Cloudflare’s Traffic Manager and Traffic Predictor: AI-Powered Tools for Reliable Global Network Performance

Cloudflare, a leading content delivery network (CDN) and security services provider has unveiled its machine learning (ML)-based Traffic Manager and Traffic Predictor tools. These tools are designed to help Cloudflare maintain reliable global network performance by automating traffic routing and predicting traffic flow shifts.

Traffic Manager

Traffic Manager is a real-time load balancer that helps to keep Cloudflare’s products fast and reliable by only shifting necessary traffic away from data centers that are having issues. Traffic Manager uses AI and ML to automatically detect data center user access troubles and withdraw anycast routes from the affected data center until users no longer see issues. Once it receives notification that the impacted data center can absorb traffic again, it puts the anycast routes back.

Traffic Predictor

Traffic Predictor is a tool designed to predict traffic flow shifts based on real-world tests. Every time Cloudflare adds a new data center or a new peering session, the distribution of traffic changes. Plus, the vendor has 12,500 peering sessions across more than 300 cities, so it’s difficult for a human to keep track of or predict how the traffic will move around the network.

Traffic Predictor carries out an ongoing series of real-world tests to check where traffic actually moves. This testing system simulates removing a data center from service and measuring where traffic would go if that data center wasn’t serving traffic. This information is used by Traffic Manager to preconfigure policies to move requests out of failover data centers to prevent a “thundering-herd scenario” where a sudden influx of requests can cause failures in a second data center if the first one has issues.

Benefits of Traffic Manager and Traffic Predictor

Traffic Manager and Traffic Predictor offer a number of benefits to Cloudflare and its customers, including:

Improved reliability: Traffic Manager and Traffic Predictor help to improve the reliability of Cloudflare’s global network by automating traffic routing and predicting traffic flow shifts. This helps to ensure that Cloudflare’s products and services remain available and performant, even when data centers experience problems.

Reduced downtime: Traffic Manager and Traffic Predictor can help to reduce downtime for Cloudflare’s customers by quickly routing traffic away from affected data centers. This is especially important for businesses that rely on Cloudflare’s products and services to keep their websites and online applications running.
Improved performance: Traffic Manager and Traffic Predictor can help to improve the performance of Cloudflare’s products and services by routing traffic to the closest and most efficient data centers. This can lead to faster page load times and a better overall user experience.

Cloudflare’s Traffic Manager and Traffic Predictor are innovative AI-powered tools that are helping to improve the reliability, performance, and availability of Cloudflare’s global network. These tools are beneficial to both Cloudflare and its customers, and they represent a significant step forward in the management of complex global networks.

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Technology

Meta Introduces Spirit LM Open Source Multimodal AI Model

by George Mensah 2024-10-20

Right in time for Halloween 2024, Meta has launched Meta Spirit LM, its first open-source multimodal language model capable of handling both text and speech inputs and outputs. This groundbreaking model directly challenges similar AI technologies such as OpenAI’s GPT-4 and Hume’s EVI 2, along with specific text-to-speech (TTS) and speech-to-text (ASR) tools like ElevenLabs.

The Future of AI Agents

Created by Meta’s Fundamental AI Research (FAIR) team, Spirit LM open source seeks to enhance AI voice systems by offering more natural and expressive speech generation. It also tackles multimodal tasks, including automatic speech recognition (ASR), text-to-speech (TTS), and speech classification.

However, for the time being, Spirit LM open source is only available for non-commercial use under Meta’s FAIR Noncommercial Research License. This allows researchers to modify and experiment with the model, but any commercial usage or redistribution of the models must adhere to the noncommercial stipulations.

A New Approach to Speech and Text AI

Most traditional AI voice models first convert spoken words into text using ASR, then process that text through a language model and finally use TTS to produce the spoken output. While this approach works, it often fails to capture the full emotional and tonal range of natural human speech.

Meta Spirit LM open source solves this issue by integrating phonetic, pitch, and tone tokens, allowing it to create more expressive and emotionally nuanced speech. The model is available in two variants:

Spirit LM Base: Focuses on phonetic tokens for speech generation and processing.

Spirit LM Expressive: Incorporates pitch and tone tokens to convey emotional cues such as excitement or sadness, bringing an added layer of expressiveness to speech.
Both models are trained on datasets that include both speech and text, allowing Spirit LM open source to excel in cross-modal tasks like converting text to speech and vice versa, all while maintaining the natural nuances of speech.

Fully Open-Source for Noncommercial Use

Consistent with Meta’s dedication to open research, Meta Spirit LM open source has been released for non-commercial research purposes. Developers and researchers have full access to the model weights, code, and accompanying documentation to advance their own projects and experiment with new applications.

Mark Zuckerberg, Meta’s CEO, has emphasized the importance of open-source AI, expressing that AI holds the potential to significantly enhance human productivity and creativity, and drive forward innovations in fields like medicine and science.

Potential Applications of Spirit LM Open Source

Meta Spirit LM open source is designed to handle a wide range of multimodal tasks, such as:

Automatic Speech Recognition (ASR): Converting spoken words into written text.
Text-to-Speech (TTS): Transforming written text into spoken words.
Speech Classification: Recognizing and categorizing speech based on content or emotional tone.

The Spirit LM Expressive model takes things further by not only recognizing emotions in speech but also generating responses that reflect emotional states like joy, surprise, or anger. This opens doors for more lifelike and engaging AI interactions in areas like virtual assistants and customer service systems.

Meta’s Larger AI Research Vision

Meta Spirit LM open source is part of a larger set of open tools and models that Meta FAIR has released. This includes advancements like Segment Anything Model (SAM) 2.1 for image and video segmentation, widely used across industries like medical imaging and meteorology, as well as research aimed at improving the efficiency of large language models.

Meta’s broader mission is to advance Advanced Machine Intelligence (AMI) while ensuring that AI tools are accessible to a global audience. For over a decade, the FAIR team has been leading research that aims to benefit not just the tech world but society at large.

What Lies Ahead for Meta Spirit LM Open Source?

With Meta Spirit LM open source, Meta is pushing the boundaries of what AI can achieve in integrating speech and text. By making the model open-source and focusing on a more human-like, expressive interaction, Meta is giving the research community the opportunity to explore new ways AI can bridge the gap between humans and machines.

Whether in ASR, TTS, or other AI-driven systems, Spirit LM open source represents a significant leap forward, shaping a future where AI-powered conversations and interactions feel more natural and engaging than ever before.

Technology

SpaceX wins $733M Space Force launch contract

by George Mensah 2024-10-19

The U.S. Space Force has awarded SpaceX a contract worth $733 million for eight launches, reinforcing the organization’s efforts to increase competition among space launch providers. This deal is part of the ongoing “National Security Space Launch Phase 3 Lane 1” program, overseen by Space Systems Command (SSC), which focuses on less complex missions involving near-Earth orbits.

Under the contract, SpaceX will handle seven launches for the Space Development Agency and one for the National Reconnaissance Office, all using Falcon 9 rockets. These missions are expected to take place no earlier than 2026.

Space Force launch contract

In 2023, the Space Force divided Phase 3 contracts into two categories: Lane 1 for less risky missions and Lane 2 for heavier payloads and more challenging orbits. Although SpaceX was chosen for Lane 1 launches, competitors like United Launch Alliance and Blue Origin were also in the running. The Space Force aims to foster more competition by allowing new companies to bid for future Lane 1 opportunities, with the next bidding round set for 2024. The overall Lane 1 contract is estimated to be worth $5.6 billion over five years.

Lt. Col. Douglas Downs, SSC’s leader for space launch procurement, emphasized the Space Force’s expectation of more competitors and greater variety in launch providers moving forward. The Phase 3 Lane 1 contracts cover fiscal years 2025 to 2029, with the option to extend for five more years, and the Space Force plans to award at least 30 missions over this period.

While SpaceX has a strong position now, emerging launch providers and new technologies could intensify the competition in the near future.

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