AI Generating Comics from Text AI Video Generation - and now AI Comic Generation - are being taken to the next level, showcased by this research paper from ByteDance and various universities in China: StoryDiffusion introduces a novel framework for generating consistent sequences of images and videos from text prompts using diffusion-based models. It proposes two main components: Consistent Self-Attention, which enhances content consistency by incorporating reference image data into the self-attention process, and Semantic Motion Predictor, which facilitates smooth video transitions by predicting motions in a semantic space. These innovations enable the production of visually coherent narratives with very impressive stability and fidelity to the original text prompts - an exciting space. |
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Stable Text-to-Motion Framework AI Generated 3D movies and video games are one step closer with this novel Stable Text-to-Motion Framework created through a collaboration from universities across US, China and Australia: This framework addresses instability issues in Text-to-Motion (T2M) models, which typically produce inconsistent motion sequences from similar textual inputs. This instability is traced back to erratic attention patterns in the text encoder module, primarily built on pre-trained CLIP models. To combat this, the authors propose the Stable Text-to-Motion Framework (SATO), comprising modules that enhance attention stability, prediction accuracy, and robustness. SATO shows significant improvements in stability and accuracy across tests with a new synonym perturbation dataset, together with impressive demos which make our imaginations run on how this will impact numerous industries. |
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Tesla Autopilot Training 5m+ Cars A deep dive into how tesla continuously improves Self-driving capability on 5M+ cars by looking at a patent by Tesla’s former Senior Director of AI Andrej Karpathy: In Testla's patent, the strategy for enhancing its Autopilot and Full Self-Driving features employs "trigger classifiers," which are specialized, lightweight machine learning models that detect unusual or rare driving conditions from data collected by its vast fleet of over 5 million vehicles. These classifiers help gather critical data, allowing Tesla to continually refine and update its models, ensuring better performance in edge cases and increasing overall system reliability. This dynamic method of targeted data acquisition and continuous model improvement demonstrates a scalable approach to advancing autonomous driving technologies. A great insight on a large-scale production machine learning system active globally. |
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Reading Papers for Career Reading research papers can take your engineering career to the next level: Great advice on the benefits of reading whitepapers for engineers - clear benefits include expanding impact with innovative insights in team projects, fostering continuous personal and professional growth, and staying up-to-date with industry trends to secure career longevity. Often it is also beneficial to look at "foundational" resources as opposed to the "latest cutting edge", as this can serve to build a robust engineering knowledge and accelerate their advancement and establish themselves as key contributors within their organizations. Also some great papers suggested, including Google File System (GFS), Google Spanner (Globally Distributed Database), Google Chubby Locking Service, Meta XFaaS: Hyperscale and Low-cost serverless functions, Facebook Cassandra (Distributed NoSQL DB), Facebook Memcache (KV store), LinkedIn Kafka (PubSub), Amazon DynamoDB, and Bitcoin (yep). |
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Measuring Personal Growth Chip Huyen shares great introspective advice on how to measure (and augment) personal growth. She covers the concept of measuring personal growth using unique metrics beyond conventional means like net worth or social media followers. There are three main heuristics for personal development presented: the rate of change in identity every 3-6 years, the efficiency in solving life's significant problems such as career, family, and finance, and the number of future options available, akin to empowerment maximization principles used in reinforcement learning. Great advice on focusing on novelty and exploration, suggesting that personal growth can be quantified by one's ability to become a different person, resolve major life challenges quickly, and maximize potential future opportunities. |
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Upcoming MLOps Events The MLOps ecosystem continues to grow at break-neck speeds, making it ever harder for us as practitioners to stay up to date with relevant developments. A fantsatic way to keep on-top of relevant resources is through the great community and events that the MLOps and Production ML ecosystem offers. This is the reason why we have started curating a list of upcoming events in the space, which are outlined below. Upcoming conferences where we're speaking:
Other upcoming MLOps conferences in 2024:
In case you missed our talks:
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Check out the fast-growing ecosystem of production ML tools & frameworks at the github repository which has reached over 10,000 ⭐ github stars. We are currently looking for more libraries to add - if you know of any that are not listed, please let us know or feel free to add a PR. Four featured libraries in the GPU acceleration space are outlined below. - Kompute - Blazing fast, lightweight and mobile phone-enabled GPU compute framework optimized for advanced data processing usecases.
- CuPy - An implementation of NumPy-compatible multi-dimensional array on CUDA. CuPy consists of the core multi-dimensional array class, cupy.ndarray, and many functions on it.
- Jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
- CuDF - Built based on the Apache Arrow columnar memory format, cuDF is a GPU DataFrame library for loading, joining, aggregating, filtering, and otherwise manipulating data.
If you know of any open source and open community events that are not listed do give us a heads up so we can add them! |
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As AI systems become more prevalent in society, we face bigger and tougher societal challenges. We have seen a large number of resources that aim to takle these challenges in the form of AI Guidelines, Principles, Ethics Frameworks, etc, however there are so many resources it is hard to navigate. Because of this we started an Open Source initiative that aims to map the ecosystem to make it simpler to navigate. You can find multiple principles in the repo - some examples include the following: - MLSecOps Top 10 Vulnerabilities - This is an initiative that aims to further the field of machine learning security by identifying the top 10 most common vulnerabiliites in the machine learning lifecycle as well as best practices.
- AI & Machine Learning 8 principles for Responsible ML - The Institute for Ethical AI & Machine Learning has put together 8 principles for responsible machine learning that are to be adopted by individuals and delivery teams designing, building and operating machine learning systems.
- An Evaluation of Guidelines - The Ethics of Ethics; A research paper that analyses multiple Ethics principles.
- ACM's Code of Ethics and Professional Conduct - This is the code of ethics that has been put together in 1992 by the Association for Computer Machinery and updated in 2018.
If you know of any guidelines that are not in the "Awesome AI Guidelines" list, please do give us a heads up or feel free to add a pull request!
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| | | | The Institute for Ethical AI & Machine Learning is a European research centre that carries out world-class research into responsible machine learning. | | | | |
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