India set to surpass US in scientific publications by 2029

by Hansraj Agrawal | Aug 18, 2024 | Science

A study conducted by the Raman Research Institute (RRI), Bengaluru, has shown that India will surpass the US in terms of the number of annual scientific publications in 2029.

According to the study, while China, the ‘giant in scientific publications’, will remain at the top, the US will lose its second rank to Indonesia from this year (2024). India will have to wait for another 5 years to better the US, the statistical analysis done by Dipak Patra of the Soft Condensed Matter Group, RRI, says.

The study analysed scientific publications of 50 countries between 1996 and 2020, and investigated how the disparity in the number of publications varies with time, and when it will go away. It has determined that from 2046, all countries excluding China will contribute equally in terms of scientific publications.

The study uses statistical tools such as entropy (a measure of randomness and therefore, unpredictability, in a data set) and linear regression analysis (relationship between two variables). “Based on the regression analysis, it is estimated that three potential countries such as Indonesia, India and Iran may take the ranks ahead of the US around the years 2024, 2029 and 2041 respectively,” the study says.

The findings of the study have been published in a yet to be peer reviewed paper. “It is found that entropy mostly increases linearly with time implying the constant involvement of the countries in the growth of science and the increasing contribution of lagging countries,” the paper says.

The entropy continues to “decay significantly” after the year 2017 as the year-wise publication of China has been surging since then. Because China “has become a large giant in science publications”, the study excluded China from its scope.

By computing entropy between the US and other countries, the research assessed the stability of the current rank of the US against other prominent countries. “Three potential countries such as Indonesia, India and Iran may contribute much more to the growth of science than the US around the years 2024, 2029 and 2041, respectively,” it says.

The study makes two caveats. First, it points out that any prediction based on linear regression analysis “strongly depends on the current pace of growth” and may not be warranted if countries change their policies towards research and development. Second, it stresses that the investigation is only on the number of scientific publications and has nothing to say about the quality of the publications. Quality is generally assessed using metrics such as citations or the ‘impact factor’, neither of which is safeguarded as they can be manipulated.

“The qualitative assessment is a major issue in the understanding of the actual growth of science as some researchers across the world publish substandard and fraudulent works to secure funds for research and uphold their academic position in the current “publish or perish” environment,” it notes.

According to Scopus (a multidisciplinary abstract and citation database), India, with 1,91,590 publications, ranked 4th in terms of number of science publications in 2020, after China (7,44,042), the US (6,24,554) and the UK (1,98,500). As per a classification done by Scimago Journal and Country Rank, India ranked was No 3 in 2023 with 3,06,647 publications, after China (1,043,131) and the US (6,24,554). (The two classifications, however, are not strictly comparable.)

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A furniture purchase receipt, 3500 years old

by Hansraj Agrawal | Aug 18, 2024 | Science

How were commercial transactions conducted 3,500 years ago? What did people buy, how did they pay and in what manner did the acknowledgement of payment come? To find answers for these intriguing questions, one must dig deep.

Dig they did, in Turkey’s Reyhanli district, but for an entirely different reason. Workers engaged in restoration work after the deadly earthquake of February 2023 were digging through the rubble when they chanced upon a curious object — a small clay tablet. It measured 4.2 cm in length, 3.5 cm in width, was 1.6 cm thick and weighed 23 grams. There was something etched on the surface. They turned it over to the authorities and it went into the hands of archaeologists.

It turns out that the tablet was actually a receipt, made out 3,500 years ago, for a purchase of large number of wooden tables, chairs and stools, and mentioned the names of the buyer and the seller. The furniture did not survive the passage of time, but the receipt did.

The receipt is in the Akkadian language, which has been deciphered. The script is one of the world’s ancient ones, in what is called ‘cuneiform writing’.

This, though, is not a unique discovery — there have been similar finds in the recent past. Last year, restoration work at an ancient palace damaged by the earthquake threw up another tablet with writings in Akkadian. It was an agreement made by Yarim-Lim, the first king of Alalakh, to purchase another city — 3,800 years ago.

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Why ionic liquids could be game changers for battery recycling

by Hansraj Agrawal | Aug 18, 2024 | Science

There is a growing body of scientific literature that thinks that ‘ionic liquids’ (IL) might just be the solution (pun intended) to the problem of extracting valuable metals from used batteries. ILs, sometimes colourfully described as ‘designer solvents’, could be the battery recycler’s dream-come-true.

That ‘battery recycling’ is an emerging, growing industry is not in doubt. A November 2023 report of Avendus Capital noted that the demand for lithium-ion batteries would touch 235 GWhr by 2030; the recycling industry would grow in sympathy, to 23 GWhr, worth $1 billion. Since batteries account for not less than 30 per cent of the cost of an electric vehicle, extracting metals such as lithium, nickel, cobalt and manganese from used batteries is useful.

While there are many ways of mining battery waste, the one that is commonly used is ‘hydrometallurgy’ — essentially ‘dissolve and separate’ which sometimes uses harmful chemicals. Now scientists are saying that ionic liquids, known for a century, could find a new purpose in extracting useful metals from used batteries.

What are ionic liquids?

Liquids are typically composed of electrically neutral molecules. In contrast, ionic liquids (IL) are made entirely of ions — positively charged cations and negatively charged anions.

Usually, cations and anions should cling together to form neutral molecules. But in ILs they don’t, because of the asymmetry of cations and anions. ILs are essentially salts that are liquid at temperatures below 100 degrees. Typically, salts are solids at such temperatures and require a large amount of heat to melt. Ionic liquids are highly adaptable, non-volatile liquid salts with a wide range of industrial and scientific applications. Their unique properties, such as low melting points and tunability, make them valuable in areas like green chemistry, electrochemistry and materials science. By selecting different cations and anions, the physical and chemical properties of ionic liquids — such as viscosity, density, solubility and conductivity — can be precisely tailored for specific applications.

In other words, you can create your own IL for a specific use, by picking up cations and anions off-the-shelf. Such ILs are called ‘task-specific ionic liquids’ (TSILs). By carefully selecting a combination of cations and anions to create a salt with desired properties. Horses for courses, you can design ILs for extracting a certain metal.

ILs are environment-friendly and can dissolve a wide range of substances — organic, inorganic and polymeric. “Creating new cations and anions, and incorporating suitable functional groups can impart the exact physical properties essential for each application at the core of the ILs designing process,” says a review study conducted by a group of scientists from CSIR and IIT-Madras. “With appropriate design, ionic liquids can exhibit advantages such as low volatility, high stability, a wide liquid range, high conductivity and high solubility,” the study says.

“Due to its heterogeneous composition, discarded rechargeable batteries (LIBs, NiMHs) are difficult to separate for nickel, cobalt, lithium, manganese, zinc and copper, says Prof Tamal Banerjee of the Department of Chemical Engineering, IIT Guwahati. “New cations and anions within new solvents such as ionic liquids have gained huge interest,” he observes, in a write-up in IIT-M TechTalk.

While the scientific world is looking at ILs with renewed interest, by all accounts, the industry is a bit circumspect. Ashish Bansal, Managing Director, Pondy Oxides & Chemicals Ltd, which is into recycling of materials and is now putting up a plant for extracting metals from lithium-ion batteries, says the use of ionic liquids as solvents for the extraction of metals from used lithium-ion batteries “is progressing well on the R&D scale. In an emailed response to quantum, Bansal observed that ILs have an “ability to selectively extract metals at a certain pH, RPM and time period in the leaching process.” Additionally, they are environment-friendly, due to their ease of disposal and restoration, and they have an inherent nature for eco-friendly recycling as compared to other leaching agents, he said.

Yet, the company is not yet ready to use ILs, because of “certain shortcomings” — mainly, the higher cost compared with conventional leaching agents. “There is a need for further commercial-scale development before the process can be profitably scaled up for the recycling of lithium-ion batteries in a sustainable manner,” Bansal said.

Recovery is key

The focus of scientific research is shifting to recycling ILs, to make them economically viable. “Numerous IL recycling techniques, such as distillation, membrane separation, ATPS, extraction and adsorption, have been introduced to recycle ILs. All these IL recovery methods have their own pros and cons,” notes a scientific paper published by a group of Singapore-based researchers.

For example, ‘membrane separation’ requires less capital investment, but the yields are low. ‘Distillation’ is effective but also energy intensive. ‘Extraction’ calls for solvents. If researchers could crack recovery of ILs, they would have a gamechanger in their hands.

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First test flight of Gaganyaan is expected in December 2024: ISRO Chairman

by Hansraj Agrawal | Aug 16, 2024 | Science

The first test flight of Gaganyaan is expected to happen in December 2024, said Indian Space Research Organisation (ISRO) Chairman S Somanath.

The three stages of the Gaganyaan rocket have arrived at the Satish Dhawan Space Centre (SDSC, SHAR). The integration of the crew module is taking place at the Vikram Sarabhai Space Centre, Thiruvananthapuram, he told newspersons after the successful launch of India’s Earth Observation Satellite-08 (EOS-08) into orbit.

All the systems for the Gaganyaan rocket – codenamed G1 – will reach SDSC in November this year and the target for the rocket flight is December, he said.

The Gaganyaan project envisages demonstration of human spaceflight capability by launching a crew of three members to an orbit of 400 km for a three-day mission and bring them back safely to earth, by landing in Indian sea waters.

The project is accomplished through an optimal strategy taking into account inhouse expertise, the experience of Indian industry, intellectual capabilities of Indian academia and research institutions, along with cutting-edge technologies available with international agencies.

The pre-requisites for the Gaganyaan mission include development of critical technologies including the human-rated launch vehicle for carrying crew safely to space, the Life Support System to provide an earth-like environment for the crew in space, crew emergency escape provision and evolving crew management aspects for training, recovery and rehabilitation of crew, according to information on the ISRO website.

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The paradox of powerful AI: When bigger isn’t always better

by Hansraj Agrawal | Aug 5, 2024 | Science

Many of us are used to setting alarms with Siri, asking Google for nearby restaurants or telling Alexa to turn up the lights. Even if we don’t use these AI-powered assistants ourselves, we often see others using them. However, when it comes to complex tasks, like drafting an email to the boss, the results can be baffling or even a confusing mess. This stark difference in outcomes raises an important question: Do large language models (LLMs) — the brains behind our virtual assistants and chatbots — really perform as we expect them to?

LLMs have become the cornerstone of modern AI applications. Models like OpenAI’s GPT-4, Google’s Gemini, Meta’s LLAMA, are capable of generating human-like text, translating languages, writing code and even crafting poetry. The excitement around LLMs stems from their ability to handle a diverse range of tasks using a single model. This versatility offers immense potential — imagine a model helping a doctor summarise patient notes while also assisting a software engineer in debugging code.

However, this very diversity also presents a significant challenge — How do we evaluate such a multifaceted tool? Traditional models are typically designed for specific tasks and evaluated against benchmarks tailored to those tasks. But with LLMs, it’s impractical to create benchmarks for every possible application they might be used for. This raises an essential question for researchers and users alike: How can we gauge where an LLM will perform well and where it might stumble?

The LLM dielemma

The crux of the problem lies in understanding human expectations. When deciding where to deploy an LLM, we naturally rely on our interactions with the model. If it performs well on one task, we might assume it will excel at related tasks. This generalisation process — where we infer the capabilities of a model based on limited interactions — is key to understanding and improving the deployment of LLMs.

In a new paper, MIT researchers Keyon Vafa, Ashesh Rambachan and Sendhil Mullainathan took a different approach. In their study — ‘Do large language models perform the way people expect? Measuring the human generalisation function’ — they have explored how humans form beliefs about LLM capabilities and whether these beliefs align with the models’ actual performance.

To start with, the researchers collected a substantial dataset of human generalisations. They surveyed participants, presenting them with examples of how an LLM responded to specific questions. The participants were then asked whether these responses influenced their beliefs about how the model would perform on other, related tasks. This data collection spanned 19,000 examples across 79 tasks, sourced from well-known benchmarks like the MMLU and BIG-Bench.

On analysing the data using sophisticated natural language processing (NLP) techniques, they found that human generalisations are not random; unsurprisingly, they follow consistent, structured patterns that can be predicted using existing NLP methods.

The researchers also evaluated how well different LLMs align with these human generalisations. They tested several models for this, including GPT-4, to see if their performance matched human expectations, and discovered a paradox: larger, more capable models like GPT-4 often performed worse in high-stakes scenarios, precisely because users overestimated their capabilities. In contrast, smaller models sometimes aligned better with human expectations, leading to more reliable deployment in critical applications.

The researchers used a novel approach to evaluate model alignment. Instead of relying on fixed benchmarks, they modelled the human deployment distribution — the set of tasks humans choose based on their beliefs about the model’s capabilities. This method acknowledges that real-world use depends not just on the model’s abilities but also on human perceptions of those abilities.

The findings of this research are both fascinating and cautionary. It highlights that while larger LLMs have impressive capabilities, their misalignment with human generalisations can lead to significant deployment errors.

On the flip side, by understanding and modelling human generalisations, we can better align LLMs with user expectations. This could involve developing better interfaces that help users accurately gauge a model’s strengths and weaknesses or creating more targeted training data that helps models perform consistently across a broader range of tasks.

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Using deep learning to predict stock prices gains maturity

by Hansraj Agrawal | Aug 5, 2024 | Science

Alchemists of yore sought to short-cut their way to richness by finding ways to convert metals into gold. Today, it appears that the job of finding a quick pathway to prosperity has been taken up by data scientists. Alchemists, of course, failed to transmute cheap metals into gold, but data scientists may be sniffing success.

Today, it is possible to take data sets of quarterly results of a company, daily high, low and close prices and traded volumes, as well as economic data, news and sentiment analysis, and use the data to train a machine learning model to make predictions. The model essentially sees patterns that we would miss.

Data scientists first clean up the data (filling in any missing values and removing outliers) and make it usable by a model. Then they select a deep learning technique (like Long Short-Term Memory) or Convolutional Neural Networks, for milking the data for insights. LSTM is particularly appropriate for time series data, such as historical stock prices, because it can remember previous information and use it to make predictions. Then the model is trained. The training process involves making the model adjust its parameters to reduce the gap between its own predictions and actual stock prices. Then comes validation of the model, using a separate set of data to verify the model’s accuracy.

In broad terms, AI and deep learning predict stock prices by analysing historical data, identifying patterns and making future price forecasts. This involves collecting and pre-processing data, selecting and training appropriate models, making predictions, and continuously evaluating and refining the models to improve their accuracy and reliability.

Hitting the marks

In the last few years, a lot of work has been done in this area, with each model getting better than the earlier ones. A recent work in this area by three data scientists, Jaydip Sen, Hetvi Waghela and Sneha Rakshit, of the Department of Data Science and Artificial Intelligence at the Praxis Business School, Kolkata, has thrown up a model that boasts of 95.8 per cent accuracy in predicting the next day’s stock prices.

Sen, Waghela and Sneha used Long Short-term Memory (LSTM), which is a special kind of artificial neural network used in deep learning, designed to remember information for long periods of time, for “accurate stock price prediction”. In a recent paper (which is yet to be peer-reviewed), they note that “despite the efficient market hypothesis suggesting that such predictions are impossible, there are propositions in the literature demonstrating that advanced algorithms and predictive models can effectively forecast future stock prices.” They add that in recent times, the use of machine learning and deep learning systems has become popular for market price prediction.

Sen, Waghela and Rakshit believe that an LSTM model could predict stock prices better than other techniques such as convolutional neural networks that has been used by others earlier. “This model automatically retrieves historical stock prices using a Python function, utilising stock ticker names from the NSE within a specified interval determined by a start and end date.”

They took historical prices of 180 stocks across 18 sectors, between January 1, 2005 and April 23, 2024. They trained the data on LSTM model to make predictions. They used three metrics — Huber Loss, Mean Absolute Error and Accuracy Score — which give an idea of how wrong a prediction could be, to assess the performance of their model. For a model to be accurate, it must have low values for Huber loss and MAE, and a high value for the accuracy score.

Golden insight

Sen Waghela and Rakshit observed that their LSTM model yielded the minimum value of Huber loss for the auto sector, the minimum value of MAE for the banking sector, and the maximum value of accuracy score for the PSU banks sector. “Hence, the model is found to be the most accurate for these three sectors,” they note. On the other hand, Huber loss and MAE are the maximum for the media sector and accuracy score is the minimum for the energy sector. Therefore, the performance of the model has been the worst for media and energy sectors on the three metrics.

However, overall, the model’s performance has been highly accurate, since the lowest value of the accuracy score is 0.958315. “In other words, in the worst case, the model correctly predicted the direction of movement of the price (upward or downward) of the stock the next day in 95.83 per cent of cases. Thus, the model can be reliably used in stock trading decisions,” they say.

The use of deep learning techniques for making stock predictions will call for radical changes in regulations, so that the market does not get skewed in favour of those who know how to use techniques at the expense of those who don’t.

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