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57 recent industry stories relevant to the field â releases, launches, and announcements beyond the papers.
The neolab is betting that automating routine computer tasks will soon outpace coding as AI's biggest use case.
AI companies, including Nvidia and Mistral, urge policymakers to avoid broad restrictions on open-weight AI models as Washington debates responses to Chinese AI and alleged model distillation.
The Trump administration unveiled the first "Genesis Mission" grants on Thursday, directing $5 billion toward hundreds of AI-driven science projects in an effort the White House has described as "comparable in urgency and ambition to the Manhattan Project." At roughly the same time, Trump's science adviser Michael Kratsios was on Capitol Hill selling lawmakers on […]
AMD is challenging its chipmaker rival with a new rack-scale system that will start shipping to customers later this year.
Etched, founded by three Harvard dropouts, has created new chips and memory components that speed up inference on any AI model -- no GPUs required, it says.
The viability of orbital data centers hosting the largest and most capable large language models (LLMs) remains hotly contested. But enormous deployments that require thousands of GPUs arenât the only way LLMs might prove useful in space. NASAâs Jet Propulsion Laboratory recently sent Googleâs Gemma 3 to space, achieving the first in-orbit demonstration of a vision-language model analyzing imagery from a satelliteâs own sensor.The system, known as NAVI-Orbital, used Gemma 3 to analyze images captured by a YAM-9 satellite built by Loft Orbital. Juan M. Delfa, technical group lead at NASA, said that though the goal in this case was image analysis, the projectâs success implies a fundamentally new way researchers on the ground can interact with spacecraft.âThis is a major shift,â said Delfa. âNow, a scientist can write a prompt, upload it to the spacecraft, and that will be taken into account by the system. Itâs different from previous paradigms, where researchers have to write very structured commands that require an operations team and process.â Google Gemma 3 goes to spaceâno modifications requiredAt its core, NAVI-Orbital is an agentic software framework developed by Delfa and his coauthors, Taran Cyriac John, an AI researcher at NASA JPL, and Andrew W. Herson, a tech lead at Loft Orbital. It coordinates operations with a LangGraph-based conductor and deploys a compressed, 4-bit format of Googleâs Gemma 3 4B, an open-weights LLM, to produce plain-text image descriptions.NAVI-Orbital was 88 percent accurate when used to classify images in a benchmark dataset of 7,960 images. Notably, Gemma 3 classified the images without being trained or fine-tuned on this particular dataset or its categories; itâs the same base model you can download from Hugging Face and use on a laptop. The benchmark was conducted on the ground to validate the system before launch.Once the system was in orbit, NASA researchers performed two live capture tests with a camera on Loftâs YAM-9 satellite: one over Toulouse, France, and a second over the coast of Argentina. Gemma 3 generated a text description of each image, and NASA also prompted the LLM with a set of scripted questions about the images, such as whether they contain commercial or residential areas or show natural features. The image analysis also took place onboard YAM-9, which carries a compute cluster of several radiation-hardened processors (FPGAs, CPUs, and GPUs) to serve multiple customer payloads simultaneously. The satellite is powered by solar panels, which provide onboard systems with between 150 and 500 watts, depending on the position of the satellite.For the live capture experiment, Gemma 3 ran on Nvidiaâs Jetson Orin AGX, a small compute module frequently used for robotics and AI tasks. The 4-bit, 4-billion-parameter model requires only 8 gigabytes of memory, which makes it possible for it to run on a lower-power device such as the Orin AGX. âIt conveys the message of how lightweight it is. You can run it in a tiny, tiny computer,â said Delfa.Getting more useful data across limited bandwidthPaul Lasserre, general manager at Loft Orbital, said vision-capable LLMs could deliver a âparadigm shiftâ for orbital operations. Contrary to what spy movies would have you believe, most satellites canât provide fast, high-fidelity image and video feeds to observers on the ground. Bandwidth is often limited and, in most cases, satellites can deliver data to ground stations only at set intervals based on their orbit. Lasserre believes AI models can work around this problem with âsemantic compression.â Instead of sending large amounts of raw image data, a satellite can report a text summary of noteworthy information. âIt doesnât matter if the link is slow, because youâre downlinking dozens of kilobytes instead of dozens or hundreds of megabytes,â said Lasserre. âIt lets you use your satellite in a tactical way, which until now was only in Hollywood movies.âDelfa expanded on this with a real-world example: wildfire detection. Satellites are currently capable of detecting wildfires, but limits in downlink bandwidth and data processing can delay results by up to 90 minutes. A satellite capable of analyzing an image in space and reporting a plain-text warning might remove this delay. NASAâs Jet Propulsion Laboratory tested NAVI-Orbital on a YAM-9 satellite, made by Loft Orbital. Loft OrbitalFrom image analysis to spacecraft controlQuicker insight is only half of what NAVI-Orbital points toward. The other half relates to the âmajor shiftâ Delfa flagged. NAVI-Orbital provides a proof of concept for an alternate means of interacting with spacecraft.Thatâs not to say Google Gemma 3 is currently at the controls. NAVI-Orbital is deliberately walled off from the flight software. It reads images and produces descriptions. The system has the capability to make decisions about how images are analyzed but has no access beyond that.Still, the interface is novel. Retooling the system to search for a different kind of targetâsuch as wildfiresâis a matter of editing a text prompt. It doesnât require rewriting and revalidating onboard software, or training and deploying a different AI model (as was often required with prior image-classification models). The long-term vision for how this capability could be deployed goes beyond uncrewed satellites and image processing. Delfa said NAVI is rooted in thinking about how AI could serve as a companion for astronauts. âWe thought, astronauts have a lot of limitations in the spacesuit in terms of dexterity, so we conceived this idea of having NAVI as a companion to the astronaut, to allow interaction via natural languageâŠ. This is the concept that we definitely want to push forward.â A great deal of additional research will be required to push the technology that far, but NAVI-Orbitalâs demonstration has shown that two elementsâdeploying a large language model in space and controlling it with promptsâare possible.
Designing and developing a new medicine is an expensive, failure-prone scientific challenge. A new drug can take many years to develop, at the cost of a significant investment. And even then, most possible candidates never reach the patient. For biologic medicines, therapies made from engineered proteins rather than synthetic chemistry (which are often used to…
Google released Gemini 3.6 Flash, 3.5 Flash-Lite, and Flash Cyber, but the continued absence of Gemini 3.5 Pro raises fresh questions about its AI strategy.
The conversation about AI often centers on algorithms, computing power, or huge investments in new semiconductor fabrication plants and hyperscale data centers. But beneath each of these advances is another layer of innovation that makes them possible: advanced materials. Every new generation of AI technology demands more processing power, more memory, greater energy efficiency, and…
Databricks has remade its image into an AI company and has published research on the cost savings of open weight AI models for coding.
A $400 million chip-backed loan points to the next wave of AI infrastructure deals.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. PsiQuantum has a plan to make a massive quantum computer out of light The machine that could change the world will be housed in a room that looks like a data…
The funding discussions point to investor interest in applying AI to make breakthroughs in life sciences.
DeepMind CEO Demis Hassabis is proposing an AI "standards body" modeled after FINRA, to test frontier models and develop best practices for their release.
New York has become the first state to temporarily halt approval of large data centers, as Gov. Kathy Hochul argues the AI-driven building boom shouldnât come at the expense of higher electricity costs, water supplies, or local control.
Reflection AI has signed a $1 billion deal to access Nebius' compute. Reflection was founded in 2024 and is developing open source AI technology.
The machine that could change the world will be housed in a room that looks like a data center crossed with an ice cream factory. Inside will be some 100 stainless-steel cabinets, each about six feet tall and connected to a supply of liquid helium that keeps them only a few degrees above absolute zero.…
Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]
The AI chip boom just produced its biggest Wall Street moment yet. Now SK Hynix and Samsung are being asked to build U.S. factories.
The company is taking a modular approach to designing these chips, anticipating that their needs will change as AI evolves rapidly by the time the chips are in production.
The large language models (LLMs) that form the basis of generative AI chatbots such as ChatGPT, Claude, and Gemini can generate uncannily human-like text and images. But these models still struggle with a skill that, ironically, looks at face value to be right in their wheelhouse: analyzing structured data. A new type of generative AI is set to change this situation.Although you can get your favorite chatbot to solve intractable math problems, review dense legal documents, compose a catchy pop song, or put together some slick PowerPoint slides, give it anything more than a small table and it doesnât have a clue what to do.For most companies and organizations, the most important data sits in spreadsheets. Whether itâs a bankâs transaction logs, a marketing agencyâs website metrics, clinical trial participantsâ vital signs, or the vast amount of proton collision information produced at atom smashers like the Large Hadron Collider, structured, row-and-column data runs the world, and LLMs canât deal with it.AI startup Fundamental is pioneering a new type of AI foundation model, known as a large tabular model (LTM), to fill the gap. Fundamental came out of stealth mode on 5 February 2026 with US $275 million in funding and a model called NEXUS, purpose-built for tabular data. Now, the model is being adopted by companies such as Amazon Web Services, while others race to build their own LTMs. Why LLMs struggle with spreadsheetsPart of why structured data has garnered less attention is a very human bias, argues Boris van Breugel, a senior AI researcher based in Amsterdam. âPeople like to see images, videos, and ChatGPT responses,â he says. âBut tabular data really lags behind because itâs not fun to look at numbers.â Different tabular datasets are also difficult to compare, explains van Breugel, who co-wrote a prescient position paper on this topic in 2024. Whereas most language has similar semantics, making LLMs well-suited to being trained on vast amounts of text data, van Breugel argues that it is much harder to train a single tabular model on tables with very different variables. Additionally, language is sequential by nature (as are music, images, and video). Changing the order of words in a sentence may change or completely destroy its meaning. But the structured data you find in spreadsheets isnât sequential. You can swap the order of columns or play around with rows, but the underlying factual meaning of the data remains the same.This independence from linear order is incompatible with an LLMâs fundamental purpose of predicting the next value in a linear sequence. âWith LLMs, even slightly changing the input, you get a different output,â says Jeremy Fraenkel, CEO of Fundamental. âThatâs fine and actually often desirable for LLMs, but when youâre making a prediction of whether a transaction is fraudulent or not, you want to make sure that the prediction is the same, or deterministic, no matter what.âDeveloping Fundamentalâs LTMCurrent tabular data solutions are limited to machine learning algorithms, such as XGBoost, that have been around for more than 15 years and are used by organizations globally. These algorithmsâcalled gradient-boosted decision treesâhave to be trained and optimized by data scientists over the course of months for each and every use case. In contrast, NEXUS and other emerging LTMs are foundational, leveraging learning amassed from pre-training on diverse databases so that they can be applied across a range of different predictive tasks with minimal bespoke feature engineering or task-specific model building.And unlike LLMs, which primarily model sequences of tokens, LTMs model the structure of tabular data directly. They jointly learn from each entryâs numerical value, what it represents, and how it relates to other entries. For example, imagine an entry in a grocery stock inventory table for bananas: The LTM can take in not just the magnitudeâsay, 500âbut the fact that the entry represents the current banana stock quantity, its category (produce), and the statistical properties that link the entry with the rest of the column. This contextual understanding enables more accurate reasoning and prediction over structured data.According to Fraenkel, one of Fundamentalâs biggest challenges in developing NEXUS was obtaining the right training data. Unlike natural language, which is abundant and broadly uniform in structure, tabular data is relatively hard to findâmuch of the data is sensitive or proprietaryâand diverse. There are very few similarities between, for instance, a biology dataset and a financial one. That combination of factors meant Fundamental needed to invest in building a huge training set.âWe pre-trained NEXUS on billions of tables using a combination of proprietary datasets acquired through partnerships and licensing, high-quality public and open-source datasets, and data augmentation techniques that expanded the diversity and coverage of our training corpus,â Fraenkel says, though he is keen to point out that NEXUS is not trained on customer data. In fact, it is a confidential computing platform, which means that Fundamental physically cannot access customer data, let alone train on it.This feature was most likely a key consideration when in June, Amazon Web Services (AWS) embedded NEXUS in Amazon SageMaker, widely considered the default operating system for secure machine learning. This brings NEXUS to many customersâ often sensitive dataâa contrasting approach to LLMs, where the data has to be imported to the model.âWith Amazon, we have a first-party partnership, which means that our model exists as if itâs a native AWS solution,â Fraenkel says. âAnd over time, the goal is to expand these types of relationships to allow [end users] to really access their data wherever they do their predictions.âThe future of data analysisThough Fundamental has taken the lead, at least in enterprise applications, the company is not alone in pursuing foundational LTMs. In March, Feedzai, which provides fraud and financial crime prevention services, and credit card company Mastercard separately launched similar proprietary technologies focused on finance. Then, in late June, Google launched its own foundational competitor, TabFM, trained entirely on hundreds of millions of synthetic datasets. And machine learning researchers are not far behind either. FlexTab, TabICL, and iLTM are just three of a raft of LTMs developed by the research community in the past year, all in the pursuit of bringing the success of LLMs to the tabular domain.For all involved, the direction of travel is clear. âI would be very surprised if most data processing and analysis is not done through an automated system in the future, whether thatâs an LLM, an LTM, or some combination,â van Breugel says. âMost people donât necessarily like to do data analysis, and these systems will be able to do it a lot better.âFraenkel agrees. âI see the relationship between LLMs and LTMs as being a bit like the human brain: The left side is good at reasoning and understanding and summarizing text, and the right side is really good at understanding numbers and statistics and patterns,â he says. âBut itâs when you combine both of those that you really get something much more powerful.â
Open source modelsâ success isnât coming at the expense of frontier labs. Instead, they each seem to capture two phases of the same life cycle.
With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future. Returning to the foundational elements of AI architectureâthe…
Weâve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.
At the event "The Briefing: AI for Science" earlier this week, Anthropic announced Claude Science, a new "AI workbench for scientists" that pulls fragmented tools and datasets into one environment, and generates figures and visuals. Anthropic, already dominating the industry with its popular coding tools and powerful AI models, framed the launch around what it […]
The rapid expansion of artificial intelligence infrastructure is typically framed as an energy problem. Data centers are projected to consume a growing share of global electricity demand: The International Energy Agency estimates they could account for 3 to 4 percent of total global consumption within this decade.Utilities are already adjusting long-term forecasts to accommodate anticipated growth from hyperscale facilities and high-density compute clusters.This framing captures scale. It misses behavior.The emerging issue is not simply how much power large-scale compute systems consume, but how increasingly dense and synchronized computational workloads are beginning to alter the operating characteristics of the electrical grid itself through increasingly unpredictable demand that varies rapidly in both time and location, creating new operational challenges for grid operators.AIâs Capricious Energy NeedsTraditional grid planning assumes relatively predictable demand behavior. Industrial, commercial, and residential loads generally follow established profiles that can be forecast with reasonable accuracy. Even substantial demand growth has historically been manageable through reserve planning, transmission upgrades, and demand management programs.Large-scale compute infrastructure introduces a different class of electrical load. Trainingâthe computational task of making AI modelsâtends to be highly synchronized across clusters of GPUs, TPUs, and specialized accelerators operating in parallel, computationally dense, and relatively scheduled. Inferenceâthe process of actually using those modelsâis generally more distributed and user-driven, making demand less predictable both in time and location. Both differ materially from traditional industrial demand profiles, though for different reasons. Unlike many conventional industrial processes, these workloads can ramp rapidly depending on model training cycles, distributed compute coordination, and workload scheduling strategies.From the perspective of the grid, this is not simply higher demand. It is more abrupt demand. High-density compute workloads can produce substantial step changes in electricity consumption over extremely short intervals, including rapid fluctuations occurring within milliseconds. Data-center operators are already deploying mitigation technologies, including batteries, power-conditioning systems, and supercapacitors. Collectively, however, data centersâ rapid load changes can place additional stress on backup-generation reserves, systems that adjust supply as demand changes, frequency-control mechanisms that maintain grid stability, and local transmission infrastructure.Compute-related variability differs from the intermittency introduced through renewable energy integration. Wind and solar variability originate primarily on the supply side and is tied to environmental conditions. Compute-related variability emerges on the demand side, driven by workload synchronization, scheduling behavior, and computational intensity. The interaction between increasingly dynamic supply and demand conditions introduces additional uncertainty into forecasting, reserve management, congestion planning, and balancing operations.Research organizations including the National Renewable Energy Laboratory have emphasized the growing complexity associated with integrating highly dynamic resources into modern grid operations.Location, Location, LocationThe issue becomes more significant when compute activity is geographically concentrated. Large-scale data centers tend to cluster in regions with favorable conditions such as fiber connectivity, access to markets, tax incentives, and historically low electricity costs. Northern Virginia, often referred to as Data Center Alley, remains the most prominent example. The region hosts the worldâs largest concentration of data centers and carries a substantial share of global internet traffic.Utilities operating in these regions have already identified data-center growth as a primary driver of future load expansion. Virginia-based electricity supplier Dominion Energy, for example, has repeatedly highlighted hyperscale demand growth in its integrated resource planning documents. Virginia has seen one of the largest data center buildouts worldwide. Here, Amazon Web Services and Iron Mountain data centers dominate the landscape in Manassas, Va. Nathan Howard/Bloomberg/Getty ImagesA sudden increase in electricity consumption within a constrained geographic area can stress substations, transmission corridors, and local balancing operations even if the broader grid maintains sufficient aggregate capacity. This creates localized reliability challenges that are not always visible through system-wide demand metrics alone.Thermal management systems further intensify these effects. Cooling infrastructure in high-density compute facilities must respond dynamically to changing workloads. As processing intensity rises, cooling demand rises as well, often nonlinearly. This coupling between compute and thermal systems means that fluctuations in workload can propagate through multiple layers of facility power consumption simultaneously.High-density compute clusters may also introduce power-quality concerns at the local level. Large concentrations of accelerators, switching power supplies, and high-frequency compute equipment can generate harmonics and nonlinear load behavior that place additional stress on distribution infrastructure. While modern facilities incorporate mitigation technologies, the scale and concentration of next-generation compute facilities may require utilities and operators to revisit assumptions surrounding localized power conditioning, harmonics management, and infrastructure resilience. These conditions can also contribute to short-duration electrical transients that place additional stress on localized infrastructure and power-conditioning systems.Regulations Need UpdatingPart of the challenge is that many existing regulatory and operational frameworks were designed around relatively stable industrial demand profiles. Large rapidly fluctuating loads have historically been constrained because abrupt cycling can complicate balancing operations, increase stress on transmission equipment, and reduce predictability in system operations. High-density compute clusters do not fit neatly within those assumptions.This creates pressure for both operational adaptation and regulatory reassessment.Demand-response mechanisms may allow certain compute workloads to be shifted or curtailed during periods of system stress. Data-center operators are exploring flexible scheduling, battery storage, and behind-the-meter generation. Grid operators, meanwhile, are evaluating planning frameworks and interconnection approaches for increasingly large flexible loads.The Electric Reliability Council of Texas (ERCOT), for example, has publicly acknowledged the growing implications of large flexible loads, including data centers, for long-term grid planning and operational stability. Interconnection queues across the United States continue to expand significantly, reflecting mounting pressure on both generation and transmission infrastructure. Grid expansion timelines, however, are measured in years rather than quarters.This creates a structural mismatch. Compute infrastructure can scale rapidly. Electrical infrastructure generally cannot.The broader implication is that large-scale compute infrastructure is not simply another industrial load category. It represents a shift in the temporal and spatial characteristics of electricity demand itself.Framing the issue solely in terms of aggregate energy consumption risks overlooking these second-order operational effects. Capacity expansion alone does not fully address rapid ramping behavior, synchronization, localized congestion, transient instability, reserve compression, or increasingly demanding load-following requirements.The challenge is not just how much electricity these systems consume. It is how they are beginning to change the operating conditions of the grid itself. The call is not to slow AI development but to recognize that hyperscale computing represents a new category of electrical demand. As AI infrastructure continues to scale, planning frameworks may need to account not only for total energy consumption but also for demand volatility, synchronization effects, and geographic concentration. Grid resilience will increasingly depend on understanding how these facilities consume power, not simply how much power they consume.
At an internal meeting, the Meta CEO reportedly said that AI development efforts were not moving as quickly as anticipated.
The news comes about a week after OpenAI announced its own custom AI chip in a partnership with Broadcom.
OpenAI CEO Sam Altman has reportedly proposed giving 5% of the companyâs equity to a U.S. sovereign wealth fund, reviving discussions about letting the public share in the financial gains from the AI boom.
Microsoft follows Amazon, OpenAI, and Anthropic with its new AI deployment group.
Meta is developing plans for a cloud infrastructure business, selling access to AI compute power and models. The move would pit it against the big cloud providers like Amazon Web Services, Google Cloud, and Microsoft Azure.
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Claude Science is Anthropicâs newest flagship product At an event for pharmaceutical executives, biotech founders, and researchers yesterday, Anthropic announced Claude Science, a major new product intended to support scientific research…
Anthropic said it would begin restoring access to the Fable on July 1.
After weeks of negotiating with the Trump administration, Anthropic is finally going to be able to bring Claude Fable 5 back online. In a post on X, Anthropic said it plans to begin restoring access Wednesday to users globally on Claude platforms, and that the company would re-enable access on AWS, Google Cloud, and Microsoft […]
At an event for pharmaceutical executives, biotech founders, and researchers on Tuesday, Anthropic announced Claude Science, a major new product intended to support scientific research in the same way that Claude Code supports software engineering. Like Claude Code, Claude Science can autonomously carry out meaningful work when given concise, high-level instructions, and it has access…
EquiLibre Technologies, a Prague-based AI lab founded by three ex-DeepMind researchers, is now valued at more than $500 million.
Anthropic's Claude Science is a workbench that gives scientists one environment to do computational research, saving them from the need to bounce between databases, pipelines, and tools.
Engineers on the new team will embed within companies to deploy purpose-built agents, focusing on fast deployments and customer self-sufficiency.
Agent-testing startup Patronus AI, founded by former Meta AI researchers, is experiencing nearly insatiable demand, its investor says.
Amazonâs latest India investment comes as global tech companies race to expand AI infrastructure in the country.
IBM has built a new prototype chip with around 100 billion transistors on an area the size of a fingernail, which is twice the density of the companyâs previous state-of-the-art technology announced in 2021. The design could pave the way for faster and more energy efficient computers for years to come. For more than half…
OpenAI has just revealed a new "intelligence processor" chip for AI servers made in partnership with Broadcom. The chip, called Jalapeño, is designed to power current and future large language models, according to an announcement on Wednesday. Jalapeño is an ASIC (Application-Specific Integrated Circuit), meaning it's designed for a specific purpose: AI inference. With AI […]
AI is booming. New use cases are emerging each day. To capitalize on the technologyâs potential, enterprises require data at scale. In many cases, though, the relevant information is blocked or unstructured, which limits its use by AI models.  To understand this challenge, consider the foundation of the web itself. The web was not designed…
Large language models have moved out of the research lab and into engineersâ daily workflow. LLMs serve as reasoning engines that can orchestrate complex tasks including identifying vulnerabilities in source code and transforming fragmented project discussions into rigorous technical specifications.While the general public uses AI tools to write email and plan vacations, technical professionals use LLMs as core architectural elements that are fundamentally changing how digital infrastructures are built and maintained. As the AI models move into mainstream engineering practice, the demand for technical expertise is rising.The LLM technology market is expected to grow by about 33 percent every year through 2030, according to MarketsandMarkets. The rapid expansion suggests that proficiency in implementing and securing the models is transitioning from a niche into a core requirement for technologists.More than just a better search engineTo use LLMs effectively, technical professionals must move beyond treating them as conversational robots. At a fundamental level, the AI systems are built on the transformer architecture, a framework that replaced the older method of processing data in a fixed, sequential order. Unlike earlier models that analyzed information one step at a time, transformers use self-attention mechanisms to ingest vast datasets simultaneously.For technical professionals, LLMs are core architectural elements that are fundamentally changing how digital infrastructures are built and maintained.Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.Four ways LLMs are changing jobsHere are areas that integrate large language models.Moving past basic prompts. Developers are using application program interfaces (APIs) to connect LLMs directly to their databases and software tools. Employing the APIs allows AI to perform work such as executing code or searching through internal repositories.Fixing the âhallucinationâ problem. LLMs are at risk of hallucinations, which are generated facts or code that looks correct but actually is wrong or broken. To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a companyâs database.Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up âprivateâ instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.The future of collaboration. By automating repetitive coding tasks and summarizing thousands of pages of documentation, LLMs let engineers spend more time on high-level designs and solving important issues.Online course program helps with mastering the techThe gap between people who use AI and those who understand how to build with it is growing wider. To help technical professionals stay ahead, IEEE offers a five-course online program, Large Language Models Demystified, available through the IEEE Learning Network.The program, developed by IEEE Educational Activities in partnership with the IEEE Computer Society, is built for people who want to understand the âhowâ and the âwhyâ behind the technology. Rather than just teaching basic prompting, the curriculum dives into the engineering behind generative AI, including:Evolution, impact, and hands-on exercises: the shift from statistical methods to modern transformers, including hands-on model optimization.Understanding transformer architectures: the mathematical core of self-attention and positional encoding, implemented in NumPy and Python.Architectural analysis and implementation: advanced LLM design with practical model-building exercises.Training and modeling with PyTorch: end-to-end pipelines in PyTorch, leveraging parameter-efficient techniques such as low-rank adaptation and quantization.Optimization, alignment, and deployment: performance scaling, reinforcement learning from human feedback (RLHF), group-relative policy optimization, RAG, and agentic AI.Upon completion of the program, participants earn professional development credits and a digital badge from IEEE to verify their expertise.Enroll in the course program on the IEEE Learning Network.Organizations looking to prepare their teams to work on LLMs can connect with an IEEE content specialist to discuss group enrollment and tailored training paths.
Miami-based AI startup Subquadratic came out of stealth mode last month with a huge claim. It announced that it had solved a mathematical bottleneck that had been holding back large language models for almost a decade. The details were thin, and many people were unconvinced. But Subquadratic has started to bring the receipts, sharing the…
OpenAI is bulking up before its IPO, landing Transformer co-inventor Noam Shazeer from Google DeepMind and former Trump AI policy official Dean Ball in the same week.
AWS is in talks to sell its chips to other data centers. CEO Andy Jassy has said this represents a $50 billion opportunity for the company.