creating games.
Who, me? They warned you about me?
Thoughts on "We are the people they warned you about."
Ethics at scale
Scale changes the problems of privacy, security, and honesty in fundamental ways.
Planning for AI
What you need know before committing to AI.
The evolution of DevOps
Understanding the impact and expanding influence of DevOps culture, and how to apply DevOps principles to make your digital operations more performant and productive.
What if we build the internet we always wanted?
It's time to stop cursing the network we have and build the network we want.
AI first—with UX
An AI-first strategy will only work if it puts the user first.
What is Jupyter?
To succeed in digital transformation, businesses need to adopt tools that enable collaboration, sharing, and rapid deployment. Jupyter fits that bill.
What are machine learning engineers?
A new role focused on creating data products and making data science work in production.
The machine learning paradox
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.
On computational ethics
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.
Are tablets the new laptops? Not yet.
It makes no sense at all for programming to be stuck on laptops, but that's where we are.
Artificial intelligence: Cooperation vs. aggression
Machines learn what we teach them. If you don't want AI agents to shoot, don't give them guns.
The ethics of face recognition
We need AI researchers who are actively trying to defeat AI systems and exposing their inadequacies.
The ethics of artificial intelligence
A framework for thinking about AI.
Is it possible for an AI to create revolutionary art?
To supervise or not to supervise in AI?
If you look carefully at how humans learn, you see surprisingly little unsupervised learning.
What is Artificial Intelligence?
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.
Making revolutionary products without genetic engineering
A lot can happen in biotechnology with plain old organisms.
Where open source hasn’t won
Open source has victories, but there are battles that still need to be fought.
Learning from Tay
Whether our prejudices are overt or hidden, our artificial intelligentsia will reflect them.
The inherent clumpiness of randomness
Finding patterns isn't really a question about random processes; it's a question about the human brain.
A poet does TensorFlow
Pete Warden’s instructions on building a deep learning classifier looked so simple, I had to try it myself.
Learning from AlphaGo
If there's anything humans should learn from AlphaGo, it's that our survival depends on constantly looking at the data.
What passes for sharing these days
The "sharing economy" has nothing at all to do with sharing.
The artist in the age of online media
A lot of young artists are building brand equity and audience, but fame doesn't equal money and you can't eat brand equity.
P-values not quite considered harmful
The crisis of reproducibility is an opportunity to get better at doing science.
Programming, oaths, and users
The Programmer's Oath is missing one essential element: the customer.
A new infrastructure for biology
The revolution in automation is fueling biology at scale.
Do one thing…
I don't want barely distinguishable tools that are mediocre at everything; I want tools that do one thing and do it well.
Building an automated future
Our fears of automation aren’t due to problems of artificial intelligence, but of human intelligence.
Amazon, boredom, and culture
Corporate leadership is as much about building people as it is about developing product.
Imagining a future biology
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.
How Data Science Is Transforming Health Care
Explore how data analysis will help us structure the business of health care more effectively around outcomes, and personalize medicine for each specific patient.
What is DevOps (yet again)?
Empathy, communication, and collaboration across organizational boundaries.
Beyond lab folklore and mythology
What the future of science will look like if we’re bold enough to look beyond centuries-old models.
What is the Internet of Things?
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.
The computing of distrust
A look at what lies ahead in the disenchanted age of postmodern computing.
The revolution in biology is here, now
Biological products have always seemed far off. BioFabricate showed that they're not.
Uber is breaking bad
Uber has built a great service. Why do they feel the need to use dirty tricks to succeed?
Artificial intelligence: summoning the demon
We need to understand our own intelligence is competition for our artificial, not-quite intelligences.
Overfocus on tech skills could exclude the best candidates for jobs
Is the unemployment problem about a lack of qualified applicants in the workforce?
Operations, machine learning, and premature babies
An astonishing connection between web ops and medical care.
The evolution of data products
The data that drives products is shifting from overt to covert.
What is data science?
The future belongs to the companies and people that turn data into products.
Specialized tools for machine learning development and model governance are becoming essential
Why companies are turning to specialized machine learning tools like MLflow.
You created a machine learning application. Now make sure it’s secure.
The software industry has demonstrated, all too clearly, what happens when you don’t pay attention to security.
Deep automation in machine learning
We need to do more than automate model building with autoML; we need to automate tasks at every stage of the data pipeline.
Case studies in data ethics
These studies provide a foundation for discussing ethical issues so we can better integrate data ethics in real life.
Building tools for the AI applications of tomorrow
We’re currently laying the foundation for future generations of AI applications, but we aren’t there yet.
Getting the most from the AI Business Summit
The AI Conference in NY will feature tutorials, conference sessions, and executive briefings to help business leaders understand and plan for AI technologies.
Join the battle for the internet
It's time to rally in defense of the internet again.
Where programming meets the real world
Greg Brown's new book, Programming Beyond Practices, is a thoughtful exploration of how software gets developed.
How do you learn?
Shared learning: It's what we do at O'Reilly, and it's what we’d like to share with you.
Cultivate in Portland: Leadership, values, diversity
Building the next generation of leaders, for any size organization.
To suit or not to suit?
At Cultivate, we'll address the issues really facing management: how to deal with human problems.
BioBuilder: Rethinking the biological sciences as engineering disciplines
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.
Announcing BioCoder issue 6
BioCoder 6: iGEM's first Giant Jamboree, an update from the #ScienceHack Hack-a-thon, the Open qPCR project, and more.