DD Dasgupta explores the edge-cloud continuum, explaining how the roles of data centers and cloud infrastructure are redefined through the mainstream adoption of AI, ML, and IoT technologies. Continue reading AI, ML, and the IoT will destroy the data center and the cloud (just not in the way you think).
Ted Dunning discusses how new tools can change the way production systems work. Continue reading The answer to life, the universe, and everything: But can you get that into production?.
Executives from Cloudera and PNC Bank look at the challenges posed by data-hungry organizations. Continue reading The future of data warehousing.
Millibytes, Webpage Bloat, Neuromorphic Computing, and UX Dark Patterns
- Measuring Information in Millibytes -- a cute conceit. Therefore, the information given by one passing test run [in our 1-in-90 failure scenario] is just a little over one millibyte.
- The Developer Experience Bait-and-Switch (Alex Russell) -- a pointed observation about bloat: If one views the web as a way to address a fixed market of existing, wealthy web users, then it’s reasonable to bias toward richness and lower production costs. If, on the other hand, our primary challenge is in growing the web along with the growth of computing overall, the ability to reasonably access content bumps up in priority.
Brainchip Launches Spiking Neural Network Hardware -- Brainchip’s claim is that while a convolutional approach is more akin to modeling the neuron as a large filter with weights, the iterative linear algebra matrix multiplication on data within an activation layer and Continue reading "Four short links: 12 September 2018"
Poll results reveal where and why organizations choose to use containers, cloud platforms, and data pipelines. Mesosphere conducted a poll of approximately 1,000 IT professionals to understand where they are on their container, cloud, and data adoption. Above all, the poll shows that companies are investing heavily in migrating to containers, running those containers in the cloud, and improving their data pipelines. From outward appearances, these three pieces don’t appear related, but they go hand-in-hand. Organizations start by looking at how to improve their data pipelines. The ops team asks how they’re going to monitor hundreds of processes running on dozens of machines. Then someone says that you can monitor and separate those processes using containers. Finally, the manager looks at their hardware budget for the year and asks how much all of this new hardware will cost. The team tells the manager about the glorious land of the cloud
Continue reading "The real story on container, cloud, and data adoption"
It has become much more feasible to run high-performance data platforms directly inside Kubernetes. Kubernetes is really cool because managing services as flocks of little containers is a really cool way to make computing happen. We can get away from the idea that the computer will run the program and get into the idea that a service happens because a lot of little computing just happens. This idea is crucial to making reliable services that don’t require a ton of heroism to stand up or keep running. But there is a dark side here. Containers want to be agile because that is the point of containers in the first place. We want containers because we want to make computing more like a gas made up of indistinguishable atoms instead of like a few billiard balls with colors and numbers on their sides. Stopping or restarting containers should be cheap so
Continue reading "Progress for big data in Kubernetes"
Serverless, Predicting Personality, Broken Design, and Hamming Lectures
- Serverless Cold Start War -- hard numbers on the cold start time on different function-as-a-service providers.
- Eye Movements During Everyday Behavior Predict Personality Traits -- Using a state-of-the-art machine learning method and a rich set of features encoding different eye movement characteristics, we were able to reliably predict four of the big five personality traits (neuroticism, extraversion, agreeableness, conscientiousness) as well as perceptual curiosity, only from eye movements.
- Broken Product Design (We Make Money Not Art) -- Not only did he ask them to fabricate items that would be unusable but he also requested that each worker had full license to decide what the error, flaw, and glitch in the final product would be. Hutchison ended up with a collection of dysfunctional objects and prints of online exchanges with baffled factory managers.
Learning to Learn (Richard Hamming) -- watch lectures in computer Continue reading "Four short links: 11 September 2018"
If we’re going to think about the ethics of data and how it’s used, then we have to take into account how data flows. Data, even “big data,” doesn’t stay in the same place: it wants to move. There’s a long history of language about moving data: we have had dataflow architectures, there's a great blog on visualization titled FlowingData, and Amazon Web Services has a service for moving data by the (literal) truckload. Although the scale and speed at which data moves has changed over the years, we’ve recognized the importance of flowing data ever since the earliest years of computing. If we’re going to think about the ethics of data and how it’s used, then, we can’t just think about the content of the data, or even its scale: we have to take into account how data flows. In Privacy in Context, Helen Nissenbaum connects data’s
Continue reading "The ethics of data flow"
Optoelectronics, Checked C, MagicScroll, Quantum AWS
- The Largest Cognitive Systems Will be Optoelectronic -- Electrons and photons offer complementary strengths for information processing. Photons are excellent for communication, while electrons are superior for computation and memory. Cognition requires distributed computation to be communicated across the system for information integration. We present reasoning from neuroscience, network theory, and device physics supporting the conjecture that large-scale cognitive systems will benefit from electronic devices performing synaptic, dendritic, and neuronal information processing operating in conjunction with photonic communication.
Checked C -- This paper presents Checked C, an extension to C designed to support spatial safety, implemented in Clang and LLVM. Checked C’s design is distinguished by its focus on backward-compatibility, incremental conversion, developer control, and enabling highly performant code. Like past approaches to a safer C, Checked C employs a form of checked pointer whose accesses can be statically or dynamically verified. Performance evaluation Continue reading "Four short links: 10 September 2018"
David Patterson explains why he expects an outpouring of co-designed ML-specific chips and supercomputers. Continue reading A new golden age for computer architecture.
Joseph Sirosh tells an intriguing story about AI-infused prosthetics that are able to see, grip, and feel. Continue reading Connected arms.
Hagay Lupesko explores key trends in machine learning, the importance of designing models for scale, and the impact that machine learning innovation has had on startups and enterprises alike. Continue reading Machine learning in the cloud.
Huma Abidi discusses the importance of optimization to deep learning frameworks. Continue reading Accelerating AI on Xeon through SW optimization.
Peter Norvig says one of the most exciting aspects of AI is the diversity of applications in fields far astray from the original breakthrough areas. Continue reading The breadth of AI applications: The ongoing expansion.
Manish Goyal shows you how to best unlock the value of enterprise AI. Continue reading Four success factors for building your AI business journey.
Dawn Song explains how AI and deep learning can enable better security and how security can enable better AI. Continue reading AI and security: Lessons, challenges, and future directions.
Levent Besik explains how enterprises can stay ahead of the game with customized machine learning. Continue reading Customized ML for the enterprise.
From chaos architecture to event streaming to leading teams, the O'Reilly Software Architecture Conference offers a unique depth and breadth of content. We received more than 200 abstracts for talks for the 2018 O'Reilly Software Architecture Conference in London—on both expected and surprising topics. We continue to see strong interest in microservices and its related ecosystem, including topics like DevOps and tools like Kubernetes. The quality of the abstracts led to a stellar lineup of speakers, talks, and keynotes. Two of the outstanding features of the O'Reilly Software Architecture Conference are the depth and breadth of our content. While most conferences have a single software architecture track, our whole conference revolves around software architecture. That means we can go much deeper, covering topics that would be too rarefied for other conferences. That also means we can spread out, tackling subjects critical to success as an architect (like soft skills) but
Continue reading "10 talks to look for at the 2018 O’Reilly Software Architecture Conference in London"
Quantifying Facebook, Deep Learning IDE, REPL + Debugger, and RPC Library
- Unveiling and Quantifying Facebook Exploitation of Sensitive Personal Data for Advertising Purposes -- This paper quantifies the portion of Facebook users in the European Union (EU) who were labeled with interests linked to potentially sensitive personal data in the period prior to when GDPR went into effect. The results of our study suggest that Facebook labels 73% of EU users with potential sensitive interests. This corresponds to 40% of the overall EU population. We also estimate that a malicious third party could unveil the identity of Facebook users who have been assigned a potentially sensitive interest at a cost as low as €0.015 per user. Finally, we propose and implement a web browser extension to inform Facebook users of the potentially sensitive interests Facebook has assigned them. (via Morning Paper)
- Subgraphs -- a deep learning IDE.
REPLugger: Continue reading "Four short links: 7 September 2018"
Akhilesh Tripathi shows you how to use machine learning to identify root causes of problems in minutes instead of hours or days. Continue reading Using machine learning in workload automation.