Showing posts with label cognitive science. Show all posts
Showing posts with label cognitive science. Show all posts




Project SyNAPSE (Systemsof Neuromorphic Adaptive Plastic Scalable Electronics) is a collaborativecognitive computing effort sponsored by the Defense Advanced Research ProjectsAgency to develop the architecture for a brain-inspired neurosynaptic computercore.

The project, which began in 2008, is a collaboration between IBM Research, HRL Laboratories, and Hewlett-Packard.

Researchers from a number of universities are also involved in the project.

The acronym SyNAPSE comes from the Ancient Greek word v, which means "conjunction," and refers to the neural connections that let information go to the brain.

The project's purpose is to reverse-engineer the functional intelligence of rats, cats, or potentially humans to produce a flexible, ultra-low-power system for use in robots.

The initial DARPA announcement called for a machine that could "scale to biological levels" and break through the "algorithmic-computational paradigm" (DARPA 2008, 4).

In other words, they needed an electronic computer that could analyze real-world complexity, respond to external inputs, and do so in near-real time.

SyNAPSE is a reaction to the need for computer systems that can adapt to changing circumstances and understand the environment while being energy efficient.

Scientists at SyNAPSE are working on neuromorphicelectronics systems that are analogous to biological nervous systems and capable of processing data from complex settings.

It is envisaged that such systems would gain a considerable deal of autonomy in the future.

The SyNAPSE project takes an interdisciplinary approach, drawing on concepts from areas as diverse as computational neuroscience, artificial neural networks, materials science, and cognitive science.

Basic science and engineering will need to be expanded in the following areas by SyNAPSE: 

  •  simulation—for the digital replication of systems in order to verify functioning prior to the installation of material neuromorphological systems.

In 2008, IBM Research and HRL Laboratories received the first SyNAPSE grant.

Various aspects of the grant requirements were subcontracted to a variety of vendors and contractors by IBM and HRL.

The project was split into four parts, each of which began following a nine-month feasibility assessment.

The first simulator, C2, was released in 2009 and operated on a BlueGene/P supercomputer, simulating cortical simulations with 109 neurons and 1013 synapses, similar to those seen in a mammalian cat brain.

Following a revelation by the Blue Brain Project leader that the simulation did not meet the complexity claimed, the software was panned.

Each neurosynaptic core is 2 millimeters by 3 millimeters in size and is made up of materials derived from human brain biology.

The cores and actual brains have a more symbolic than comparable relationship.

Communication replaces real neurons, memory replaces synapses, and axons and dendrites are replaced by communication.

This enables the team to explain a biological system's hardware implementation.

HRL Labs stated in 2012 that it has created the world's first working memristor array layered atop a traditional CMOS circuit.

The term "memristor," which combines the words "memory" and "transistor," was invented in the 1970s.

Memory and logic functions are integrated in a memristor.

In 2012, project organizers reported the successful large-scale simulation of 530 billion neurons and 100 trillion synapses on the Blue Gene/Q Sequoia machine at Lawrence Livermore National Laboratory in California, which is the world's second fastest supercomputer.

The TrueNorth processor, a 5.4-billion-transistor chip with 4096 neurosynaptic cores coupled through an intrachip network that includes 1 million programmable spiking neurons and 256 million adjustable synapses, was presented by IBM in 2014.

Finally, in 2016, an end-to-end ecosystem (including scalable systems, software, and apps) that could fully use the TrueNorth CPU was unveiled.

At the time, there were reports on the deployment of applications such as interactive handwritten character recognition and data-parallel text extraction and recognition.

TrueNorth's cognitive computing chips have now been put to the test in simulations like a virtual-reality robot driving and playing the popular videogame Pong.

DARPA has been interested in the construction of brain-inspired computer systems since the 1980s.

Dharmendra Modha, director of IBM Almaden's Cognitive ComputingInitiative, and Narayan Srinivasa, head of HRL's Center for Neural and Emergent Systems, are leading the Project SyNAPSE project.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Cognitive Computing; Computational Neuroscience.

References And Further Reading

Defense Advanced Research Projects Agency (DARPA). 2008. “Systems of Neuromorphic Adaptive Plastic Scalable Electronics.” DARPA-BAA 08-28. Arlington, VA: DARPA, Defense Sciences Office.

Hsu, Jeremy. 2014. “IBM’s New Brain.” IEEE Spectrum 51, no. 10 (October): 17–19.

Merolla, Paul A., et al. 2014. “A Million Spiking-Neuron Integrated Circuit with a Scalable Communication Network and Interface.” Science 345, no. 6197 (August): 668–73.

Monroe, Don. 2014. “Neuromorphic Computing Gets Ready for the (Really) Big Time.” Communications of the ACM 57, no. 6 (June): 13–15.

Artificial Intelligence - Who Is Aaron Sloman?


Aaron Sloman (1936–) is a renowned artificial intelligence and cognitive science philosopher.

He is a global expert in the evolution of biological information processing, an area of study that seeks to understand how animal species have acquired cognitive levels that surpass technology.

He's been debating if evolution was the first blind mathematician and whether weaver birds are actually capable of recursion in recent years (dividing a problem into parts to conquer it).

His present Meta-Morphogenesis Project is based on an idea by Alan Turing (1912–1954), who claimed that although computers could do mathematical brilliance, only brains could perform mathematical intuition.

According to Sloman, not every aspect of the cosmos, including the human brain, can be represented in a sufficiently massive digital computer because of this.

This assertion clearly contradicts digital physics, which claims that the universe may be characterized as a simulation running on a sufficiently big and fast general-purpose computer that calculates the cosmos's development.

Sloman proposes that the universe has developed its own biological building kits for creating and deriving other—different and more sophisticated—construction kits, similar to how scientists have evolved, accumulated, and applied increasingly complex mathematical knowledge via mathematics.

He refers to this concept as the Self-Informing Universe, and suggests that scientists build a multi-membrane Super-Turing machine that runs on subneural biological chemistry.

Sloman was born to Jewish Lithuanian immigrants in Southern Rhodesia (now Zimbabwe).

At the University of Cape Town, he got a bachelor's degree in Mathematics and Physics.

He was awarded a Rhodes Scholarship and earned his PhD in philosophy from Oxford University, where he defended Immanuel Kant's mathematical concepts.

He saw that artificial intelligence had promise as the way forward in philosophical understanding of the mind as a visiting scholar at Edinburgh University in the early 1970s.

He said that using Kant's recommendations as a starting point, a workable robotic toy baby could be created, which would eventually develop in intellect and become a mathematician on par with Archimedes or Zeno.

He was one of the first scholars to refute John McCarthy's claim that a computer program capable of operating intelligently in the real world must use structured, logic-based ideas.

Sloman was one of the founding members of the University of Sussex School of Cognitive and Computer Sciences.

There, he collaborated with Margaret Boden and Max Clowes to advance artificial intelligence instruction and research.

This effort resulted in the commercialization of the widely used Poplog AI teaching system.

Sloman's The Computer Revolution in Philosophy (1978) is famous for being one of the first to recognize that metaphors from the realm of computers (for example, the brain as a data storage device and thinking as a collection of tools) will dramatically alter how we think about ourselves.

The epilogue of the book contains observations on the near impossibility of AI sparking the Singularity and the likelihood of a human Society for the Liberation of Robots to address possible future brutal treatment of intelligent machines.

Sloman held the Artificial Intelligence and Cognitive Science chair in the School of Computer Science at the University of Birmingham until his formal retirement in 2002.

He is a member of the Alan Turing Institute and the Association for the Advancement of Artificial Intelligence.

~ Jai Krishna Ponnappan

Find Jai on Twitter | LinkedIn | Instagram

You may also want to read more about Artificial Intelligence here.

See also: 

Superintelligence; Turing, Alan.

References & Further Reading:

Sloman, Aaron. 1962. “Knowing and Understanding: Relations Between Meaning and Truth, Meaning and Necessary Truth, Meaning and Synthetic Necessary Truth.” D. Phil., Oxford University.

Sloman, Aaron. 1971. “Interactions between Philosophy and AI: The Role of Intuition and Non-Logical Reasoning in Intelligence.” Artificial Intelligence 2: 209–25.

Sloman, Aaron. 1978. The Computer Revolution in Philosophy: Philosophy, Science, and Models of Mind. Terrace, Hassocks, Sussex, UK: Harvester Press.

Sloman, Aaron. 1990. “Notes on Consciousness.” AISB Quarterly 72: 8–14.

Sloman, Aaron. 2018. “Can Digital Computers Support Ancient Mathematical Conscious￾ness?” Information 9, no. 5: 111.

What Is Artificial General Intelligence?

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