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Lack of focus doesn’t equal lack of intelligence — it’s proof of an intricate brain

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Imagine a busy restaurant: dishes clattering, music playing, people talking loudly over one another. It’s a wonder that anyone in that kind of environment can focus enough to have a conversation. A new study by researchers at Brown University’s Carney Institute for Brain Science provides some of the most detailed insights yet into the brain mechanisms that help people pay attention amid such distraction, as well as what’s happening when they can’t focus.

In an earlier psychology study, the researchers established that people can separately control how much they focus (by enhancing relevant information) and how much they filter (by tuning out distraction). The team’s new research, published in Nature Human Behaviour, unveils the process by which the brain coordinates these two critical functions.

Lead author and neuroscientist Harrison Ritz likened the process to how humans coordinate muscle activity to perform complex physical tasks.

“In the same way that we bring together more than 50 muscles to perform a physical task like using chopsticks, our study found that we can coordinate multiple different forms of attention in order to perform acts of mental dexterity,” said Ritz, who conducted the study while a Ph.D. student at Brown.

The findings provide insight into how people use their powers of attention as well as what makes attention fail, said co-author Amitai Shenhav, an associate professor in Brown’s Department of Cognitive, Linguistic and Psychological Sciences.

“These findings can help us to understand how we as humans are able to exhibit such tremendous cognitive flexibility — to pay attention to what we want, when we want to,” Shenhav said. “They can also help us better understand limitations on that flexibility, and how limitations might manifest in certain attention-related disorders such as ADHD.”

The focus-and-filter test

To conduct the study, Ritz administered a cognitive task to participants while measuring their brain activity in an fMRI machine. Participants saw a swirling mass of green and purple dots moving left and right, like a swarm of fireflies. The tasks, which varied in difficulty, involved distinguishing between the movement and colors of the dots. For example, participants in one exercise were instructed to select which color was in the majority for the rapidly moving dots when the ratio of purple to green was almost 50/50.

Ritz and Shenhav then analyzed participants’ brain activity in response to the tasks.

Ritz, who is now a postdoctoral fellow at the Princeton Neuroscience Institute, explained how the two brain regions work together during these types of tasks.

“You can think about the intraparietal sulcus as having two knobs on a radio dial: one that adjusts focusing and one that adjusts filtering,” Ritz said. “In our study, the anterior cingulate cortex tracks what’s going on with the dots. When the anterior cingulate cortex recognizes that, for instance, motion is making the task more difficult, it directs the intraparietal sulcus to adjust the filtering knob in order to reduce the sensitivity to motion.

“In the scenario where the purple and green dots are almost at 50/50, it might also direct the intraparietal sulcus to adjust the focusing knob in order to increase the sensitivity to color. Now the relevant brain regions are less sensitive to motion and more sensitive to the appropriate color, so the participant is better able to make the correct selection.”

Ritz’s description highlights the importance of mental coordination over mental capacity, revealing an often-expressed idea to be a misconception.

“When people talk about the limitations of the mind, they often put it in terms of, ‘humans just don’t have the mental capacity’ or ‘humans lack computing power,'” Ritz said. “These findings support a different perspective on why we’re not focused all the time. It’s not that our brains are too simple, but instead that our brains are really complicated, and it’s the coordination that’s hard.”

Ongoing research projects are building on these study findings. A partnership with physician-scientists at Brown University and Baylor College of Medicine is investigating focus-and-filter strategies in patients with treatment-resistant depression. Researchers in Shenhav’s lab are looking at the way motivation drives attention; one study co-led by Ritz and Brown Ph.D. student Xiamin Leng examines the impact of financial rewards and penalties on focus-and-filter strategies.

The study was funded by the National Institutes of Health (R01MH124849, S10OD02518), the National Science Foundation (2046111) and by a postdoctoral fellowship from the C.V. Starr Foundation.

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Roger Guillemin (1924–2024), neuroscientist who showed how the brain controls hormones

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Black and white portrait of Roger Guillemin pictured in 1977

Credit: Hulton Archive/Getty

Roger Guillemin identified the molecules in the brain that control the production of hormones in endocrine glands such as the pituitary and thyroid. His work led to a torrent of advances in neuroendocrinology, with far-reaching effects on studies of metabolism, reproduction and growth. For his discoveries on peptide-hormone production in the brain, Guillemin shared the 1977 Nobel Prize in Physiology or Medicine with Andrew Schally and Rosalyn Yalow. He has died at the age of 100.

In the autumn of 1969, after analysing millions of sheep brains for more than a decade, Guillemin and his colleagues determined the structure of thyrotropin-releasing factor (TRF). This small peptide is produced in the hypothalamus, a small region at the base of the brain, and is transported to the anterior lobe of the nearby pituitary gland, where it triggers the release of the hormone thyrotropin. Thyrotropin, in turn, stimulates the thyroid gland to produce the hormone thyroxine, which regulates metabolic activity in nearly every tissue of the body. More than two dozen drugs use such hypothalamic hormones to treat endocrine disorders and cancers, and the worldwide market for these drugs is worth several billion dollars.

Guillemin was born in Dijon, France, and came of age at the end of the Second World War. He graduated from medical school in the University of Lyon, France, in 1949 and worked as a country doctor in the small commune of Saint-Seine-l’Abbaye in Burgundy. He found the work satisfying but intellectually limiting, noting that “in those days I could take care of all my patients with three prescriptions, including aspirin”. Fascinated by how the brain and pituitary gland control the body’s response to stress, he attended lectures in Paris by the Hungarian–Canadian endocrinologist Hans Selye, after which Selye accepted Guillemin’s request to spend a year doing research in his laboratory at the University of Montreal, Canada.

The consciousness wars: can scientists ever agree on how the mind works?

This turned into a four-year project, for which Guillemin was awarded a PhD in 1953. His studies with Selye were impactful, but it was meeting the UK physiologist Geoffrey Harris in Canada that would shape Guillemin’s subsequent science. Harris argued that the hypothalamus controls the anterior pituitary not through nerve signals, but rather through blood-borne factors that reach the pituitary through the capillaries of an interconnecting stalk. Recruited to the faculty of the Baylor College of Medicine in Houston, Texas, Guillemin decided to tackle Harris’s hypothesis head on. His initial aim was to purify and determine the structure of corticotropin-releasing factor (CRF), the hypothalamic hormone that stimulates the anterior pituitary to produce adrenocorticotropic hormone, the driver of the stress response described by Seyle. Progress towards this goal was slow, so Guillemin turned his attention to other putative releasing factors, including TRF.

The scale of his efforts at purification in the late 1950s and 1960s was enormous. These releasing factors were peptides — short chains of amino acids — present in only tiny amounts in the hypothalamus. Together with the fact that the hypothalamus is itself a small part of the brain, this meant that purification began with extracts prepared from millions of sheep hypothalami obtained from slaughterhouses. Peptides were separated on 3-metre-tall chromatography columns that extended through the lab’s ceiling. One set of columns was packed with the then-new resin Sephadex, released by the Stockholm-based biotechnology company Pharmacia in 1959. Guillemin sent a postdoc in his lab, Andrew Schally, that year to Sweden to procure much of the world’s supply of Sephadex.

Schally, who had worked on releasing factors for his PhD, joined the expanding team in Houston in 1957. He chafed under Guillemin’s leadership, however, viewing his years in Houston as a struggle in which he and Guillemin had a “very bitter, unpleasant relationship”. Guillemin suggested that Schally should move on, and himself accepted a simultaneous appointment at the Collège de France in Paris in 1960. Schally established his own competing research operation at Tulane University in New Orleans, Louisiana. Guillemin and Schally would remain competitors for more than two decades, a state of affairs not changed by their shared Nobel Prize.

Signs of ‘transmissible’ Alzheimer’s seen in people who received growth hormone

The Houston and New Orleans teams succeeded in purifying TRF and determining its amino-acid sequence at around the same time. Immediately thereafter, Guillemin moved his lab to the Salk Institute for Biological Studies in La Jolla, California. There, his team identified a raft of hypothalamic releasing factors, now referred to as hormones. These included gonadotropin-releasing hormone, which drives the release of hormones that stimulate the reproductive organs; somatostatin, which inhibits the release of growth hormones; and growth-hormone-releasing hormone. In 1981, a Salk Institute team headed by US endocrinologist Wylie Vale, who was a student of Guillemin, finally purified and sequenced the elusive CRF, Selye’s obsession and Guillemin’s initial target from the 1950s. Drugs built on these discoveries have proved to be among the farthest-reaching medical translations of research from the institute.

Guillemin was the recipient of multiple honours and awards as well as the Nobel Prize. He was a connoisseur of the wines of Burgundy, and during his tenure as president of the Salk Institute in 2007–09, white wine was served at lunchtime faculty meetings. Roger lived an art- and music-filled life, and was close to the artists Françoise Gilot and Niki de Saint Phalle. He cherished his ties to family, students, postdocs, colleagues and friends. He leaves a vibrant scientific legacy.

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Sam Altman developing new AI “Brain Chips” or Neuromorphic chips

AI Brain Chips under development by Sam Altman

Sam Altman, a leading figure in the tech industry and head of OpenAI, is spearheading an ambitious project to raise funds for the development and worldwide production of advanced AI chips. These chips are designed to operate similarly to the human brain, which could lead to significant improvements in the efficiency and cost-effectiveness of AI computations.

The rapid advancement of AI technology has led to an increase in computational demands that current hardware is struggling to meet. Neuromorphic chips could be the solution to this problem, offering a more natural and efficient way to handle AI tasks than traditional processors. The success of these chips could have a profound impact on the future of AI, making Altman’s fundraising efforts crucial.

However, Altman’s initiative has not been without controversy. Some have questioned his decision to seek funding outside of OpenAI, while others have misinterpreted his actions as being at odds with the organization’s goals. One of the first attempts to secure funding involved investors from the Middle East, which raised concerns and prompted the U.S. government to intervene, citing national security interests. This has highlighted the importance of where chip manufacturing takes place and the need for domestic production to maintain hardware sovereignty and avoid risks associated with blacklisted entities.

Sam Altman investment to make new “Brain Chips” for AI

Learn more about the new development and start-up backed by CEO Sam Altman that OpenAI has already agreed to buy $51 million worth of AI chips from.

The search for financial backers and partners to build chip fabrication facilities is ongoing. OpenAI and other major tech companies need to secure investment to maintain their lead in the AI race. Neuromorphic chips have the potential to revolutionize AI, but realizing their full capabilities will require collaboration across various sectors.

As Altman continues to push for the global production of these advanced AI chips, the implications for U.S. national security and the global AI infrastructure will be closely watched. The success of this initiative could mark a significant moment for investors, policymakers, and the AI community as a whole.

What are Neuromorphic chips?

Neuromorphic chips are a type of hardware designed to mimic the neural structure and functioning of the human brain. This approach to chip design is fundamentally different from traditional computing paradigms. Traditional computers use the von Neumann architecture, which separates memory and processing units, leading to a bottleneck in data transfer. Neuromorphic chips, on the other hand, integrate memory and processing, similar to how neurons in the brain function.

The core concept behind neuromorphic computing is to emulate the brain’s massively parallel computational approach. Neurons in the brain are interconnected through synapses, and they work in parallel to process information. Neuromorphic chips use artificial neurons and synapses to replicate this architecture. These artificial neurons and synapses are typically implemented using silicon-based technologies, although other materials are also being explored.

Ability to learn and adapt

One key feature of neuromorphic chips is their ability to learn and adapt. In traditional computing, tasks are performed based on pre-written algorithms and require explicit programming. Neuromorphic chips, however, can change their internal connections (synapses) in response to incoming data, a process akin to learning in the human brain. This adaptability makes them well-suited for tasks like pattern recognition, sensory data processing, and decision-making in unstructured environments.

Energy efficiency is another significant advantage of neuromorphic chips. The human brain is remarkably energy-efficient when compared to traditional computers. Neuromorphic chips emulate this efficiency by using a method called “spiking neural networks” (SNNs). In SNNs, information is processed and transmitted in the form of spikes, which are discrete events that occur only when needed, rather than the continuous signal processing used in conventional computers. This event-driven processing significantly reduces power consumption.

Application of AI brain chips

Applications of neuromorphic chips are diverse and growing. They are particularly useful in areas where real-time processing, low power consumption, and the ability to handle complex, unstructured data are crucial. Examples include autonomous vehicles, where they can process sensory data in real-time; robotics, for more adaptive and efficient processing; and edge computing, where processing data on-site can reduce the need for data transmission to centralized cloud servers.

However, there are challenges in the development and adoption of neuromorphic chips. One major challenge is the complexity of designing and manufacturing these chips, as they require new materials and fabrication techniques. Additionally, developing software and algorithms that can fully utilize their unique architecture is an ongoing area of research.

In summary, neuromorphic chips represent a significant shift in computing, drawing inspiration from the human brain to create hardware that is efficient, adaptable, and capable of learning. Their development is still at a relatively early stage, but they hold great promise for a wide range of applications, particularly in areas where traditional computing architectures fall short. To learn more about the new AI chips being developed by Sam Altman jump over to Bloomberg.

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How to Use Google Bard to Challenge Your Brain

google Bard

This guide will show you how to use AI tools like Google Bard to challenge your brain. Are you finding yourself weary of the monotonous routine of Sudoku grids and the familiar patterns of crossword puzzles? Are you yearning for a cerebral challenge that not only stimulates but also surprises? Your search ends with Google Bard, an approachable and innovative AI companion nestled right in your neighborhood, endowed with a remarkable ability to devise an array of ingenious puzzles and games.

However, Google Bard transcends the traditional roles of a mere search engine or a basic chatbot. It’s a veritable oasis for those driven by curiosity, a digital playground that sparks joy and ignites the intellect. Armed with an expansive repository of knowledge and a flair for creativity, Bard is adept at presenting a rich array of challenges. These challenges are not just about putting your brain to the test; they’re about embarking on a journey of discovery, learning intriguing new facts and concepts, and enjoying every moment of this stimulating experience. This AI platform is designed to transform the mundane into the extraordinary, turning every interaction into an opportunity for both learning and enjoyment. Whether you’re a puzzle aficionado or someone looking to diversify their intellectual pursuits, Google Bard stands ready to redefine the way you engage with puzzles and games, offering a delightful blend of entertainment and education.

So, how do you unlock this intellectual smorgasbord? Here’s your Bard-approved guide to using Google Bard for some serious brain-bending fun:

1. Embrace the Power of Prompt Play:

Bard thrives on prompts. The more specific and creative your questions and requests, the more imaginative its responses. Start by asking Bard to generate puzzles for you. Specify the type of puzzle you’re in the mood for – a logic riddle, a lateral thinking brain teaser, even a cryptic poem with hidden clues. Let your imagination run wild!

2. Dive into the Depths of Dialogue:

Don’t just ask Bard questions, have conversations with it! Engage in a back-and-forth puzzle-solving dialogue. Throw out a problem, bounce ideas off each other, and see where the conversation takes you. You might uncover unexpected solutions or stumble upon entirely new puzzle formats. Remember, Bard is an active participant, not just a passive answer machine.

3. Unleash the Bardic Bard:

Bard can even become your personal puzzle-generating AI bard. Ask it to write you a story or poem with built-in puzzles woven into the narrative. Challenge yourself to decipher hidden meanings, solve riddles disguised as metaphors, or untangle clues embedded in the plot. This is a fantastic way to combine creative writing with mental gymnastics.

4. Get Competitive – Bard Edition:

Turn Bard into your friendly (or not-so-friendly) puzzle rival. Challenge it to solve a puzzle you’ve created, or set a timer and see who can crack a Bard-generated brain teaser first. You can even involve friends and family, making it a collaborative or competitive game night with Bard as the ultimate puzzle master.

5. Beyond the Binary – Embrace Open-Ended Exploration:

Remember, Google Bard isn’t about finding one right answer. It’s about exploring possibilities, testing assumptions, and expanding your thinking. Don’t be afraid to throw out open-ended prompts and see where they lead. Ask Bard to create a puzzle with multiple solutions, or challenge it to come up with the most creative or absurd answer to a problem. This is where you truly tap into the divergent thinking power of AI and your own brain in tandem.

6. Remember, the Journey is the Reward:

Don’t get hung up on finding the “correct” solution. The beauty of using Google Bard for puzzles and games is the process itself. Enjoy the back-and-forth, the unexpected twists, the moments of “aha!” and the head-scratching quandaries. Let Bard be your guide on a journey of intellectual exploration, where learning and amusement go hand in hand.

Summary

Are you finding yourself weary of the monotonous routine of Sudoku grids and the familiar patterns of crossword puzzles? Are you yearning for a cerebral challenge that not only stimulates but also surprises? Your search ends with Google Bard, an approachable and innovative AI companion nestled right in your neighborhood, endowed with a remarkable ability to devise an array of ingenious puzzles and games.

However, Google Bard transcends the traditional roles of a mere search engine or a basic chatbot. It’s a veritable oasis for those driven by curiosity, a digital playground that sparks joy and ignites the intellect. Armed with an expansive repository of knowledge and a flair for creativity, Bard is adept at presenting a rich array of challenges. These challenges are not just about putting your brain to the test; they’re about embarking on a journey of discovery, learning intriguing new facts and concepts, and enjoying every moment of this stimulating experience. This AI platform is designed to transform the mundane into the extraordinary, turning every interaction into an opportunity for both learning and enjoyment. Whether you’re a puzzle aficionado or someone looking to diversify their intellectual pursuits, Google Bard stands ready to redefine the way you engage with puzzles and games, offering a delightful blend of entertainment and education.

Here are some more helpful Google Bard guides:

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Create an AI second brain and chat to your documents

Create an AI second brain and chat to your documents

If you are interested in having the ability to give your large language model or preferred AI model a memory you might be interested in Quivr. A unique LLM data storage and query interface, designed to function as a second brain. Read this guide and watch the videos below to learn how to set up and use Quivr on a local machine.

Quivr has been specifically designed to use the power of Generative AI such as that provided by OpenAI’s ChatGPT,  Anthropic AI,  and other large language models to store and retrieve unstructured information. At the current time Quivr only supports a connection to ChatGPT but is looking to expand its capabilities as the project progresses to connect to other LLMs.

Quivr is an open-source software that accepts almost every type of document or media, including pictures, videos, PDFs, CSVs, and PowerPoint documents. It can be used with chat CPT and local models on a user’s computer, allowing users to upload files and ask questions about their content.

Adding memory to your AI model

One of the features that make Quivr unique is the ability to create different “brains” for different sets of documents. This allows users to separate personal and work documents or categorize documents based on projects, topics, or any other criteria. The software is production-ready and can be used to chat with documents at any time. Users can select different models, set the temperature, set the number of tokens, and even create API keys to code on top of Quivr

Other articles you may find of interest on the subject of improving your productivity :

What is a second brain and how is it used to improve productivity?

The term “second brain” in the context of activity and document handling refers to an external system for storing, organizing, and managing information, tasks, and documents that you encounter in your personal and professional life. The idea is to offload the cognitive work of remembering, sorting, and synthesizing information to this external system, thereby freeing up mental space and improving productivity and creativity.

The concept is rooted in productivity methods like Getting Things Done (GTD) by David Allen and takes advantage of modern digital tools to make the process more efficient. Tools like note-taking apps Notion, Obsidian, Devon Think 3 and others as well as task managers such as Asana, Omnifocus and even more specialized software for document handling can serve as components of one’s second brain.

However with the explosion of artificial intelligence over the last two years it has now become even easier to build your very own AI second brain. Using large language models or connections to existing GPT style chatbots you can quickly create a personal assistant and organise your documents that can be accessed using AI queries.

Here’s how a second brain might handle activities and documents:

  • Capture: The first step involves capturing all relevant information, tasks, and documents as they come. This could be meeting notes, ideas, to-do items, or important documents like invoices and contracts.
  • Organize: Once captured, these pieces of information are sorted into appropriate folders, tags, or databases. The system should be intuitive enough that you can find items easily.
  • Review: Periodically, the stored information is reviewed to update tasks, delete or archive obsolete information, and integrate new data.
  • Execute: With a well-organized second brain, you can focus on executing tasks more efficiently, knowing that all the contextual information you might need is readily available.
  • Synthesize: Finally, the second brain can help in synthesizing new ideas by connecting disparate pieces of information stored within it.
  • Search and Retrieve: Because the second brain is well-organized, searching for and retrieving documents or information becomes much easier than if they were scattered in various places.

The second brain essentially serves as an extension of your cognitive faculties, allowing you to manage activities and documents more efficiently than relying solely on your own memory and intuition.

Building an AI second brain with Quivr

Features of Quivr

  • Universal Data Acceptance: Quivr is capable of managing a wide range of data types, including text, images, and code snippets.
  • Generative AI: The platform utilizes advanced AI algorithms to aid in the creation and retrieval of information.
  • Speed and Efficiency: Quivr is engineered for quick and efficient data access.
  • Security: The platform offers robust security features, ensuring that you have full control over your data.
  • OS Compatibility: The software is compatible with Ubuntu 22 and newer versions.
  • File Compatibility: Quivr supports a variety of file types: Text, Markdown, PDF, PowerPoint, Excel (Upcoming), CSV, Word, Audio and Video.
  • Open Source: Quivr is an open-source platform, making it free to use and modify.

One of the key advantages of the Quivr AI second brain is its flexibility. The project can be put on the internet and walled behind authorization, allowing users to give access to friends or colleagues to chat with documents they create. This feature is particularly useful for collaborative projects where multiple users need to interact with the same set of documents.

Quivr is a powerful tool that takes document interaction to a new level. Its unique approach of creating an ‘AI second brain’ allows users to interact with their documents in a conversational manner, making it easier to extract information and insights. Whether you are a student, a professional, or just someone who deals with a lot of documents, building and AI second brain  using Quivr can be a valuable addition to your AI toolkit. For more information and to download the project to install locally jump over to the official GitHub repository

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