The O'Reilly Radar Podcast: Dele Atanda and Mutaz Qubbaj talk about their startup platforms and the disruption in fintech.
Learn more about Next:Money, O’Reilly’s conference focused on the fundamental transformation taking place in the finance industry.
In this Radar Podcast episode, I chat with Dele Atanda, founder and CEO of Digitteria, about the disruptive state of the financial tech industry, what he thinks is driving that disruption, and why smart data (as opposed to big data) is going to revolutionize finance. I also talk with Mutaz Qubbaj, CEO and co-founder of Squirrel, about about the Squirrel platform, accelerator programs, and how he views the big disruptors in fintech landscape.
We’ve started an investigation — and launched a new summit, Next:Money — here at O’Reilly to look into the disruption happening in the fintech industry, as burgeoning startups create services and products that threaten to disaggregate traditional finance incumbents. I recently had the opportunity to sit down with a number of fintech startup founders and will be featuring several of those conversations in upcoming Radar Podcasts.
Dele Atanda founded Digitteria, a startup developing sustainable identity and personal data management solutions for both consumers and enterprise. I asked him why the time is ripe for disruption — he pointed to the growing complexity of the landscape and compared the current state of the finance industry to the early stages of the Web:
There’s a significant increase in complexity, and in that complexity there’s a much more detailed and rich ecosystem. Banks, it’s difficult for them to be able to tackle all the ends and elements efficiently, so it’s interesting because it’s almost representative of, it’s lacking the evolution of the Web, but it mirrors it in very many ways. Initially, you had these monolithic sort of applications, or browsers, or services that tried to do lots of things, and then we moved into the mobile era where things became much more siloed and application centric, where you did one thing particularly well. That’s inevitably going to happen in the fintech space. They say that the currency of the industrial era was paper, and the currency of the knowledge era is the electron.
Money is primarily electronic now, so it’s inevitable that there’s going to become this confluence between the Web and finance in that regard. I think, of course, because of security, regulatory issues, and the cultural dimension of money, there’s been a lag and resistance. Now that the Web has reached a level of maturity that it can address those issues, I think that’s inevitable.
A look at the issues and trends in deploying beacon-based solutions.
Save 25% on registration for Solid with code SLD25. Solid, O’Reilly’s conference on hardware and the Internet of Things, covers topics like beacons — and everything else you need to know in order to build intelligent, connected devices.
In this post, the third of our series, I’ll look at some of the issues in using and deploying beacon-based solutions, some of the trends in both hardware and software offerings, and share some resources where you can find more detail on everything I’ve covered so far.
First off, let’s take a look at some of the topics that can cause issues, or trip us up when starting to consider a new iBeacon-based solution. In no particular order, these include:
Notifications, Notifications, Notifications
I have many conversations with potential clients that go something like this (in condensed form):
Client: “We want to do a beacon project”
Me: “Great, what are you thinking of?”
Client: “We want to trigger messages to people as they come past or in to our place on their phones”
Me: “OK, is this iOS only?”
Client: “No – of course not – in fact, most of our users are on Android”
Me: “Ah – ok… well…”
(long conversation follows)
With the initial hype and excitement around iBeacon, it’s understandable that there’s some confusion around what’s possible, and what’s not, across all the mobile handsets available today. For now, I’ll focus on just iOS and Android, though we could usefully expand to include Windows and Blackberry. Read more…
Cheap, accessible, open hardware is driving the IoT.
The Internet of Things (IoT) has been committing a lot of buzzword imperialism lately. It’s a hot term, marching across the technological countryside and looking for rich disciplines to capture. Electronics, manufacturing, and robotics, among others, have all become dominions of the IoT. The result is that the meaning of IoT has broadened to include practically anything that involves 1. technology, and 2. something physical.
At the same time, practitioners have been trying to escape the IoT — and its early association with Internet-connected refrigerators — for years. Big enterprises that want to develop serious applications for the Internet of Things have come up with other terms for what they’re doing, like Internet of Everything (Cisco) and Industrial Internet (GE).
Let’s put a stop to this and define some boundaries. In my view, the Internet of Things is the result of a much larger and more important movement that’s about making the physical environment accessible in the same way that the Internet has become accessible over the last 20 years. I’ll call this the “new hardware movement.” Read more…
The conference will take place January 20-22, 2016, in San Francisco, and we want to hear from you.
Submit a proposal to speak at The O’Reilly Design Conference: Design the Future, our new design conference looking at the convergence of design, programming, and business.
Last year, I had a conversation with Tim O’Reilly about the emergence of design in unexpected places — in the data community, in the programming community, and in the business community. Design has become increasingly important as technology and bandwidth become commoditized. Our conversation initiated what would become a new program for us here at O’Reilly, and our relatively recent focus on design has reached a milestone: I’m thrilled to announce the first O’Reilly Design Conference: Design the Future, coming to San Francisco in January 2016. Read more…
The O'Reilly Data Show Podcast: Patrick Wendell on the state of the Spark ecosystem.
As organizations shift their focus toward building analytic applications, many are relying on components from the Apache Spark ecosystem. I began pointing this out in advance of the first Spark Summit in 2013 and since then, Spark adoption has exploded.
With Spark Summit SF right around the corner, I recently sat down with Patrick Wendell, release manager of Apache Spark and co-founder of Databricks, for this episode of the O’Reilly Data Show Podcast. (Full disclosure: I’m an advisor to Databricks). We talked about how he came to join the UC Berkeley AMPLab, the current state of Spark ecosystem components, Spark’s future roadmap, and interesting applications built on top of Spark.
User-driven from inception
From the beginning, Spark struck me as different from other academic research projects (many of which “wither away” when grad students leave). The AMPLab team behind Spark spoke at local SF Bay Area meetups, they hosted 2-day events (AMP Camp), and worked hard to help early users. That mindset continues to this day. Wendell explained:
We were trying to work with the early users of Spark, getting feedback on what issues it had and what types of problems they were trying to solve with Spark, and then use that to influence the roadmap. It was definitely a more informal process, but from the very beginning, we were expressly user-driven in the way we thought about building Spark, which is quite different than a lot of other open source projects. We never really built it for our own use — it was not like we were at a company solving a problem and then we decided, “hey let’s let other people use this code for free”. … From the beginning, we were focused on empowering other people and building platforms for other developers, so I always thought that was quite unique about Spark.
AI scares us because it could be as inhuman as humans.
Although I believe we’ve entered the age of postmodern computing, when we don’t trust our software, and write software that doesn’t trust us, I’m not particularly concerned about AI. AI will be built in an era of distrust, and that’s good. But there are some bigger issues here that have nothing to do with distrust.
What do we mean by “artificial intelligence”? We like to point to the Turing test; but the Turing test includes an all-important Easter Egg: when someone asks Turing’s hypothetical computer to do some arithmetic, the answer it returns is incorrect. An AI might be a cold calculating engine, but if it’s going to imitate human intelligence, it has to make mistakes. Not only can it make mistakes, it can (indeed, must be) be deceptive, misleading, evasive, and arrogant if the situation calls for it.
That’s a problem in itself. Turing’s test doesn’t really get us anywhere. It holds up a mirror: if a machine looks like us (including mistakes and misdirections), we can call it artificially intelligent. That begs the question of what “intelligence” is. We still don’t really know. Is it the ability to perform well on Jeopardy? Is it the ability to win chess matches? These accomplishments help us to define what intelligence isn’t: it’s certainly not the ability to win at chess or Jeopardy, or even to recognize faces or make recommendations. But they don’t help us to determine what intelligence actually is. And if we don’t know what constitutes human intelligence, why are we even talking about artificial intelligence? Read more…