Trends in AI, COVID-19, Programming and more.
September 1, 2020
Compared to the last few months, there are relatively few items about COVID. And almost no items about Blockchains, though the one item I’ve listed (China’s Blockchain Services Network) may be the most important item here. On the other hand, I’m seeing a steady stream of articles about various forms of no-code/low-code programming. While many programmers scoff at the idea of programming-without-programming, spreadsheets are an early example of low-code programing. Excel is hardly insignificant. It’s also worth noting how the discussion of ethics and AI has changed in the past few months: most discussions now explicitly raise the issue of power and domination.
- There is serious talk of a “Deep Learning recession” due, among other things, to a collapse in job postings. Short-term effect of COVID or long term trend?
- An excellent analysis of participation in machine learning: how it is used, and how it could be used to build fair systems and mitigate power imbalances.
- Fairness and Machine Learning is an important new (academic) book by Solon Barocas, Moritz Hardt, and Arvind Narayanan. It’s currently an incomplete draft, available (free) online.
- A draft document from NIST describes Four Principles of Explainable Artificial Intelligence. The ability to explain decisions made by AI systems is already important, and will become more so.
- SAIL-ON is a DARPA-funded research project to develop AI systems that can deal with novelty (such as the COVID pandemic), starting with unexpected situations (such as changes to the rules) in board games.
- Is NLP research pursuing the right goals? While GPT-3 is impressive, it doesn’t demonstrate anything like comprehension (ordering the relationships found in a story). (David Ferrucci, Elemental Cognition). Likewise, Gary Marcus argues that GPT-3 can put together sentences that sound good, but that it has no idea what it’s talking about.
- What happens when you combine a relational database with git? You get a Dolt, a database that enables collaboration on datasets. You might get a solution to the problem of data versioning–and a big step towards CI/CD pipelines for AI applications.
- Cough recognition? AI to locate people who cough in space and time. A very questionable tool for pandemic fighting.
- Patient-led research on COVID-19 is an organization to help long-term COVID patients share their observations. This is reminiscent of PatientsLikeMe, and related to other trends in re-envisioning healthcare.
- Turning a Google Sheet into an app without code is yet another example of the low-code trend.
- Chris Lattner (one of the co-creators of LLVM and of Swift) has an interesting AMA that, among other things, talks about integrating machine learning with compilers, and machine learning as its own programming paradigm.
- Contextual engineering is fundamentally simple: consider “why” before thinking about “how.” But ignoring “why” is at the heart of so many engineering failures throughout history. And understanding “why” often requires “immersion in the local culture.” This is starting to sound like an extended version of bounded context.
Cloud and Microservices
- K3s is a stripped-down Kubernetes designed (among other things) for IoT and Edge Computing. I’ve thought for some time that Kubernetes needs simplification. Is this it?
- Microsoft announces Open Service Mesh for managing communications between microservices. OSM is based on the Service Mesh Interface, and is an alternative to Google’s Istio, which has a reputation for being difficult, and has become controversial.
- SMOKEstack is Redmonk’s “alternative stack” for multi-cloud environments (and perhaps doing an end-run around Amazon’s hegemony). SMOKE stands for Serviceful, Mashable, Open, K(C)composable, Event-driven.
- IBM’s RoboRXN is a “chemistry lab in a cloud” that’s designed for drug synthesis; you design an experiment, which executes in a robotic lab. The idea is similar to Max Hodak’s Transcriptic (now Strateos; Max is now founder and president of Elon Musk’s Neuralink). But IBM adds some twists: you design a molecule with a graphical (low-code) interface, and the actual process is filled in using AI.
- Amazon’s Braket service: true quantum computing in the cloud, and now available to the public. IBM and Microsoft already have quantum computers available through their cloud offerings; Google will eventually follow. We’re still at the tire kicking stage, since none of these machines can do real work yet.
- NIST has announced a number of cryptographic algorithms that can’t currently be broken by quantum computers. This is a significant step towards quantum-proof encryption.
- TRUSTS is a data market funded by the European Commission. MIT Tech Review has a good explanation. It’s a little surprising to see this coming out of the EU, but once people have the right to privacy, the right to sell data is not far behind. Individuals don’t participate in the market as individuals; the trust handles (and enforces) the transactions and pays dividends.
- One of the biggest problems with privacy and identity has been developing the infrastructure for managing public keys. Sidetree is a protocol for decentralized public key infrastructure based on a blockchain.
- China’s blockchain infrastructure (BSN) supplies the infrastructure for a global financial services network. It is not a blockchain, but a network for building interoperable blockchain applications that is targeted at small to medium businesses, with the intent of making RMB a viable global currency. The US Federal Reserve has also released plans for cryptocurrency: They are planning to launch a service called FedNow in 2-3 years. They are way behind the Chinese.
- A pre-release of a paper describes zero-downtime deployments at Facebook scale. There’s a good thread on Twitter discussing the paper.
- An algorithm for controlling fairness and bias in search results rations exposure, preventing a popular link from gaining clicks relative to similar links, while still maximizing usefulness.
- Twitter has released their new API. After abusing their developers a decade ago, will this make any difference?
- In iOS 14, Apple will be requiring opt-in for tracking users’ web activity. Facebook is not happy about this; targeted advertising depends critically on user tracking. Google (which has been gradually implementing other limitations on advertising technology) has been quiet about it.