- Apple’s Incredible Platform Advantage (Steve Cheney) — the best people in chip design no longer want to work at Intel or Qualcomm. They want to work at Apple. I have plenty of friends in the Valley who affirm this. Sure Apple products are cooler. But Apple has also surpassed Intel in performance. This is insane. A device company – which makes CPUs for internal use – surpassing Intel, the world’s largest chip maker that practically invented the CPU and has thousands of customers.
- Data Center’s Days are Numbered — Adrian Cockroft says, the investments going into bolstering security on AWS and other clouds are set to pay off to the point where within five years, “it will be impossible to get security certification if you’re not running in the cloud because the tools designed for data centers are sloppily put together and can’t offer the auditing for PCI and other regulators.”
- A Peek Inside IBM’s R&D Lab — IBM still has a physics department, but at this point, almost every physicist is somehow linked to a product plan or customer plan.
- Building Scalable Stateful Services (High Scalability) — elucidation of a talk by Caitie McCaffrey (YouTube), tech lead for observability at Twitter.
Bryan Liles, who works on strategic initiatives for DigitalOcean, gave a great thought-provoking talk on bias and diversity. “If your company is creating a diversity plan and you’ve actually gone and counted people,” Liles said, “you’ve already lost.” Read more...
Our program will emphasize the full stack of skills designers need to work smarter.
Register now for the O’Reilly Design Conference, which will be held January 19-22, 2015, in San Francisco.
When reviewing the schedule for the O’Reilly Design Conference, you may wonder what shoe shopping with elders and the fisheries business have to do with design. Design’s impact is felt in every corner of our lives. And as a result, if you’re a designer you need to know more about more.
Remaining true to our roots, the O’Reilly Design Conference is all about learning new skills and bringing together different voices. We’ve planned several days of training for designers who want to increase their skill sets and widen their perspectives, including sessions on voice, industrial design, design thinking, prototyping, and running design reviews together with sessions like creative coding, and discussions on data, ethics, and privacy. Our program emphasizes the full stack of skills designers need to work smarter.
The keynotes we’ve lined up for Thursday and Friday morning will provide new perspectives, and a sense of how design is impacting business and society. You’ll hear designers talk about their experiences in the VC world and what it’s like when the experiences you craft live in both the physical and the digital worlds. After lunch on Thursday and Friday, the program gets broad. Whether you come for the conference, workshops, or intensive training days — or all three — I’ve laid out a few themes that can help to shape the sessions you choose to attend.
Here are some possible learning paths:
- I want to learn how to design for the digital and physical world
- I want to learn how to manage, lead, and have a larger impact within my organization
- I want to learn how to learn how to work effectively in cross discipline teams.
- I want to learn how to be better at user research and using data to create better products
- I want to learn how to apply my design skills to societal issues
With more companies focusing on design as a competitive advantage, it seems as if everyone is suddenly a designer.
The more that I talk to people about what it means to explain design, the more I realize that everyone across all types of organizations — from product companies to nonprofits to universities to health care — is intensely interested in it. Everyone now has an opinion about design, and we’ve all been in the position of having to defend our choices or suggestions.
Developers, product owners, project managers, and even CEOs are intimately involved in design processes now — increasingly, it seems as if everyone is a designer. But it hasn’t always been this way — so, why now do so many people have an opinion about design?
In the past decade, design and UX has gone “mainstream.” The most popular and interesting companies have put design at the forefront of their product offerings, creating a buzz culture that drools over every new release and a fan following that promotes their brand for them. I’m not only thinking of Apple, but also brands such as IKEA, innovators like Tesla, and unique problem-solving designs from Dyson, Segway, or Nest. These brands command respect, elicit strong opinions, and foster loyalty from the people who follow them. This elevation of design conversations within today’s companies , organizations, and throughout the public in general exemplifies a democratization of design that we haven’t before experienced.
Here, I’ll explore several factors contributing to design’s growing ubiquity.
Social media has changed how people view digital products
It’s not only physical products that have transformed our understanding of the value of design. Social media platforms have shown that UX is a critical component to success. Millions of people use Facebook every single day. Each minor tweak to the UI or change to the design incurs the praise or wrath of every user. Why? Because Facebook (and other services like it) is a very personal part of our lives. Never before have we had a platform for sharing the most intimate and mundane details of our everyday experiences. Read more…
The O'Reilly Radar Podcast: Rajiv Maheswaran on the science of moving dots, and Claudia Perlich on big data in advertising.
Subscribe to the O’Reilly Radar Podcast to track the technologies and people that will shape our world in the years to come.
In this week’s Radar Podcast episode, O’Reilly’s Mac Slocum chats with Rajiv Maheswaran, CEO of Second Spectrum. Maheswaran talks about machine learning applications in sports, the importance of context in measuring stats, and the future of real-time, in-game analytics.
Here are some highlights from their chat:
There’s a lot of parts of the game of basketball — pick and rolls, dribble hand-offs — that coaches really care about, about analyzing how it works on offense, how to guard them. Before big data and machine learning, people basically watched the games and marked them. It turns out that people are pretty bad at marking them accurately, and they also miss a ton of stuff. Right now, machine learning tells coaches, ‘This is how many pick and rolls these two players have had over the course of the season, how often they do all the different variations, what they’re good at, what they’re bad at.’ Coaches can really find tendencies that can help them play offense, play defense, far more efficiently, based off of machine learning.
What we’re doing is having the machine match human intuition. If I’m watching a game, I know that the shot is harder if I’m farther away, if I have multiple defenders, if they’re close, if they’re closing in on me, if I’m dribbling, the type of shot I’m taking. As a human, I watch this and I have an intuition about it. Now, by giving all that data to the machine, it can make a predictor that actually matches our intuition, and goes beyond it because it can put a number onto what our intuition tells us.
The O’Reilly Data Show podcast: Todd Lipcon on hybrid and specialized tools in distributed systems.
Subscribe to the O’Reilly Data Show Podcast to explore the opportunities and techniques driving big data and data science.
In recent months, I’ve been hearing about hybrid systems designed to handle different data management needs. At Strata + Hadoop World NYC last week, Cloudera’s Todd Lipcon unveiled an open source storage layer — Kudu — that’s good at both table scans (analytics) and random access (updates and inserts).
While specialized systems will continue to serve companies, there will be situations where the complexity of maintaining multiple systems — to eke out extra performance — will be harder to justify.
During the latest episode of the O’Reilly Data Show Podcast, I sat down with Lipcon to discuss his new project a few weeks before it was released. Here are a few snippets from our conversation:
HDFS and Hbase
[Hadoop is] more like a file store. It allows you to upload files onto an arbitrarily sized cluster with 20-plus petabytes, in single clusters. The thing is, you can upload the files but you can’t edit them in place. To make any change, you have to basically put in a new file. What HBase does in distinction is that it has more of a tabular data model, where you can update and insert individual row-by- row data, and then randomly access that data [in] milliseconds. The distinction here is that HDFS is pretty good for large scans where you’re putting in a large data set, maybe doing a full parse over the data set to train a machine learning model or compute an aggregate. If any of that data changes on a frequent basis or if you want to stream the data in or randomly access individual customer records, you’re kind of out of luck on HDFS. Read more…