Neural networks keep getting larger and more energy-intensive. As a result, the future of AI depends on making AI run more efficiently and on smaller devices.
That’s why it’s alarming that progress is slowing on making AI more efficient.
The most resource-intensive aspect of AI is data transfer. Transferring data often takes more time and power than actually computing with it. To tackle this, popular approaches today include reducing the distance that data needs to travel and the data size. There is a limit to how small we can make chips, so minimizing distance can only do so much. Similarly, reducing data precision works to a point but then starts to hurt performance.
Local consciousness, or our phenomenal mind, is emergent, whereas non-local consciousness, or universal mind, is immanent. Material worlds come and go, but fundamental consciousness is ever-present, according to the Cybernetic Theory of Mind. From a new science of consciousness to simulation metaphysics, from evolutionary cybernetics to computational physics, from physics of time and information to quantum cosmology, this novel explanatory theory for a deeper understanding of reality is combined into one elegant theory of everything.
Based on The Cybernetic Theory of Mind eBook series (2022) by Alex M. Vikoulov as well as his magnum opus The Syntellect Hypothesis: Five Paradigms of the Mind’s Evolution (2020), comes a recently-released documentary Consciousness: Evolution of the Mind.
This film, hosted by the author of the book from which the narrative is derived, is now available for viewing on demand on Vimeo, Plex, Tubi, Xumo, Social Club TV and other global networks with its worldwide premiere aired on June 8, 2021. IMDb-accredited film, rated TV-PG. This is a futurist’s take on the nature of consciousness and reverse engineering of our thinking in order to implement it in cybernetics and advanced AI systems.
What mechanism may link quantum physics to phenomenology? What properties are inherently associated with consciousness? What is Experiential Realism? How can we successfully approach the Hard Problem of Consciousness, or perhaps, circumvent it? What is the Quantum Algorithm of Consciousness? Are free-willing conscious AIs even possible? These are some of the questions addressed in this Part V of the documentary.
Why do industrial robots require teams of engineers and thousands of lines of code to perform even the most basic, repetitive tasks while giraffes, horses, and many other animals can walk within minutes of their birth?
My colleagues and I at the USC Brain-Body Dynamics Lab began to address this question by creating a robotic limb that learned to move, with no prior knowledge of its own structure or environment [1,2]. Within minutes, G2P, our reinforcement learning algorithm implemented in MATLAB®, learned how to move the limb to propel a treadmill (Figure 1).
Siemens and Roboze have announced that they are collaborating to develop workflows dedicated to the industrialization of 3D printing. This includes an emphasis on expanding the use of the technology in energy, mobility, and aerospace. Though the exact nature of the agreement isn’t fully elucidated, it marks a significant shift for both firms.
Siemens is the largest industrial manufacturer in Europe, with a storied history spanning nearly two centuries and annual revenues totaling €62.3 billion, as of 2021. In contrast, Roboze is a comparatively new firm, established in Italy in 2013. The company has since built itself up into a leader in industrial-grade material extrusion 3D printers, earning such customers as Ducati, GE, and the U.S. Army.
The partners do not exactly clarify their intent except to say that they will work together to “increase the productivity, competitiveness and efficiency of manufacturers that have embarked on the path to the future of industry.” They do mention focusing on “digitalization and automation projects”.
Reward maximisation is one strategy that works for reinforcement learning to achieve general artificial intelligence. However, deep reinforcement learning algorithms shouldn’t depend on reward maximisation alone.
Identifying dual-purpose therapeutic targets implicated in aging and disease will extend healthspan and delay age-related health issues.
AI is all that matters now, and reaching Agi before 2030 is all that matters for this decade.
A substantial percentage of the human clinical trials, including those evaluating investigational anti-aging drugs, fail in Phase II, a phase where the efficacy of the drug is tested. This poor success is in part due to inadequate target choice and the inability to identify a group of patients who will most likely respond to specific agents. This challenge is further complicated by the differences in the biological age of the patients, as the importance of therapeutic targets varies between the age groups. Unfortunately, most targets are discovered without considering patients’ age and being tested in a relatively younger population (average age in phase I is 24). Hence, identifying potential targets that are implicated in multiple age-associated diseases, and also play a role in the basic biology of aging, may have substantial benefits.
Identifying dual-purpose targets that are implicated in aging and disease at the same time will extend healthspan and delay age-related health issues – even if the target is not the most important in a specific patient, the drug would still benefit that patient.
“When it comes to targets identification in chronic diseases, it is important to prioritize the targets that are implicated in age-associated diseases, implicated in more than one hallmark of aging, and safe,” said Zhavoronkov. “So that in addition to treating a disease, the drug would also treat aging – it is an off-target bonus.”
