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AI Hunter:Mark Zuckerberg’s new goal is creating artificial general intelligence

Post at:January 19, 2023 at 12:57 PM

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Mark Zuckerberg’s new goal is creating artificial general intelligence

Mark Zuckerberg, CEO of Meta (formerly Facebook), has announced his new objective of creating artificial general intelligence (AGI). He has initiated reorganization in the company by moving Meta’s AI research group closer to the team that’s creating generative AI products across Meta’s apps. The company aims to use AI to develop products that have broader reach across its billions of users.

The decision to focus more on AGI was influenced by the launch of Llama 2, Meta’s large language model. With training for Llama 3 currently underway, Zuckerberg hopes to include code-generating capabilities in the new model. Zuckerberg didn’t specify an exact timeline or definition for AGI, but emphasized the importance of the breadth of capabilities it offers, such as reasoning and intuition.

AI Hunter Tech

OpenAI Quietly Deletes Ban on Using ChatGPT for “Military and Warfare”

The article reports that OpenAI, an artificial intelligence research lab, has removed explicit language prohibiting military use of its technology from its usage policy. Previously, OpenAI had a ban on activities with high risk of physical harm, which included weapons development and military and warfare. However, this language has been deleted in the most recent policy update.

What TinyML is

TinyML, or Tiny Machine Learning, refers to the application of Machine Learning (ML) on microcontrollers with limited resources including low CPU power, minimal RAM, and extreme power consumption efficiency of milliwatts or microwatts. The goal is to downsize larger ML models for microcontrollers. This practice is particularly prevalent amongst the Makers.

Embedded systems using microcontrollers with up to 256kB memory cannot accommodate large models so techniques are used to compress these models to effectively identify patterns in data. These include Pruning (Synapses and Neurons) and Quantization which are essentially optimization techniques. Knowledge distillation is also leveraged to shrink the model size alongside tools like TensorFlow Lite.

TinyML’s applications span multiple areas:

  1. For DIY enthusiasts, Makers, and Hackers
  2. Industries use it mainly for preventive maintenance to provide alerts on potential breakdowns due to sudden alterations in vibration patterns.
  3. In environmental monitoring, for instance detecting changes in wildlife activity.
  4. Assisting individuals with disabilities to carry out various tasks, making UI/UX more accessible and user-friendly.

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