We propose the In-context Autoencoder (ICAE), leveraging the power of a large language models (LLM) to compress a long context into short compact memory slots that can be directly conditioned on by the LLM for various purposes. ICAE is first pretrained using both autoencoding and language modeling objectives on massive text data, enabling it to generate memory slots that accurately and comprehensively represent the original context; Then, it is fine-tuned on instruction data for producing desirable responses to various prompts. Experiments demonstrate that our lightweight ICAE, introducing fewer than 1% additional parameters, effectively achieves 4X context compression based on Llama, offering advantages in both improved latency and GPU memory cost during inference, and showing an interesting insight in memorization as well as potential for scalability. These promising results imply a novel perspective on the connection between working memory in cognitive science and representation learning in LLMs, revealing ICAE's significant implications in addressing the long context problem and suggesting further research in LLM context management. Our data, code and model are released at https://github.com/getao/icae.

Future focused-organizations are harnessing decoupled, composable, microservices, API-first technologies to increase speed to market, reduce costs and move away from monolithic platforms, enterprise software and vendor lock-ins. According to the minds behind DCPL, the decoupled accelerator, (pronounced decouple) a revolution among businesses of all sizes is definitely underway. Today's developers are ditching legacy tools and techniques and in favor of best-of-breed components easily available via the cloud and APIs. This switch has led to explosive growth with composable infrastructure, a $4 billion market, projected to exceed $23 billion by 2030. AI and ML News: An Investment Into Artificial Intelligence as Daktela Buys Coworkers.ai

Web3 is a new iteration of the web driven by blockchain technology. It has the potential to form a new digital economy with a greater social impact than conventional economies, providing clearly defined benefits and secure environments for success. NTT DOCOMO will bring its expertise in telecommunications networks and digital services, as well as its experience working on society-wide issues. Accenture will help build an operational foundation for the initiatives with a view to future global expansion, leveraging the knowledge gained through its work on regional development efforts, including that with Aizu Wakamatsu City in Fukushima. Web3 is already being used in Japan to provide valuable solutions for society.

Peach, the global market leader in video advertising workflow and delivery has announced support of Netflix's new ad-supported service Basic with Ads. To coincide with the launch of the service, Peach has launched new destinations enabling clients to deliver ads to Netflix across multiple territories including UK, Australia, Germany, France, Italy, Spain, Mexico, Brazil with more to follow. Peach provides a connected advertising workflow, enabling clients to get their ads delivered to Netflix straight from the edit suite, while ensuring the highest possible quality, formatting and accuracy. Doug Conely, Chief Product and Technology Officer at Peach, said: "This is a pivotal moment for TV advertising. As leaders in global creative ad delivery for over 25 years, we've seen ad spend in Connected TV grow rapidly in the UK* and the rest of the world, and we expect to see further acceleration of growth driven by ad-supported tiers such as Netflix. AI and ML News: An Investment Into Artificial Intelligence as Daktela Buys Coworkers.ai "Netflix's Basic with Ads will bring our clients new audiences in a premium environment, creating opportunities for more addressable and premium content.

Lockheed Martin has partnered with Red Hat to enable small military platforms to handle increased artificial intelligence workloads.

Red Hat, the world's leading provider of open source solutions, and IBM Research announced Project Wisdom, the first community project to create an intelligent, natural language processing capability for Ansible and the IT automation industry. Using an artificial intelligence (AI) model, the project aims to boost the productivity of IT automation developers and make IT automation more achievable and understandable for diverse IT professionals with varied skills and backgrounds. According to a 2021 IDC prediction1, "by 2026, 85% of enterprises will combine human expertise with AI, ML, NLP, and pattern recognition to augment foresight across the organization, making workers 25% more productive and effective. Technologies such as machine learning, deep learning, natural language processing, pattern recognition, and knowledge graphs are producing increasingly accurate and context-aware insights, predictions, and recommendations." Project Wisdom – underpinned by AI foundation models derived from IBM's AI for Code efforts – works by enabling a user to input a command as a straightforward English sentence.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! More often than not, when organizations deploy applications across hybrid and multicloud environments, they use the open-source Kubernetes container orchestration system. Kubernetes itself helps to schedule and manage distributed virtual compute resources and isn't optimized by default for any one particular type of workload, that's where projects like Kubeflow come into play. For organizations looking to run machine learning (ML) in the cloud, a group of companies including Google, Red Hat and Cisco helped to found the Kubeflow open-source project in 2017.

AI can be an intimidating field to get into, and there is a lot that goes into deploying an AI application. But if you don't choose the right tools, it can be even more difficult than it needs to be. Luckily, the work that Intel and Red Hat are doing is easing the burden for businesses and developers. They'll discuss machine learning and natural language processing; using the OpenVINO AI toolkit with Red Hat OpenShift; and the life cycle of an AI intelligent application. Ryan Loney: Everything today has some intelligence embedded into it.

This month, we get stuck into AI and consciousness. This topic has long been much-discussed, and especially so recently with one tweet in particular sparking a debate online. Joining the discussion this time are: Tom Dietterich (Oregon State University), Stephen Hanson (Rutgers University), Sabine Hauert (University of Bristol), Holger Hoos (Leiden University), Sarit Kraus (Bar-Ilan University) and Michael Littman (Brown University). Stephen Hanson: So, the topic of consciousness has come up a lot recently in discussions on the Connectionists. This area of cognitive science was pretty much wiped out in the first five years of NeurIPS [Conference on Neural Information Processing Systems].

The distributed, granular nature of edge computing – where an "edge device" could mean anything from an iPhone to a hyper-specialized IoT sensor on an oil rig in the middle of an ocean – is reflected in the variety of its enterprise use cases. There are some visible common denominators powering edge implementations: Containers and other cloud-native technologies come to mind, as does machine learning. But the specific applications of edge built on top of those foundations quickly diversify. "Telco applications often have little in common with industrial IoT use cases, which in turn differ from those in the automotive industry," says Gordon Haff, technology evangelist, Red Hat. This reflects the diversity of broader edge computing trends he sees expanding in 2022.