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

Check out these Books and Making Sense of Math.

Photo: Heather Eyster via Flickr

 

New Sinc Methods of Numerical Analysis - Festschrift in Honor of Frank Stenger's 80th Birthday

New Sinc Methods of Numerical Analysis:
Festschrift in Honor of Frank Stenger's 80th Birthday

 

This contributed volume honors the 80th birthday of Frank Stenger who established new Sinc methods in numerical analysis.The contributions, written independently from each other, show the new developments in numerical analysis in connection with Sinc methods and approximations of solutions for differential equations, boundary value problems, integral equations, integrals, linear transforms, eigenvalue problems, polynomial approximations, computations on polyhedra, and many applications...

Publisher: Princeton University Press; Illustrated edition (October 23, 2011)

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Magical Mathematics - The Mathematical Ideas That Animate Great Magic Tricks  

 Magical Mathematics:
The Mathematical Ideas That Animate Great Magic Tricks

Magical Mathematics reveals the secrets of fun-to-perform card tricks―and the profound mathematical ideas behind them―that will astound even the most accomplished magician. Persi Diaconis and Ron Graham provide easy, step-by-step instructions for each trick, explaining how to set up the effect and offering tips on what to say and do while performing it. Each card trick introduces a new mathematical idea, and varying the tricks in turn takes readers to the very threshold of today's mathematical knowledge...

Publisher: Princeton University Press; Illustrated edition (October 23, 2011) 

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Read 📚books and drink ☕️coffee!   

Source: Amazon

2021: The Year of Robotics and Artificial Intelligence | Artificial Intelligence - TechNewsWorld

I'm writing this just before New Year's Day, and like many of you I'm hoping to reach my one remaining goal for 2020, which is to be alive in 2021, as Rob Enderle, has been an ECT News Network columnist since 2003 reports.

2021 will see advances in robotics and artificial intelligence
Photo: TechNewsWorld

I'm looking forward to putting 2020 behind, and while the pandemic has been painful, it has caused things to accelerate in several areas impressively rapidly. Two of those areas are robotics and artificial intelligence, which we'll see adapted broadly this decade with a considerable bump in 2021.

Let's talk about all of that this week, and we'll close with my first product of the week in 2021, the Somnofy AI Sleep Monitor.

Let's begin.

Robots, Robots, Robots

Robots, robots, and more robots will increasingly surround us in 2021...

AI Domination 

Another place we are going to see a ton of deployments is AI. From temperature monitors at entrances to transportation, offices, hospitals, and restaurants that alert and sometimes prevent entry for those running a bit hot, to radically improved digital assistants, we are going to be surrounded by AI. 

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Source: TechNewsWorld

Ethics and artificial intelligence: Wide-ranging AI Winter School organised by Open University | Education - Cyprus Mail

Aiming to cover topics related to artificial intelligence and to promote the multidisciplinarity of fairness, accountability, transparency and ethics, the Cyprus Centre for Algorithmic Transparency (CyCAT), hosted at the Open University of Cyprus, organises the Winter School: Fairness, Accountability, Transparency and Ethics (FATE) in AI. CyCAT’s FATE in AI Winter School will be taking place online between January 11 and 15 by Press Release.

The FATE in AI Winter School is targeting students at Masters and PhD levels, and early career postdoctoral researchers. The disciplines of the organisers aim to attract can be aligned, but not necessarily limited to the following:

Human computer interaction

Data science

Information systems

Cognitive systems

Computer science...

The goal of the FATE in AI Winter School is to bring people into a holistic environment, with participants, as well as speakers, coming from different disciplines, allowing them to get a different perspective than the one they hold, which will result in new insights on the topics of FATE.

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Source: Cyprus Mail

Researchers find machine learning models still struggle to detect hate speech | Machine learning - VentureBeat

Kyle Wiggers, writes about artificial intelligence for VentureBeat notes, Detecting hate speech is a task even state-of-the-art machine learning models struggle with.

Photo: Ian Williams via Flickr

That’s because harmful speech comes in many different forms, and models must learn to differentiate each one from innocuous turns of phrase. Historically, hate speech detection models have been tested by measuring their performance on data using metrics like accuracy. But this makes it tough to identify a model’s weak points and risks overestimating a model’s quality, due to gaps and biases in hate speech datasets.

In search of a better solution, researchers at the University of Oxford, the Alan Turing Institute, Utrecht University, and the University of Sheffield developed HateCheck, an English-language benchmark for hate speech detection models created by reviewing previous research and conducting interviews with 16 British, German, and American nongovernmental organizations (NGOs) whose work relates to online hate. Testing HateCheck on near-state-of-the-art detection models — as well as Jigsaw’s Perspective tool — revealed “critical weaknesses” in these models, according to the team, illustrating the benchmark’s utility.

HateCheck’s tests canvass 29 modes that are designed to be difficult for models relying on simplistic rules, including derogatory hate speech, threatening language, and hate expressed using profanity...

The researchers suggest targeted data augmentation, or training models on additional datasets containing examples of hate speech they didn’t detect, as one accuracy-improving technique. But examples like Facebook’s uneven campaign against hate speech show significant technological challenges. Facebook claims to have invested substantially in AI content-filtering technologies, proactively detecting as much as 94.7% of the hate speech it ultimately removes. But the company still fails to stem the spread of problematic posts, and a recent NBC investigation revealed that on Instagram in the U.S. last year, Black users were about 50% more likely to have their accounts disabled by automated moderation systems than those whose activity indicated they were white.

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Source: VentureBeat

Dive into Deep Learning & Data Analysis With These Expert-Led Classes | Technology - IFLScience

There are plenty of reasons to brush up on your data analytics skills in 2020, from helping you better understand your business to securing a good salary in a growing industry, but these unprecedented times have put a halt on in-person education by StackCommerce.

Dive into Deep Learning & Data Analysis With These Expert-Led Classes
Photo: IFLScience

Don't let social distancing put a damper on your ever-evolving need to learn with the Deep Learning & Data Analysis Certification Bundle.

More than just a study in data analytics, this online training also focuses on deep learning technology, which can take the data you gather and use it to more accurately predict the future. These in-demand skills can help you land a better job or make you a better candidate for promotion in nearly any economy.

Over about 325 different lessons in 8 courses, you'll cover topics including H2O framework for data analysis, visualization, statistics, deep learning, machine learning, regression modeling, business data, and more...

You don't need a data analysis degree to sort out that getting the Deep Learning & Data Analysis Certification Bundle valued at $1600 for $39.99 is a savings of 97%.   

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Source: IFLScience 

Best of arXiv.org for AI, Machine Learning, and Deep Learning – December 2020 | AI, Machine Learning, and Deep Learning - insideBIGDATA

In this recurring monthly feature, we filter recent research papers appearing on the arXiv.org preprint server for compelling subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the past month by Daniel Gutierrez, Author at insideBIGDATA.

Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. arXiv contains a veritable treasure trove of statistical learning methods you may use one day in the solution of data science problems. The articles listed below represent a small fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Links to GitHub repos are provided when available. Especially relevant articles are marked with a “thumbs up” icon. Consider that these are academic research papers, typically geared toward graduate students, post docs, and seasoned professionals. They generally contain a high degree of mathematics so be prepared.

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Source: insideBIGDATA 

Watch a Robot Dog Learn How to Deftly Fend Off a Human | Science - WIRED

Kick over this robot and it’ll quickly right itself—not because someone told it how, but because it taught itself to overcome the embarrassment, summarizes Matt Simon, science journalist at WIRED.

Dog illustration on white steps
Photo: YANG ET AL., SCI ROBOT. 5, EABB2174 (2020)

Study hard enough, kids, and maybe one day you’ll grow up to be a professional robot fighter. A few years ago, Boston Dynamics set the standard for the field by having people wielding hockey sticks try to keep Spot the quadrupedal robot from opening a door. Previously, in 2015, the far-out federal research agency Darpa hosted a challenge in which it forced clumsy humanoid robots to embarrass themselves on an obstacle course way outside the machines’ league. (I once asked you, dear readers, to stop laughing at them, but have since changed my mind.) And now, behold: The makers of the Jueying robot dog have taught it a fascinating way to fend off a human antagonizer who kicks it over or pushes it with a stick.

A team of researchers from China’s Zhejiang University—where the Jueying’s hardware was also developed—and the University of Edinburgh didn’t teach the Jueying how to recover after an assault, so much as they let the robot figure it out. It’s a dramatic departure from how a hardware developer like Boston Dynamics goes about teaching a robot how to move, using decades of human experience to hard code, line by line, the way a robot is supposed to react to stimuli like, um, a person’s foot...

Once the eight algorithmic experts were trained up, they needed to learn to work together as a team. So the researchers combined them into an overarching network to act as a kind of coach or team captain. This allows the Jueying’s artificial brain to tap into the knowledge of each expert—how to run, or turn, or right itself. “The coach or the captain will tell who is doing what, or who should do work together, at which time,” says Li. “So all experts can collaborate together as a whole team, and this drastically improves the capability of skills.” For example, when the robot is falling down and needs to recover, the system can detect that movement and trigger the expert that handles balancing.

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Source: WIRED

New Quantum Algorithms Finally Crack Nonlinear Equations | Mathematics - Quanta Magazine

Two teams found different ways for quantum computers to process nonlinear systems by first disguising them as linear ones, argues Max G. Levy, science journalist based in Los Angeles.

Sometimes, it’s easy for a computer to predict the future. Simple phenomena, such as how sap flows down a tree trunk, are straightforward and can be captured in a few lines of code using what mathematicians call linear differential equations. But in nonlinear systems, interactions can affect themselves: When air streams past a jet’s wings, the air flow alters molecular interactions, which alter the air flow, and so on. This feedback loop breeds chaos, where small changes in initial conditions lead to wildly different behavior later, making predictions nearly impossible — no matter how powerful the computer.

“This is part of why it’s difficult to predict the weather or understand complicated fluid flow,” said Andrew Childs, a quantum information researcher at the University of Maryland. “There are hard computational problems that you could solve, if you could [figure out] these nonlinear dynamics.”

That may soon be possible. In separate studies posted in November, two teams — one led by Childs, the other based at the Massachusetts Institute of Technology — described powerful tools that would allow quantum computers to better model nonlinear dynamics...

Knowing One’s Limits

While these are significant steps, they are still among the first in cracking nonlinear systems. More researchers will likely analyze and refine each method — even before the hardware needed to implement them becomes a reality. “With both of these algorithms, we are really looking in the future,” Kieferová said. Using them to solve practical nonlinear problems requires quantum computers with thousands of qubits to minimize error and noise — far beyond what’s possible today.

And both algorithms can realistically handle only mildly nonlinear problems. The Maryland study quantifies exactly how much nonlinearity it can handle with a new parameter, R, which represents the ratio of a problem’s nonlinearity to its linearity — its tendency toward chaos versus the friction keeping the system on the rails.

“[Childs’ study is] mathematically rigorous. He gives very clear statements of when it will work and when it won’t work,” Osborne said. “I think that’s really, really interesting. That’s the core contribution.”

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Source: Quanta Magazine

Why more employees need data literacy skills | Evaluate - TechTarget

Maria Korolov, Technology Journalist at TechTarget writes, Encouraging employees to learn data literacy skills can benefit any enterprise. Read on for some of the benefits and resources to take advantage of in building those skills.

Why more employees need data literacy skills
Photo: TechTarget

 

Research shows that data-driven organizations are more successful, but employees often lack needed data literacy skills.

According to a 2020 survey by Sapio Research, 80% of decision-makers believed opening up access to data has a positive impact on their organizations and 74% said employees have access to the data they need. But 53% of respondents reported employee resistance to using the data.

Meanwhile, research has consistently shown that data-driven companies are more successful. A 2019 survey by McKinsey & Company, a management consulting firm, showed that companies where employees consistently use data in decision-making are one-and-a-half times more likely to report revenue growth of more than 10% in the past three years.

The difference comes down to data literacy...

What is data literacy?

Data literacy is the ability to write and comprehend data similar to how we view literacy with reading. This can include an understanding of where data comes from, communicating information derived from data to others and knowing where to use different analytical tools and methods.

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Source: TechTarget

The Alignment Problem: Machine Learning and Human Values | Books - Amazon

Check out this book about artificial intelligence by Brian Christian, American non-fiction author, poet, programmer and researcher.

The Alignment Problem:
Machine Learning and Human Values

Today’s “machine-learning” systems, trained by data, are so effective that we’ve invited them to see and hear for us―and to make decisions on our behalf. But alarm bells are ringing. Recent years have seen an eruption of concern as the field of machine learning advances. When the systems we attempt to teach will not, in the end, do what we want or what we expect, ethical and potentially existential risks emerge. Researchers call this the alignment problem...

The mathematical and computational models driving these changes range in complexity from something that can fit on a spreadsheet to a complex system that might credibly be called “artificial intelligence.” They are steadily replacing both human judgment and explicitly programmed software.

Publisher: W. W. Norton & Company; 1st edition (October 6, 2020)  

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Source: Amazon