Wednesday, June 28, 2023

Journey to infinite possibilities through mathematics exploration


Mathematics provides amazing tools for science and study that has been in existence for centuries! It gives us a better understanding of the world around us, and can help us uncover the mysteries of the universe. An essential part of this discipline is mathematics research. It’s all about proving and creating theories, finding new applications, and exploring unsolved problems. Plus, it helps us to develop new technologies and advance science. Pretty cool, right?

Two winners of the Hang Lung Mathematics Awards (HLMA), Dr. Kero Lau (2004 Bronze Award winner) and Ms. Ewina Pun (2012 Bronze Award winner), recently met with more than 100 secondary students from 50-plus schools in an online sharing session. The two promising young scientists shared their own experiences and thoughts about pursuing careers in science and research in a highly informative session where Professor Shing Yu Leung, Associate Dean of Science at HKUST, served as moderator.

Are memes a helpful way to learn?

Recalling their time in secondary school, both speakers had a keen interest in mathematics, which led them to the world of scientific research. Mathematics is a language used daily in research. Research requires perseverance. Although difficulties in research and calculations may be encountered, they pointed out that students should not be afraid of making mistakes. These experiences may lead to the discovery of something new.

They also encouraged students to exchange ideas with their friends when solving problems. Doing research requires a lot of teamwork, strong communication skills, and cooperative spirit. That’s something you don’t learn by taking exams. Being able to work collaboratively and apply knowledge skilfully to real-life situations requires many soft skills that are not tested in exams.

The biennial HLMA provides opportunities for secondary school students to hone their research skills in this discipline and compete for HK$1 million in prizes.

Students debate whether smaller class sizes are always better

If you want to dive deeper into the fascinating world of mathematics as Kero and Ewina did, research is the way to go! Through research, you can explore infinite possibilities, think critically, create innovative solutions to problems, and learn more about the world and yourself. With the right guidance and resources, you can embark on your own journey of discovery. And who knows what you might come up with!

All interested students are encouraged to take the first step by signing up for 2023 HLMA Student Information Session which will take place virtually on Friday, 24 February from 6-7 pm. For 2023 HLMA registration, please scan the QR code below:

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Recounting the History of Math’s Transcendental Numbers




In 1886 the mathematician Leopold Kronecker famously said, “God Himself made the whole numbers — everything else is the work of men.” Indeed, mathematicians have introduced new sets of numbers besides the ones used to count, and they have labored to understand their properties.

Although each type of number has its own fascinating and complicated history, today they are all so familiar that they are taught to schoolchildren. Integers are just the whole numbers, plus the negative whole numbers and zero. Rational numbers are those that can be expressed as a quotient of integers, such as 3, −‍1/2 and 57/22. Their decimal expansions either terminate (−‍1/2 = −‍0.5) or eventually repeat (57/22 = 2.509090909…). That means if a number has decimal digits that go on forever without repeating, it’s irrational. Together the rational and irrational numbers comprise the real numbers. Advanced students learn about the complex numbers, which are formed by combining the real numbers and imaginary numbers; for instance, i=√−1.

One set of numbers, the transcendentals, is not as well known. Paradoxically, these numbers are both plentiful and exceedingly difficult to find. And their history is intertwined with a question that plagued mathematicians for millennia: Using only a compass and a straightedge, can you draw a square with the same area as a given circle? Known as squaring the circle, the question was answered only after the invention of algebra and a deeper understanding of π — the ratio of the circumference of any circle to its diameter.

What does it mean to discover a new set of numbers? Today we say that Hippasus of Metapontum, who lived in approximately the fifth century BCE, discovered irrational numbers. In fact, his discovery was geometric, not arithmetic. He showed that it’s possible to find two line segments, like the side and diagonal of a square, that can’t be divided into parts of equal length. Today we would say that their lengths are not rational multiples of each other. Because the diagonal is √2 times as long as the side, √2 is irrational.

It is impossible to divide the side and diagonal of a square into parts of equal length. Here a length divides the side into 10 equal parts, but the diagonal is divided into 14 equal parts with a small remainder.

In terms of constructions possible with just a compass and straightedge — the mathematical tools of antiquity — if we begin with a unit-length line segment, it’s possible to construct a segment with any positive rational length. However, we can also construct some irrational lengths. For instance, we’ve seen how to make √2; another famous irrational number, the golden ratio, (1+√5)/2, is the diagonal of a regular pentagon with side length 1.

Roughly 2,000 years after the Greeks first posed the question of squaring the circle, René Descartes applied new algebraic techniques to show in his 1637 treatise La Géométrie that the constructible lengths are precisely those that can be expressed using integers and the operations of addition, subtraction, multiplication, division and the calculation of square roots. Notice that all positive rational numbers have this form, as do √2 and the golden ratio. If π could be written in this way, it would finally let geometers square the circle — but π was not so easy to classify.

In the next 200 years, algebra matured significantly, and in 1837 a little-known French mathematician named Pierre Wantzel connected constructible numbers to polynomials — mathematical expressions that involve variables raised to various powers. In particular, he proved that if a length is constructible, then it must also be a root, or value that produces zero, of a certain type of polynomial, namely one that can’t be factored, or simplified, further, and whose degree (the largest exponent of x) is a power of 2 (so 2, 4, 8, 16 and so on).

For instance, √2 and the golden ratio are constructible, and they are roots of the polynomials x2–2 and x2–x–1, respectively. On the other hand, 3√2 is a root of the degree 3 polynomial x3–2, which doesn’t qualify, so it is impossible to construct a segment of this length.

Wantzel used his results to resolve other classical problems by proving that they can’t be solved — it is impossible to trisect some angles, it is impossible to double the cube and it is impossible to construct certain regular polygons. But because the exact nature of π remained a mystery, the question of squaring the circle remained open.

The key to resolving the problem, it turned out, was to cleverly divide the set of complex numbers into two sets, much as earlier generations partitioned the real numbers into rational and irrational numbers. Many complex numbers are the root of some polynomial with integer coefficients; mathematicians call these numbers algebraic. But this isn’t true for all numbers, and these non-algebraic values are called transcendental.

Every rational number is algebraic, and some irrational numbers are too, like 3√2. Even the imaginary number i is algebraic, as it is a root of x2+1.


This diagram shows the relationships between the various kinds of numbers. An irrational number is any real number that is not rational, and a transcendental number is any complex number that is not algebraic.

It was not obvious that transcendental numbers should exist. Moreover, it’s challenging to prove that a given number is transcendental because it requires proving a negative: that it is not the root of any polynomial with integer coefficients.

In 1844, Joseph Liouville found the first one by coming at the problem indirectly. He discovered that irrational algebraic numbers cannot be approximated well by rational numbers. So if he could find a number that was approximated well by fractions with small denominators, it would have to be something else: a transcendental number. He then constructed just such a number.

Liouville’s manufactured number,

L=0.1100010000000000000000010…,

contains only 0s and 1s, with the 1s occurring in certain designated places: the values of n!. So the first 1 is in the first (1!) place, the second is in the second (2!) place, the third is in the sixth (3!) place, and so on. Notice that as a result of his careful construction, 1/10, 11/100, and 110,001/1,000,000 are all very good approximations of L — better than one would expect given the size of their denominators. For instance, the third of these values has 3! (six) decimal digits, 0.110001, but agrees with L for a total of 23 digits, or 4!−1.

Despite L proving that transcendental numbers exist, π does not satisfy Liouville’s criterion (it can’t be well approximated by rational numbers), so its classification remained elusive.

The key breakthrough occurred in 1873, when Charles Hermite devised an ingenious technique to prove that e, the base of the natural logarithm, is transcendental. This was the first non-contrived transcendental number, and nine years later it allowed Ferdinand von Lindemann to extend Hermite’s technique to prove that π is transcendental. In fact he went further, showing that ed is transcendental whenever d is a nonzero algebraic number. Rephrased, this says that if ed is algebraic, then d is either zero or transcendental.

To prove that π is transcendental, Lindemann then made use of what many people view as the most beautiful formula in all of mathematics, Euler’s identity: eπi=−1. Because −‍1 is algebraic, Lindemann’s theorem states that πi is transcendental. And because i is algebraic, π must be transcendental. Thus, a segment of length π is impossible to construct, and it is therefore impossible to square the circle.

Although Lindemann’s result was the end of one story, it was just an early chapter in the story of transcendental numbers. Much still had to be done, especially, as we’ll see, given how prevalent these misfit numbers are.

Shortly after Hermite proved that e was transcendental, Georg Cantor proved that infinity comes in different sizes. The infinity of rational numbers is the same as the infinity of whole numbers. Such sets are called countably infinite. However, the sets of real numbers and irrational numbers are larger; in a sense that Cantor made precise, they are “uncountably” infinite. In the same paper, Cantor proved that although the set of algebraic numbers contains all rational numbers and infinitely many irrational numbers, it is still the smaller, countable size of infinity. Thus, its complement, the transcendental numbers, is uncountably infinite. In other words, the vast majority of real and complex numbers are transcendental.

Yet even by the turn of the 20th century, mathematicians could conclusively identify only a few. In 1900, David Hilbert, one of the most esteemed mathematicians of the era, produced a now-famous list of the 23 most important unsolved problems in mathematics. His seventh problem, which he considered one of the harder ones, was to prove that ab is transcendental when a is algebraic and not equal to zero or 1, and b is an algebraic irrational number.

In 1929, the young Russian mathematician Aleksandr Gelfond proved the special case in which b=±i√r and r is a positive rational number. This also implies that eπ is transcendental, which is surprising because neither e nor π is algebraic, as required by the theorem. However, by cleverly manipulating Euler’s identity again, we see that

eπ=e−iπi = (eπi)−i = (−1)−i.

Shortly afterward, Carl Siegel extended Gelfond’s proof to include values of b that are real quadratic irrational numbers, allowing him to conclude that 2√2 is transcendental. In 1934, Gelfond and Theodor Schneider independently solved the entirety of Hilbert’s problem.

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Wednesday, June 21, 2023

Whole College Approach effective in improving maths learning, research project finds



Findings from the pilot year of a programme taking a Whole College Approach (WCA) to improving maths learning indicate that it is an effective means of supporting colleges to improve student attendance and learning experiences. Furthermore, the approach was found to have the potential to bring about sustainable organisational change in the way that colleges organise and manage students’ maths learning.

Delivered in four phases – discovery, planning, intervention and review – the Whole College Approach pilot involved a process of organisational change through which student learning of maths became a shared responsibility and all staff were actively involved in a collaborative effort to improve students’ understanding of the subject. The project began in April 2021 as a strand of the Education and Training Foundation (ETF) Centres for Excellence in Maths (CfEM) programme, funded by the Department for Education, and followed the publication of the Nuffield-funded Mathematics in Further Education Colleges (MiFEC) project (Noyes & Dalby 2017–20).

That work evidenced broad agreement from a cross-section of staff in England’s FE colleges about the importance of maths and students with low attainment improving their mathematics skills. However, it also found that students can receive inconsistent messages, explicitly and implicitly, about the need to engage with mathematics; and that combinations of strategic or operational approaches can produce variations in students’ experiences and sometimes hinder their participation or progress.

In the WCA pilot project, which was delivered by the University of Nottingham’s Centre for Research in Mathematics Education (CRME) on behalf of the ETF, three elements were identified as being effective in guiding and supporting colleges through a process of organisational change:The support and guidance given by each college’s ‘critical friend’ was a key factor in the success. Participating colleges reported that having an external facilitator to work through the self-assessment tasks with them was an important early step. Through meetings with their critical friend, colleges reported that their thinking was challenged. They found the interaction and feedback to be an effective means of support that helped them review and refine their analysis of the problem and develop action plans with more focused and appropriate interventions.
Colleges also agreed that the self-assessment activities were an important element of the programme. The first activity was useful in starting the group thinking about the context in which they were working and its contextual affordances and constraints. This was followed by activities to explore the college culture and use different perspectives to analyse the issues thoroughly. Colleges valued the way these tasks stimulated rich, purposeful discussion about the problems they wanted to address.
Colleges found that the constitution of a cross-college team to collaborate and lead their college WCA was an essential element of the programme. It was important to include representatives from vocational and maths departments, including both managers and teachers, and to secure the active involvement of a senior leader.

“It has been encouraging to see how the WCA programme has helped colleges develop purposeful collaboration between maths and vocational staff and supported the co-design of effective interventions to improve their maths provision. By working across traditional silo-structures and sharing different perspectives, staff have gained a better understanding of the problems and found new ways of tackling key issues such as student motivation and engagement collaboratively.”

Steve Pardoe, Head of Centres for Excellence in Maths at the ETF, said:

“This research project demonstrates that success in FE maths is down to more than just maths teaching. The Whole College Approach has proved to be an effective process for bringing people together from across a college to support improvement processes for maths. In doing so, it has achieved its objectives of translating MiFEC and other related ‘whole organisation’ research into practice; building sector knowledge about WCAs; and developing support mechanisms and producing support material. It has also identified moderating factors that can affect the implementation of the approach, such as college readiness and stability, time pressures and the extra pressure put on staff by the Covid pandemic.”

Case studies of some of the 16 colleges that participated in the project – Harlow College, Leyton Sixth Form College, Stamford College, the Lakes College, Weston College, and Wilberforce Sixth Form College – are available on the ETF website.

For further details of the wider CfEM programme please visit the CfEM resources and evidence hub.

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Research Team Uses Math To Help Allocate Resources During Natural Disasters



New research by a team led by an engineering professor at Northeastern University has found that a mathematical model can predict human movement during natural disasters.

The research team looked at events like the COVID-19 pandemic, Hurricane Dorian, and the Kincade Wildfire to predict patterns of human movement and used anonymous information from 90 million Americans to create the mathematical model used during the study.

According to Qi Ryan Wang, an associate professor of environmental and civil engineering at Northeastern University, the findings from this research study can help governments and emergency responders properly allocate resources during a range of disasters.

What Did The Research Team Find?

The research team led by Wang looked at people’s mobility behavior during six major disasters, and their findings discovered a disparity in movement between economic groups. They concluded that those with little means are exposed to more risk during natural disasters like diseases.

For example, during the height of the COVID-19 pandemic, people that lived in poorer neighborhoods left home more frequently because they were essential workers and because they did not have the ability to stock up on water and food for days and weeks like wealthier individuals and families.

These communities also did not have access to emergency generators and other important technologies. In an interview published on Northeastern’s news platform, Wang stated similar mobility patterns were observed during weather-related disasters.

This need for access to food, water, and other supplies during emergencies is one of the reasons experts tell people to keep survival bags in their homes full of non-perishable foods, water bottles, and medical supplies.

According to ExpressVPN’s emergency survival article, people are also meant to store different tech products in their emergency bags, such as satellite phones, medical flash drives, and portable power banks. When you have these bags already in place, you can limit how many times you leave the house for food, water, and essentials.

How Will This Information Help Governments and Emergency Responders?

According to Wang, governments and emergency responders can use this information on human mobility during disasters to better understand resource allocation and which communities need to be helped first. Understanding this will help institutions produce more effective responses to disasters like diseases, earthquakes, and wildfires.

The Northeastern News board also said the research study touched on the concept of temporal decay, which refers to when people’s attention moves away from certain information or situations as time passes. This phenomenon occurred as the COVID-19 pandemic moved into its second year, and governments can remember this when implementing year-long restrictions and regulations.

As per Access Partnership, the annual number of natural disasters is projected to increase by 37% (541 occurrences) by 2025. With rising concerns over floods, earthquakes, sinkholes, and diseases, it is more important than ever that Wang and his research team have provided a human mobility model that will help governments and emergency respondents better allocate resources and supplies during these events.

Highlights from the study found that less wealthy communities are at greater risk of exposure to events like diseases because they don’t have the freedom to work from home or stock up on supplies; this information is crucial if another pandemic ever arises.

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National Mathematics Day is observed on December 22 every year. This date marks the birth anniversary of legendary mathematician Srinivasa Ramanujan. In 2012, then Prime Minister Manmohan Singh declared December 22 as National Mathematics Day to honor the life and achievements of Ramanujan.

Hee are 10 points on life and work of the great mathematician:Srinivasa Ramanujan was born on December 22, 1887, in Tamil Nadu’s Erode to a Brahmin Iyengar family. He had developed a liking for mathematics at a very young age, mastering trigonometry at 12 and was eligible for a scholarship at the Government Arts College in Kumbakonam.
He studied at the Government College in Kumbakonam in 1903. Due to his dislike for non-mathematical subjects, he failed exams there. He had enrolled in Madras’ Pachaiyappa College at the age of 14.
In 1912, Ramanujan started working as a clerk in the Madras Port Trust. There, his mathematical genius was recognised by some of his colleagues and one of them referred him to Professor GH Hardy of Trinity College, Cambridge University. He met Hardy in 1913, after which he went to Trinity College.
In 1916, Ramanujan received his Bachelor of Science (BSc) degree. He went on to publish several papers on his subject with Hardy’s help. The two even collaborated on several joint projects.
Ramanujan was elected to the London Mathematical Society in 1917. Next year, he was elected to the prestigious Royal Society for his research on Elliptic Functions and theory of numbers. He was also the first Indian to be elected a Fellow of the Trinity College.
Despite not receiving any formal training in pure maths, Ramanujan made impactful contribution to the discipline in his short life. His areas of work include infinite series, continued fractions, number theory and mathematical analysis.
He also made notable contributions like the hypergeometric series, the Riemann series, the elliptic integrals, the theory of divergent series, and the functional equations of the zeta function. He is said to have discovered his own theorems and independently compiled 3,900 results.
In 1919, Ramanujan returned to India. A year later, on April 26, he breathed his last owing to deteriorating health. He was just 32 years old. His biography ‘The Man Who Knew Infinity’ by Robert Kanigel depicts his life and journey to fame.
A film of the same name was released in 2015 in which British-Indian actor Dev Patel played Ramanujan. The film shed light on Ramanujan’s childhood in India, his time in Britain, and his journey to becoming the great mathematician.
An anecdote from his biography shows Ramanujan's brilliance. In this, GH Hardy said: I remember once going to see him when he was ill at Putney. I had ridden in taxi cab number 1729 and remarked that the number seemed to me rather a dull one, and that I hoped it was not an unfavourable omen. "No," he replied, "it is a very interesting number; it is the smallest number expressible as the sum of two cubes in two different ways." Thus, 1729 became the Hardy-Ramanujan number – definitely not the greatest contribution of Ramanujan, but perhaps the easiest one to remember

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Monday, June 19, 2023

Whole College Approach effective in improving maths learning, research project finds




Findings from the pilot year of a programme taking a Whole College Approach (WCA) to improving maths learning indicate that it is an effective means of supporting colleges to improve student attendance and learning experiences. Furthermore, the approach was found to have the potential to bring about sustainable organisational change in the way that colleges organise and manage students’ maths learning.

Delivered in four phases – discovery, planning, intervention and review – the Whole College Approach pilot involved a process of organisational change through which student learning of maths became a shared responsibility and all staff were actively involved in a collaborative effort to improve students’ understanding of the subject. The project began in April 2021 as a strand of the Education and Training Foundation (ETF) Centres for Excellence in Maths (CfEM) programme, funded by the Department for Education, and followed the publication of the Nuffield-funded Mathematics in Further Education Colleges (MiFEC) project (Noyes & Dalby 2017–20).

That work evidenced broad agreement from a cross-section of staff in England’s FE colleges about the importance of maths and students with low attainment improving their mathematics skills. However, it also found that students can receive inconsistent messages, explicitly and implicitly, about the need to engage with mathematics; and that combinations of strategic or operational approaches can produce variations in students’ experiences and sometimes hinder their participation or progress.

In the WCA pilot project, which was delivered by the University of Nottingham’s Centre for Research in Mathematics Education (CRME) on behalf of the ETF, three elements were identified as being effective in guiding and supporting colleges through a process of organisational change:The support and guidance given by each college’s ‘critical friend’ was a key factor in the success. Participating colleges reported that having an external facilitator to work through the self-assessment tasks with them was an important early step. Through meetings with their critical friend, colleges reported that their thinking was challenged. They found the interaction and feedback to be an effective means of support that helped them review and refine their analysis of the problem and develop action plans with more focused and appropriate interventions.
Colleges also agreed that the self-assessment activities were an important element of the programme. The first activity was useful in starting the group thinking about the context in which they were working and its contextual affordances and constraints. This was followed by activities to explore the college culture and use different perspectives to analyse the issues thoroughly. Colleges valued the way these tasks stimulated rich, purposeful discussion about the problems they wanted to address.
Colleges found that the constitution of a cross-college team to collaborate and lead their college WCA was an essential element of the programme. It was important to include representatives from vocational and maths departments, including both managers and teachers, and to secure the active involvement of a senior leader.

“It has been encouraging to see how the WCA programme has helped colleges develop purposeful collaboration between maths and vocational staff and supported the co-design of effective interventions to improve their maths provision. By working across traditional silo-structures and sharing different perspectives, staff have gained a better understanding of the problems and found new ways of tackling key issues such as student motivation and engagement collaboratively.”

Steve Pardoe, Head of Centres for Excellence in Maths at the ETF, said:

“This research project demonstrates that success in FE maths is down to more than just maths teaching. The Whole College Approach has proved to be an effective process for bringing people together from across a college to support improvement processes for maths. In doing so, it has achieved its objectives of translating MiFEC and other related ‘whole organisation’ research into practice; building sector knowledge about WCAs; and developing support mechanisms and producing support material. It has also identified moderating factors that can affect the implementation of the approach, such as college readiness and stability, time pressures and the extra pressure put on staff by the Covid pandemic.”
Case studies of some of the 16 colleges that participated in the project – Harlow College, Leyton Sixth Form College, Stamford College, the Lakes College, Weston College, and Wilberforce Sixth Form College – are available on the ETF website

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Automating the math for decision-making under uncertainty




One reason deep learning exploded over the last decade was the availability of programming languages that could automate the math — college-level calculus — that is needed to train each new model. Neural networks are trained by tuning their parameters to try to maximize a score that can be rapidly calculated for training data. The equations used to adjust the parameters in each tuning step used to be derived painstakingly by hand. Deep learning platforms use a method called automatic differentiation to calculate the adjustments automatically. This allowed researchers to rapidly explore a huge space of models, and find the ones that really worked, without needing to know the underlying math.

But what about problems like climate modeling, or financial planning, where the underlying scenarios are fundamentally uncertain? For these problems, calculus alone is not enough — you also need probability theory. The "score" is no longer just a deterministic function of the parameters. Instead, it's defined by a stochastic model that makes random choices to model unknowns. If you try to use deep learning platforms on these problems, they can easily give the wrong answer. To fix this problem, MIT researchers developed ADEV, which extends automatic differentiation to handle models that make random choices. This brings the benefits of AI programming to a much broader class of problems, enabling rapid experimentation with models that can reason about uncertain situations.

Lead author and MIT electrical engineering and computer science PhD student Alex Lew says he hopes people will be less wary of using probabilistic models now that there’s a tool to automatically differentiate them. “The need to derive low-variance, unbiased gradient estimators by hand can lead to a perception that probabilistic models are trickier or more finicky to work with than deterministic ones. But probability is an incredibly useful tool for modeling the world. My hope is that by providing a framework for building these estimators automatically, ADEV will make it more attractive to experiment with probabilistic models, possibly enabling new discoveries and advances in AI and beyond.”

Sasa Misailovic, an associate professor at the University of Illinois at Urbana-Champaign who was not involved in this research, adds: "As the probabilistic programming paradigm is emerging to solve various problems in science and engineering, questions arise on how we can make efficient software implementations built on solid mathematical principles. ADEV presents such a foundation for modular and compositional probabilistic inference with derivatives. ADEV brings the benefits of probabilistic programming — automated math and more scalable inference algorithms — to a much broader range of problems where the goal is not just to infer what is probably true but to decide what action to take next."

In addition to climate modeling and financial modeling, ADEV could also be used for operations research — for example, simulating customer queues for call centers to minimize expected wait times, by simulating the wait processes and evaluating the quality of outcomes — or for tuning the algorithm that a robot uses to grasp physical objects. Co-author Mathieu Huot says he’s excited to see ADEV "used as a design space for novel low-variance estimators, a key challenge in probabilistic computations."

The research, awarded the SIGPLAN Distinguished Paper award at POPL 2023, is co-authored by Vikash Mansighka, who leads MIT's Probabilistic Computing Project in the Department of Brain and Cognitive Sciences and the Computer Science and Artificial Intelligence Laboratory, and helps lead the MIT Quest for Intelligence, as well as Mathieu Huot and Sam Staton, both at Oxford University. Huot adds, "ADEV gives a unified framework for reasoning about the ubiquitous problem of estimating gradients unbiasedly, in a clean, elegant and compositional way." The research was supported by the National Science Foundation, the DARPA Machine Common Sense program, and a philanthropic gift from the Siegel Family Foundation.

"Many of our most controversial decisions — from climate policy to the tax code — boil down to decision-making under uncertainty. ADEV makes it easier to experiment with new ways to solve these problems, by automating some of the hardest math," says Mansinghka. "For any problem that we can model using a probabilistic program, we have new, automated ways to tune the parameters to try to create outcomes that we want, and avoid outcomes that we don't."

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Intervention based on science of reading and math boosts comprehension and word problem-solving skills New research from the University of ...