Technology
Drone versus drone combat is bringing a new kind of warfare to Ukraine
As both sides of the ongoing Russia-Ukraine war attempt to claim technological supremacy, drone versus drone combat has become routine – and thanks to the ubiquity of cameras, this new kind of warfare is being documented in real time across air, land and sea.
Russia’s invasion of Ukraine has already been dubbed the “first drone war”, with some sources suggesting that small drones are inflicting as much as 80 per cent of the damage to military hardware and personnel. Now, the war has evolved, with machines…
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Inference framework Archon promises to make LLMs quicker, without additional costs
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Researchers from Stanford University‘s Scaling Intelligence Lab introduced a new inference framework that could help large language models (LLMs) go through potential responses faster.
The framework, Archon, uses an inference-time architecture search (ITAS) algorithm to improve LLMs performance without additional training. It is model agnostic, open-source and designed to be plug-and-play for large and small models.
Archon could ideally help developers design AI model systems using multiple inference-time techniques to cut down on models to determine responses. The Scaling Intelligence Lab said techniques like Archon would help cut down on costs related to building models and inference. As LLM development turns toward larger parameters or more advanced reasoning, costs could increase despite companies like OpenAI anticipating more affordability.
According to the researchers, Archon automatically designs architectures that improve task generalization, enabling models to perform tasks beyond those they were initially trained on.
“Our Archon framework and ITAS algorithm draw inspiration from neural architectures and neural architecture search, respectively,” the researchers said in their paper. “Archon is constructed of layers of LLMs, in which models in the same layer run in parallel but each later runs sequentially.”
These layers perform different inference-time techniques, “either transforming the number of candidate responses through generation and fusion (like linear transformations) or reducing the number of candidate responses to improve quality (like non-linearities).”
Archon outperformed GPT-4o and Claude 3.5 Sonnet by 15.1 percentage points in benchmark tests such as MT-Bench, Arena-Hard-Auto, Alpaca-2.0 Eval, MixEval, MixEval Hard, MATH and CodeContests. When Archon faced open-source LLMs, it outperformed them by 11.2 percentage points.
Archon components
The ITAS algorithm is comprised of several LLM components and can do inference-time techniques.
The first component is the Generator, which creates possible answers for the model. The second component, the Guser, will take these responses and combine them into one. An example would be if the question posed to a model wants to know the capital of France, the fuser will take the generated responses of “the capital of France is Paris,” France is in Europe,” and turn it to “the capital of France, a country in Europe, is Paris.”
Next, Archon moves to the Ranker component, which ranks the best answers. A Critic component evaluates the ranked answers to determine whether they’re good or bad. The Verifier checks for logic and correctness before moving on to the Unit Test Generator and Evaluator, which do small tests to see if the response works and check the test results.
By building Archon this way, the researchers said the framework improves the quality of LLMs’ responses faster and without additional fine-tuning.
Archon’s limitations
So far, the Archon framework works best with LLMs with 70B parameters or more like Meta’s Code Llama 70B, making it difficult to point to most LLMs right now. The researchers said most of the challenge comes from the smaller model’s limited capabilities to follow instructions due to the smaller context windows.
“When we utilize the Archon architecture with only 7B open-source models, we get a notable decrease of 16%,” in performance, the paper stated.
Smaller models using the Archon framework lagged behind single-turn models by 15.7%.
The Stanford lab also said Archon “is not ideal for tasks that prefer the latency of a single LLM call,” such as chatbots. The framework makes multiple LLM calls because of the different operations it does so single question-and-answer queries won’t benefit from its capabilities. Archon may work better for tasks involving complex instructions like solving equations, programming, or even complicated customer service issues.
Despite its limitations, the researchers behind Archon said they hope it can accelerate the development of high-performing models without requiring more inference and training capital.
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Technology
Instant harkens back to a pre-Google Firebase
Instant wants to recapture some of the magic of the pre-Google-acquisition Firebase by building a modern, Postgres-based open source version of it.
Like the original Firebase, San Francisco-based Instant focuses on giving front-end developers easy access to a real-time database with offline capabilities without the need to manage any of the back-end architecture. The service supports applications written in React, React Native, and vanilla JavaScript. In addition to the database, it also offers tools to manage authentication and permissions, as well as real-time features like shared cursors, presence, and more.
The team recently open sourced the project.
Instant was founded by former roommates Joe Averbukh (CEO) and Stepan Parunashvili, who previously worked at companies like Wit.ai, Facebook, Airbnb, and others.
At Facebook, Averbukh told me, the architecture was set up to ensure that as a front-end engineer, he was able to move quickly. Meanwhile, at Airbnb, the company’s intricate microservices architecture meant it took a long time to try a new idea. “Thankfully, we already had Firebase inside Airbnb, and we could use it for notifications. So we would actually just use Firebase to prototype these ideas — and that was a huge win,” he explained.
Before starting Instant, the co-founders worked on a fitness app, based on Google’s Firebase. While they loved all of the features like optimistic updates and offline mode that Firebase offers out of the box, they missed having access to a relational database since Firebase’s focus is on its NoSQL database.
“You see apps like Figma, Notion, and Linear — they’re winning the market because of this feature set that they have,” Averbukh said. “And we think that basically, in the same way that apps today are much better than apps before, the expectations are going to rise for what people want in their apps. But the reality is, the way that people build these features today is that they have this whole team that’s building all of these sync services.”
At the core of all of this is Aurora, AWS’s relational database service, which is cost-effective and allows Instant to quickly spin up new databases inside of a multi-tenant architecture that still offers strong security guarantees. Averbukh argues that competitors like Supabase have to spin up a new database for every project (and free user), while Instant essentially lives in one large Postgres instance. He also stressed that if a large enterprise customer would sign up for the service, the team could offer them to spin them up a separate database.
On Wednesday, the company announced that it has raised a $3.4 million seed funding round. It’s backed by Y Combinator and SV Angel, as well as prominent angel investors like former Firebase CEO James Tamplin, Paul Graham, Greg Brockman, and Jeff Dean.
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A Bank of America outage showed customers blanked-out account balances
First, it was Spotify, then Verizon, then PlayStation, and now it’s apparently Bank of America’s turn to have an outage this week. Some customers reported problems accessing their bank accounts on Wednesday, with the balances showing $0 or “—-” instead of what should be there.
“Some mobile and online banking clients experienced an issue accessing their accounts and balance information earlier today,” writes Bank of America media relations executive Matt Card in an email to The Verge. “These technology issues have been fully resolved. We apologize for any inconvenience.”
Reports spiked just before 1PM ET on Downdetector, with posts across X and Reddit from people reporting that their account balances don’t show anything (some mentioned that the amounts they owe, however, appear to be displaying accurately).
We verified this afternoon that the bank’s app was showing customers a notification that “Accounts temporarily unavailable” and that “Some accounts and/or balances are temporarily unavailable,” as reported previously by CNN. Some people have reported their accounts are working or intermittently gaining access to their information, and it’s unclear how many people have been affected. In July, Bank of America said 58 million clients use its “digital capabilities to help manage their financial lives” and that they connected a record 23.4 billion times last year.
There’s no word on what might have caused the problem, but let us know if things are working for you now.
Update, October 3rd: Added updated Bank of America statement.
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Home Server SET-UP with the New Riveco 18U Server Rack
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