We introduce EgoSchema, a very long-form video question-answering dataset, and benchmark to evaluate long video understanding capabilities of modern vision and language systems. Derived from Ego4D, EgoSchema consists of over 5000 human curated multiple choice question answer pairs, spanning over 250 hours of real video data, covering a very broad range of natural human activity and behavior. For each question, EgoSchema requires the correct answer to be selected between five given options based on a three-minute-long video clip. While some prior works have proposed video datasets with long clip lengths, we posit that merely the length of the video clip does not truly capture the temporal difficulty of the video task that is being considered. To remedy this, we introduce temporal certificate sets, a general notion for capturing the intrinsic temporal understanding length associated with a broad range of video understanding tasks & datasets.

We would like to thank all the reviewers for their thoughtful comments. We will respond to each reviewer's questions Itô diffusion processes with tractable finite-dimensional distributions (FDD). To show the correctness of Eqs. Since our experiments focus on low-dimensional data, the time cost is not a major bottleneck. We agree with the reviewer's comment on Eq.(12):

We introduce EgoSchema, a very long-form video question-answering dataset, and benchmark to evaluate long video understanding capabilities of modern vision and language systems. Derived from Ego4D, EgoSchema consists of over 5000 human curated multiple choice question answer pairs, spanning over 250 hours of real video data, covering a very broad range of natural human activity and behavior. For each question, EgoSchema requires the correct answer to be selected between five given options based on a three-minute-long video clip. While some prior works have proposed video datasets with long clip lengths, we posit that merely the length of the video clip does not truly capture the temporal difficulty of the video task that is being considered. To remedy this, we introduce temporal certificate sets, a general notion for capturing the intrinsic temporal understanding length associated with a broad range of video understanding tasks & datasets.

From 2001: A Space Odyssey and Ex Machina to Wall-E and Her, artificial intelligence has reliably been a subject of fascination in modern culture. But AI is no longer a thing of imagination, books or film scripts – it is already playing a pivotal role in both our professional and personal lives. And when it comes to the capability of this next-generation technology, we are now on the precipice of an exponential leap. The potential impact of AI on our lives cannot be understated, so the growing AI skills gap must be addressed if we are to ensure that businesses are prepared to take this jump. AI has already transformed the way we interact with banks, how we shop and how we manufacture.

Nikunj Oza So yeah, so NASA Ames itself has a pretty broad range of work that it does. So there's work going on, for example, in life science, there's work going on things like the heat shields for space vehicles to ensure that on reentry, it does not get too hot. There's certainly significant work in aeronautics things like management of airspace, they are researching new approaches to allow the airspace to be better managed, as we have more and more air traffic not only from commercial vehicles, but from new entrants, like UAVs. So really, AI has quite a broad range of applications within all of these pretty much most of these systems, the engineered systems that we produce, and also natural systems that we study, such as the Earth and space through all the various sensors that we have, they produce a significant amount of data. And oftentimes, what we want to do is sort of reverse engineer those data, so to speak, I mean, we have some understanding of the processes that generate these data.

David Hockney, one of the world's most famous living artists, is also a proponent of digital art. Hockney would argue significant technological advances occurred in the 15th Century with the arrival of optical devices. Centring around the mid 15th Century a radical transformation in the visual quality of painting happened. What we would call photorealistic today replaced the stylised rendering of the likes of Giotto. An understanding of optics and lenses gave artists a new way to capture the reality that the eye could see.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! A couple posts a holiday selfie to keep friends updated on their travels. Unwittingly, each one is adding fuel to an emerging fraud vector that could become enormously challenging for businesses and consumers alike: Deepfakes. Deepfakes get their name from the underlying technology: Deep learning, a subset of artificial intelligence (AI) that imitates the way humans acquire knowledge.

When it comes to building any platform, the hardware is the easiest part and, for many of us, the fun part. But more than anything else, particularly at the beginning of any data processing revolution, it is experience that matters most. Whether to gain it or buy it. With AI being such a hot commodity, many companies that want to figure out how to weave machine learning into their applications are going to have to buy their experience first and cultivate expertise later. This realization is what caused Christopher Ré, an associate professor of computer science at Stanford University and a member of its Stanford AI Lab, Kunle Olukotun, a professor of electrical engineer at Stanford, and Rodrigo Liang, a chip designer who worked at Hewlett-Packard, Sun Microsystems, and Oracle, to co-found SambaNova Systems, one of a handful of AI startups trying to sell complete platforms to customers looking to add AI to their application mix. The company has raised an enormous $1.1 billion in four rounds of venture funding since its founding in 2017, and counts Google Ventures, Intel Capital, BlackRock, Walden International, SoftBank, and others as backers as it attempts to commercialize its DataScale platform and, more importantly, its Dataflow subscription service, which rolls it all up and puts a monthly fee on the stack and the expertise to help use it. SambaNova's customers have been pretty quiet, but Lawrence Livermore National Laboratory and Argonne National Laboratory have installed DataScale platforms and are figuring out how to integrate its AI capabilities into the simulation and modeling applications. Timothy Prickett Morgan: I know we have talked many times before during the rise of the "Niagara" T series of many-threaded Sparc processors, and I had to remind myself of that because I am a dataflow engine, not a storage device, after writing so many stories over more than three decades. I thought it was time to have a chat about what SambaNova is seeing out there in the market, but I didn't immediately make the connection that it was you.

Data-centric AI, on the other hand, began in earnest in the 1970s with the invention of methods for automatically constructing "decision trees" and has exploded in popularity over the last decade with the resounding success of neural networks (now dubbed "deep learning"). Data-centric artificial intelligence has also been called "narrow AI" or "weak AI," but the rapid progress over the last decade or so has demonstrated its power. Deep-learning methods, coupled with massive training data sets plus unprecedented computational power, have delivered success on a broad range of narrow tasks from speech recognition to game playing and more. The artificial-intelligence methods build predictive models that grow increasingly accurate through a compute-intensive iterative process. In previous years, the need for human-labeled data to train the AI models has been a major bottleneck in achieving success.

Stethoscopes are among doctors' most important instruments, yet there have not been any essential improvements to the device since the 1960s. Now, researchers at Aalto University have developed a device that analyzes a broad range of bodily functions and offers physicians a probable diagnosis as well as suggestions for appropriate further examinations. The researchers believe that the new device could eventually replace the stethoscope and enable quicker and more precise diagnoses. A startup called Vital Signs is taking the device to the market. The researchers are currently testing the device in a clinical pilot trial.