Welcome! Here, we showcase the latest advancements and insights in data science and artificial intelligence. Dive into our curated collection of trends, topics, and discussions, and stay at the forefront of Data Science / AI developments. Join us in exploring the cutting-edge of technology and innovation.
Posted on: December 19, 2023
The AI Alliance’s 57 founding members include established companies like AMD, Intel, Oracle, and Sony; startups like Cerebras and Stability AI; nonprofits such as HuggingFace and the Linux Foundation, public institutes like the European Council for Nuclear Research (CERN) and... ...moreThe AI Alliance’s 57 founding members include established companies like AMD, Intel, Oracle, and Sony; startups like Cerebras and Stability AI; nonprofits such as HuggingFace and the Linux Foundation, public institutes like the European Council for Nuclear Research (CERN) and U.S. National Aeronautics and Space Administration (NASA); and universities in Asia, Europe, and North America. The group stated its intention to pursue a variety of projects:
Develop open foundation models, especially multilingual and multimodal models
Provide free benchmarks, standards, and safety and security tools to aid responsible development of AI systems
Encourage development of hardware that benefits open AI
Educate and lobby policymakers to encourage open development ...less
Posted on: December 19, 2023
Intel delivered a fantastic portfolio of announcements and product releases this past week. For a summary and on-demand video of the event where this news was announced, look here: https://www.intel.com/content/www/us/en/events/ai-everywhere.html Specifically, Intel announced new and improved AI-ready future-now next-gen processors:... ...moreIntel delivered a fantastic portfolio of announcements and product releases this past week. For a summary and on-demand video of the event where this news was announced, look here: https://www.intel.com/content/www/us/en/events/ai-everywhere.html
Specifically, Intel announced new and improved AI-ready future-now next-gen processors:
1) 5th Generation Xeon® Scalable Processor (“The Processor Designed for AI”), with built-in #AI acceleration in every core.
2) The Intel® Core™ Ultra mobile processor family, the company’s largest architectural shift in 40 years, delivering Intel’s most power-efficient client processor, ushering in the age of the AI PC.
3) The unveiling of the first-ever seen Intel® Gaudi®3 AI accelerator, scheduled to arrive in their portfolio next year. ...less
Posted on: December 19, 2023
If you\'re interested in learning the fundamentals behind machine/deep learning, this tweet consists of list of free courses and helpful resources. The Little Book of Deep Learning, by @fchollet https://fleuret.org/public/lbdl.pdf Practical Deep Learning, by @jeremyphoward https://course.fast.ai Understanding Deep Learning, by... ...moreIf you\'re interested in learning the fundamentals behind machine/deep learning, this tweet consists of list of free courses and helpful resources.
The Little Book of Deep Learning, by
@fchollet
https://fleuret.org/public/lbdl.pdf
Practical Deep Learning, by
@jeremyphoward
https://course.fast.ai
Understanding Deep Learning, by Simon J.D. Prince
https://udlbook.github.io/udlbook/
Deep Learning, by
@goodfellow_ian
https://deeplearningbook.org ...less
Posted on: December 19, 2023
With the recent and accelerated advances in machine learning (ML), machines can understand natural language, engage in conversations, draw images, create videos and more. Modern ML models are programmed and trained using ML programming frameworks, such as TensorFlow, JAX, PyTorch,... ...moreWith the recent and accelerated advances in machine learning (ML), machines can understand natural language, engage in conversations, draw images, create videos and more. Modern ML models are programmed and trained using ML programming frameworks, such as TensorFlow, JAX, PyTorch, among many others. These libraries provide high-level instructions to ML practitioners, such as linear algebra operations (e.g., matrix multiplication, convolution, etc.) and neural network layers (e.g., 2D convolution layers, transformer layers). Importantly, practitioners need not worry about how to make their models run efficiently on hardware because an ML framework will automatically optimize the user\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\'s model through an underlying compiler. The efficiency of the ML workload, thus, depends on how good the compiler is. A compiler typically relies on heuristics to solve complex optimization problems, often resulting in suboptimal performance. ...less