Consciousness: Evolution of the Mind Documentary (2021), a film by Alex Vikoulov, Part I, WHAT IS CONSCIOUSNESS?
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We know that a daily diet that includes lots of fruit and vegetables is healthier, but now it seems that it can also help prevent one from being infected with COVID-19.
A new study from Boston published in the journal Gut reports that consuming healthy food like produce may lower the risk of contracting the virus, in addition to lowering the severity of symptoms if one is infected. Although doctors have stated that metabolic conditions including obesity and type 2 diabetes can cause severe coronavirus complications, this study is among the first to add nutrition to the equation.
A new study claims that one-third of coronavirus cases could have been avoided if people had healthier eating habits.
Continue reading “Does eating healthy prevent people from getting, dying from COVID?” »
Reservoir computing, a machine learning algorithm that mimics the workings of the human brain, is revolutionizing how scientists tackle the most complex data processing challenges, and now, researchers have discovered a new technique that can make it up to a million times faster on specific tasks while using far fewer computing resources with less data input.
With the next-generation technique, the researchers were able to solve a complex computing problem in less than a second on a desktop computer — and these overly complex problems, such as forecasting the evolution of dynamic systems like weather that change over time, are exactly why reservoir computing was developed in the early 2000s.
These systems can be extremely difficult to predict, with the “butterfly effect” being a well-known example. The concept, which is closely associated with the work of mathematician and meteorologist Edward Lorenz, essentially describes how a butterfly fluttering its wings can influence the weather weeks later. Reservoir computing is well-suited for learning such dynamic systems and can provide accurate projections of how they will behave in the future; however, the larger and more complex the system, more computing resources, a network of artificial neurons, and more time are required to obtain accurate forecasts.
As developers unlock new AI tools, the risk for perpetuating harmful biases becomes increasingly high — especially on the heels of a year like 2020, which reimagined many of our social and cultural norms upon which AI algorithms have long been trained.
A handful of foundational models are emerging that rely upon a magnitude of training data that makes them inherently powerful, but it’s not without risk of harmful biases — and we need to collectively acknowledge that fact.
Recognition in itself is easy. Understanding is much harder, as is mitigation against future risks. Which is to say that we must first take steps to ensure that we understand the roots of these biases in an effort to better understand the risks involved with developing AI models.
Dowsett’s algorithm was recently published in npj Breast Cancer, a Nature Partner Journal supported by the Breast Cancer Research Foundation. It is intended to help physicians triage postmenopausal women with ER+ HER2–breast cancers, which represent around 70% of breast cancer cases.1 During the pandemic, many within this patient group were prescribed neoadjuvant endocrine therapy (NeoET), rather than surgery, as a disease management strategy.
Analysis of biomarkers in biopsies helps identify breast cancer patients in need of urgent surgery or chemotherapy during COVID-19 pandemic.
Researchers at Skolkovo Institute of Science and Technology (Skoltech) in Russia have recently developed an innovative system for human-swarm interactions that allows users to directly control the movements of a team of drones in complex environments. This system, presented in a paper pre-published on arXiv is based on an interface that recognizes human gestures and adapts the drones’ trajectories accordingly.
Quadcopters, drones with four rotors that can fly for long periods of time, could have numerous valuable applications. For instance, they could be used to capture images or videos in natural or remote environments, can aid search-and–rescue missions and help to deliver goods to specific locations.
New chip eliminates the need for specific decoding hardware, could boost efficiency of gaming systems, 5G networks, the internet of things, and more.
A new silicon chip can decode any error-correcting code through the use of a novel algorithm known as Guessing Random Additive Noise Decoding (GRAND). The work was led by Muriel Médard, an engineering professor in the MIT Research Laboratory of Electronics.
Is an academic doctor and medical technology entrepreneur, working in the field of the computational biology of aging.
Dr. Radenkovic is also a Partner at the SALT Bio-Fund, and a co-founder of Hooke, an elite longevity research clinic in London.
Circa 2021
A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a “machine” that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques.
Learning how to learn is something most humans do well, by leveraging previous experiences to inform the learning processes for new tasks. Endowing AI systems with such abilities however remains challenging, as it requires the machine learners to learn update rules, which typically have been manually tuned for each task.
The field of meta-learning studies how to enable machine learners to learn how to learn, and is a critical research area for improving the efficiency of AI agents. One of the approaches is for learners to learn an update rule by applying it on previous steps and then evaluating the corresponding performance.
To fully unlock the potential of meta-learning, it is necessary to overcome both the meta-optimization problem and myopic meta objectives. To tackle these issues, a research team from DeepMind has proposed an algorithm designed to enable meta-learners to teach themselves.
