Lifeboat Foundation

Researchers solved a differential equation behind the interaction of two neurons through synapses, creating a faster AI algorithm.

Artificial intelligence uses a technique called artificial neural networks (ANN) to mimic the way a human brain works. A neural network uses input from datasets to “learn” and output its prediction based on the given information.

Continue reading “MIT reveals a new type of faster AI algorithm for solving a complex equation” »

Take with a grain of salt and use at own risk:

Galactica is an AI trained on humanity’s scientific knowledge. You can use it as a new interface to access and manipulate what we know about the universe.

Last year, MIT developed an AI/ML algorithm capable of learning and adapting to new information while on the job, not just during its initial training phase. These “liquid” neural networks (in the Bruce Lee sense) literally play 4D chess — their models requiring time-series data to operate — which makes them ideal for use in time-sensitive tasks like pacemaker monitoring, weather forecasting, investment forecasting, or autonomous vehicle navigation. But, the problem is that data throughput has become a bottleneck, and scaling these systems has become prohibitively expensive, computationally speaking.

On Tuesday, MIT researchers announced that they have devised a solution to that restriction, not by widening the data pipeline but by solving a differential equation that has stumped mathematicians since 1907. Specifically, the team solved, “the differential equation behind the interaction of two neurons through synapses… to unlock a new type of fast and efficient artificial intelligence algorithms.”

Continue reading “MIT solved a century-old differential equation to break ‘liquid’ AI’s computational bottleneck” »

Physical dynamical processes can be modelled with differential equations that may be solved with numerical approaches, but this is computationally costly as the processes grow in complexity. In a new approach, dynamical processes are modelled with closed-form continuous-depth artificial neural networks. Improved efficiency in training and inference is demonstrated on various sequence modelling tasks including human action recognition and steering in autonomous driving.

Meta introduces ‘Tulip,’ a binary serialization protocol supporting schema evolution. This simultaneously addresses protocol reliability and other issues and assists us with data schematization. Tulip has multiple legacy formats. Hence, it is used in Meta’s data platform and has seen a considerable increase in performance and efficiency. Meta’s data platform is made up of numerous heterogeneous services, such as warehouse data storage and various real-time systems exchanging large amounts of data and communicating among themselves via service APIs. As the number of AI and machine learning ML-related workloads in Meta’s system increase that use data for training these ML models, it is necessary to continually work on making our data logging systems efficient. The schematization of data plays a huge role in creating a platform for data at Meta’s scale. These systems are designed based on the knowledge that every decision and trade-off impacts reliability, data preprocessing efficiency, performance, and the engineer’s developer experience. Changing serialization formats for the data infrastructure is a big bet but offers benefits in the long run that make the platform evolve over time.

The Data Analytics Logging Library is present in the web tier and the internal services, and this is also responsible for logging analytical and operational data using Scribe-a durable message queuing system used by Meta. Data is read and ingested from Scribe, which also includes a data platform ingestion service and real-time processing systems. The data analytics reading library helps deserialize data and rehydrate it into a structured payload. Logging schemas are created, updated, and deleted every month by thousands of engineers at Meta, and these logging schema data flows in petabytes range each and every day over Scribe.

Schematization is necessary to ensure that any message logged in the past, present, or future, depending on the (de) serializer’s version, can be reliably (de)serialized at any time with the utmost fidelity and no data loss. Safe schema evolution via backward and forward compatibility is the name given to this characteristic. The article’s main focus lies on the on-wire serialization format used to encode the data that is finally processed by the data platform. Compared to the two serialization formats previously utilized, Hive Text Delimited and JSON serialization, the new encoding format is more efficient, requiring 40 to 85 percent fewer bytes and 50 to 90 percent fewer CPU cycles to (de)serialize data.

You might expect a comic book series featuring art generated entirely by artificial intelligence to be full of surreal images that have you tilting your head trying to grasp what kind of sense-shifting madness you’re looking at.

Not so with the images in The Bestiary Chronicles, a free, three-part comics series from Campfire Entertainment, a New York-based production house focused on creative storytelling.

The visuals in the trilogy — believed to be the first comics series made with AI-assisted art — are stunning. They’re also stunningly precise, as if they’ve come straight from the hand of a seasoned digital artist with a very specific story and style in mind.

Bringing together concepts from electrical engineering and bioengineering tools, Technion and MIT scientists collaborated to produce cells engineered to compute sophisticated functions— biocomputers of sorts.

Graduate students and researchers from Technion—Israel Institute of Technology Professor Ramez Daniel’s Laboratory for Synthetic Biology & Bioelectronics worked together with Professor Ron Weiss from the Massachusetts Institute of Technology to create genetic “devices” designed to perform computations like artificial neural circuits. Their results were recently published in Nature Communications.

The genetic material was inserted into the bacterial cell in the form of a plasmid: a relatively short DNA molecule that remains separate from the bacteria’s “natural” genome. Plasmids also exist in nature, and serve various functions. The research group designed the plasmid’s genetic sequence to function as a simple computer, or more specifically, a simple artificial neural network. This was done by means of several genes on the plasmid regulating each other’s activation and deactivation according to outside stimuli.

It’s not really a Star Wars story unless there’s a lovable or memorable droid stealing the spotlight. But, when you really stop and think about it, there’s also something profoundly tragic about the role artificial lifeforms play in the Star Wars universe.

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Continue reading “The Tragedy of Droids in Star Wars” »

It’s commonly believed that many of these prehistoric animals were vicious carnivores.

Researchers used artificial intelligence (AI) to discover information on dinosaurs, specifically to find out if a certain dinosaur was a predator or not. AI has shown that the dinosaur typically thought to be a carnivore may actually have been an herbivore.

The new analysis was done by using deep convolutional neural networks (DCNNs) — computing systems based on neural networks of the brain — to track the fossils of these prehistoric reptiles.

Continue reading “AI predicts that a dinosaur thought to be predatory may have been an herbivore” »