The O'Reilly Data Show Podcast: Ben Recht on optimization, compressed sensing, and large-scale machine learning pipelines.
As we put the finishing touches on what promises to be another outstanding Hardcore Data Science Day at Strata + Hadoop World in New York, I sat down with my co-organizer Ben Recht for the the latest episode of the O’Reilly Data Show Podcast. Recht is a UC Berkeley faculty member and member of AMPLab, and his research spans many areas of interest to data scientists including optimization, compressed sensing, statistics, and machine learning.
At the 2014 Strata + Hadoop World in NYC, Recht gave an overview of a nascent AMPLab research initiative into machine learning pipelines. The research team behind the project recently released an alpha version of a new software framework called KeystoneML, which gives developers a chance to test out some of the ideas that Recht outlined in his talk last year. We devoted a portion of this Data Show episode to machine learning pipelines in general, and a discussion of KeystoneML in particular.
Since its release in May, I’ve had a chance to play around with KeystoneML and while it’s quite new, there are several things I already like about it:
KeystoneML opens up new data types
Most data scientists don’t normally play around with images or audio files. KeystoneML ships with easy to use sample pipelines for computer vision and speech. As more data loaders get created, KeystoneML will enable data scientists to leverage many more new data types and tackle new problems. Read more…
Robot wealth managers and approaches will grow and offer alternative ways of investing.
Editor’s note: This post originally published in Big Data at Mary Ann Liebert, Inc., Publishers, in Volume 3, Issue 2, on June 18, 2015, under the title “Should You Trust Your Money to a Robot?” It is republished here with permission.
Financial markets emanate massive amounts of data from which machines can, in principle, learn to invest with minimal initial guidance from humans. I contrast human and machine strengths and weaknesses in making investment decisions. The analysis reveals areas in the investment landscape where machines are already very active and those where machines are likely to make significant inroads in the next few years.
Computers are making more and more decisions for us, and increasingly so in areas that require human judgment. Driverless cars, which seemed like science fiction until recently, are expected to become common in the next 10 years. There is a palpable increase in machine intelligence across the touchpoints of our lives, driven by the proliferation of data feeding into intelligent algorithms capable of learning useful patterns and acting on them. We are living through one of the greatest revolutions in our lifestyles, in which computers are increasingly engaged in our lives and decision-making, to a degree that it has become second nature. Recommendations on Amazon or auto-suggestions on Google are now so routine, we find it strange to encounter interfaces that don’t anticipate what we want. The intelligence revolution is well under way, with or without our conscious approval or consent. We are entering the era of intelligence as a service, with access to building blocks for building powerful new applications. Read more…
As augmented reality technologies emerge, we must place the focus on serving human needs.
Register now for Solid Amsterdam, October 28, 2015 — space is limited.Augmented reality (AR), wearable technology, and the Internet of Things (IoT) are all really about human augmentation. They are coming together to create a new reality that will forever change the way we experience the world. As these technologies emerge, we must place the focus on serving human needs.
The Internet of Things and Humans
Tim O’Reilly suggested the word “Humans” be appended to the term IoT. “This is a powerful way to think about the Internet of Things because it focuses the mind on the human experience of it, not just the things themselves,” wrote O’Reilly. “My point is that when you think about the Internet of Things, you should be thinking about the complex system of interaction between humans and things, and asking yourself how sensors, cloud intelligence, and actuators (which may be other humans for now) make it possible to do things differently.”
I share O’Reilly’s vision for the IoTH and propose we extend this perspective and apply it to the new AR that is emerging: let’s take the focus away from the technology and instead emphasize the human experience.
The definition of AR we have come to understand is a digital layer of information (including images, text, video, and 3D animations) viewed on top of the physical world through a smartphone, tablet, or eyewear. This definition of AR is expanding to include things like wearable technology, sensors, and artificial intelligence (AI) to interpret your surroundings and deliver a contextual experience that is meaningful and unique to you. It’s about a new sensory awareness, deeper intelligence, and heightened interaction with our world and each other. Read more…
Our Next:Economy event aims to inspire industry leaders to rebuild the economy by solving the hard problems.
Request an invitation to Next:Economy, our event aiming to shed light on the transformation in the nature of work now being driven by algorithms, big data, robotics, and the on-demand economy.
WTF?! In San Francisco, Uber has 3x the revenue of the entire prior taxi and limousine industry.
WTF?! Without owning a single room, Airbnb has more rooms on offer than some of the largest hotel groups in the world. Airbnb has 800 employees, while Hilton has 152,000.
WTF?! Top Kickstarters raise tens of millions of dollars from tens of thousands of individual backers, amounts of capital that once required top-tier investment firms.
WTF?! What happens to all those Uber drivers when the cars start driving themselves? AIs are flying planes, driving cars, advising doctors on the best treatments, writing sports and financial news, and telling us all, in real time, the fastest way to get to work. They are also telling human workers when to show up and when to go home, based on real-time measurement of demand.The algorithm is the new shift boss.
WTF?! A fabled union organizer gives up on collective bargaining and instead teams up with a successful high tech entrepreneur and investor togo straight to the people with a local $15 minimum wage initiative that is soon copied around the country, outflanking a gridlocked political establishment in Washington.
What do on-demand services, AI, and the $15 minimum wage movement have in common? They are telling us, loud and clear, that we’re in for massive changes in work, business, and the economy.
Access not just to content, but technology.
“They learn a bit of Web stuff, and the next thing you know they think they understand programming.” I’ve heard variations of that lament for the past two decades. I’ve heard it less lately, because so many recent computing professionals built their technical careers from that bit of Web stuff.
The Web succeeded in large part because it was the easiest way for people to create electronic content and share it. Indexes and search engines made it easier to find that content. While administering files on a server and learning HTML weren’t trivial, they were much more approachable tasks than creating and distributing traditional (now largely considered desktop or native) applications.
For the most part, they still are easier.