As large language models (LLMs) continue to scale, their workloads increasingly rely on distributed execution across multiple GPUs. However, the conventional bulk synchronous parallel~(BSP) model used in such settings introduces significant performance inefficiencies. To characterize these bottlenecks, we introduce the ''Three Taxes'' (Bulk Synchronous, Inter-Kernel Data Locality, and Kernel Launch Overhead) as an analytical framework. We propose moving beyond the rigid BSP model to address key inefficiencies in distributed GPU execution. By exploiting libraries like Iris for Triton, we gain access to in-kernel communication primitives that enable the design of novel fine-grained programming patterns, offering greater flexibility and performance than traditional BSP-based approaches. These patterns systematically eliminate the three taxes by creating direct, tile-level producer-consumer pipelines and replacing global barriers with fine-grained dataflow synchronization. Applying this methodology to critical kernels, from the foundational All-Gather + general matrix multiplication operation to the complex Flash Decode algorithm, we observe a 10-20% speedup in end-to-end latency over BSP-based approaches, establishing a more programmable and efficient paradigm for distributed LLM workloads.

Voice assistants are deployed widely and provide useful functionality. However, recent work has shown that commercial systems like Amazon Alexa and Google Home are vulnerable to voice-based confusion attacks that exploit design issues. We propose a systems-oriented defense against this class of attacks and demonstrate its functionality for Amazon Alexa. We ensure that only the skills a user intends execute in response to voice commands. Our key insight is that we can interpret a user's intentions by analyzing their activity on counterpart systems of the web and smartphones. For example, the Lyft ride-sharing Alexa skill has an Android app and a website. Our work shows how information from counterpart apps can help reduce dis-ambiguities in the skill invocation process. We build SkilIFence, a browser extension that existing voice assistant users can install to ensure that only legitimate skills run in response to their commands. Using real user data from MTurk (N = 116) and experimental trials involving synthetic and organic speech, we show that SkillFence provides a balance between usability and security by securing 90.83% of skills that a user will need with a False acceptance rate of 19.83%.

In this article, we will look at the development of AI and the field of deep learning. Deep learning originated in the era of vacuum tube computers. In 1958, Frank Rosenblatt of Cornell University designed the first artificial neural network. This was later named "deep learning". Rosenblatt knew that this technology surpassed the computing power at that time.

Meadows and Dinwiddie: The Venus Project is a non-profit organization that presents a new socio-economic model utilizing science and technology. For the past 40 years, we have maintained a 21-acre research center in Venus, Florida. We propose a new scientific foundation in transcending humanity's current problems by testing a new social design for organizing our society as a global "operating system". Taken as a whole, the Venus Project fills the egregious gap between the sciences and the humanities by combining a social philosophy of the future with technical knowledge applied at a global scale to solve the problems of the human condition. Our methodologies are designed to realize the full potential of science and technology to achieve social betterment for all living systems-- without exception. Our approach to social organization calls for changes in governance, economics, urban planning, education, human relationships, language, and values.

To scale the benefits of AI-innovations, companies need to stop thinking of AI tools and applications -- such as natural language processing or computer vision -- as standalone solutions. Otherwise, the opportunity cost could be as large as 41% of revenue by 2023. Companies that see AI as components of next-generation enterprise IT systems stand to grow revenues by as much as one-third over the next five years. And as systems evolve, so must the IT workforce. Companies will need multidisciplinary talent that can bridge infrastructure, development tools, programming languages, AI, and machine learning.

Today, some 80% of large companies have adopted machine learning and other forms of artificial intelligence (AI) in their core business. Five years ago, the figure was less than 10%. Nevertheless, the majority of companies still use AI tools as point solutions -- discrete applications, isolated from the wider enterprise IT architecture. That's what we found in a recent analysis of AI practices at more than 8,300 large, global companies in what we believe is one of the largest-scale studies of enterprise IT systems to date. To scale the benefits of AI-innovations, those companies need to stop thinking of AI tools and applications -- such as natural language processing or computer vision -- as standalone solutions.

The current resurgent interest in Artificial Intelligence (AI) has been driven by the availability of data (particularly labelled data), the democratisation of computing infrastructure and tooling, and the ability to combine these elements to create AI algorithms. Benefit is achieved once an algorithm is deployed into an operational system to achieve an operational advantage. The ability to exploit the opportunities offered by AI within UK Defence calls for an understanding of systemic issues required to achieve an effective operational capability. This paper provides the authors' views of issues which currently block UK Defence from fully benefitting from AI technology. These are situated within a reference model for the AI Value Train, so enabling the community to address the exploitation of such data and software intensive systems in a systematic, end to end manner. The paper sets out the conditions for success including: - Researching future solutions to known problems and clearly defined use cases; - Addressing achievable use cases to show benefit; - Enhancing the availability of Defence-relevant data; - Enhancing Defence'know how' in AI; - Operating Software Intensive supply chain ecosystems at required breadth and pace; - Governance and, the integration of software and platform supply chains and operating models.

To realize the full potential of the Internet of Things, businesses need to move beyond short-sighted use cases and consider its broader impact on the world. The content on this page was provided by our sponsor, Teradata. The MIT SMR Editorial Staff was not involved in the selection, writing or editing of the content on this page. Most business and technology leaders today agree that the Internet of Things (IoT) represents an unprecedented opportunity in terms of ground-breaking insights and entirely new ways to understand and engage with both customers and "things." But in actuality, IoT's full promise has thus far failed to materialize, in part because of the narrow ways in which organizations define and pursue the potential capabilities.

Microsoft's vision of enterprise mobility and how to improve employee engagement for digital transformation has long been mooted in this publication – yet at the Redmond giant's Envision conference today, CEO Satya Nadella discussed themes taking the collaboration element a step further. Building on the'conversation as a platform' concept discussed at Build last week, the Microsoft chief exec described how bots and artificial intelligence (AI) could transform business conversations by'taking the human language but applying it much more pervasively to computing'. "Think of bots that you will build as the new websites or mobile apps, and your customers will interact with your business through these bots," he told delegates. "We think it is going to be much more ubiquitous in terms of its deployment." These bots already exist in some contexts; New York-based startup x.ai offers an AI-powered personal assistant called Amy which schedules meetings.