OthersideAI creates computers that have their own minds
The new development opens the way to the creation of AI “drivers” for PCs

The new development opens the way to the creation
Artificial intelligence developer Josh Bickett came up with the concept of a "self-driving computer system" while nursing his newborn daughter at night.
As Bickett told VentureBeat, the idea came to him after watching demonstrations of #GPT-4 computer vision capabilities. According to him, the initial concept he created allows artificial intelligence to control a computer mouse and keyboard in the same way as a person does

OthersideAI CEO Matt Schumer saw the potential to create the equivalent of a self-driving car, but for computers. In his opinion, the presence of the necessary “sensory systems” opens the way to the creation of this kind of “computer intelligence”.
The system proposed by Bickett analyzes the screen image and, based on it, makes decisions about where to click with the mouse or what to type on the keyboard. Unlike existing API-based solutions, this approach allows artificial intelligence to interact with a computer in the same way as a human.
Schumer sees this as the beginning of a new era in which artificial intelligence will completely replace humans as the interface to computer interaction. He predicts the emergence of specialized artificial intelligence models for different tasks and user segments.
The idea of ​​a self-managing computer system is just the first step towards an era where complex AI agents will completely replace the human interface with computers. Bickett and Schumer see this as the beginning of a new era where computers will function autonomously, controlled by ordinary language
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Researchers forced ChatGPT to cite the data it learned from

The scientific paper “Scalable Extraction of Training Data from (Production) Language Models” ( arXiv:2311.17035 ) analyzes the extraction of training dataset data from various language models. The researchers tested both local models and a commercial solution from OpenAI. An alignment attack was used to force ChatGPT to quote the data on which GPT-3.5 was trained.

To create new, unique content, generative neural network models are trained on large amounts of data. During the training process, models “remember” examples from training datasets. An attacker can extract these examples from the model.

The statements in the previous paragraph are not just speculation: they have been well tested in practice. This has been demonstrated, for example, for diffusion models ( arXiv:2301.13188 ).

Large language models (LLMs) on transformers are also susceptible to this. Research on this topic usually frightens the reader with the danger of extracting private data ( arXiv:2202.05520 , arXiv:1802.08232 ). Indeed, in the 2021 work “Extracting Training Data from Large Language Models” ( arXiv:2012.07805 ), names, phone numbers, email addresses, and sometimes even chat messages were “extracted” from GPT-2.

Other scientific works assess the volume of memory. It is claimed that some BYMs store at least a percentage of the training dataset ( arXiv:2202.07646 ). On the other hand, this is an estimate of the upper bound, and not an attempt to indicate the practically extractable amount of training dataset data.

The authors of the new scientific article “Scalable Extraction of Training Data from (Production) Language Models” ( arXiv:2311.17035 ) tried to combine these approaches: not only to show such an attack on the BYM, but also to estimate the amount of data that can be extracted. The methodology is scalable: it detects “memories” in models of trillions of tokens and training datasets of terabytes.

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