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- Learn how to improve coding performance based on GenAI in one article
- Hellofolks, my name is Luga, and today we will talk about technologies related to the artificial intelligence (AI) ecological field - GenAI. Facing the challenges of rapid technological innovation and differentiated business scenarios, traditional coding methods have begun to become acclimated and cannot fully cope with the growing demands. At the same time, emerging general-purpose GenAI (artificial intelligence technology) has great potential to meet this demand. As a representative of artificial intelligence technology, GenAI has begun to be widely used in all walks of life with its strong potential and capabilities. It can automatically learn and adapt to coding needs in different scenarios, greatly improving coding efficiency and quality. Through deep learning and model optimization, GenAI is able to accurately understand different
- AI 816 2024-04-01 18:49:14
- Adopting generative AI systems could transform enterprise cloud architectures
- From data availability and security to large language models and selection and monitoring, enterprise adoption of generative AI means the need to reexamine their cloud architecture. Therefore, many companies are rebuilding their cloud architecture and developing generative artificial intelligence systems. So, what changes do these enterprises need to make? What are the emerging best practices? Industry experts said that in the past 20 years, especially in the past two years, he has helped enterprises build some such platforms. Here are his Some advice for enterprises: Understand your own use cases Enterprises need to clearly define the purpose and goals of generative AI in cloud architecture. If you see some false feedback, it's because they don't understand what it means to generate artificial intelligence in business systems. Businesses need to understand their goals
- AI 348 2024-04-01 17:34:12
- 0 threshold for free commercial use! Mencius 3-13B large model is officially open source and trained with trillions of token data
- Lanzhou Technology officially announced: The Mencius 3-13B large model is officially open source! This large, cost-effective lightweight model is fully open to academic research and supports free commercial use. Mencius 3-13B has shown good performance in various benchmark evaluations such as MMLU, GSM8K, and HUMAN-EVAL. Especially in the field of lightweight large models with parameters within 20B, his Chinese and English language skills are particularly outstanding. Mathematics and programming skills are also at the forefront. △The above results are based on 5-shot. According to reports, the Mencius 3-13B large model is based on the Llama architecture, and the data set size is as high as 3TTokens. The corpus is selected from web pages, encyclopedias, social media, media, news, and high-quality open source data sets. By in trillion toke
- AI 605 2024-04-01 17:01:22
- Unifying characters and changing scenes, PixVerse, a video generation artifact, has been played out by netizens, and its super consistency has become a 'killer move'
- Another double click is the debut of a new feature. Have you ever wanted to change the background of a character in a picture, but the AI always produces the effect of "the object is neither the person nor the object". Even in mature generation tools such as Midjourney and DALL・E, some prompt skills are required to maintain character consistency, otherwise the characters will change around and you will not achieve the results you want. However, this time it’s your chance. The new "Character-Video" function of the AIGC tool PixVerse can help you achieve all this. Not only that, it can generate dynamic videos to make your characters more vivid. Enter a picture and you will be able to get the corresponding dynamic video results. On the basis of maintaining the consistency of the characters, the rich background elements and character dynamics allow the generated results to be
- AI 768 2024-04-01 14:11:12
- 'Tiangong Big Model 3.0' was officially released on April 17th - a 400 billion parameter MoE super model that is simultaneously open source and has performance exceeding Grok1.0
- On April 17, 2023, Kunlun Wanwei released its self-developed dual-hundred-billion-level large language model "Tiangong 1.0", officially paving the way for the rise of domestic large-scale models. On the upcoming April 17, 2024, on the first anniversary of the "Tiangong" large model, Kunlun Wanwei announced that "Tiangong 3.0" has officially launched public beta! "Tiangong 3.0" adopts a 400-billion-level parameter MoE hybrid expert model, and will simultaneously select open source. It is one of the MoE models with the largest model parameters and the strongest performance in the world. Compared with the previous generation "Tiangong 2.0" MoE large model, "Tiangong 3.0" has amazing performance improvements in areas such as model semantic understanding, logical reasoning, versatility, generalization, uncertainty knowledge, and learning capabilities. Its model technical knowledge and capabilities have improved by more than 20
- AI 732 2024-04-01 14:01:27
- GPT-4 scored only 7.1 points in each category, revealing three major shortcomings in large model code capabilities. The latest benchmark test is here
- Devin, the first AI software engineer, was officially unveiled, immediately detonating the entire technology community. Although Devin cannot easily solve coding tasks, he can complete the entire software development cycle independently - from project planning to deployment. He tries his best to dig, but is not limited to building websites, independently finding and fixing bugs, training and fine-tuning AI models, etc. This kind of "unbelievably strong" software development ability has made many coders despair, calling out: "Is the doom of programmers really coming?" Among the test results, Devin's best in the SWE-Bench benchmark test The performance is particularly striking. SWE-Bench is a test to evaluate AI software engineering capabilities, focusing on the ability of large models to solve actual GitHub problems. Devin is independent
- AI 573 2024-04-01 12:41:01
- Musk says there's a 20% chance of artificial intelligence destroying humanity, but it's still worth the risk
- According to news from this site on April 1, Elon Musk said at the "Artificial Intelligence Debate" seminar held at the Abundance Summit earlier that even artificial intelligence technology has a 1/5 chance of posing a threat to humans. , but the advantages outweigh the disadvantages, and it is still worth the risk for us to conduct research and development. Musk re-evaluated his previous risk assessment of artificial intelligence. He said at the seminar: "I think artificial intelligence has the potential to end human civilization. I probably agree with Geoffrey Hinton that the probability is about 10% to 20%." But he added: "I think the positive possibility scenario is greater than the negative possibility scenario." Musk did not mention how he calculated that risk. This site noticed, go
- AI 400 2024-04-01 12:21:20
- To make the video pose Transformer fast, Peking University proposes an efficient 3D human pose estimation framework HoT
- Currently, VideoPoseTransformer (VPT) has achieved the most leading performance in the field of video-based 3D human pose estimation. In recent years, the computational workload of these VPTs has become increasingly large, and these huge computational workloads have also limited further development in this field. It is very unfriendly to researchers with insufficient computing resources. For example, training a 243-frame VPT model usually takes several days, seriously slowing down the progress of research and becoming a major pain point in the field that needs to be solved urgently. So, how to effectively improve the efficiency of VPT without losing almost any accuracy? A team from Peking University proposed an efficient 3D human pose estimation framework HoT based on hourglass Tokenizer to
- AI 535 2024-04-01 11:31:32
- 'It's hard to distinguish between true and false'! Clever use of autonomous driving simulation data generated by NeRF
- Written earlier & the author’s personal understanding Neural Radiation Fields (NeRF) have become a tool that advances the prelude to the re-search for autonomous driving (AD), providing scalable closed-loop simulation and data enhancement capabilities. However, in order to trust the results obtained in the simulation, it is necessary to ensure that the AD system perceives the real data and the rendered data in the same way. Although the performance of rendering methods is improving, many scenes remain inherently challenging to faithfully reconstruct. To this end, we propose a new perspective to address the gap between real and simulated data. We not only focus on improving rendering fidelity, but explore simple yet effective methods to enhance the robustness of perceptual models to NeRF artifacts without affecting real data performance. Additionally, we use state-of-the-art neural
- AI 478 2024-04-01 11:31:16
- Robotics and Biomedical Engineering: Artificial Tissues
- In recent years, the intersection of robotics and biomedical engineering has led to breakthrough innovations in regenerative medicine. One of the most exciting developments is the creation of artificial tissue, which holds great promise for revolutionizing medical treatments and therapies. This article explores innovative efforts in the fields of robotics and biomedical engineering to develop artificial tissues and their potential applications in healthcare. Conventional medical treatments and therapies can often only repair damaged tissue by transplanting human organs or using synthetic materials. However, these approaches come with many limitations and risks, including a shortage of donated organs and the risk of immune rejection. Therefore, the development of artificial tissues has become an urgent need. Robotics and biomedicine Artificial tissues, also known as tissue engineering or regenerative medicine, involve creating tissues that mimic the body's natural
- AI 940 2024-04-01 09:56:19
- How the telecom industry is using AI to solve its biggest problems
- As the industry becomes increasingly complex and uncertain, the telecom industry must embrace AI as a strategic tool to address challenges, improve decision-making, and transform businesses. The telecommunications industry faces huge challenges. In addition to tough macroeconomic conditions, they face stiff competition from new entrants, rising costs due to inflation, and competition to find new revenue streams in a crowded market. The telecommunications industry is rapidly adopting AI to overcome obstacles and transform the way business is run. In fact, one survey found that 95% of the telecom industry is using AI, and 65% of respondents believe AI is critical to the success of the industry. By integrating artificial intelligence into daily operations, the telecommunications industry has the opportunity to stand out in a highly competitive market. This will allow them to streamline processes and more efficiently analyze
- AI 912 2024-04-01 09:36:20
- Shanghai Jiao Tong University's new framework unlocks CLIP long text capabilities, grasps the details of multi-modal generation, and significantly improves image retrieval capabilities
- CLIP long text capabilities are unlocked, and the performance of image retrieval tasks is significantly improved! Some key details can also be captured. Shanghai Jiao Tong University and Shanghai AI Laboratory proposed a new framework Long-CLIP. △The brown text is the key detail that distinguishes the two images. Based on maintaining the original feature space of CLIP, Long-CLIP can be plug-and-play in downstream tasks such as image generation to achieve fine-grained image generation of long text. Long text-image retrieval increased by 20%, and short text-image retrieval increased by 6%. Unlocking CLIP's long text capabilities CLIP aligns visual and text modalities and has powerful zero-shot generalization capabilities. Therefore, CLIP is widely used in various multi-modal tasks, such as image classification, text image retrieval, and image generation.
- AI 437 2024-04-01 09:26:33
- The international actress of 'Mamma Mia' lost her job to AI overnight!
- A few days ago, OpenAI just announced its entry into Hollywood and released a wave of shocking videos of directors and artists experiencing Sora. In just a few days, internationally famous celebrities have lost their jobs to AI overnight! She is Sara Poyzer, the star of the musical "Mamma Mia". For more than ten years, she has dominated London's West End with her performance as the heroine Donna Sheridan in the play. She has had a very successful career as an actress and voiceover artist. Recently, Sara starred in the London West End musical "Come From Away", which also won the British Laurence Olivier Award (Olivieraward). Who would have thought that after working hard at the BBC for at least 20 years, he would suddenly be told that "AI
- AI 1055 2024-04-01 09:16:09
- My leader Musk: Hates meetings, doesn't want non-technical middle managers, and advocates layoffs
- Musk is already famous for being a "devil boss." Now, his old subordinate Andrej Karpathy "hammered" him again (doge) in the latest interview: I had to beg him to allow me to recruit people. He (Musk) always defaults to laying off employees. In addition to his preference for layoffs, at this AIAscent event organized by Sequoia, Kapasi also revealed more details about Musk's management company: he hates meetings, refuses to lie down, and prefers to chat directly with engineers than with VPs. Work... In addition, he also talked about a lot of large model topics that everyone cares about, including: Is the scale of LLMOS important? How can young startups compete with OpenAI? For more details, please share the text version below~(
- AI 1062 2024-04-01 09:01:33
- Why is generative AI sought after by various industries?
- Generative AI is a type of human artificial intelligence technology that can generate various types of content, including text, images, audio and synthetic data. So what is artificial intelligence? What is the difference between artificial intelligence and machine learning? Artificial intelligence is the discipline, a branch of computer science, that studies the creation of intelligent agents, which are systems that can reason, learn, and perform actions autonomously. At its core, artificial intelligence is concerned with the theories and methods of building machines that think and act like humans. Within this discipline, machine learning ML is a field of artificial intelligence. It is a program or system that trains a model based on input data. The trained model can make useful predictions from new or unseen data derived from the unified data on which the model was trained.
- AI 721 2024-03-30 19:36:03
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