Toggle light / dark theme

The problem of AI identity

The problem of personal identity is a longstanding philosophical topic albeit without final consensus. In this article the somewhat similar problem of AI identity is discussed, which has not gained much traction yet, although this investigation is increasingly relevant for different fields, such as ownership issues, personhood of AI, AI welfare, brain–machine interfaces, the distinction between singletons and multi-agent systems as well as to potentially support finding a solution to the problem of personal identity. The AI identity problem analyses the criteria for two AIs to be considered the same at different points in time. Two approaches to tackle the problem are proposed: One is based on the personal identity problem and the concept of computational irreducibility, while the other one applies multi-factor authentication to the AI identity problem. Also, a range of scenarios is examined regarding AI identity, such as replication, fission, fusion, switch off, resurrection, change of hardware, transition from non-sentient to sentient, journey to the past, offspring and identity change.

Like Recommend

Eureka-research/DrEureka

From UPenn, Google Deepmind, & NVIDIA Introducing🎓, our latest effort pushing the frontier of robot learning using LLMs!

From upenn, google deepmind, & NVIDIA

Introducing🎓, our latest effort pushing the frontier of robot learning using LLMs!


Contribute to eureka-research/DrEureka development by creating an account on GitHub.

Microsoft Reportedly Building a GPT-4 Competitor Despite $10 Billion OpenAI Partnership

Microsoft is said to be building an OpenAI competitor despite its multi-billion-dollar partnership with the firm — and according to at least one insider, it’s using GPT-4 data to do so.

First reported by The Information, the new large language model (LLM) is apparently called MAI-1, and an inside source told the website that Microsoft is using GPT-4 and public information from the web to train it out.

MAI-1 may also be trained on datasets from Inflection, the startup previously run by Google DeepMind cofounder Mustafa Suleyman before he joined Microsoft as the CEO of its AI department earlier this year. When it hired Suleyman, Microsoft also brought over most of Inflection’s staff and folded them into Microsoft AI.

Google’s Top AI Scientists On Quantum Superpositions Creating Consciousness

In this talk at Mindfest 2024, Hartmut Neven proposes that conscious moments are generated by the formation of quantum superpositions, challenging traditional views on the origins of consciousness. Please consider signing up for TOEmail at https://www.curtjaimungal.org.

Support TOE: — Patreon: https://patreon.com/curtjaimungal (early access to ad-free audio episodes!) — Crypto: https://tinyurl.com/cryptoTOE — PayPal: https://tinyurl.com/paypalTOE — TOE Merch: https://tinyurl.com/TOEmerch … see more.

This Highly Reflective Black Paint Makes Objects More Visible to Autonomous Cars

Driving at night might be a scary challenge for a new driver, but with hours of practice it soon becomes second nature. For self-driving cars, however, practice may not be enough because the lidar sensors that often act as these vehicles’ “eyes” have difficulty detecting dark-colored objects. Research published in ACS Applied Materials & Interfaces describes a highly reflective black paint that could help these cars see dark objects and make autonomous driving safer.

Lidar, short for light detection and ranging, is a system used in a variety of applications, including geologic mapping and self-driving vehicles. The system works like echolocation, but instead of emitting sound waves, lidar emits tiny pulses of near-infrared light. The light pulses bounce off objects and back to the sensor, allowing the system to map the 3D environment it’s in. But lidar falls short when objects absorb more of that near-infrared light than they reflect, which can occur on black-painted surfaces. Lidar can’t detect these dark objects on its own, so one common solution is to have the system rely on other sensors or software to fill in the information gaps. However, this solution could still lead to accidents in some situations. Rather than reinventing the lidar sensors, though, Chang-Min Yoon and colleagues wanted to make dark objects easier to detect with existing technology by developing a specially formulated, highly reflective black paint.

/* */