The concept of neural correlates of consciousness (NCC), which suggests that specific neural activities are linked to conscious experiences, has gained widespread acceptance. This acceptance is based on a wealth of evidence from experimental studies, brain imaging techniques such as fMRI and EEG, and theoretical frameworks like integrated information theory (IIT) within neuroscience and the philosophy of mind. This paper explores the potential for artificial consciousness by merging neuromorphic design and architecture with brain simulations. It proposes the Neuromorphic Correlates of Artificial Consciousness (NCAC) as a theoretical framework. While the debate on artificial consciousness remains contentious due to our incomplete grasp of consciousness, this work may raise eyebrows and invite criticism. Nevertheless, this optimistic and forward-thinking approach is fueled by insights from the Human Brain Project, advancements in brain imaging like EEG and fMRI, and recent strides in AI and computing, including quantum and neuromorphic designs. Additionally, this paper outlines how machine learning can play a role in crafting artificial consciousness, aiming to realise machine consciousness and awareness in the future.

In 2008, the journalist Jonah Lehrer paid a visit to a lab in Lausanne, Switzerland, to profile Henry Markram, a world-renowned neuroscientist. Markram, a South African, had trained at a series of élite institutions in Israel, the United States, and Germany; in the nineties, he published foundational papers on neural connections and synaptic activity. Markram's work in the laboratory, which involved piercing neural membranes with what Lehrer described as an "invisibly sharp glass pipette," was known for its painstaking precision. Lehrer's visit, however, had been occasioned not by Markram's incremental contributions to the field--it's not easy to sell a colorful profile on the basis of such publications as "The neural code between neocortical pyramidal neurons depends on neurotransmitter release probability"--but by Markram's pivot, in the early two-thousands, to brain simulation. Neuroscience, Markram declaimed to Lehrer, had reached an impasse. Researchers had generated an enormous wealth of fine-grained data, but the marginal returns had begun to diminish.

Few times in history has mankind ever united to solve a single goal. Even the ultimate moonshot in history--putting a man on the moon--was driven by international competition rather than unification. So it's perhaps fitting that mankind is now uniting to understand the organ that fundamentally makes us human: our brain. First envisioned in 2016 through a series of discussions on the "grand challenges" in neuroscience at Johns Hopkins University, the International Brain Initiative (IBI) "came out" this week in a forward-looking paper in Neuron. Rather than each country formulating their own brain projects independently, the project argues, it's high time for the world to come together and share their findings, resources, and expertise across borders.

Andrew Ng, one of the world's best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years. AI experts have become some of the most sought-after and well-paid employees in today's tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see "10 Breakthrough Technologies 2013: Deep Learning").

Andrew Ng, one of the world's best-known artificial-intelligence experts, is launching an online effort to create millions more AI experts across a range of industries. Ng, an early pioneer in online learning, hopes his new deep-learning course on Coursera will train people to use the most powerful idea to have emerged in AI in recent years. AI experts have become some of the most sought-after and well-paid employees in today's tech economy. Deep learning involves teaching a machine to perform a complex task using large amounts of data along with a large simulated neural network. The technique has typically required deep technical knowledge and expertise to master (see "10 Breakthrough Technologies 2013: Deep Learning").

Our world has been revolutionized by Artificial Intelligence. A subject hotly glorified by popular sci-fi movies, AI has now penetrated various spheres of our life. It is widely used in applications such as aerospace, bio-informatics, business intelligence, financial advisory systems, emergency response, homeland security, logistics and supply chain. In recent years, we have witnessed a rebirth of AI through the use of cloud, with technology firms such as Google leading the way in showing the power of data-driven computing. Artificial intelligence systems are extensively used by researchers at technology firms, universities and government labs.

Artificial intelligence will surpass human intelligence after 2020, predicts Vernor Vinge, a world-renowned pioneer in AI, who has warned about the risks and opportunities that an electronic super-intelligence would offer to mankind. "It seems plausible that with technology we can, in the fairly near future," says scifi legend Vernor Vinge, "create (or become) creatures who surpass humans in every intellectual and creative dimension. Events beyond such an event -- such a singularity -- are as unimaginable to us as opera is to a flatworm." There was the psychotic HAL 9000 in "2001: A Space Odyssey," the humanoids which attacked their human masters in "I, Robot" and, of course, "The Terminator", where a robot is sent into the past to kill a woman whose son will end the tyranny of the machines. Experts interviewed by AFP were divided.

The data perspective can be found in Huerta for Neuroscience, for example, has can be stored in robust databases and and Koslow (1996).