Large language models (LLMs) have demonstrated remarkable capabilities across a variety of tasks. One of the main challenges towards the successful deployment of LLMs is memory management, since they typically involve billions of parameters. To this end, architectures based on Mixture-of-Experts have been proposed, which aim to reduce the size of the parameters that are activated when producing a token. This raises the equally critical issue of efficiently managing the limited cache of the system, in that frequently used experts should be stored in the fast cache rather than in the slower secondary memory. In this work, we introduce and study a new paging problem that models expert management optimization. Our formulation captures both the layered architecture of LLMs and the requirement that experts are cached efficiently. We first present lower bounds on the competitive ratio of both deterministic and randomized algorithms, which show that under mild assumptions, LRU-like policies have good theoretical competitive performance. We then propose a layer-based extension of LRU that is tailored to the problem at hand. Extensive simulations on both synthetic datasets and actual traces of MoE usage show that our algorithm outperforms policies for the classic paging problem, such as the standard LRU.

One-max search is a classic problem in online decision-making, in which a trader acts on a sequence of revealed prices and accepts one of them irrevocably to maximise its profit. The problem has been studied both in probabilistic and in worst-case settings, notably through competitive analysis, and more recently in learning-augmented settings in which the trader has access to a prediction on the sequence. However, existing approaches either lack smoothness, or do not achieve optimal worst-case guarantees: they do not attain the best possible trade-off between the consistency and the robustness of the algorithm. We close this gap by presenting the first algorithm that simultaneously achieves both of these important objectives. Furthermore, we show how to leverage the obtained smoothness to provide an analysis of one-max search in stochastic learning-augmented settings which capture randomness in both the observed prices and the prediction.

The advent of Machine Learning as a Service (MLaaS) has heightened the trade-off between model explainability and security. In particular, explainability techniques, such as counterfactual explanations, inadvertently increase the risk of model extraction attacks, enabling unauthorized replication of proprietary models. In this paper, we formalize and characterize the risks and inherent complexity of model reconstruction, focusing on the "oracle'' queries required for faithfully inferring the underlying prediction function. We present the first formal analysis of model extraction attacks through the lens of competitive analysis, establishing a foundational framework to evaluate their efficiency. Focusing on models based on additive decision trees (e.g., decision trees, gradient boosting, and random forests), we introduce novel reconstruction algorithms that achieve provably perfect fidelity while demonstrating strong anytime performance. Our framework provides theoretical bounds on the query complexity for extracting tree-based model, offering new insights into the security vulnerabilities of their deployment.

Artificial intelligence is an everlasting bridge between evolvement and people who want to evolve. Soon after the existence of artificial intelligence, the world witnessed many incredible innovations, and still continues to. As we have shined light upon the innovations of artificial intelligence, let's talk about chatbots which are one of the prime features of AI. Chatbots have come a long way when it comes to chatbot applications, like Replika, Wysa, Chai, Messenger, and other popular chatbot apps in AI. With that said, our motive for gathering here for discussion is to create a foolproof AI-based chatbot app like Replika.

In the online (time-series) search problem, a player is presented with a sequence of prices which are revealed in an online manner. In the standard definition of the problem, for each revealed price, the player must decide irrevocably whether to accept or reject it, without knowledge of future prices (other than an upper and a lower bound on their extreme values), and the objective is to minimize the competitive ratio, namely the worst-case ratio between the maximum price in the sequence and the one selected by the player. The problem formulates several applications of decision-making in the face of uncertainty on the revealed samples. Previous work on this problem has largely assumed extreme scenarios in which either the player has almost no information about the input, or the player is provided with some powerful, and error-free advice. In this work, we study learning-augmented algorithms, in which there is a potentially erroneous prediction concerning the input. Specifically, we consider two different settings: the setting in which the prediction is related to the maximum price in the sequence, as well as the setting in which the prediction is obtained as a response to a number of binary queries. For both settings, we provide tight, or near-tight upper and lower bounds on the worst-case performance of search algorithms as a function of the prediction error. We also provide experimental results on data obtained from stock exchange markets that confirm the theoretical analysis, and explain how our techniques can be applicable to other learning-augmented applications.

Resident Moz search scientist Dr. Pete Meyers returns to the MozCon stage this year, and we're so excited for his presentation: Rule Your Rivals: From Data to Action. In our last interview before the show, we talked with Dr. Pete about 2020, the trends he's seeing in the SERPs, and what makes competitive analysis effective. Read the full interview below, and don't forget to grab your ticket to see Dr. Pete and our other amazing speakers at MozCon Virtual 2021 (ticket sales end Friday, July 9!): Question: 2020 was quite a year, how was this year for you? Did you have any favorite projects? Dr. Pete: Honestly, there were a lot of days this past year when it felt like just staying alive and sane were our main project (and I'm not sure I completed the sane part).

The Global Machine Learning as a Service (MLaaS) Market is anticipated to rise at a considerable rate over the estimated period between 2016 and …

Market Expertz has recently published a new study in its database that highlights the in-depth market analysis with the future prospects of the Machine Learning in Retail market. The study covers significant data which makes the research document a handy resource for the managers, industry executives and other key people. It provides them with a ready-to-access and self analyzed study along with the graphs and tables that will help them understand the market trends, drivers, restraints and the market challenges. The research report covers the current market size of the Global Machine Learning in Retail market and its growth rates based on historical analysis. This study also contains company profiling, product picture and specifications, sales, market share, and contact information of the various international, regional, and local vendors Machine Learning in Retail Market.

No need to look for competitors since your business offering is so unique, there's nothing out there remotely like it? You are going to find competition, if you are thorough, and tenacious, and keep searching until you find offerings on the market directly or indirectly related to yours. No one wants to find their brilliant idea has been produced by someone else. But it's smart business to look for the same, or similar offerings to yours before investing a lot of effort in developing and/or marketing what's already available. "Find and Utilize Competition with Competitive Analysis," is a 50 minute talk detailing the Competitive Analysis process--efficiently, proactively, step-by-step.

The and Regional Deep Learning Market report gives a purposeful depiction of the area by the practice for research, amalgamation, and review of data taken from various sources. The market analysts have displayed the different sidelines of the area with a point on recognizing the top players (Amazon Web Services (AWS), Google, IBM, Intel, Micron Technology, Microsoft, Nvidia, Qualcomm, Samsung Electronics, Sensory Inc., Skymind, Xilinx, AMD, General Vision, Graphcore, Mellanox Technologies, Huawei Technologies, Fujitsu, Baidu, Mythic, Adapteva, Inc., Koniku) of the industry. The and Regional Deep Learning market report correspondingly joins a predefined business market from a SWOT investigation of the real players. Thus, the data summarized out is, no matter how you look at it is, reliable and the result of expansive research. This report mulls over and Regional Deep Learning showcase on the classification, for instance, application, concords, innovations, income, improvement rate, import, and others (Automotive, Home & Building Automation, Food & Beverages) in the estimated time from 2019–2025 on a global stage.