Computer Science

Xometry’s essential guide reveals the transformative power of artificial intelligence in supply chain optimisation. It lifts the lid on how machine learning, natural language processing, and big data, can streamline procurement and enhance operations efficiency. The guide showcases applications across various sectors such as healthcare, construction, retail, and more, offering actionable insights and strategies. Readers will explore the workings of AI technologies, their implementation in manufacturing, and future trends in supply chain management, making it a valuable resource for professionals aiming to harness AI’s potential to innovate and optimise their supply chain processes.Downlo..

One of the management guru Peter Drucker’s most over-quoted turns of phrase is “what gets measured gets improved.” But it’s over-quoted for a reason: It’s true. Nowhere is it truer than in technology over the past 50 years. Moore’s law—which predicts that the number of transistors (and hence compute capacity) in a chip would double every 24 months—has become a self-fulfilling prophecy and north star for an entire ecosystem. Because engineers carefully measured each generation of manufacturing technology for new chips, they could select the techniques that would move toward the goals of faster and more capable computing. And it worked: Computing power, and more impressively co..

AI hiring has been growing at least slightly in most regions around the world, with Hong Kong leading the pack; however, AI careers are losing ground compared with the overall job market, according to the 2024 AI Index Report. This annual effort by Stanford’s Institute for Human-Centered Artificial Intelligence (HAI) draws from a host of data to understand the state of the AI industry today.Stanford’s AI Index looks at the performance of AI models, investment, research, and regulations. But tucked within the 385 pages of the 2024 Index are several insights into AI career trends, based on data from LinkedIn and Lightcast, a labor market analytics firm.Here’s a quick look at that analysi..

Each year, the AI Index lands on virtual desks with a louder virtual thud—this year, its 393 pages are a testament to the fact that AI is coming off a really big year in 2023. For the past three years, IEEE Spectrum has read the whole damn thing and pulled out a selection of charts that sum up the current state of AI (see our coverage from 2021, 2022, and 2023). This year’s report, published by the Stanford Institute for Human-Centered Artificial Intelligence (HAI), has an expanded chapter on responsible AI and new chapters on AI in science and medicine, as well as its usual roundups of R&D, technical performance, the economy, education, policy and governance, diversity, and public opini..

Neural networks that imitate the workings of the human brain now often generate art, power computer vision, and drive many more applications. Now a neural network microchip from China that uses photons instead of electrons, dubbed Taichi, can run AI tasks as well as its electronic counterparts with a thousandth as much energy, according to a new study.AI typically relies on artificial neural networks in applications such as analyzing medical scans and generating images. In these systems, circuit components called neurons—analogous to neurons in the human brain—are fed data and cooperate to solve a problem, such as recognizing faces. Neural nets are dubbed “deep“ if they possess multi..

Last December, the AI Institute announced that it was opening an office in Zurich as a European counterpart to its Boston headquarters and recruited Marco Hutter to helm the office. Hutter also runs the Robotic Systems Lab at ETH Zurich, arguably best known as the origin of the ANYmal quadruped robot (but it also does tons of other cool stuff).We’re doing our best to keep close tabs on the institute, because it’s one of a vanishingly small number of places that currently exist where roboticists have the kind of long-term resources and vision necessary to make substantial progress on really hard problems that aren’t quite right for either industry or academia. The institute is still sc..

Although the race to power the massive ambitions of AI companies might seem like it’s all about Nvidia, there is a real competition going in AI accelerator chips. The latest example: At Intel’s Vision 2024 event this week in Phoenix, Ariz., the company gave the first architectural details of its third-generation AI accelerator, Gaudi 3. With the predecessor chip, the company had touted how close to parity its performance was to Nvidia’s top chip of the time, H100, and claimed a superior ratio of price versus performance. With Gaudi 3, it’s pointing to large-language-model (LLM) performance where it can claim outright superiority. But, looming in the background is Nvidia’s next GPU..

A new breed of AI-powered coding tools have emerged—and they’re claiming to be more autonomous versions of earlier assistants like GitHub Copilot, Amazon CodeWhisperer, and Tabnine.One such new entrant, Devin AI, has been dubbed an “AI software engineer” by its maker, applied AI lab Cognition. According to Cognition, Devin can perform all these tasks unassisted: build a website from scratch and deploy it, find and fix bugs in codebases, and even train and fine-tune its own large language model.Following its launch, open-source alternatives to Devin have cropped up, including Devika and OpenDevin. Meanwhile makers of established assistants have not been standing still. Researchers at ..

A new machine learning system can create height maps of urban environments from a single synthetic aperture radar (SAR) image, potentially accelerating disaster planning and response. Aerospace engineers at the University of the Bundeswehr in Munich claim their SAR2Height framework is the first to provide complete—if not perfect—three-dimensional city maps from a single SAR satellite.When an earthquake devastates a city, information can be in short supply. With basic services disrupted, it can difficult to assess how much damage occurred or where the need for humanitarian aid is greatest. “Being able to produce 3D from SAR alone would be really be a revolution.” —Carl Pucci, EO59Ae..

Liquid circuits that mimic synapses in the brain can for the first time perform the kind of logical operations underlying modern computers, a new study finds. Near-term applications for these devices may include tasks such as image recognition, as well as the kinds of calculations underlying most artificial intelligence systems.Just as biological neurons both compute and store data, brain-imitating neuromorphic technology often combines both operations. These devices may greatly reduce the energy and time lost in conventional microchips shuttling data back and forth between processors and memory. They may also prove ideal for implementing neural networks—AI systems increasingly finding use..

Times change, and so must benchmarks. Now that we’re firmly in the age of massive generative AI, it’s time to add two such behemoths, Llama 2 70B and Stable Diffusion XL, to MLPerf’s inferencing tests. Version 4.0 of the benchmark tests more than 8,500 results from 23 submitting organizations. As has been the case from the beginning, computers with Nvidia GPUs came out on top, particularly those with its H200 processor. But AI accelerators from Intel and Qualcomm were in the mix as well. MLPerf started pushing into the LLM world last year when it added a text summarization benchmark GPT-J (a 6 billion parameter open-source model). With 70 billion parameters, Llama 2 is an order of mag..

In our last article, A How-To Guide on Acquiring AI Systems, we explained why the IEEE P3119 Standard for the Procurement of Artificial Intelligence (AI) and Automated Decision Systems (ADS) is needed. In this article, we give further details about the draft standard and the use of regulatory “sandboxes” to test the developing standard against real-world AI procurement use cases. Strengthening AI procurement practicesThe IEEE P3119 draft standard is designed to help strengthen AI procurement approaches, using due diligence to ensure that agencies are critically evaluating the AI services and tools they acquire. The standard can give government agencies a method to ensure transparency fro..

Last weekend, X.ai released the world’s largest “open-source” large language model (LLM), Grok-1. At 314 billion parameters, it’s the largest open-source model yet, far exceeding predecessors like Falcon 180B at 180 billion. That’s impressive, but the timing of X.ai’s release—just a few weeks after Elon Musk, founder of X.ai, filed suit against OpenAI for an alleged lack of openness—raised eyebrows. Is Grok-1 a useful effort to move open-source AI forward, or more of a ploy to prove Musk’s company is more open than its rival? “One strategy is truly open source. And the second strategy is more of an ‘open weight’ strategy. What X.ai has done here is gone with the open ..

In just 30 years, the South Indian city of Bengaluru has gone from a sleepy retirement town to the capital of the country’s burgeoning technology sector. But such rapid development has left it with some of the most congested roads in the world. Now, a former chip engineer has been drafted in to act as the city’s traffic czar, and he’s turning to artificial intelligence to tackle the problem. Indian streets are disorderly at the best of times. Pedestrians, cyclists, hand-drawn carts, scooters, auto-rickshaws, and a menagerie of ramshackle goods vehicles of varying sizes and speeds all vie for space on roads that are often in poor condition, lacking sidewalks, and largely devoid of signa..

Computer operations are a straightforward process: Specific sets of transistors carry out predetermined logical functions. But computer researchers have hoped that someday, electronic circuits would not be stuck doing only one specific task. Instead, the idea is that circuits could be switched to perform various functions as the situation demanded—a kind of on-the-go reprogramming.In a development that could reshape electronic circuits and chip design, a research team at TU Wien in Vienna has introduced a new generation of intelligent, controllable transistors capable of flexible reprogramming at the hardware level. This innovation represents a departure from conventional semiconductor tec..

Today at Nvidia’s developer conference, GTC 2024, the company revealed its next GPU, the B200. The B200 is capable of delivering four times the training performance, up to 30 times the inference performance, and up to 25 times better energy efficiency, compared to its predecessor, the Hopper H100 GPU. Based on the new Blackwell architecture, the GPU can be combined with the company’s Grace CPUs to form a new generation of DGX SuperPOD computers capable of up to 11.5 billion billion floating point operations (exaflops) of AI computing using a new, low-precision number format.“Blackwell is a new class of AI superchip,” says Ian Buck, Nvidia’s vice president of high-performance comput..

Nvidia’s ongoing GTC developer conference in San Jose is, unsurprisingly, almost entirely about AI this year. But in between the AI developments, Nvidia has also made a couple of significant robotics announcements.First, there’s Project GR00T (with each letter and number pronounced individually so as not to invoke the wrath of Disney), a foundation model for humanoid robots. And secondly, Nvidia has committed to be the founding platinum member of the Open Source Robotics Alliance, a new initiative from the Open Source Robotics Foundation intended to make sure that the Robot Operating System (ROS), a collection of open-source software libraries and tools, has the support that it needs to ..

In February, OpenAI announced the arrival of Sora, a stunning “text-to-video” tool. Simply enter a prompt, and Sora generates a realistic video within seconds. But it wasn’t immediately available to the public. Some of the delay is because OpenAI reportedly has a set of experts called a red team who, the company has said, will probe the model to understand its capacity for deepfake videos, misinformation, bias, and hateful content.Red teaming, while having proved useful for cybersecurity applications, is a military tool that was never intended for widespread adoption by the private sector. “Done well, red teaming can identify and help address vulnerabilities in AI,” says Brian Chen..

For 19 months, the sociologist Benjamin Shestakofsky embedded himself in an early-stage tech startup to study its organization and culture. The company went on to become one of Silicon Valley’s “unicorns,” valued at over US $1 billion. This article is adapted from an excerpt of the author’s new book, Behind the Startup: How Venture Capital Shapes Work, Innovation, and Inequality (University of California Press, 2024). The names of staff members and the company have been changed to preserve privacy. When I began my research, AllDone had just secured its first round of venture capital funding to fuel its quest to build an “Amazon for local services.” The company had built a digita..

Sunnyvale, Calif., AI supercomputer firm Cerebras says its next generation of waferscale AI chips can do double the performance of the previous generation while consuming the same amount of power. The Wafer Scale Engine 3 (WSE-3) contains 4 trillion transistors, a more than 50 percent increase over the previous generation thanks to the use of newer chipmaking technology. The company says it will use the WSE-3 in a new generation of AI computers, which are now being installed in a datacenter in Dallas to form a supercomputer capable of 8 exaflops (8 billion billion floating point operations per second). Separately, Cerebras has entered into a joint development agreement with Qualcomm that ai..

With the rapid proliferation of AI systems, public policymakers and industry leaders are calling for clearer guidance on governing the technology. The majority of U.S. IEEE members express that the current regulatory approach to managing artificial intelligence (AI) systems is inadequate. They also say that prioritizing AI governance should be a matter of public policy, equal to issues such as health care, education, immigration, and the environment. That’s according to the results of a survey conducted by IEEE for the IEEE-USA AI Policy Committee.We serve as chairs of the AI Policy Committee, and know that IEEE’s members are a crucial, invaluable resource for informed insights into the ..

Since ChatGPT dropped in the fall of 2022, everyone and their donkey has tried their hand at prompt engineering—finding a clever way to phrase your query to a large-language model (LLM) or AI art or video generator to get the best results or side-step protections. The internet is replete with prompt engineering guides, cheat sheets, and advice threads to help you get the most out of an LLM.In the commercial sector, companies are now wrangling LLMs to build product co-pilots, automate tedious work, create personal assistants, and more, says Austin Henley, a former Microsoft employee who conducted a series of interviews with people developing LLM-powered co-pilots. “Every business is tryin..

In the past few months, we’ve seen a deepfake robocall of Joe Biden encouraging New Hampshire voters to “save your vote for the November election” and a fake endorsement of Donald Trump from Taylor Swift. It’s clear that 2024 will mark the first “AI election” in United States history.With many advocates calling for safeguards against AI’s potential harms to our democracy, Meta (the parent company of Facebook and Instagram) proudly announced last month that it will label AI-generated content that was created using the most popular generative AI tools. The company said it’s “building industry-leading tools that can identify invisible markers at scale—specifically, the ‘AI..

A microchip that uses light instead of electricity can potentially be faster and more energy-efficient at the complex computations essential to training AI than conventional electronics. In addition, researchers say the new chips may be significantly more secure against hacking.AI typically relies on neural networks in applications such as analyzing medical scans and supporting autonomous vehicles. In these systems, components known as neurons are fed data and cooperate to solve a problem, such as recognizing faces.As neural networks grow in size and power, they are becoming more energy hungry when run on conventional electronics. This has led some researchers to investigate optical computin..

The term “social media influencer” may call to mind Instagram accounts shilling hair growth gummies and cute outfits—but in reality, influencers influence all types of things. Including artificial intelligence research trends.Mainstream interest in AI and machine learning is at an all-time high, and the industry is responding—churning out thousands of AI and ML works for conferences and journals. The AI/ML community is also particularly active in posting non-peer-reviewed preprints via online platforms like ArXiv. Given this glut of work, what rises to the top and receives attention?The answer, at least in part, is: the research that a pair of highly influential users of X (formerly ..

Diversity of thought in industrial design is crucial: If no one thinks to design a technology for multiple body types, people can get hurt. The invention of seat belts is an oft-cited example of this phenomenon, as they were designed based on crash dummies that had traditionally male proportions, reflecting the bodies of the team members working on them.The same phenomenon is now at work in the field of motion-capture technology. Throughout history, scientists have endeavored to understand how the human body moves. But how do we define the human body? Decades ago many studies assessed “healthy male” subjects; others used surprising models like dismembered cadavers. Even now, some modern ..

Today, Figure is announcing an astonishing US $675 million Series B raise, which values the company at an even more astonishing $2.6 billion. Figure is one of the companies working towards a multi or general purpose (depending on who you ask) bipedal or humanoid (depending on who you ask) robot. The astonishing thing about this valuation is that Figure’s robot is still very much in the development phase—although they’re making rapid progress, which they demonstrate in a new video posted this week.This round of funding comes from Microsoft, OpenAI Startup Fund, Nvidia, Jeff Bezos (through Bezos Expeditions), Parkway Venture Capital, Intel Capital, Align Ventures, and ARK Invest. Figure ..

It’s been just about a year now—a nonprofit called the Future of Life Institute posted an open letter reflecting people’s darkest fears about artificial intelligence. “Contemporary AI systems are now becoming human-competitive at general tasks,” it said. It called for a pause in training of the most advanced AI, so that technology companies could develop safety protocols. It expressed worry about disinformation and out-of-control machines. And it struck a nerve with its concerns that some AI could make human work irrelevant. “AI is a tool.... And tools generally aren’t substitutes for expertise but rather levers for its application.” —David Autor, MIT “Should we automate ..

2023 wasn’t a great year for AI detectors. Leaders like GPTZero surged in popularity but faced a backlash as false positives led to incorrect accusations. Then OpenAI quietly tossed ice-cold water on the idea with an FAQ to answer whether AI detectors work. The verdict? “No, not in our experience.”OpenAI’s conclusion was correct—at the time. Yet the demise of AI detectors is greatly exaggerated. Researchers are inventing new detectors that perform better than their predecessors and can operate at scale. And these come alongside “data poisoning” attacks that individuals can use to safeguard their work from being scooped up against their wishes to train AI models.“Language mode..

Generative AI is today’s buzziest form of artificial intelligence, and it’s what powers chatbots like ChatGPT, Ernie, LLaMA, Claude, and Command—as well as image generators like DALL-E 2, Stable Diffusion, Adobe Firefly, and Midjourney. Generative AI is the branch of AI that enables machines to learn patterns from vast datasets and then to autonomously produce new content based on those patterns. Although generative AI is fairly new, there are already many examples of models that can produce text, images, videos, and audio. Many “foundation models” have been trained on enough data to be competent in a wide variety of tasks. For example, a large language model can generate essays, c..