People from across the AI world came together in London for the Artificial Intelligence Conference. Below you'll find links to highlights from the event. Raffaello D'Andrea presents his vision of how autonomous indoor drones will drive the next wave of robotics development. Get a free trial today and find answers on the fly, or master something new and useful. Marta Kwiatkowska provides an overview of techniques being developed to help improve the robustness, safety, and trust in AI systems.

Academic machine learning involves almost exclusively offline evaluation of machine learning models. In the real world this is, somewhat surprisingly, often only good enough for a rough cut that eliminates the real dogs. For production work, online evaluation is often the only option to determine which of several final-round candidates might be chosen for further use. As Einstein is rumored to have said, theory and practice are the same, in theory. So it is with models.

People from across the artificial intelligence (AI) world came together in San Jose, Calif. Below you'll find links to highlights from the event. Kenneth Stanley discusses how open-ended algorithms can offer an entirely different level of automated creation. Get a free trial today and find answers on the fly, or master something new and useful. Srinivas Narayanan takes a deep look into the next change we're seeing in AI--going beyond fully supervised learning techniques.

The use of AI is growing rapidly and expanding into applications that impact people's lives. Autonomous vehicles must make instant decisions with multiple outcomes, and AI is already being used in judiciary systems to help make sentencing decisions. While researchers are driven by enthusiasm to harness the power of AI, they also have an obligation to consider the impact of intelligent applications. This is a question of trust and ethics, not just technology. While we may believe that machines don't make value judgments and are inherently unbiased, assumptions made in the design process can be based on subjective value judgments, such as choosing datasets, selecting weighting schemes, balancing precision, and recall.

At O'Reilly, we believe that true innovation depends on hearing from and engaging with people with a variety of perspectives. Read our Diversity Statement and learn about our Diversity & Inclusion scholarship program. At the conference, we provide a nursing room and all-gender restroom access; we also offer childcare expenses reimbursement. To support diversity in the tech community, we work with inclusion-focused nonprofits. We're raising funds for AI4ALL and invite you to join us.

The O'Reilly AI Conference Expo Plus pass includes a lot more than access to the Expo Hall. In addition to visiting the thriving ecosystem of cutting-edge exhibitors in AI, you'll be able to test drive new tools, compare products, attend any sponsored sessions, meet with speakers and authors, get access to 90 days of O'Reilly online learning, and much more.

Subscribe to the O'Reilly Data Show Podcast to explore the opportunities and techniques driving big data, data science, and AI. Find us on Stitcher, TuneIn, iTunes, SoundCloud, RSS. Specialists describe deep learning as akin to a rocketship that needs a really big engine (a model) and a lot of fuel (the data) in order to go anywhere interesting. To get a better understanding of the issues involved in building compute systems for deep learning, I spoke with one of the foremost experts on this subject: Greg Diamos, senior researcher at Baidu. Diamos has long worked to combine advances in software and hardware to make computers run faster.

New York City was all about AI this week, with AI experts and business leaders coming together at the sold-out O'Reilly Artificial Intelligence Conference. One notable announcement from the event is that Intel is now joining forces with O'Reilly to co-present the Artificial Intelligence Conference series going forward. This new collaboration will offer exciting ways to define and drive advances in AI. The first event with Intel is the AI Conference in San Francisco, happening Sept. 17-20, with more events planned in New York and San Francisco in 2018. The AI Conference stands out for bringing together business leaders and AI innovators to talk about the most promising applied AI developments across all fields--from logistics and transportation to health and media.

Yes, you can run TensorFlow on a $39 Raspberry Pi, and yes, you can run TensorFlow on a GPU powered EC2 node for about $1 per hour. And yes, those options probably make more practical sense than building your own computer. But if you're like me, you're dying to build your own fast deep learning machine. OK, a thousand bucks is way too much to spend on a DIY project, but once you have your machine set up, you can build hundreds of deep learning applications, from augmented robot brains to art projects (or at least, that's how I justify it to myself). At the very least, this setup will easily outperform a $2,800 Macbook Pro on every metric other than power consumption and, because it's easily upgraded, stay ahead of it for a few years to come.

Underneath the hype, real breakthroughs in AI are transforming businesses. Intelligence engineers are creating software that doesn't just do what it's told, but also anticipates the needs of its users and customers through knowledge, pattern recognition, and reasoning. O'Reilly AI brings together leaders and innovators to explore the most promising applied deep learning and edge intelligence topics, including: If you're looking for groundbreaking research, compelling use cases, rock-solid technical skills--and maybe one or two crazy ideas that just might change the world--join us in New York June 26–29 for the O'Reilly AI Conference. Need help convincing your manager? Each training course takes place June 26–27 and is limited in size to maintain a high level of learning and instructor interaction.