Suggested Books Today | Books - Helge Scherlund's eLearning News

Check out these books below by Cambridge University Press.

Photo: JumpStory

The Cambridge Foucault Lexicon

The Cambridge Foucault Lexicon

The Cambridge Foucault Lexicon is a reference tool that provides clear and incisive definitions and descriptions of all of Foucault's major terms and influences, including history, knowledge, language, philosophy and power...

Together, they shed light on concepts key to Foucault and to ongoing discussions of his work today.

• The only book like it in print, in any language, offering concise and accessibly-written entries on Foucault's key concepts
• Provides the most comprehensive collection of dictionary-style entries written about Foucault
• Includes entries written by the world's most prominent Foucault scholars

Date Published: March 2020
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Resisting Scientific Realism 

Resisting Scientific Realism

In this book K. Brad Wray provides a comprehensive survey of the arguments against scientific realism. In addition to presenting logical considerations that undermine the realists' inferences to the likely truth or approximate truth of our theories, he provides a thorough assessment of the evidence from the history of science...

His arguments are supported and illustrated by cases from the history of science, including a sustained study of the Copernican Revolution, and a study of the revolution in early twentieth century chemistry, when chemists came to classify elements by their atomic number rather than by their atomic weight.

• Includes a thorough examination of the historical evidence and logical considerations that threaten scientific realism
• Presents a compelling defense of anti-realism
• Provides a sustained study of the Copernican Revolution in astronomy to illustrate some of the key issues in the realism/anti-realism debate, and a study of a hitherto unnoticed revolution in early twentieth-century chemistry

Date Published: March 2020
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The Attending Mind 

The Attending Mind

An ancient metaphor likens attention to an archer pulling her bow - the self directing her mind through attention. Yet both the existence of such a self, and the impact of attention on the mind, have been debated for millennia. Advancements in science mean that we now have a better understanding of what attention is and how it works, but philosophers and scientists remain divided as to its impact on the mind...

It thus provides a new way of thinking about the mind - as something that can either shape itself through attention or engage with the world as it is given, relying on its habits and skills.

• Helps the reader to navigate both historical and contemporary research on attention in philosophy, cognitive science, psychology and neuroscience
• Explores key topics such as mental causation, top-down attention and working memory
• Suggests new theoretical approaches towards the self, perception, consciousness and action

Date Published: March 2020 
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An Introduction to Functional Analysis
 

An Introduction to Functional Analysis

This accessible text covers key results in functional analysis that are essential for further study in the calculus of variations, analysis, dynamical systems, and the theory of partial differential equations. The treatment of Hilbert spaces covers the topics required to prove the Hilbert–Schmidt theorem, including orthonormal bases, the Riesz representation theorem, and the basics of spectral theory...

Familiarity with the basic theory of vector spaces and point-set topology is assumed, but knowledge of measure theory is not required, making this book ideal for upper undergraduate-level and beginning graduate-level courses.

• Includes an extensive source of homework problems for instructors and independent study
• Presents functional analytical methods without a reliance on measure-theoretic results, making the topics more widely accessible
• Provides readers with a sense of accomplishment and closure by showing how both Hilbert space theory and Banach space theory aim towards major results with important applications

Date Published: March 2020
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During Coronavirus - Social Distancing:
Stay home and switching to eLearning and read 📚books.

Source: Cambridge University Press.

Henniker students learn about robotics | Community - Concord Monitor

All students in seventh and eighth grades at the Henniker Community School all take a six-week intro to robotics class taught by Aaron Boucher as part of their STEM learning during the school year by Concord Monitor.
 

Jacob Winn and Peyton Sterling test their robot in class in early March. Courtesy of SAU 24.

During the six-week session, students become familiar with the basics of programming a robot and can complete individual challenge programming tasks. They also work in teams to complete their tasks as a group and build upon on one another’s challenge successes so it takes less time to complete all of the group’s assigned challenges in the least amount of time.

“No question, the learning curve with the robots, in the beginning, is steep, said Boucher. “But in the end, everyone seems to enjoy the class as they see their robot zipping around the mat and doing exactly the task they were programmed to do.”...

... “There are a lot of jobs in programing and this class gives you a preview of what you might want in a career using technology.”
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Source: Concord Monitor

How a Real Dog Taught a Robot Dog to Walk | Science - WIRED

Matt Simon, science journalist at WIRED summarizes, Instead of coding a mechanical quadruped's movements line by line, Google researchers fed it videos of real-life pups. Now it can even chase its tail.

Photo: Kiyoshi Ota/Getty Images

What you see when Boston Dynamics’ humanoid robot does a backflip or its Spot dog robot fights off a human and opens a door is incredible hardware engineering, to be sure. But what you don’t see is the wildly complex underlying code that makes it possible. What comes so easily to you—OK maybe not backflips, just walking—requires extreme coordination, which roboticists have to replicate, a kind of dance of motors working in concert.

Pity the engineers who have to write out all that code. Over at Google, researchers have a secret weapon to teach robots to move that’s both less taxing and more adorable: dogs. They put the canines on treadmills and take motion-capture videos, then feed that data into a simulator to create a digital version of the pooch. The researchers then translate the digital version of the real dog into a digital version of their four-legged robot—Laikago, which has a rectangular body and skinny legs. Then they port those algorithms into the physical version of Laikago. (The robot is named, by the way, after Laika, the Soviet space dog who was the first animal to orbit Earth.)

A robot works quite differently than a biological dog; it has motors instead of muscles, and in general it’s a lot stiffer. But thanks to this translation work, Laikago has learned to move like a real-life canine. Not only that, its learned gait is faster than the fastest gait provided by the manufacturer of the robot—though in fairness it’s not yet as stable. The new system could be the first steps (sorry) toward robots that learn to move not thanks to exhaustive coding, but by watching videos of animals running and jumping...

The next challenge is known as sim-to-real; that is, taking what the system has learned in simulation and getting it to work in a physical robot. This is tricky because a simulation is an imperfect and highly-simplified version of the real world. Mass and friction are represented as accurately as possible, but not perfectly. The actions of the simulated robot in the digital world don’t map precisely to movements of the real robot in the lab.
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Source: WIRED

The AI ethics review - eight sticking points we haven't resolved | Machine intelligence and AI - Diginomica

AI tech is moving quickly - but the ethical problems aren't going away. Here's eight AI ethics issues that persist, explains Neil Raden, active industry analyst, consultant.

problems-and-questions
Photo: Diginomica

Well over three years ago, I started to research and write about AI Ethics. Den Howlett of diginomica interviewed me about the topic in an article in September, 2018 - Can AI be bounded by an ethical framework?

I have since written about various aspect of AI and AI ethics for diginomica. Though I stand by the principles in those documents, they are neither comprehensive, nor completely current.

AI is moving so fast, and new ethical issues are apparent. It is time to review the subject, first by commenting on what has materially changed in the last few years, and what ethical issues have arisen...

This is hardly a complete list - so it will be a recurring series.
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Source: Diginomica

Can data save us from coronavirus? | Technology - Financial Times

Big data and machine learning should be helping contain the pandemic — but their usefulness has been limited, continues Financial Times.

Will data science save us from the pandemic? 

Photo: JumpStory

Big data and machine learning — the twin engines behind the recent boom in artificial intelligence — have been touted in the tech world as technologies capable of delivering huge social benefit. 

Watching them being applied to a global health crisis unfolding in real time shows both their promise and their shortcomings. Machine-learning systems employ a form of pattern recognition that is of broad use at a time like this, according to Fei-Fei Li, co-director of Stanford University’s Institute for Human-Centered AI. She was speaking at a hastily-arranged online conference this week to consider the many ways that AI is being brought to bear...

It is too soon to tell whether these and many other attempts to break down the barriers and make existing bodies of information more useful are coming soon enough to have a marked impact on the fight against the threat from Covid-19. But they at least point to one silver lining from this pandemic. The new forms of data sharing they are forcing should provide a template for when the next health crisis hits, as well as better co-ordination inside and between healthcare systems to improve the quality of care even in normal times.
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Source: Financial Times

How To Leverage Artificial Intelligence And Machine Learning During A Pandemic | AI - Forbes

After the COVID 19 crisis is over, business success or failure may come down to whether companies have taken advantage of Artificial Intelligence (AI) and Machine Learning (ML) technologies, as Emil Sayegh, President and CEO at Ntirety Inc. reports.

Digital Landscape (Black)
Photo: Forbes

To say that change is a constant is an understatement with the coronavirus turning the whole world upside down.

Paired with accelerating cloud technologies where there seems to be no “finish line,” we find ourselves in an environment that is more and more of a challenge for the IT skills of internal teams to keep up.

In one of my previous articles “3 Steps To Address The Cloud Talent Drought,” we found that relieving the growing skills gap is becoming a great motivator for increased automation, driven by artificial intelligence (AI) and Machine Learning (ML). After this pandemic is over, there will be business winners and losers. Organizations that view these technologies as a critical differentiator will create a wide range of business advantages for themselves both during and after the pandemic subsides. With a combination of AI and ML, executives – and especially CIOs – will be able to view and act on better information and more in-depth analytics, enabling them to drive a faster business transformation...

Winners and Losers
Intelligent systems increasingly speed up and disrupt status-quo processes, freeing up personnel to engage better, create more with their time, and explore new possibilities. As competitive advantages line up along the powerful technologies of cloud, AI, machine learning, and automation, those that have not deployed these tools will soon be in the loser category – meaning that their competitive advantage will undoubtedly be lost. Better products, increased efficiency, minimal errors, better work conditions, and costs savings are just some of the benefits of this new realm of AI and ML that companies cannot miss out on.
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Source: Forbes

Lack of women and non-binary people in computer science remains a systemic and social issue | Campus - The Eyeopener

Students, faculty and tech workers believe more needs to be done to create supportive environments for women and non-binary people in tech, Kayla Zhu, Journalism student and freelance content creator reports.

Illustration: Nathaniel Crouch

In Vanessa Landayan’s high school computer science and robotics class, no one wanted to be her partner for projects—she was the only girl in the class.

Her classmates would refer to her as “woman,” and it “became like a meme,” says Landayan. 

Landayan, who is now a second-year Ryerson computer science student, says she has faced similar experiences in one of her predominantly male labs in her first year of university...

Women at tech companies are often evaluated for promotions differently than men, says Inmar Givoni, director of engineering at Uber Advanced Technologies Group Toronto—an Uber branch that works on machine learning for self-driving cars.

“What we know from studies, and what I’ve observed around me, is that men often get promoted based on potential and women get promoted based on what they’ve already proven,” says Givoni. 
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Source: The Eyeopener 

What does that graph mean? University statistician on understanding COVID-19 numbers | Social Distancing - Mirage News

/Public Release. View in full here.

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What does that graph mean? University statistician on understanding COVID-19 numbers

Photo: Jeffrey Rosenthal

But making sense of all this data can be a challenge, particularly if you’re not mathmatically inclined.

“There are a lot of numbers out there and it can be hard to follow and track them,” says Jeffrey Rosenthal, a professor in the department of statistical sciences in the University of Toronto’s Faculty of Arts & Science.

“Every different way of visualizing data is going to give a somewhat different impression. So you have to understand what they mean.”

Arts & Science writer Chris Sasaki recently asked Rosenthal what advice he’d give to Canadians trying to make sense of the numbers behind the pandemic. All figures and trends current as of March 31.

Most of the graphs we see showing the total number of cases or deaths are linear graphs, but there are also logarithmic graphs. What’s the difference between the two and what do they tell us about the pandemic?...

What else should Canadians be mindful of as we track the numbers?

When you look at total numbers, you should look at the numbers as a percentage of the population or per capita. For example, if we compare Canada to the U.S., we might expect the U.S. to have about 10 times the number of cases because they have about 10 times the population. In fact, they have more than 20 times as many cases as us right now. So, it’s not simply that they have more cases because they have more people – they have twice the cases per capita.
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Source: Mirage News

Half of Sweden's population could be infected by coronavirus in April, statistician warns | Coronavirus - Daily Mail

• Sweden has confirmed 5,466 cases of coronavirus, 282 deaths from the disease 
• But mathematician thinks that up to 1million Swedes could already be infected 
• Based on his calculations, 5million people could become infected by April 30
• He expects infections to peak around April 15, with hospitals hit two weeks later
• Sweden has so-far resisted calls to go on lockdown like other European nations

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Half of Sweden's population could be infected with coronavirus by the end of the month, a statistician has warned.

Sweden has so far resisted calls to lock the country down like most other European nations, and instead advised people to 'act like adults' and socially distance themselves.
Photo: Via Reuters

Tom Britton, a mathematics professor from Stockholm University, said it is possible that up to a million people are already infected with the virus - though the country has only confirmed 5,466 cases.

Using mathematical models he believes the number of new daily infections will peak around the middle of the month, with up to 5million people infected by April 30...

Since Sweden's social distancing measures were only first introduced two weeks ago, it means they will not yet show in that data. 

Using statistical modelling, Mr Britton explained that he can work forwards from the number of infections three weeks ago to estimate how many are infected now.

The calculations are based on a number known as 'R' - which stands for the number of people the average person with the virus infects before they stop being infectious. 
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Source: Daily Mail 

Why it’s almost impossible to insure these 5 emerging technologies | Insurance - The Next Web

Sam Golden, tech storyteller, strategist and communications pro., We’re exposed to risk every day. 

Photo: The Next Web

From crossing the road to using our phones on the toilet. Every decision is a gamble and, subconsciously or consciously, we weigh up the likelihood of something going wrong.

The insurance industry is built on this principle. Policies offer protection in the event the odds aren’t in your favor. The trick is, insurers need to be able to protect their customers 
 he field of actuarial science is devoted to this cause. Actuaries use mathematical formulas based on historical trends to model events of uncertainty. This gives insurers an idea of the likelihood of a certain event taking place.
 
But what about when there’s no historical data to base these assumptions on?..

These changes won’t happen overnight but the sooner we accept our insurer can help us not hinder us, the sooner the industry can transform itself from a boring afterthought into an essential part of our everyday lives.
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Source: The Next Web