Lifeboat Foundation

In artificial neural networks, many models are trained for a narrow task using a specific dataset. They face difficulties in solving problems that include dynamic input/output data types and changing objective functions. Whenever the input/output tensor dimension or the data type is modified, the machine learning models need to be rebuilt and subsequently retrained from scratch. Furthermore, many machine learning algorithms that are trained for a specific objective, such as classification, may perform poorly at other tasks, such as reinforcement learning or quantification.

Even if the input/output dimensions and the objective functions remain constant, the algorithms do not generalize well across different datasets. For example, a neural network trained on classifying cats and dogs does not perform well on classifying humans and horses despite both of the datasets having the exact same image input¹. Moreover, neural networks are highly susceptible to adversarial attacks². A small deviation from the training dataset, such as changing one pixel, could cause the neural network to have significantly worse performance. This problem is known as the generalization problem³, and the field of transfer learning can help to solve it.

Transfer learning^{4,5,6,7,8,9,10} solves the problems presented above by allowing knowledge transfer from one neural network to another. A common way to use supervised transfer learning is obtaining a large pre-trained neural network and retraining it for a different but closely related problem. This significantly reduces training time and allows the model to be trained on a less powerful computer. Many researchers used pre-trained neural networks such as ResNet-50¹¹ and retrained them to classify malicious software^12,13,14,15. Another application of transfer learning is tackling the generalization problem, where the testing dataset is completely different from the training dataset. For example, every human has unique electroencephalography (EEG) signals due to them having distinctive brain structures. Transfer learning solves the generalization problem by pretraining on a general population EEG dataset and retraining the model for a specific patient^{16,17,18,19,20}. As a result, the neural network is dynamically tailored for a specific person and can interpret their specific EEG signals properly. Labeling large datasets by hand is tedious and time-consuming. In semi-supervised transfer learning^21,22,23,24, either the source dataset or the target dataset is unlabeled. That way, the neural networks can self-learn which pieces of information to extract and process without many labels.

Digital twin models may enhance future autonomous systems.

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests.

Using machine learning tools to create a digital twin, or a virtual copy, of an electronic circuit that exhibits chaotic behavior, researchers found that they were successful at predicting how it would behave and using that information to control it.

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Previous guest and friend of the show, Sir Roger Penrose, argues that human consciousness is not algorithmic and, therefore, cannot be modeled by Turing machines. In fact, he believes in a quantum mechanical understanding of human consciousness. However, as with any issue related to human consciousness, many disagree with him. One of his opponents is Daniel Dennett, with whom I recently had the pleasure of talking. Tune in to find out why Dennett thinks Penrose is wrong!

Continue reading “Dan Dennett: Sir Roger Penrose Is WRONG About Human Consciousness!” »

Learn what you always wish you knew about Google’s algorithms.

As the world’s population continues to grow and age, the healthcare system in different geographies is inching closer to the brink of collapse. According to the World Health Organization, the current number of health workers, including physicians, radiologists, and other professionals, is not sufficient to handle the rising caseload. On top of it, the increased stress and burnout stemming from the surge in cases is pushing many to exit the field, further reducing the number of practicing workers. Becker Health estimates show that nearly 72,000 American physicians left the workforce between 2021 and 2022, and some 30,000 who will join the workforce will not be enough to meet the growing demand.

At the core, both these challenges – the rising caseload and dwindling workforce – are leaving one major impact: diminished quality of patient care. This is where the much talked about generative AI can come in, saving healthcare staffers valuable time and resources and enabling them to focus on enhancing clinical outcomes.

First off, it’s important to understand AI is not new in healthcare. Organizations have been experimenting with predictive and computer vision algorithms for a while now, most notably to forecast the success of treatments and diagnose dangerous diseases earlier than humans. However, when it comes to generative AI, things are still pretty fresh, given the technology came to the forefront just a couple of years ago with the launch of ChatGPT. Gen AI models use neural networks to identify patterns and structures in existing data and generate new content such as text and images. They are applicable across sectors, including healthcare – where organizations cumulatively generate about 300 petabytes of data every single day.

While tantalizing, Alcubierre’s design has a fatal flaw. To provide the necessary distortions of spacetime, the spacecraft must contain some form of exotic matter, typically regarded as matter with negative mass. Negative mass has some conceptual problems that seem to defy our understanding of physics, like the possibility that if you kick a ball that weighs negative 5 kilograms, it will go flying backwards, violating conservation of momentum. Plus, nobody has ever seen any object with negative mass existing in the real universe, ever.

These problems with negative mass have led physicists to propose various versions of “energy conditions” as supplements to general relativity. These aren’t baked into relativity itself, but add-ons needed because general relativity allows things like negative mass that don’t appear to exist in our universe—these energy conditions keep them out of relativity’s equations. They’re scientists’ response to the unsettling fact that vanilla GR allows for things like superluminal motion, but the rest of the universe doesn’t seem to agree.

The energy conditions aren’t experimentally or observationally proven, but they are statements that concord with all observations of the universe, so most physicists take them rather seriously. And until recently, physicists have viewed those energy conditions as making it absolutely 100 percent clear that you can’t build a warp drive, even if you really wanted to.

A study from Japan published in the International Journal of Computer Aided Engineering and Technology reveals a way to optimize the composition of functionally graded materials (FGMs). FGMs are advanced composite materials with a gradual variation in composition and properties across their volume, designed to optimize performance under specific loading conditions.

Scientists pitted OpenAI and Meta chatbots against over 1,900 humans. The algorithms bested participants in areas like the detection of irony and faux pas.

Sean Carroll, a physicist at Johns Hopkins University, spoke at the Bell House in Brooklyn, New York, in an event presented by New York City’s Secret Science Club. He talked about quantum field theory, which is now considered the definitive explanation of what reality is made of. So, pretty important stuff.

His new book The Biggest Ideas in the Universe: Quanta and Fields was released this week. It’s the second in a three-book series in which he goes through the important ideas of Physics for non-academics, but actually using and carefully explaining the equations that physicists use.