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.
True artificial intelligence will require rich models that incorporate real-world phenomena.
Delving into deep learning and the inner workings of neural networks.
Why my understanding of artificial intelligence is different from yours.
Some of AI's viable approaches lie outside the organizational boundaries of Google and other large Internet companies.
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