Today's youngsters will never know the painstaking task of going to a library and searching for an article or a particular book. This tedious undertaking involved hours upon hours of trawling through drawers filled with index cards – typically sorted by author, title or subject. An explosion in research publications during the 1940s made it especially time-consuming to locate what you wanted, especially as this was before the invention of the internet. Now, an expert has lifted the lid on the man and the device that changed everything – and it could also be the key to surviving AI. Dr Martin Rudorfer, a lecturer in Computer Science at Aston University, said an American engineer called Vannevar Bush first came up with a solution, dubbed the'memex'.

Is there anything more tedious than the endless drive to make every waking moment more productive? "If I can save ten seconds on a process that happens ten times per day, that's a minute and 40 seconds saved per day," a productivity guru recently advised in Time magazine, to take just one example. "Over the course of a year, that's ten hours saved." If that sounds exhausting, the good news is that there are many reasons to avoid putting ourselves under this kind of relentless pressure – not least the latest neuroscience on how the brain regulates focus, as we explore in "How to shift the brain's inner gearstick to optimise the way you think". It has to do with a tiny bundle of blue-tinged neurons in the brainstem called the locus coeruleus.

From'The Terminator' to'I, Robot', killer robots have been a staple feature in science fiction blockbusters for years. But nightmares of AI overtaking humanity might be further away than we thought, according to scientists. New research from the University of Oxford suggests that the human brain learns information in a fundamentally different and more efficient way to machines. This allows humans to learn something after seeing it once, while AI needs to be trained hundreds of times on the same information. Unlike AI, humans can also learn new information without it interfering with the knowledge we already have.

This will help scientists to understand the basic principles by which signals travel through the brain at the neural level and lead to behaviour and learning. An organism's nervous system, including the brain, is made up of neurons that are connected to each other via synapses. Information in the form of chemicals passes from one neuron to another through these contact points. The map of the 3016 neurons that make up the larva of the fruit fly Drosophila melanogaster's brain, and the detailed circuitry of neural pathways within it, is known as a'connectome'. This is the largest complete brain connectome ever to have been mapped.

Artificial Intelligence (AI) and neuroscience have been intertwined since the start of the 20th century. Both disciplines seek to understand how the brain works, and both are exploring ways to use technology to improve our lives. AI and neuroscience are similar in many ways but differ in key aspects. In this blog post, we will explore the relationship between AI and neuroscience and how they interact with each other. We'll discuss topics such as neural networks, machine learning, natural language processing, and more to gain a better understanding of how these two disciplines complement one another.

An AI has created hilarious postcard images of popular British tourist attractions, based solely on snippets from one-star Trip Advisor reviews. Text-to-image tool DALL-E, released by artificial intelligence firm OpenAI, is able to create images and artwork from text prompts. UK rental agency My Favourite Cottages used it to reimagine tourist spots including Stonehenge, Angel of the North, Brighton Palace Pier and Cornwall's Eden Project. Some of the results have a passing resemblance to the real thing, while others are like a window into a dystopian nightmare. DALL-E relies on artificial neural networks (ANNs), which simulate the way the brain works in order to learn.

Neural networks are a type of machine learning algorithm that are inspired by the way the human brain works. They are used to recognize patterns and make decisions based on input data. Although there are several different types of neural networks, and each has its own unique characteristics and applications. Let's discuss the three most prominent ones, starting with, An artificial neural network (or ANN) is a type of machine learning algorithm that is inspired by the way the human brain works. It is made up of a network of artificial "neurons," which are inspired by the way biological neurons in the brain work.

Neural networks, a type of computing system loosely modeled on the organization of the human brain, form the basis of many artificial intelligence systems for applications such speech recognition, computer vision, and medical image analysis. In the field of neuroscience, researchers often use neural networks to try to model the same kind of tasks that the brain performs, in hopes that the models could suggest new hypotheses regarding how the brain itself performs those tasks. However, a group of researchers at MIT is urging that more caution should be taken when interpreting these models. In an analysis of more than 11,000 neural networks that were trained to simulate the function of grid cells -- key components of the brain's navigation system -- the researchers found that neural networks only produced grid-cell-like activity when they were given very specific constraints that are not found in biological systems. "What this suggests is that in order to obtain a result with grid cells, the researchers training the models needed to bake in those results with specific, biologically implausible implementation choices," says Rylan Schaeffer, a former senior research associate at MIT.

How does your brain innovate? How does the human brain learn to innovate? The Thousand Brain theory, proposed by Silicon Valley innovator-turned-scientist Jeff Hawkins, is the first proposal to explain how the brain functions at the cellular level. This theory has direct, important, unexpected implications for how people learn to innovate, and therefore for how teachers, coaches, managers, and other professionals teach innovation. This article provides a summary of Jeff's conclusions.

Call it the Skynet hypothesis, Artificial General Intelligence, or the advent of the Singularity -- for years, AI experts and non-experts alike have fretted (and, for a small group, celebrated) the idea that artificial intelligence may one day become smarter than humans. According to the theory, advances in AI -- specifically of the machine learning type that's able to take on new information and rewrite its code accordingly -- will eventually catch up with the wetware of the biological brain. In this interpretation of events, every AI advance from Jeopardy-winning IBM machines to the massive AI language model GPT-3 is taking humanity one step closer to an existential threat. Except that it will never happen. Co-authors University at Buffalo philosophy professor Barry Smith and Jobst Landgrebe, founder of German AI company Cognotekt argue that human intelligence won't be overtaken by "an immortal dictator" any time soon -- or ever.