Lifeboat Foundation

BlueOcean, a startup leveraging AI and machine learning to measure brand sentiment, has raised $15 million in capital.

Saving $100 billion in lost productivity over the next decade.

Boston Dynamics’ Spot could be going to Mars. The dog-like robot is now equipped with AI and could be sent on missions to autonomously explore caves.

GPT-J and GPT-Neo, the open-source alternatives to GPT-3, are among the best NLP models as of this writing. But using them effectively can take practice. Few-shot learning is an NLP technique that works very well with these models.

GPT-J and GPT-Neo.

GPT-Neo and GPT-J are both open-source NLP models, created by EleutherAI (a collective of researchers working to open source AI).

The future of fighter jets is coming, seemingly with more international power and disruptive technologies than predicted. As the US forges ahead to become 1st nation field sixth-generation fighter jet, other major air forces fear falling behind in the competitive race. The US, Europe, Japan, and China have made unbelievable investments looking for unique next-level capabilities like stealth, robust avionics, and navigation systems to present the most technologically advanced fighter jet. But one particular trend is crucial for all 6th gen prototypes. Artificial Intelligence is about to begin a new era of air combat.

Nvidia today announced the release of TensorRT 8, the latest version of its software development kit (SDK) designed for AI and machine learning inference. Built for deploying AI models that can power search engines, ad recommendations, chatbots, and more, Nvidia claims that TensorRT 8 cuts inference time in half for language queries compared with the previous release of TensorRT.

Models are growing increasingly complex, and demand is on the rise for real-time deep learning applications. According to a recent O’Reilly survey, 86.7% of organizations are now considering, evaluating, or putting into production AI products. And Deloitte reports that 53% of enterprises adopting AI spent more than $20 million in 2019 and 2020 on technology and talent.

TensorRT essentially dials a model’s mathematical coordinates to a balance of the smallest model size with the highest accuracy for the system it’ll run on. Nvidia claims that TensorRT-based apps perform up to 40 times faster than CPU-only platforms during inference, and that TensorRT 8-specific optimizations allow BERT-Large — one of the most popular Transformer-based models — to run in 1.2 milliseconds.

Untether AI, a startup developing custom-built chips for AI inferencing workloads, today announced it has raised $125 million from Tracker Capital Management and Intel Capital. The round, which was oversubscribed and included participation from Canada Pension Plan Investment Board and Radical Ventures, will be used to support customer expansion.

Increased use of AI — along with the technology’s hardware requirements — poses a challenge for traditional datacenter compute architectures. Untether is among the companies proposing at-memory or near-memory computation as a solution. Essentially, this type of hardware builds memory and logic into an integrated circuit package. In a “2.5D” near-memory compute architecture, processor dies are stacked atop an interposer that links the components and the board, incorporating high-speed memory to bolster chip bandwidth.

Founded in 2018 by CTO Martin Snelgrove, Darrick Wiebe, and Raymond Chik, Untether says it continues to make progress toward mass-producing its RunA1200 chip, which boasts efficiency with computational robustness. Snelgrove and Wiebe claim that data in their architecture moves up to 1000 times faster than is typical, which would be a boon for machine learning, where datasets are frequently dozens or hundreds of gigabytes in size.

Building machines that can make decisions based on common sense is no easy feat. A machine must be able to do more than merely find patterns in data; it also needs a way of interpreting the intentions and beliefs behind people’s choices.

At the 2021 International Conference on Machine Learning (ICML), Researchers from IBM, MIT, and Harvard University have come together to release a DARPA “Common Sense AI” dataset for benchmarking AI intuition. They are also releasing two machine learning models that represent different approaches to the problem that relies on testing techniques psychologists use to study infants’ behavior to accelerate the development of AI exhibiting common sense.

This research work is the first of its kind to use psychology to create more fluid and better AI systems. It aims to develop machine commonsense that makes sense because it has innate human qualities – such as intuition, common knowledge, or understanding of social cues.