Can conscious self-awareness be coded in an algorithm? According to distinguished computer scientist Lenore Blum and Turing Award Laureate Manuel Blum the answer is \.
Category: information science – Page 33
How can machine learning help individuals with type 1 diabetes (T1D)? This is what a study presented at this year’s Annual Meeting of the European Association for the Study of Diabetes (EASD) hopes to address as a team of researchers have developed a system using machine learning capable of managing blood sugars levels with such proficiency that those using system were able to lead lives far more active than the average T1D patient.
For the study, the researchers developed the AID system, which uses closed-loop technology that delivers insulin based on readings from the machine learning algorithm, resulting in a 50-year-old man, a 40-year-old man, and a 34-year-old woman with T1D being able to run hours-long marathons in Tokyo, Santiago, and Paris, respectively. This study holds the potential to help develop better technology capable of allowing T1D diabetes patients to stay in shape without constantly fearing for their blood sugar levels, which can lead to long-term health problems, including hyperglycemia, nerve damage, or a heart attack.
“Despite better systems for monitoring blood sugars and delivering insulin, maintaining glucose levels in target range during aerobic training and athletic competition is especially difficult,” said Dr. Maria Onetto, who is in the Department of Nutrition at the Pontifical Catholic University of Chile and lead author of the study. “The use of automated insulin delivery technology is increasing, but exercise continues to be a challenge for individuals with T1D, who can still struggle to reach the recommended blood sugar targets.”
A wormhole is a hypothetical structure connecting disparate points in spacetime, and is based on a special solution of the Einstein field equations. [ 1 ]
A can be visualized as a tunnel with two ends at separate points in spacetime (i.e., different locations, different points in time, or both).
Wormholes are consistent with the general theory of relativity, but whethers actually exist is uncertain. Many scientists postulate thats are merely projections of a fourth spatial dimension, analogous to how a two-dimensional (2D) being could experience only part of a three-dimensional (3D) object. [ 2 ] A well-known analogy of such constructs is provided by the Klein bottle, displaying a hole when rendered in three dimensions but not in four or higher dimensions.
Scientists Create Matter from Pure Light, Demonstrating Einstein’s E=mc² Equation in Action.
Physicists at Brookhaven National Laboratory have achieved a groundbreaking experiment, creating matter from light by demonstrating the Breit-Wheeler process. Using the Relativistic Heavy Ion Collider, they accelerated heavy ions to generate nearly real photons, leading to the formation of electron-positron pairs. This experiment showcases Einstein’s E=mc² equation in action, aligning with predictions for transforming energy into matter. While these virtual photons act similarly to real ones, the experiment is a crucial step towards proving the process with real photons when technology advances to create gamma-ray lasers. Don’t forget to comment your thought about this!
A team of roboticists at the German Aerospace Center’s Institute of Robotics and Mechatronics finds that combining traditional internal force-torque sensors with machine-learning algorithms can give robots a new way to sense touch.
In their study published in the journal Science Robotics, the group took an entirely new approach to give robots a sense of touch that does not involve artificial skin.
For living creatures, touch is a two-way street; when you touch something, you feel its texture, temperature and other features. But you can also be touched, as when someone or something else comes in contact with a part of your body. In this new study, the research team found a way to emulate the latter type of touch in a robot by combining internal force-torque sensors with a machine-learning algorithm.
Game Developer jourverse, who is currently working on a tutorial series focused on building a traffic system in Unreal Engine 5, shared a demo project file for this procedural road network integrated with vehicle AI for obstacle avoidance, using A* for pathfinding.
The developer explained that both the A* algorithm and the road editor mode are implemented in C++, with no use of neural networks. Vehicle AI operations like spline following, reversing, and performing 3-point turns are handled through Blueprints. The vehicle AI navigates using two paths: the green spline for the main route and the blue spline for obstacle avoidance. The main spline leverages road network nodes to determine the path to the target via A* on FPathNode, which includes adjacent road nodes.
For obstacle detection, the vehicle employs polynomial regression to predict its future position. Upon detecting an obstacle, a grid of sphere traces is generated to map the obstacle’s location, and another A* algorithm is employed to create a path around the obstacle.
In an exciting development for quantum computing, researchers from the University of Chicago’s Department of Computer Science, Pritzker School of Molecular Engineering, and Argonne National Laboratory have introduced a classical algorithm that simulates Gaussian boson sampling (GBS) experiments.
In this sense, the cemi theory incorporates Chalmers’ (Chalmers 1995) ‘double-aspect’ principle that information has both a physical, and a phenomenal or experiential aspect. At the particulate level, a molecule of the neurotransmitter glutamate encodes bond energies, angles, etc. but nothing extrinsic to itself. Awareness makes no sense for this kind matter-encoded information: what can glutamate be aware of except itself? Conversely, at the wave level, information encoded in physical fields is physically unified and can encode extrinsic information, as utilized in TV and radio signals. This EM field-based information will, according to the double-aspect principle, be a suitable substrate for experience. As proposed in my earlier paper (McFadden 2002a) ‘awareness will be a property of any system in which information is integrated into an information field that is complex enough to encode representations of real objects in the outside world (such as a face)’. Nevertheless, awareness is meaningless unless it can communicate so only fields that have access to a motor system, such as the cemi field, are candidates for any scientific notion of consciousness.
I previously proposed (McFadden 2013b), that complex information acquires its meaning, in the sense of binding of all of the varied aspects of a mental object, in the brain’s EM field. Here, I extend this idea to propose that meaning is an algorithm experienced, in its entirety from problem to its solution, as a single percept in the global workspace of brain’s EM field. This is where distributed information encoded in millions of physically separated neurons comes together. It is where Shakespeare’s words are turned into his poetry. It is also, where problems and solutions, such as how to untangle a rope from the wheels of a bicycle, are grasped in their entirety.
There are of course many unanswered questions, such as degree and extent of synchrony required to encode conscious thoughts, the influence of drugs or anaesthetics on the cemi field or whether cemi fields are causally active in animal brains. Yet the cemi theory provides a new paradigm in which consciousness is rooted in an entirely physical, measurable and artificially malleable physical structure and is amenable to experimental testing. The cemi field theory thereby delivers a kind of dualism, but it is a scientific dualism built on the distinction between matter and energy, rather than matter and spirit. Consciousness is what algorithms that exist simultaneously in the space of the brain’s EM field, feel like.
An international research team, led by Professor Gong Xiao from the National University of Singapore, has achieved a groundbreaking advancement in photonic-electronic integration. Their work, published in Light: Science & Applications (“Thin film ferroelectric photonic-electronic memory”), features Postdoc Zhang Gong and PhD student Chen Yue as co-first authors. They developed a non-volatile photonic-electronic memory chip utilizing a micro-ring resonator integrated with thin-film ferroelectric material.
This innovation successfully addresses the challenge of dual-mode operation in non-volatile memory, offering compatibility with silicon-based semiconductor processes for large-scale integration. The chip operates with low voltage, boasts a large memory window, high endurance, and multi-level storage capabilities. This breakthrough is poised to accelerate the development of next-generation photonic-electronic systems, with significant applications in optical interconnects, high-speed data communication, and neuromorphic computing.
As big data and AI grow, traditional computers struggle with large-scale tasks. Photonic computing offers potential, but interfacing with electronic chips is challenging. Current storage can’t handle dual-mode operations, and OEO conversion adds losses and delays. A non-volatile memory for efficient data exchange between photonic and electronic chips is essential.
But what if you’re a manufacturer without the budget, bandwidth or time to invest in advanced digital transformation right now? You can still take practical steps to move forward. Start with fundamental data collection and analytic tools to lay the groundwork. Leveraging visibility solutions like barcode scanning, wearables or other basic Internet of Things (IoT) devices can help monitor machines and provide insights and improvements.
Quality is the final piece of the equation. Once you’re further down the path to transformation, implement visibility solutions and augment and upskill workers with technology to optimize quality. To drive quality even further, add advanced automation solutions. You don’t have to boil the ocean on your digital transformation journey—take it one step at a time from wherever you’re starting.
Most manufacturers (87%) in Zebra’s study agree it’s a challenge to pilot new technologies or move beyond the pilot phase, yet they plan to advance digital maturity by 2029. With the right technology tools and solutions in place to advance visibility, augment workers and optimize quality, they will get there.