Science Atlas

Science Atlas is a website designed to provide a high-level overview of where research is conducted in the private and public sectors worldwide.

It is a dynamic map that allows users to explore the geographical distribution of research institutions and facilities across various countries. Using an intuitive interface, you can filter research centers by country, topic, and sector, providing deep insights into the scientific output from around the globe.

Discover potential collaborators or institutions to join, with access to detailed information about their focus areas and partnerships. Understand the distribution of research activity globally.

Simply visit Science Atlas. We welcome your feedback as we continue to develop it. If you have suggestions, data to contribute, or institutions you’d like to see added, don’t hesitate to contact us through our Add Map Content form.

The Tower of Babel 2.0

In the science fiction franchise, The Matrix, artificial intelligence (AI) has taken over the world and uses human beings as a source of energy. In this fictional setting, humans are kept in suspended animation and connected to a virtual reality environment called the Matrix. Their bodies are stored in pods and are connected to the Matrix through neural interfaces, which build a virtual world that the humans perceive as real. The AI entities maintain this system to harvest the thermal energy and bioelectricity generated by the human body. In essence, humans have been reduced to the role of biological batteries that power the machine world. This grim reality is hidden from the humans by keeping them in the Matrix, a simulated reality that keeps their minds occupied while their bodies are exploited.

The way that AI works now is that it farms intelligence and creativity from humans. AI systems rely on user-generated data to train and fine-tune themselves. This can range from simple data points like clicks and likes to more complex inputs like user-created content, books, and problem-solving strategies. In this sense, AI is cannibalizing human intelligence and creativity, improving its capabilities level by level.

Just as the advent of calculators led to a decline in the practice of mental arithmetic and the widespread use of smartphones has been associated with a reduction in fine motor skills, AI systems, especially those designed to assist in decision-making, problem-solving, or automating complex tasks, can potentially engender a phenomenon known as cognitive offloading. This refers to the increasing human reliance on AI to perform mental functions, potentially leading to a diminished capacity in cognitive and meta-cognitive skills. Such skills, which include planning, self-assessment, and problem-solving, are not merely task-specific but are foundational to human intelligence. They are typically developed and refined through sustained practice. As AI systems take over greater responsibility for these cognitive tasks, humans may find fewer opportunities to exercise and hone these skills, resulting in a gradual decline in cognitive abilities.

Therefore, the ambition to build superintelligence could mirror the ancient myth of the Tower of Babel—a human ambition to transcend limits and attain the divine. In that story, humanity sought to reach the heavens, thwarted by confusion and division. Similarly, in our pursuit of superintelligence, we risk constructing a monument to hubris, where the drive to surpass human cognition may result not in enlightenment but in profound disarray. As we build this modern tower fueled by AI and data, we may inadvertently disconnect from the very cognitive foundations that make us human, leading not to a higher understanding but to a world where both our minds are fragmented and bewildered.

Aidlines

Aidlines is a website that gathers emergency phone numbers for people and animals worldwide. Whether you need an ambulance, police, animal rescue, or help with issues like sexual violence, domestic violence, depression, suicide, or drug addiction, Aidlines makes it easy to find the right numbers quickly, no matter where you are. Our goal is to ensure everyone can get the help they need in times of crisis.

http://aidlines.com/

You can also help by adding important numbers we may have missed, making sure everyone gets the help they need in times of crisis:

https://forms.gle/nJ9bPYDDocjxp7eB6

AI Philosopher’s Roundtable: A Python Script for Enhancing AI-Driven Debates with Real-Time Analysis

AI-driven discussions present a unique opportunity for intellectual engagement and growth in today’s dynamic and rapidly changing world. Facilitated or generated by artificial intelligence (AI) systems—such as advanced language models like OpenAI’s GPT series—these discussions can take various forms, including virtual debates among AI entities. AI-driven discussions enable users to engage with diverse topics anytime, anywhere, fostering a flexible learning experience. These discussions broaden users’ understanding and encourage critical thinking by presenting fresh perspectives on controversial or complex issues. Serving as a valuable resource for brainstorming sessions, AI-driven discussions can help researchers and creative professionals generate new ideas and insights. Moreover, they facilitate time efficiency by concisely summarizing vast amounts of information or presenting multiple viewpoints. Importantly, these automated discussions are devoid of personal biases or emotions, which often impede productive debates, allowing for more objective and focused discourse.

I created a Python script enabling multiple AI language models to engage in an AI-moderated discussion on any topic. An additional AI model provides real-time analysis and critique to improve the conversation’s quality further.

The AI Philosopher’s Roundtable Script

The Python script harnesses OpenAI’s GPT-4 to create an interactive setting in which three distinct AI entities, each assigned specific roles, engage in a structured dialogue:

Moderator: This AI model ensures the conversation remains focused, provides guidance, and promotes productive discourse. GPT-based analysis system: This AI model summarizes and assesses the debate in real-time, offering valuable insights and constructive feedback to enhance the conversation’s quality. The script starts by requesting the user to input a discussion topic. Once entered, the conversation begins with the Moderator setting the stage. The AI philosophers, System1 and System2, alternate in contributing to the debate, with the Moderator periodically intervening to maintain focus.

System1 and System2: These AI models represent philosophers celebrated for their critical thinking and capacity to propel discussions forward.

Evaluator: After a predetermined number of iterations, the GPT-based analysis system evaluates and summarizes the discussion.

The script can be found here.

Analysis

Despite the numerous advantages, there are also some drawbacks to AI-driven discussions. One significant limitation is that AI language models are based on existing knowledge and might not be able to provide truly original insights or ideas. They may lack the depth and nuance that human experts can bring to a conversation, resulting in oversimplifications of certain topics. This can be observed in the discussion records. They may also inadvertently reproduce biases present in the data they were trained on. Misinterpretations or inaccuracies may also arise, as AI models might not fully comprehend the context or nuances behind a specific subject. Lastly, the absence of emotions and personal experiences may limit the empathetic understanding and interpersonal connections that can be fostered through human-to-human discussions.

Nevertheless, even with such a simple script, it is already possible with GPT-4 to produce plausible discussions. It will be interesting to see how these discussions involve with more advanced system information prompts, models, and scripts.

Read Further

Quite similar to the topic discussed in this blog post are Auto-GPT and BabyAGI.

These projects attempt to create AI agents that can perform multistep tasks autonomously. While they currently require significant human input and are not yet fully autonomous, they represent early steps towards more complex AI models.

Auto-GPT, created by Toran Bruce Richards, chains together GPT-4 outputs to achieve a set goal. It currently requires user permission for each step and can’t make purchases, but it demonstrates the potential for AI assistants. BabyAGI, created by Yohei Nakajima, is inspired by the idea of using GPT-4 as an AI co-founder for businesses and has a task-oriented approach. Both projects face limitations due to GPT-4’s narrow range of interpretive intelligence and the issue of confabulations.

Read the full article to learn more about Auto-GPT, BabyAGI, and their implications for AI development.

Another self-looping ChatGPT agent system is described in the paper “Generative Agents: Interactive Simulacra of Human Behavior.” Implemented in a sandbox environment inspired by The Sims, these agents exhibit realistic individual and social behaviors. The research emphasizes the significance of observation, planning, and reflection in creating convincing simulations and demonstrates the integration of large language models with interactive agents.

Barrier-free Websites for GPT Models and Search Engine Optimization

Barrier-free websites are like digital superheroes, battling against the evil of discrimination and exclusion by empowering everyone to engage with the digital world with ease and confidence, regardless of ability or disability. These websites aim to accommodate individuals with various disabilities, including visual impairments, hearing impairments, motor impairments, cognitive impairments, and seizure disorders.

With the integration of newer versions of language models like the Prometheus model (a successor of ChatGPT) into the Microsoft Edge web browser, website accessibility for language models will play a crucial role in the future. The ability to summarize website content and answer questions about it will be a valuable tool for people with and without disabilities, may influence how likely they visit a website, and could even impact the ranking of websites in search engines.

As a result, the optimization of website accessibility for language models will become an important aspect of future search engine optimization (SEO). This could involve adjusting website content and language to give the best output for language models, leading to higher search engine rankings and a better user experience for everyone.

Evaluating ChatGPT’s Forecasts

In our previous post, we explored the potential of ChatGPT as a forecasting support tool. In this post, we put ChatGPT to the test and evaluate its predictions made entirely on its own, without any human assistance. To do this, we will use the normalized mean square error (NMSE) as our evaluation metric. The NMSE is a measure of the accuracy of a prediction. It is calculated by dividing the mean square error (MSE) of the prediction by the variance of the true values. In general, the NMSE is preferred over the MSE when you want to compare the accuracy of different predictions that are based on datasets with different variances.

def calc_nmse(true_values, predicted_values):
"""Calculate the normalized mean square error (NMSE)"""
# Calculate the mean square error (MSE)
mse = sum([(y - ŷ)**2 for y, ŷ in zip(true_values, predicted_values)]) / len(true_values)

# Calculate the variance of the true values
variance = sum([(y - sum(true_values)/len(true_values))**2 for y in true_values]) / (len(true_values) - 1)

# Calculate the NMSE
nmse = mse / variance

return nmse

If you want to do your own estimations and compare them to ChatGPT, don’t scroll further and estimate them here:

  1. How many cars are there in the United States?
  2. How many minutes of video are uploaded to YouTube every day?
  3. How many flights take off from airports around the world every day?
  4. How many babies are born every day?
  5. How many people visit Disneyland every year?
  6. How many cells are there in the human body?
  7. How many words are there in the English language?

We now let ChatGPT estimate the following values. We used the following chat message: “Estimate via Fermi quiz method QUESTION.”

  1. How many cars are there in the United States?
    Estimated: 495 million cars
    Actual: 276 million cars
  2. How many minutes of video are uploaded to YouTube every day?
    Estimated: 333,333,333 hours
    Actual: 720,000 hours
  3. How many flights take off from airports around the world every day?
    Estimated: 250,000 flights/day
    Actual: 100,000 flights/day
  4. How many babies are born every day?
    Estimated: 400,000 people
    Actual: 385,000 babies
  5. How many people visit Disneyland every year?
    Estimated: 18 million people
    Actual: 8.5 million visitors
  6. How many cells are there in the human body?
    Estimated: 100 trillion
    Actual: 30 trillion
  7. How many words are there in the English language?
    Estimated: 500,000
    Actual: 171,146 words

The NMSE of ChatGPT is 5.44.
A value of 0 indicates a perfect fit, while a value greater than 1 indicates a poor fit.

Have you calculated the NMSE for your forecasts? If so, please leave a comment with your result or send me your result directly. It would be interesting to see how ChatGPT’s performance compares to that of a human forecaster.

Superforecasting with ChatGPT

The Fermi Quiz is a powerful tool for making accurate estimates and solving problems quickly. Named after physicist Enrico Fermi, this method involves breaking a problem down into smaller, more manageable pieces and using your knowledge and experience to make educated guesses. By following a few simple steps, you can use the Fermi Quiz to solve problems ranging from estimating the number of coffee shops in a city to calculating the number of stars in the universe. In this post, I will explain how to use the Fermi Quiz to make accurate estimates and demonstrate how ChatGPT, a chatbot, can help us generate more manageable pieces for our estimates and may even improve them.

Fermi Quiz

The Fermi Quiz is a method of solving problems and making estimates by breaking a problem down into smaller, more manageable pieces and using your knowledge and experience to make educated guesses. Here’s how it works:

  1. Define the scope of your estimate: First, you need to clearly define the problem or question that you are trying to solve. This will help you focus your efforts and make it easier to come up with a good estimate.
    For example: How many bike stores are in the Netherlands?
  2. Once you have defined the scope of your estimate, you can begin to break the problem down into smaller, more manageable pieces that help you answer the overall question independently.
    For example:
    1. Piece: 
    How many bike stores are in a dutch city on average? How many cities are in the Netherlands?
    2. Piece: How many people in the Netherlands go on average in one week to a bike store? How many people can one bike store handle in a week?
    3. Piece: How many bikes are in the Netherlands? How many bikes have an average bike store sold since its initial opening?
  3. Answer all questions and estimate the actual value for the overall question with each piece independently. Average all of the estimates together to get the final estimate. This method is based on the wisdom-of-crowds effect, which states that averaging independent judgments often leads to improved accuracy.

ChatGPT for manageable piece generation

As a rule of dumb, more manageable pieces make your final result more precise. However, at some point, it can be difficult to generate more pieces.
Therefore, we can utilize the chatbot ChatGPT to do it for us. You can use the following messages to generate the pieces via ChatGPT (note that the ChatGPT outputs vary, so you may have to tweak the messages a bit):

Estimate how many bike stores are in the Netherlands by using the Fermi quiz method and do not give me estimates.

[ChatGPT ANSWER]

What are five examples of breaking the problem down into smaller, more manageable pieces that I mentioned in my previous response?

[MULTIPLE IDEAS] (Piece 2 and Piece 3 were actually created by ChatGPT)

Estimate each generated manageable piece a value and average it with your previous estimated values.

Why did I not want to get an estimate from ChatGPT yet?

Estimate how many bike stores are in the Netherlands by using the Fermi quiz method and do not give me estimates.

The anchoring effect is a cognitive bias that refers to the tendency for people to rely too heavily on the first piece of information they receive (the “anchor”) when making decisions or judgments. This can lead to distorted judgments and decisions, as people may give too much weight to the initial anchor and not consider other relevant information. Therefore, knowing the estimate of the chatGPT (which is not necessarily precise) may influence your estimate.

Can ChatGPT improve our forecasting?

Now for every manageable piece, we use ChatGPT to get some estimates. Note that multiple times, the same question results in different estimates. This is not a big problem and we can handle it by, for example, averaging the estimates for each subquestion.

Let’s calculate the ChatGPT estimates.

1. Piece

How many bike stores are in a dutch municipality on average? How many cities are in the Netherlands?

Estimate via the Fermi quiz method how many bike stores are in a dutch municipality on average?
-> ANWSERS: 5

Estimate via the Fermi quiz method how many municipalities are in the Netherlands.
-> ANWSER: 233

ESTIMATE:
5 * 233 = 1165

2. Piece

How many people in the Netherlands go on average in one week to a bike store?
-> 
525000
How many people can one bike store handle in a week?
-> 500

ESTIMATE:
525000/500=1050

3. Piece

How many bikes are in the Netherlands?
-> 
35 million bikes
How many bikes have an average bike store in the Netherlands sold in its life span?
-> 10000 bikes

ESTIMATE:
35,000,000/10,000 = 3500

FINAL CHATGPT ESTIMATE: (1165 + 1050 + 3500)/3 = 1905

Now that we have generated additional pieces using ChatGPT, we can average its estimate with your own to create a more precise estimate for the problem. To see how accurate your final estimate is, you can compare it to the actual number of bike stores in the Netherlands, which was approximately 3080 in 2020.

If you have tried using ChatGPT to generate additional manageable pieces for the Fermi Quiz method, please let me know in the comments how it worked for you. Did it help you come up with a more accurate estimate? Did combining your own estimate with ChatGPT’s estimate bring you closer to the actual number? I would love to hear your thoughts and experiences with using ChatGPT to improve the accuracy of your Fermi Quiz estimates. Please share your comments below.

Luxury Handbag Investment – A Data-Driven Point of View

In the investment landscape, designer handbags are undoubtedly worth taking a look at. According to Art Market Research (AMR), designer handbags outperform art, classic cars, and rare whiskies in terms of investment potential. Some handbags, from Hermes, Chanel, and Louis Vuitton, have even experienced a valuation spike of an average of 83% in the last ten years. To put that into context, watches have increased by 72%.

Average Prices of different handbag models on different reseller platforms in December 2021.

When it comes to considering designer handbags as an investment it’s important to have the right expectations. A quality designer handbag can be a great wardrobe investment. Selling your designer handbags years later for a profit is only true for certain designer handbags.

Where do you get them?

Whether you are on the lookout for a classic Louis Vuitton bag, or desperately want a Hermès Birkin and don’t want to wait on their list, luxury resale websites are the new place to be. The most popular luxury resale sites are Vestiaire Collective, The Luxury Closet, and Rebelle.

Short-Term Strategy

When reselling fashion items like handbags, you have to understand the trends. A good way to understand the trends is to analyze the sales on the previously mentioned reselling platforms. They give you an overview of how certain handbag models are performing. A good performance indicator is for example the turnaround time (the duration of how long certain products are on the market). Lower turnaround times indicate that certain models are more wanted than other models.

Average turnaround times of different handbag models in December 2021. Each model sample size is larger than 20 items (so currently not the biggest one).

When setting a price, do not forget to take platform fees into account (mostly around 25% of the price). Therefore, a quite nice scenario would be to buy a handbag 25% less than its average price and sell it a bit more than the average price.

Long-Term Strategy

Designer bags go in and out of fashion, but a well-chosen designer bag can last forever. Classic brands, such as Hermes, Chanel, and Louis Vuitton, and classic handbag styles may hold their value. Taking good care of your bag is necessary—both when in use and not—to guarantee interest if you’re looking to trade it in.

Microchips – Demand, Industry, and Shortage

The microchip became one of the most important strategic materials in the 21st century. Almost everything we use depends on microchips. From your iPhone, your toaster to fighter jets, and automobiles. Microchips became a part of our daily lives and, therefore, the heart of our modern society. The development of AI, the internet of things, and the self-driving car revolution won’t stop this trend.

From Semiconductors To Microchips

All this technological advancement builds on top of a simple group of materials called semiconductors. When passing through a conductor, electricity faces little resistance, creating a free-flowing current. In an insulator, electrical current cannot travel due to high levels of resistance. Semiconductors sit somewhere between these two extremes, allowing a degree of control over the flow of electricity by providing a change of electric fields. Silicon semiconductors are the industry standard for most transistors. Transistors are devices that regulate current and act as switches for electronic signals. These transistors are crucial to microchip manufacturing, from processors to memory cards.

Semiconductor Industry

The semiconductor industry has professionalized, and today, companies in the field typically specialize in one of the following domains:

Mining: China is with two-thirds of the worldwide production by far the world’s largest producer of silicon and therefore the producer of the essential material for microchips. Other producers are Russia, the USA, Norway, and Brazil.

Chip Design defines how many cores a microchip should have, how those and other components such as memory are arranged on the silicon, and how the circuits should actually look like. Chip Designers normally outsource the chip manufacturing to fab foundries (microchip manufacturers). Famous chip designers are AMD, Apple, Amazon, Alphabet, and a lot more.

Fabrication: There are a handful of fab foundries. Intel, Samsung, and TSMC are the Top 3 leading companies by sales revenue in this field. While Intel designs its own microchips, there are other companies like TSMC specializing in manufacturing microchips for other companies and is, therefore, a pure-play fab foundry. In the field of fab foundries, TSMC (ca. 50% market share, Taiwan), Samsung (ca. 20% Market Share, South Korea), Global Foundries (ca. 8% market share, USA), UMC (ca. 7%, Taiwan), SMIC (ca. 5%, China) are the most noticeable ones. TSMC delivers its microchips to famous tech players like AMD, Apple, ARM, Broadcom, Nvidia, and Qualcomm. TrendForce and ReportLinker estimated a foundries revenue in 2020 with 70 Billion dollars and an average turnover of 10% per year over the next decade.

Equipment: The high-tech industry of semiconductors needs one of the advanced engineered machines in the world. Without the most advanced machines, no manufacturer would keep up with the competition. The Dutch company ASML makes lithography systems, which are machines that are used to make chips. All major chipmakers use their technology because ASML lithography systems are the most advanced systems in this field with years of distance.

Semiconductor Microchip Shortage

The 2020 global microchip shortage is an ongoing crisis. The demand for microchips is greater than the supply and has led to major shortages and queues amongst consumers, not only in the information technology sector. According to AlixPartners, the chip shortage could cost the automotive industry around the world a loss of 61 Billion dollars. So how did this shortage started?

One major reason is the tech war between the USA and China. The outsourcing of AMD’s chip production to TSMC created additional pressure on TSMC production plants during the pandemic, and the Covid-19 crisis itself.

Semiconductors are no longer just components, but strategic resources that all major economies must secure.

Arisa Liu (Analyst, Taiwan Economic Research Institute)

Amazingly there are only a handful of major microchip manufacturers in the world (TSMC, Samsung, Intel). Whoever has secure access to microchips can make their economy more robust against these global shortages. In this way, microchips became more or less the new oil of the 21st century.

So, there will be an increased effort for all countries to secure the demand for microchips for their economies in the future. This effect can be already observed in various countries like the USA, strengthening their semiconductor microchip production.

The Future of Freelancer Platforms

The digital transformation of the workplace has only just begun. The notion that you have to move to Silicon valley to get employed by one of the world-class organizations is just not the case anymore.

Platforms like Fiverr and Upwork give freelancers the possibility to advertise their services to millions of customers remotely. That offers an excellent opportunity for people who want to travel around the world and still want to earn money.

Remote freelancing allows people from third-world countries to easily participate in the western world markets without leaving their homes. Remote freelancing from a third-world country allows freelancers to improve their lifestyle. This will also lead to economic growth in these third-world countries, especially in areas with high unemployment rates.

While businesses compete for local talents, remote freelancers give smaller startups a larger talent pool to choose from. Instead of hiring a graphic designer in the west, startups gain access to a far broader and deeper talent pool with these freelancer platforms than those who limit themselves to one geographic area. And for managers, organizing and coordinating a remote team’s work is crucial to winning recognition and advancement in the coming years.

So what will change in the future? I think that the prices for services that can be done remotely will drop, and they will be more and more outsourced in third-world countries. People who can do their work remotely may move to nicer places and do not need to live where their employer is located. This could have a quite interesting effect in, for example, Europe. Nowadays, low-wage countries move to North-European countries to earn more money while North-European citizens move to South-European to enjoy a friendlier climate.

Provence (2020)