Lili Cheng provides insights into the planning and release of Microsoft's bot, Tay.
Are bots your new best friend?
Andy Mauro discusses pitfalls, opportunities, and the future of conversational bot interactions.
A framework for thinking about AI.
Improving prediction accuracy using deep compression and DSD training.
It's the ensemble of technologies that will make the impossible possible.
Watching the appeal and applications of machine intelligence expand.
Is it possible for an AI to create revolutionary art?
Overcoming the dearth of labeled data, deployment issues, and regulation fears to increase the use of AI in health care.
More adventures in deep learning and cheap hardware.
Understanding AV technologies and how to integrate them.
Watch highlights covering artificial intelligence, machine learning, intelligence engineering, and more. From the O'Reilly AI Conference in New York 2016.
Gary Marcus discusses the machine-human connection.
Jim McHugh shares real-world examples of companies solving problems once thought unsolvable.
Significant progress in AI will require breakthroughs in unsupervised/predictive learning, as well as in reasoning, attention, and episodic memory.
Rana El Kaliouby explores why emotion in AI is critical to accelerating adoption of AI systems.
Aparna Chennapragada discusses Google's process for developing data products.
Naveen Rao outlines deep learning challenges and explores how changes to the organization of computation and communication can lead to advances in capabilities.
Tim O’Reilly explains why we can’t just use technology to replace people; we must use it to augment them so they can do things that were previously impossible.
Building reliable, robust software is hard. It is even harder when we move from deterministic domains, such as balancing a checkbook, to uncertain domains, such as recognizing speech or objects in an image.
Shahin Farshchi examines role artificial intelligence will play in driverless cars.
Genevieve Bell explores the meaning of “intelligence” within the context of machines and its cultural impact on humans and their relationships.
Lili Cheng discusses the human aspects of artificial intelligence.
Watch keynotes from the O'Reilly artificial intelligence conference in New York City.
A look at the artificial intelligence and messaging platforms behind the fast-growing chatbot community
Christopher Nguyen explores where advances in machine learning and AI will take us over the next 50 years.
How algorithms will optimize everything.
Qi Lu explores data-model intelligence, the Bing Knowledge Graph, the Microsoft Graph, and Cortana SDKs.
In this O’Reilly report, you’ll explore the potential of—and impediments to—widespread adoption of AI in the medical field.
November 1-2, 2016, join Google’s Eli Bixby and Amy Unruh for a two-day, hands-on, in-depth exploration of TensorFlow.
Training deep learning models to code solutions: An interview with Oriol Vinyals.
Exploring the rise of conversational interfaces, how AI will change the way programmers create software, and open source tools for AI and machine learning.
The world of conversational interfaces is very young. Here are some early questions that it’s working out.
If you look carefully at how humans learn, you see surprisingly little unsupervised learning.
Computational approaches for creating machines at scale: An interview with Daniela Rus.
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.
Stuart Russell argues for a fundamental reorientation of the field artificial intelligence.
Natural language understanding: What it is and where to begin.
Introducing the O’Reilly Artificial Intelligence Conference.
An interview with Brendan Frey about realizing new possibilities in genomic medicine.
Understanding intelligence: An interview with Yoshua Bengio.
As a technical founder at Siri, Sentient, and Viv Labs, Adam Cheyer has helped design and develop a number of intelligent systems. Drawing on specific examples, Adam reveals techniques he uses to maximize the impact of the AI technologies he employs.
Jana Eggers has been in and around the field of artificial intelligence for more than 25 years, which gives her a unique perspective on what's been accomplished in AI and what we're still missing.
Alyosha Efros discusses using computer vision to understand big visual data.
The field of machine learning is growing fast—read interviews with leading practitioners to learn where it's headed.
Whether our prejudices are overt or hidden, our artificial intelligentsia will reflect them.
The next big technologies are defined by their emerging market value.
Tensor methods to solve machine learning challenges: An interview with Anima Anandkumar.
Beyond data processing: Utilizing Spark for individual predictions.
Emerging trends in intelligent mobile applications and distributed computing.
Autonomous systems and focused startups among major changes seen in past year.
A bridge between robust control and reinforcement learning.
An interview with Risto Miikkulainen.
How to almost necessarily succeed: An interview with Google research scientist Ilya Sutskever.
Using topology to uncover the shape of your data: An interview with Gurjeet Singh.
AI scares us because it could be as inhuman as humans.
From linear models to neural networks: An interview with Reza Zadeh.
We need to understand our own intelligence is competition for our artificial, not-quite intelligences.
Exploring open web crawl data — what if you had your own copy of the entire web, and you could do with it whatever you want?
How neuroscience is benefiting from distributed computing, and how computing might learn from neuroscience.