We introduce a high-performance fingerprint liveness feature extraction technique that secured first place in LivDet 2023 Fingerprint Representation Challenge. Additionally, we developed a practical fingerprint recognition system with 94.68% accuracy, earning second place in LivDet 2023 Liveness Detection in Action. By investigating various methods, particularly style transfer, we demonstrate improvements in accuracy and generalization when faced with limited training data. As a result, our approach achieved state-of-the-art performance in LivDet 2023 Challenges.

So much has changed in the past year, it can feel like everything is ripe for disruption, but in spite of the most disruptive year in tech I have ever seen, the biggest surprise for me on this year's list is how little the framework ecosystem has changed. There are lots of new players on the board (shout out to SolidJS) but the big winners from last year still dominate this year and don't seem to be giving up much if any ground in the job market, yet (see below for data-backed evidence). When I conducted my first video interview with GPT-3 in 2020, few people believed that it actually understood anything, let alone that it could produce useful code. Fast forward to today -- every developer is already at a huge disadvantage if they're not using an AI tool like Copilot or reviewing their code for issues, bugs, and suggestions with ChatGPT. GitHub ran a test to discover the impact of AI development tools on developer productivity (specifically, GitHub Copilot), and what they found was very interesting.

Seven papers submitted by OPPO were selected for presentation at the 2022 Computer Vision and Pattern Recognition Conference, breaking a new record for the company. The selected papers cover OPPO's various R&D breakthroughs in a range of artificial intelligence disciplines OPPO received a total of eight prizes in the CVPR challenges, including three first-place, one second place, and four third place prizes SHENZHEN, CHINA - Media OutReach - 23 June 2022 - The annual Computer Vision and Pattern Recognition Conference (CVPR) came to an end in New Orleans today, with globally leading technology company OPPO successfully having seven of its submitted papers selected for the conference, putting it among the most successful technology companies at the event. OPPO also placed in eight of the widely watched competition events at the conference, taking home three first place, one second place, and four third place prizes. As deep learning technology has developed over the years, artificial intelligence has shifted from perceptual intelligence to cognitive intelligence. In addition to being able to'see' or'hear' like humans, modern AI technology is now able to demonstrate a similar level of cognitive ability to humans too.

Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. Pronouns: He/hi (show all) Tristan covers human-centric artificial intelligence advances, quantum computing, STEM, Spiderman, physics, and space stuff. There are few more compelling story lines at the intersection of Wall Street and Fear Street than China's rise to global prominence in the field of artificial intelligence. You don't have to look very far to find a military or financial expert who believes China's AI program will some day surpass the capabilities of its democratic counterparts in Silicon Valley. But, as we've written before, the idea that China is in second place behind the US is a bit misleading. Currently, it would be a huge stretch to call it a race.

Four years ago, organizers created the international AI City Challenge to spur the development of artificial intelligence for real-world scenarios like counting cars traveling through intersections or spotting accidents on freeways. In the first years, teams representing American companies or universities took top spots in the competition. Last year, Chinese companies won three out of four competitions. Last week, Chinese tech giants Alibaba and Baidu swept the AI City Challenge, beating competitors from nearly 40 nations. Chinese companies or universities took first and second place in all five categories.

Upon film premiere, a major form of speculation concerns the relative success of the film. This relativity is in particular regards to the film's original budget, as many a time have big-budget blockbusters been met with exceptional success as met with abject failure. So how does one predict the success of an upcoming film? In this paper, we explored a vast array of film data in an attempt to develop a model that could predict the expected return of an upcoming film. The approach to this development is as follows: First, we began with the MovieLens dataset having common movie attributes along with genome tags per each film. Genome tags give insight into what particular characteristics of the film are most salient. We then included additional features regarding film content, cast/crew, audience perception, budget, and earnings from TMDB, IMDB, and Metacritic websites. Next, we performed exploratory data analysis and engineered a wide range of new features capturing historical information for the available features. Thereafter, we used singular value decomposition (SVD) for dimensionality reduction of the high dimensional features (ex. genome tags). Finally, we built a Random Forest Classifier and performed hyper-parameter tuning to optimize for model accuracy. A future application of our model could be seen in the film industry, allowing production companies to better predict the expected return of their projects based on their envisioned outline for their production procedure, thereby allowing them to revise their plan in an attempt to achieve optimal returns.

They like to play until the winner takes all of the winnings, which ultimately includes all my money. Imagine running a business where your main competitor has the dominant market share, and you are in second place. You have been struggling for years to overtake your primary competitor, but they have advantages in product, in costs, and in marketing that you can't match. You are improving, but your competitor is improving at the same rate. You are stuck in a perpetual second place.

The spread of artificial intelligence (AI) is not slowing down: 85% of organizations said they are evaluating or using AI in production, a report from the technology and business training company O'Reilly found. More than half of companies identified themselves as mature adopters of AI, or as using AI for analysis or in production. O'Reilly's AI Adoption in the Enterprise 2020 report, released on Wednesday, determined that AI growth and popularity is continuing apace. To prepare for this onset of AI use, organizations must make sure they have a solid foundation for the technology to flourish, it found. The 2019 edition of O'Reilly's report indicated that AI was still in the experimental phase.

"TensorFlow is by far the most popular tool among our respondents, with Keras in second place, and PyTorch in third. Other frameworks like MXNet, CNTK, and BigDL have growing audiences as well" As if businesses today didn't already have enough to worry about, then along comes a new wave of game-changing technologies that they must master quickly if they are not to fall behind their competitors, with pressure mounting to start using AI. . Artificial Intelligence is the most visible of these technologies – and arguably the most important. Open a newspaper, and it might seem as if every business is making great strides towards developing and using AI applications that will transform their operations and enable them to deliver new products and services to their customers. It's easy for businesses yet to achieve success by using AI – or even to get started on their journey – to get despondent about the lead they perceive their competitors to have.

The courtroom fight between Uber and Waymo is over; now the race to get an autonomous ride-hailing service to market is back on. On Friday, we learned that Waymo--the self-driving car arm of Google parent Alphabet--has made one huge stride: The company applied to become a transportation network company in Arizona on Jan. 12, and its permit was approved on Jan. 24, Quartz reports. This nod from the Copper State means Waymo can begin operating a commercial service that would compete with human-powered ride-hail companies like Uber and Lyft, charging passengers for rides in its self-driving Chrysler Pacifica minivans. "As we continue to test drive our fleet of vehicles in greater Phoenix, we're taking all the steps necessary to launch our commercial service this year," a Waymo spokesperson told Slate. Waymo has slowly been making progress toward this goal, beginning with extensive real-world testing in Phoenix.