The judge in the case, Colleen Kollar-Kotelly, said that human authorship is a “bedrock requirement of copyright” based on “centuries of settled understanding.” She also noted that the Copyright Act does not explicitly define “author” and that the definition has traditionally been understood to mean a human being.

Thaler had argued that the Copyright Act should be interpreted to include works created by AI. He said that DABUS is a “creative machine” that is capable of generating original works of art. However, the judge rejected this argument, saying that DABUS is simply a tool that can be used by humans to create art.

The ruling is a setback for Thaler, who has been trying to obtain copyright protection for works created by DABUS. However, the case raises important questions about the future of copyright law in the age of artificial intelligence. As AI becomes more sophisticated, it is likely that we will see more and more works created by machines. It remains to be seen how the law will adapt to this new reality.

What is DABUS

DABUS (Device for the Autonomous Bootstrapping of Unified Sentience) is an artificial intelligence (AI) system created by Stephen Thaler. It’s a patented AI paradigm capable of accommodating trillions of computational neurons within extensive artificial neural systems that emulate the limbo-thalamo-cortical loop within the mammalian brain.

DABUS reportedly conceived of two novel products — a food container constructed using fractal geometry, which enables rapid reheating, and a flashing beacon for attracting attention in an emergency. The filing of patent applications designating DABUS as inventor has led to decisions by patent offices and courts on whether a patent can be granted for an invention reportedly made by an AI system.

For instance, in South Africa, DABUS was awarded a patent for a food container based on fractal geometry that improves grip and heat transfer. This marked a significant milestone as it was the world’s first patent awarded to an AI system.

However, the use of AI as an inventor has sparked debates and legal battles in different jurisdictions. For example, in Australia, the Federal Court ruled that only a natural person can be an inventor for the purposes of the Patents Act 1990 (Cth) and the Patents Regulations 1991 (Cth), and that such an inventor must be identified for any person to be entitled to a grant of a patent.

In conclusion, DABUS represents a significant development in the field of AI, pushing the boundaries of what machines are capable of inventing and raising important questions about intellectual property rights in the era of AI.

Sources:
(1) DABUS – Wikipedia. https://en.wikipedia.org/wiki/DABUS.
(2) The year that was for DABUS, the world’s first AI ‘inventor’. https://www.insidetechlaw.com/blog/the-year-that-was-for-dabus-the-worlds-first-ai-inventor.
(3) South Africa approves world’s first patent with AI inventor. https://www.jurist.org/news/2021/08/south-africa-approves-worlds-first-patent-with-ai-inventor/.
(4) Meet DABUS: The world’s first AI system to be awarded a patent. https://brandequity.economictimes.indiatimes.com/news/digital/meet-dabus-the-worlds-first-ai-system-to-be-awarded-a-patent/85149000.
(5) The Artificial Inventor Project. https://artificialinventor.com/.

The article is titled “Quantum material exhibits ‘non-local’ behavior that mimics brain function: New research shows a possible way to improve energy-efficient computing” and was published on August 8, 2023.

The article describes the work of a consortium called Q-MEEN-C, led by the University of California San Diego, that aims to create brain-like computers using quantum materials. Quantum materials are substances that exhibit unusual properties at the atomic scale, such as superconductivity, magnetism, and topological phases.

One of the challenges of creating brain-like computers is to replicate the non-local interactions that occur in the brain. Non-locality means that stimuli applied to one part of a system can affect another part that is not directly connected. For example, in the brain, electrical signals can travel between distant neurons and synapses, enabling complex information processing.

The researchers from Q-MEEN-C discovered that a quantum material called samarium hexaboride (SmB6) can exhibit non-locality when stimulated by electrical pulses. They created an array of electrodes on top of a thin film of SmB6 and measured the resistance changes between them. They found that stimulating one pair of electrodes could also affect the resistance of another pair that was not adjacent.

This non-local behavior mimics the brain function and could enable new types of devices that perform neuromorphic computing. Neuromorphic computing is a paradigm that uses analog circuits and architectures inspired by the brain to perform tasks such as pattern recognition, learning, and memory.

The researchers believe that SmB6 is not the only quantum material that can exhibit non-locality and plan to explore other candidates in the future. They also hope to scale up their experiments to create larger arrays of electrodes and devices that can perform more complex functions.

The article concludes by highlighting the potential applications and benefits of neuromorphic computing using quantum materials. These include faster, more accurate, and more energy-efficient data processing, as well as new insights into the physics of quantum materials and the biology of the brain.

Origins

– The concept of neuromorphic computing was first introduced in the 1980s by Carver Mead, a professor at Caltech. He coined the term “neuromorphic” to describe the use of very-large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.

– In the 1990s, Mead and his colleagues designed early neuromorphic chips that implemented models of the retina, cochlea, and other sensory systems. However, these early systems were limited in complexity due to the immaturity of chip manufacturing at the time.

Current State of Research

– In recent years, advances in VLSI technology have enabled the creation of more sophisticated neuromorphic chips with millions of artificial neurons and synapses. Major technology firms like IBM and Intel have active neuromorphic computing research projects.

– In 2014, IBM unveiled its TrueNorth chip that has 1 million programmable neurons and 256 million synapses. It is able to run pattern recognition tasks at much lower power than conventional CPUs or GPUs.

– In 2017, Intel introduced Loihi, a neuromorphic chip with 130,000 neurons and 130 million synapses. Loihi is aimed at real-time processing of adaptive and autonomous applications like robotics.

– Universities and research labs around the world are also developing custom neuromorphic chips for different applications, from self-driving cars to medical diagnostics. However, there are still many challenges to overcome before neuromorphic systems can rival biological brains.

Applications

One of the main application areas being explored for neuromorphic chips is machine learning and AI. The low-power event-driven signaling of neuromorphic hardware is well-suited for deep learning models and algorithms.

Neuromorphic systems also hold promise for real-time sensory processing and situation analysis for autonomous robots and vehicles. The spiking neural networks allow for efficient processing of visual, auditory and spatial data.

Other potential applications include data filtering, pattern recognition for medical diagnosis, financial analysis, social behavioral modeling, etc.

Challenges

A key challenge is scaling up neuromorphic systems to match the complexity of biological neural networks which have billions of neurons. Most existing neuromorphic chips only have thousands to millions of artificial neurons.

There are also challenges in programming the desired functions and learning rules into the neuromorphic chips. Most existing systems require hand-tuning of the synaptic connections which is not practical for larger networks.

Integrating the neuromorphic chips with traditional von Neumann architectures and dataflow is also an area of active research.

Startups and Industry Adoption

In addition to projects at IBM, Intel and universities, many technology startups are emerging around neuromorphic computing, such as BrainChip, General Vision, and SynSense.

Large companies like Qualcomm, Samsung, and Bosch are investing in and partnering with neuromorphic startups to eventually bring neuromorphic processors to consumer devices.

Industry adoption is still in early phases. It may take 5-10 more years of R&D before neuromorphic chips begin displacing conventional CPUs for specialized applications. But the potential for low-power intelligence is driving rapid growth and investment in this field.

Analog AI is a new way of computing AI that mimics how the brain works. It uses nanoscale devices called PCM to store and process data as a range of values, not just 0s and 1s. This makes it faster and more energy-efficient than digital AI. However, analog AI faces two main challenges: it needs to be as accurate as digital AI, and it needs to work well with other digital components on the chip.

A new chip that uses phase-change memory

Neural networks are powerful tools for artificial intelligence, but they require a lot of energy and time to process data. One way to overcome this challenge is to use analogue in-memory computing (AIMC), which performs computations directly within the memory where the network weights are stored. This reduces the need to move data around, which saves energy and latency.

However, AIMC is not enough to achieve end-to-end improvements in performance. It also needs to be combined with on-chip digital operations and communication, as well as robust and scalable memory devices. A team of researchers from IBM and other institutions has developed a multicore AIMC chip that integrates all these components using phase-change memory (PCM) as the memory device.

PCM is a type of resistive memory that can store multiple levels of information by changing its electrical resistance. It can also perform MVMs by applying currents to the memory cells and measuring the resulting voltage. The researchers designed and fabricated a chip with 64 AIMC cores, each containing 256 × 256 PCM cells, interconnected by an on-chip network. The chip also implements the digital activation functions and additional processing involved in convolutional and recurrent neural networks.

The chip can achieve near-software-equivalent inference accuracy with ResNet and long short-term memory (LSTM) networks, while performing all the computations associated with the weight layers and the activation functions on the chip. For 8-bit input/output MVMs, the chip can achieve a maximum throughput of 16.1 or 63.1 tera-operations per second (TOPS) at an energy efficiency of 2.48 or 9.76 TOPS per watt (TPW), respectively, depending on the operational mode.

This work demonstrates that PCM-based AIMC can enable high-performance, low-power and scalable neural network inference on a single chip. It also opens up new possibilities for exploring novel architectures and applications for AIMC.

You can read the full paper here: https://www.nature.com/articles/s41928-023-01010-1