Skip to content


Links for 2014-12-17

  • ‘Machine Learning: The High-Interest Credit Card of Technical Debt’ [PDF]

    Oh god yes. This is absolutely spot on, as you would expect from a Google paper — at this stage they probably have accumulated more real-world ML-at-scale experience than anywhere else. ‘Machine learning offers a fantastically powerful toolkit for building complex systems quickly. This paper argues that it is dangerous to think of these quick wins as coming for free. Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning. The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible. These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns. [….] ‘In this paper, we focus on the system-level interaction between machine learning code and larger systems as an area where hidden technical debt may rapidly accumulate. At a system-level, a machine learning model may subtly erode abstraction boundaries. It may be tempting to re-use input signals in ways that create unintended tight coupling of otherwise disjoint systems. Machine learning packages may often be treated as black boxes, resulting in large masses of “glue code” or calibration layers that can lock in assumptions. Changes in the external world may make models or input signals change behavior in unintended ways, ratcheting up maintenance cost and the burden of any debt. Even monitoring that the system as a whole is operating as intended may be difficult without careful design. Indeed, a remarkable portion of real-world “machine learning” work is devoted to tackling issues of this form. Paying down technical debt may initially appear less glamorous than research results usually reported in academic ML conferences. But it is critical for long-term system health and enables algorithmic advances and other cutting-edge improvements.’

    (tags: machine-learning ml systems ops tech-debt maintainance google papers hidden-costs development)

  • The FBI Used the Web’s Favorite Hacking Tool to Unmask Tor Users | WIRED

    Since Operation Torpedo [use of a Metasploit side project], there’s evidence the FBI’s anti-Tor capabilities have been rapidly advancing. Torpedo was in November 2012. In late July 2013, computer security experts detected a similar attack through Dark Net websites hosted by a shady ISP called Freedom Hosting—court records have since confirmed it was another FBI operation. For this one, the bureau used custom attack code that exploited a relatively fresh Firefox vulnerability—the hacking equivalent of moving from a bow-and-arrow to a 9-mm pistol. In addition to the IP address, which identifies a household, this code collected the MAC address of the particular computer that infected by the malware. “In the course of nine months they went from off the shelf Flash techniques that simply took advantage of the lack of proxy protection, to custom-built browser exploits,” says Soghoian. “That’s a pretty amazing growth … The arms race is going to get really nasty, really fast.”

    (tags: fbi tor police flash security privacy anonymity darknet wired via:bruces)

Comments closed