Radar Trends to Watch: September 2022 – O’Reilly
It’s hardly news to talk about the AI developments of the last month. DALL-E is increasingly popular, and being used in production. Google has built a robot that incorporates a large language model so that it can respond to verbal requests. And we’ve seen a plausible argument that natural language models can be made to reflect human values, without raising the question of consciousness or sentience.
For the first time in a long time we’re talking about the Internet of Things. We’ve got a lot of robots, and Chicago is attempting to make a “smart city” that doesn’t facilitate surveillance. We’re also seeing a lot in biology. Can we make a real neural network from cultured neurons? The big question for biologists is how long it will take for any of their research to make it out of the lab.
- Stable Diffusion is a new text-to-image model that has been designed to run on consumer GPUs. It has been released under a license that is similar to permissive open source licenses, but has restrictions requiring the model to be used ethically.
- Researches claim that they can use a neural network to reconstruct images (specifically, faces) that humans are seeing. They use fMRI to collect brain activity, and a neural decoding algorithm to turn that activity into images that are scarily similar to the photos the subjects were shown.
- Research from Google and other institutions investigates the emergent properties of large language models: their ability to do things that can’t be predicted by scale alone.
- DALL-E’s popularity is soaring and, like Copilot, it’s being adopted as a tool. It’s fun to play with, relatively inexpensive, and it’s increasingly being used for projects like designing logos and generating thumbnail images for a blog.
- Elon Musk has announced that Tesla will have a robot capable of performing household chores by the end of 2022. That is almost certainly overly ambitious (and we hope it works better than his self-driving vehicles), but it’s no doubt coming.
- Google has demonstrated a robot that can respond to verbal statements (for example, bringing food when you say “I’m hungry”) without being trained on those specific statements; it uses a large language model to interpret the statement and determine a response.
- Molecular modeling with deep learning has been used to predict the way ice forms. This can be very important for understanding weather patterns; the technique may be applicable to developing new kinds of materials.
- Graph neural networks may be able to predict sudden flareups in burning homes, the largest cause of death among firefighters.
- While avoiding the question of whether language models are “intelligent,” Blaise Aguera y Arcas argues that language models can be trained to reflect particular moral values and standards of behavior.
- A webcam mounted on a 3-D gimbal uses AI to automatically track moving objects. This could be a step towards making virtual reality less virtual.
- A new political party in Denmark has policies determined entirely by AI. The Synthetic Party plans to run candidates for parliament in 2023.
- One irony of AI work is that neural networks are designed by human intuition. Researchers are working on new AutoML systems that can quickly and efficiently design neural networks for specific tasks.
- To succeed at deploying AI, AI developers need to understand and use DevOps methods and tools.
- Cerebras, the company that released a gigantic (850,000 core) processor, claims their chip will democratize the hardware needed to train and run very large language models by eliminating the need to distribute computation across thousands of smaller GPUs.
- Large language models are poor at planning and reasoning. Although they have done well on “common sense” benchmarks, they fail at planning tasks requiring more than one or two steps and that can’t be solved by doing simple statistics on their training data. Better benchmarks for planning and reasoning are needed to make progress.
- A GPT-3 based application can answer philosophical questions well enough to fool philosophers. The authors make it clear that the machine is not “thinking”; it was intended as an experiment to demonstrate the danger of automated plagiarism.
- DoNotPay has built a tool that finds racist language in real estate documents, and automates the process of having it removed. Not very surprisingly, it quickly discovered that clauses preventing the sale of property to non-Whites are extremely common.
- Researchers have developed analog “neurons” that can build analog neural networks programmed similarly to digital neural networks. They are potentially much faster and require much less power.
- A startup called Language I/O does machine translation by leveraging translations from Google, Facebook, and Amazon, then uses AI to choose the best and fine-tune the result, using customer-supplied vocabularies with minimal training.
- KSplit is an automated framework for isolating operating system device drivers from each other and the OS kernel. Isolating device drivers is very difficult for human programmers, but greatly reduces vulnerabilities and bugs.
- A report on the API economy says that one of the biggest obstacles is the lack of API design skills.
- A bit of history comes back to life: An archive of everything written by why the lucky stiff (aka _why) is now online. _why was a mainstay of the Ruby community in the early 2000s; he disappeared from the community and took all of his content offline when a reporter revealed his name. Well worth reading; maybe even well worth re-acquainting yourself with Ruby.
- Observability needs to “shift left”: that is, become a primary concern of developers, in addition to operations. Most observability tools are oriented towards operations, rather than software development.
- mCaptcha is a proof-of-work-based Captcha system that avoids any human interaction like identifying images. It imposes a small penalty on genuine users that actors who want to pound web sites at scale won’t be willing to pay.
- RStudio is renaming itself Posit. We don’t normally deal in corporate names, but this change is significant. Although R will remain a focus, RStudio has been looking beyond R; specifically, they’re interested in Python and their Jupyter-based Quarto publishing system.
- Google is releasing open source tools for designing chips, and funding a program that allows developers to have their custom designs built at a fabrication facility. The goal is to jump-start an open source ecosystem for silicon.
- Zero trust adoption has soared in the past year. According to Okta, 97% of the respondents to their recent “state of zero trust” survey say they have zero trust initiatives in place, or will have them within the next year.
- Google blocked a distributed denial of service attack (DDOS) against one of its cloud customers that peaked at 26 million requests per second, a record. The customer was using Google’s Cloud Armor service.
- Chatbots backed by AI and NLP are becoming a significant problem for security. Well-designed chatbots can perform social engineering, execute denial of service attacks on customer service by generating complaints, and generate fake account credentials in bulk.
- A security researcher has created a $25 tool that allows users to run custom code on terminals for the Starlink network. It requires attaching a board to your dish, but we suspect that enough Starlink users would be interested in “exploring” the satellite network to become a serious problem.
- Message Franking is a cryptographic technology that includes end-to-end encryption, but also allows abusers to be held to account for misinformation–without revealing the content of the message.
- One trick for detecting live deepfakes in video calls: ask the caller to turn sideways. Deepfake software is good at generating head-on views, but tends to fail badly at profiles.
- Bruce Schneier on cryptographic agility: We need the ability to swap in cryptographic algorithms quickly, in light of the possibility that quantum computers will soon be able to break current codes. Industry adoption of new algorithms takes a long time, and we may not have time.
- SHARPEXT is malware that installs a browser extension on Chrome or Edge that allows an attacker to read gmail. It can’t be detected by email services. Users are tricked into installing it through a phishing attack.
- Passage offers biometric authentication services that work across devices using WebAuthn. Biometric data is encrypted, of course, and never leaves the user’s device.
- Watch the progress of the American Data Privacy Protection Act, which has bipartisan support in Congress. This is the first serious attempt to provide nationwide digital privacy standards in the US.
- A lawsuit filed in California claims that Oracle is selling a detailed social graph that incorporates information about 5 billion distinct users, roughly ⅔ the population of the planet. This information was gathered almost entirely without consent.
- Finland is planning to test digital passports later this year. Volunteers with digital passports will be issued a smartphone app, rather than papers. Digital passports will require travelers to send plans to border control agencies, and a photo of them will be taken at the border.
- A startup is attempting to grow a new liver inside a human body, as an alternative to a transplant. They will inject the patient’s lymph nodes with cells that will hopefully be able to reproduce and function as an alternate liver.
- Tiny caps for tiny brains: Researchers have developed “caps” that can measure activity in brain organoids (cultured clusters of human neurons). It’s possible that groups of organoids can then be connected and networked. Is this the next neural network?
- A bioengineered cornea made from collagen collected from pig skin, could be an important step in treating keratoconus and other causes of blindness. Artificial corneas would eliminate the problem of donor shortage, and can be stored for much longer than donated corneas.
- A startup in Israel is creating artificial human embryos from human cells. These embryos, which survive for several days but are not viable, could be used to harvest very early-stage organs for transplants.
- Materials that can think: Researchers have developed a mechanical integrated circuit that can respond to physical stresses, like touch, and perform computation on those stresses, and generate digital output.
- Eutelsat, a European satellite operator, has launched a commercial “software defined satellite”: a satellite that can be reconfigured for different missions once it’s in space.
- Developing robots just got easier. Quad-SDK is an open source stack for four-legged locomotion that’s compatible with ROS, the Robot Operating System.
- Artificial intelligence isn’t just about humans. A startup is reverse-engineering insect brains to develop efficient vision and motion systems for robots.
- A Japanese firm has developed robots that are being used to stock shelves in a convenience store chain.
- Chicago’s Array of Things is an edge network for a smart city: an array of inexpensive temporary sensors to report on issues like traffic, safety, and air quality. Although the sensors include cameras, they only send processed data (not video) and can’t be used for surveillance.
- The US Department of Energy is funding research on using sensors, drones, and machine learning to predict and detect wildfires. This includes identifying power line infrastructure that’s showing signs of arcing and in need of maintenance.
- The UK is developing “flyways” for drones: Project Skyway will reserve flight paths for drone aircraft between six major cities.
- Ethereum will be moving to proof-of-stake in September. Fred Wilson has an analysis of what this will mean for the network. The current proof-of-work blockchain will continue to exist.
- Beginning in November, international payments will begin moving to blockchains, based on the ISO 20022 standard. A small number of cryptocurrencies comply with this standard. (Bitcoin and Ethereum are not on the list.)
- Application-specific blockchains, or appchains, may be the way to go, rather than using a Layer 1 blockchain like Ethereum. Appchains can be built to know about each other, making it easier to develop sophisticated applications; and why let fees go to the root blockchain’s miners?
- Cryptocurrency scans and thefts are old news these days, but now we’ve seen the first decentralized robbery. The attackers posted a “how to” on public servers, allowing others to join in the theft, and giving the original thieves cover.
- Practical quantum computers may still be years away, but Quantum Serverless is coming. For almost all users, quantum computers will be in some provider’s cloud, and they’ll be programmed using APIs that have been designed for serverless access.