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Algorithms are complex mathematical formulas used to perform tasks in our digital world. They are programmed to process information, make decisions, and take actions. Algorithms are used in various applications, such as search engines, social media, autonomous vehicles, and digital assistants.

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OpenAI has released a new version of ChatGPT, claiming that the new language learning model is capable of passing – and even excelling in – a variety of academic exams.

ChatGPT-4, which will be available on Bing as well as the OpenAI website, is more reliable and more creative than its predecessor, according to OpenAI. The team tested the model on a number of exams designed for humans, from the bar exam to biology, using publicly available papers. While no additional training was given to the model ahead of the tests, it was able to perform well on most subjects, performing in the estimated 90th percentile for the bar exam and the 86th-100th in art history.

Just as the previous model was accused of being bad at math, this version struggled more with calculus, scoring in the 43rd-59th percentile.

An international team of planetary scientists has characterized some of the features of an exoplanet named HD-207496-b, located approximately 138 light years from Earth. In their paper accepted for publication in the journal Astronomy & Astrophysics, and currently posted on the arXiv preprint server, the group describes their study of the exoplanet and the two theories regarding its likely makeup.

The HD-207496-b was discovered as part of a larger effort to characterize naked core planets. As such, the team was analyzing HARPS of HD-207496—a bright k dwarf. By adding TESS photometry data, the group was able to measure the stars’ brightness and wavelength, and by studying the exoplanet’s transit characteristics, the team was able to calculate its period, mass, radius and density. That led them to a bit of a conundrum—was the exoplanet gaseous or watery?

The researchers calculated that the exoplanet had a radius 2.25 times that of Earth, with an orbit of 6.44 days. And it had a mass that was approximately 6.1 times Earth’s. Simple math showed that the exoplanet had a density of 3.27 grams per cubic centimeter, which is less than that of Earth.

He was ranked the number 1 most influential neuroscientist in the world by Semantic Scholar in 2016, and has received numerous awards and accolades for his work. His appointment as chief scientist of Verses not only validates their platform’s framework for advancing AI implementations but also highlights the company’s commitment to expanding the frontier of AI research and development.

Friston is short listed for a Nobel Prize, is one of the most cited scientists in human history with over 260,000 academic citations, and invented all of the mathematics behind the fMRI scan. As one pundit put it, “what Einstein was to physics, Friston is to Intelligence.”

Indeed Friston’s expertise will be invaluable in helping the company execute its vision of deploying a plethora of technologies working toward a smarter world through AI.

Through a vast network of nerve fibers, electrical signals are constantly traveling across the brain. This complicated activity is what ultimately gives rise to our thoughts, emotions, and behaviors – but also possibly to mental health and neurological problems when things go wrong.

Brain stimulation is an emerging treatment for such disorders. Stimulating a region of your brain with electrical or magnetic pulses will trigger a cascade of signals through your network of nerve connections.

However, at the moment, scientists are not quite sure how these cascades travel to impact the activity of your brain as a whole – an important missing piece that limits the benefits of brain stimulation therapies.

Check out the Machine Learning Course on Coursera: https://click.linksynergy.com/deeplink?id=vFuLtrCrRW4&mid=40…p_ml_nov18

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What are the neurons, why are there layers, and what is the math underlying it?
Help fund future projects: https://www.patreon.com/3blue1brown.
Written/interactive form of this series: https://www.3blue1brown.com/topics/neural-networks.

Additional funding for this project provided by Amplify Partners.

Typo correction: At 14 minutes 45 seconds, the last index on the bias vector is n, when it’s supposed to in fact be a k. Thanks for the sharp eyes that caught that!

For those who want to learn more, I highly recommend the book by Michael Nielsen introducing neural networks and deep learning: https://goo.gl/Zmczdy.

There are two neat things about this book. First, it’s available for free, so consider joining me in making a donation Nielsen’s way if you get something out of it. And second, it’s centered around walking through some code and data which you can download yourself, and which covers the same example that I introduce in this video. Yay for active learning!
https://github.com/mnielsen/neural-networks-and-deep-learning.

I also highly recommend Chris Olah’s blog: http://colah.github.io/

LLMs stands for Large Language Models. These are advanced machine learning models that are trained to comprehend massive volumes of text data and generate natural language. Examples of LLMs include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers). LLMs are trained on massive amounts of data, often billions of words, to develop a broad understanding of language. They can then be fine-tuned on tasks such as text classification, machine translation, or question-answering, making them highly adaptable to various language-based applications.

LLMs struggle with arithmetic reasoning tasks and frequently produce incorrect responses. Unlike natural language understanding, math problems usually have only one correct answer, making it difficult for LLMs to generate precise solutions. As far as it is known, no LLMs currently indicate their confidence level in their responses, resulting in a lack of trust in these models and limiting their acceptance.

To address this issue, scientists proposed ‘MathPrompter,’ which enhances LLM performance on mathematical problems and increases reliance on forecasts. MathPrompter is an AI-powered tool that helps users solve math problems by generating step-by-step solutions. It uses deep learning algorithms and natural language processing techniques to understand and interpret math problems, then generates a solution explaining each process step.

To keep his Universe static, Einstein added a term into the equations of general relativity, one he initially dubbed a negative pressure. It soon became known as the cosmological constant. Mathematics allowed the concept, but it had absolutely no justification from physics, no matter how hard Einstein and others tried to find one. The cosmological constant clearly detracted from the formal beauty and simplicity of Einstein’s original equations of 1915, which achieved so much without any need for arbitrary constants or additional assumptions. It amounted to a cosmic repulsion chosen to precisely balance the tendency of matter to collapse on itself. In modern parlance we call this fine tuning, and in physics it is usually frowned upon.

Einstein knew that the only reason for his cosmological constant to exist was to secure a static and stable finite Universe. He wanted this kind of Universe, and he did not want to look much further. Quietly hiding in his equations, though, was another model for the Universe, one with an expanding geometry. In 1922, the Russian physicist Alexander Friedmann would find this solution. As for Einstein, it was only in 1931, after visiting Hubble in California, that he accepted cosmic expansion and discarded at long last his vision of a static Cosmos.

Einstein’s equations provided a much richer Universe than the one Einstein himself had originally imagined. But like the mythic phoenix, the cosmological constant refuses to go away. Nowadays it is back in full force, as we will see in a future article.