Specifications - precise mathematical representations of correct domain-specific behaviors - are crucial to guarantee the trustworthiness of computer systems. With the increasing development of neural networks as computer system components, specifications gain more importance as they can be used to regulate the behaviors of these black-box models. Traditionally, specifications are designed by domain experts based on their intuition of correct behavior. However, this is labor-intensive and hence not a scalable approach as computer system applications diversify. We hypothesize that the traditional (aka reference) algorithms that neural networks replace for higher performance can act as effective proxies for correct behaviors of the models, when available. This is because they have been used and tested for long enough to encode several aspects of the trustworthy/correct behaviors in the underlying domain. Driven by our hypothesis, we develop a novel automated framework, SpecTRA to generate specifications for neural networks using references. We formulate specification generation as an optimization problem and solve it with observations of reference behaviors. SpecTRA clusters similar observations into compact specifications. We present specifications generated by SpecTRA for neural networks in adaptive bit rate and congestion control algorithms. Our specifications show evidence of being correct and matching intuition. Moreover, we use our specifications to show several unknown vulnerabilities of the SOTA models for computer systems.

Existing LLM serving systems are stateless across In the conversational setup, the user and the chatbot are requests. Consequently, when LLMs are used in the common engaged in a dialogue that may last many rounds. In order setting of multi-turn conversations, a growing log of the conversation for the chatbot not to "lose memory" of what has been said so history must be processed alongside any request far when responding, the cumulative history of the dialogue by the serving system at each turn, resulting in repeated must be part of the context for LLM's autoregressive generation.

Optimal adaptive bitrate (ABR) decision depends on a comprehensive characterization of state transitions that involve interrelated modalities over time including environmental observations, returns, and actions. However, state-of-the-art learning-based ABR algorithms solely rely on past observations to decide the next action. This paradigm tends to cause a chain of deviations from optimal action when encountering unfamiliar observations, which consequently undermines the model generalization. This paper presents Karma, an ABR algorithm that utilizes causal sequence modeling to improve generalization by comprehending the interrelated causality among past observations, returns, and actions and timely refining action when deviation occurs. Unlike direct observation-to-action mapping, Karma recurrently maintains a multi-dimensional time series of observations, returns, and actions as input and employs causal sequence modeling via a decision transformer to determine the next action. In the input sequence, Karma uses the maximum cumulative future quality of experience (QoE) (a.k.a, QoE-to-go) as an extended return signal, which is periodically estimated based on current network conditions and playback status. We evaluate Karma through trace-driven simulations and real-world field tests, demonstrating superior performance compared to existing state-of-the-art ABR algorithms, with an average QoE improvement ranging from 10.8% to 18.7% across diverse network conditions. Furthermore, Karma exhibits strong generalization capabilities, showing leading performance under unseen networks in both simulations and real-world tests.

Recently, deep learning has been successfully applied to a variety of networking problems. A fundamental challenge is that when the operational environment for a learning-augmented system differs from its training environment, such systems often make badly informed decisions, leading to bad performance. We argue that safely deploying learning-driven systems requires being able to determine, in real time, whether system behavior is coherent, for the purpose of defaulting to a reasonable heuristic when this is not so. We term this the online safety assurance problem (OSAP). We present three approaches to quantifying decision uncertainty that differ in terms of the signal used to infer uncertainty. We illustrate the usefulness of online safety assurance in the context of the proposed deep reinforcement learning (RL) approach to video streaming. While deep RL for video streaming bests other approaches when the operational and training environments match, it is dominated by simple heuristics when the two differ. Our preliminary findings suggest that transitioning to a default policy when decision uncertainty is detected is key to enjoying the performance benefits afforded by leveraging ML without compromising on safety.

Harry Potter fans will remember Professor Albus Dumbledore's nifty memory reviewer - the Pensieve. Throughout the series, several characters used it to store their memories and rewatch them to derive insights. Taking this concept from fiction to reality, Gaurav Shrivastava, Co-founder of Zimmber, built a transparent data machine - called Pensieve - AI with Co-founder Prahlad Routh. The two were ecstatic about implementing text analytics in the legal domain and the ample growth of Machine Learning (ML) and Artificial Intelligence (AI). The duo identified that legal tech could be a thing if Natural Language Processing (NLP) is used rightly, and started Pensieve in Mumbai in 2017.

"The Pensieve is an object used to review memories. It has the appearance of a shallow stone or metal basin, into which runes and strange symbols are carved and precious stones are fitted. It is filled with a silvery substance that appears to be a cloud-like liquid/gas; the collected memories of people who have siphoned their recollections into it." The images are a few photos from a roadtrip i took last summer. The model is trained on nearly 200 total. The caption is layered on via giphy.

According to Massachusetts Institute of Technology (MIT), Artificial Intelligence could be the answer to reducing headaches for viewers and streaming services. Video buffering and pixelation continues to be problematic for those who rely a ton on video streaming services for catching up on the newest movies and the latest TV shows. Buffering and pixelation can make people to switch over to other content which can result in poor viewership and advertising. The way it works is that Streaming services use ABR (Adaptive BitRate) algorithms to ascertain what the resolution of the video playback is at that time which varies according to changing network conditions. Videos are typically chopped into smaller chunks and transmitted in a sequential manner to the device that viewers are watching it on.

Don't you just hate it when the YouTube clip you're trying to watch pauses midway to buffer, or drastically lowers the resolution to a pixelated mess? Using machine learning, the Pensieve system figures out the optimal algorithm to use for delivering video at the best possible resolution while avoiding buffering breaks, no matter what connection you're on. By using an AI to learn what algorithm works best in various conditions – including, for example, instances when you're heading into a tunnel where connectivity is sketchy, and when you're in a crowded area with thousands of other network users – Pensieve is said to cut rebuffering by up to 30 percent. The team says it's tested its system with just a month's worth of video content; exposing it to more data, like Netflix's entire catalog, could help boost its performance even further.

MIT's Computer Science and Artificial Intelligence Lab (CSAIL) wants to ensure your streaming video experience stays smooth. A research team led by MIT professor Mohammad Alizadeh has developed an artificial intelligence (dubbed'Pensieve') that can select the best algorithms for ensuring video streams both without interruption, and at the best possible playback quality. The method improves upon existing tech, including the adaptive bitrate (ABR) method used by YouTube that throttles back quality to keep videos playing, albeit with pixelation and other artifacts. The AI can select different algorithms depending on what kind of network conditions a device is experiencing, cutting down on the downsides associated with any one method. During experimentation, the CSAIL research team behind this method found that video streamed with between 10 and 30 percent less rebuffing, with 10 to 25 percent improved quality.

MIT discovered a way to improve video streaming by reducing buffering times and pixelation. A new AI developed at the university's Computer Science and Artificial Intelligence Laboratory uses machine learning to pick different algorithms depending on network conditions. In doing so, the AI, called Pensieve, has been shown to deliver a higher-quality streaming experience with less buffering than existing systems. Streaming sites use ABR algorithms to determine which resolution videos will play at. Instead of sending a video to your computer in one complete piece, it breaks it up into smaller pieces and sends them sequentially.