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“The Alignment Problem”, Linking Machine Learning And Human Values

“the alignment problem”, linking machine learning and human values

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Artificial Intelligence

Sergey Tarasov – stock.adobe.com

I’ve finished reading “The Alignment Problem” (ISBN: 9780393635829), by Brian Christian. As the subtitle states, it’s an attempt to discuss fuzzier aspects of human value with the growing relevance of machine learning (ML). By ML, the author almost exclusively means neural networks. Overall, it was a good book. As with most, though, it missed a few things. It’s definitely worth a read for those who are interested in the potential social impact of artificial intelligence (AI).
The book is organized in three sections of three chapters. I have a problem with the naming of the three sections, but the chapter organization makes sense. The first section is “Prophecy”, which I feel would have been a better title for the last section. The first three chapters are, in opposition to the title, setting the foundation of the discussion by defining and discussing representation, fairness and transparency. Plenty of articles have covered the first two issues.
Representation, the first chapter, focuses on the need to represent a wide variety of people by describing the evolution of machine vision systems. That includes the inherent bias of mainly white, male images in many of …

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Australians have low trust in artificial intelligence and want it to be better regulated

australians have low trust in artificial intelligence and want it to be better regulated

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Credit: Shutterstock

Every day we are likely to interact with some form of artificial intelligence (AI). It works behind the scenes in everything from social media and traffic navigation apps to product recommendations and virtual assistants.

AI systems can perform tasks or make predictions, recommendations or decisions that would usually require human intelligence. Their objectives are set by humans but the systems act without explicit human instructions.
As AI plays a greater role in our lives both at work and at home, questions arise. How willing are we to trust AI systems? And what are our expectations for how AI should be deployed and managed?
To find out, we surveyed a nationally representative sample of more than 2,500 Australians in June and July 2020. Our report, produced with KPMG and led by Nicole Gillespie, shows Australians on the whole don’t know a lot about how AI is used, have little trust in AI systems, and believe it should be carefully regulated.
Most accept or tolerate AI, few approve or embrace it
Trust is central to the widespread acceptance and adoption of AI. However, our research suggests the Australian public is ambivalent about trusting AI systems.
Nearly half of our respondents (45%) are unwilling …

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Fleet of robotic probes will monitor global warming’s impact on microscopic ocean life

fleet of robotic probes will monitor global warming’s impact on microscopic ocean life

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Since 2014, researchers have deployed more than 150 biogeochemical Argo floats in the Southern Ocean.

GRETA SHUM/SOCCOM

By Paul VoosenOct. 29, 2020 , 10:00 AM

A single drop of seawater holds millions of phytoplankton, a mix of algae, bacteria, and protocellular creatures. Across the world’s oceans these photosynthesizing microbes pump out more than half of the planet’s oxygen, while slowing climate change by capturing an estimated 25% of the carbon dioxide (CO2) released from humanity’s burning of fossil fuels. But the scale of this vital chemistry is mostly a guess, and there’s little sense of how it will change as temperatures rise. “What’s happening out there? We have no idea really,” says Susan Wijffels, a physical oceanographer at the Woods Hole Oceanographic Institution.

Soon, 500 drifting ocean floats studded with biogeochemical sensors will deliver answers. Today, the National Science Foundation (NSF) announced it will spend $53 million to fund the new floats, marking the first major expansion of the Argo array, a set of 4000 floats that for 15 years has tracked rising ocean temperatures. “This is going to be revolutionary,” says Wijffels, a leader of the original Argo program.

The biogeochemical (BGC) Argo floats, in development for nearly as long as Argo itself, will …

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SAS a leader in IDC MarketScape for advanced machine learning software platforms

sas a leader in idc marketscape for advanced machine learning software platforms

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CARY, N.C., Oct. 29, 2020 /PRNewswire/ — SAS has been named a leader in the IDC MarketScape: Worldwide Advanced Machine Learning Software Platforms 2020 Vendor Assessment (doc #US45358820, October 2020). The report noted “artificial intelligence and machine learning are the most transformative technologies of our time, and SAS is more committed than ever to investing in its potential for enterprises.”
The IDC MarketScape report is the latest recognition from top industry analyst firms for SAS® artificial intelligence (AI), machine learning and advanced analytics capabilities.
“Organizations with large amounts of data – which today is most organizations – value machine learning because it helps them quickly discover insights in their data and improve decision making,” said Susan Kahler, SAS Global Marketing Manager for AI and Machine Learning. “SAS machine learning technologies and the larger SAS® Viya® analytics platform help people at all skill levels – executives, data scientists, business analysts and more – transform data into decisions and bottom-line results through a powerful, collaborative and cloud-native environment.”
According to David Schubmehl, Research Director for AI Software Platforms at IDC, “Success in the rapidly evolving AI software platforms market requires advanced machine learning software platform vendors to continue to innovate and provide tools to help customers accelerate development and …

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How wearable sensor algorithms powered by machine learning could prevent injuries that sideline runners

how wearable sensor algorithms powered by machine learning could prevent injuries that sideline runners

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Vanderbilt technology provides a unique new capability to estimate internal tissue forces and damage due to repetitive loading, which is lacking in existing wearables. Credit: Karl Zelik

A trans-institutional team of Vanderbilt engineering, data science and clinical researchers has developed a novel approach for monitoring bone stress in recreational and professional athletes, with the goal of anticipating and preventing injury. Using machine learning and biomechanical modeling techniques, the researchers built multisensory algorithms that combine data from lightweight, low-profile wearable sensors in shoes to estimate forces on the tibia, or shin bone—a common place for runners’ stress fractures.
The research builds off the researchers’ 2019 study, which found that commercially available wearables do not accurately monitor stress fracture risks. Karl Zelik, assistant professor of mechanical engineering, biomedical engineering and physical medicine and rehabilitation, sought to develop a better technique to solve this problem. “Today’s wearables measure ground reaction forces—how hard the foot impacts or pushes against the ground—to assess injury risks like stress fractures to the leg,” Zelik said. “While it may seem intuitive to runners and clinicians that the force under your foot causes loading on your leg bones, most of your bone loading is actually from muscle …

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AI and Machine Learning Awards – and the winners are…

ai and machine learning awards – and the winners are…

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AI and Machine Learning Awards – and the winners are…

Artificial intelligence might not be a brand new concept, but its use in enterprise IT is certainly growing at start-up rates. Every vendor seemingly has an AI-based tool, or is using machine learning to solve common problems. The challenge is how to sort fact from fiction – or, to put it more charitably, find the absolute best of the best.
We launched the AI & Machine Learning Awards last year to solve exactly this issue. AI has applications in every sector and at every level of business: from simple automation to full-scale cyber defences. Even the most basic implementations can free a workforce from time-consuming manual tasks, with the most recent developments providing practical insights into customer data.

Now in its second year, the AI & Machine Learning Awards celebrate the companies, individuals, products and projects that are changing what it means to use artificial intelligence in the workplace today. Perhaps they are simplifying massive data sets, raising manufacturing efficiency, or even helping to counter the spread of COVID-19. Every finalist in the Awards was a standout example of how AI could be developed and used to change the world we live …

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The Next Generation Of Artificial Intelligence (Part 2)

the next generation of artificial intelligence (part 2)

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Deep learning pioneer Yoshua Bengio has provocative ideas about the future of AI.

Maryse Boyce, IEEE Spectrum

For the first part of this article series, see here.
The field of artificial intelligence moves fast. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless.
If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

What will the next generation of artificial intelligence look like? Which novel AI approaches will unlock currently unimaginable possibilities in technology and business?
My previous column covered three emerging areas within AI that are poised to redefine the field—and society—in the years ahead. This article will cover three more.

4. Neural Network Compression
Recommended For YouAI is moving to the edge.
There are tremendous advantages to being able to run AI algorithms directly on devices at the edge—e.g., phones, smart speakers, cameras, vehicles—without sending data back and forth …

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Machine learning finds heart faults

machine learning finds heart faults

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The team tested their approach on 11 donated human hearts and located AF drivers with an accuracy of up to 81%.
Multi-electrode mapping (MEM) is a technique that can be applied during an operation, in which an array of electrodes is pressed against tissue to measure electrical activity. But AF drivers have proved difficult to locate with sufficient accuracy using this technique – as the remedy is to stop the AF driver by burning it away from within the heart tissue – called targeted ablation.
There is a technique that can accurately locate AF drivers, called sub-surface near-infrared optical mapping (NIOM), which has a resolution of 0.3mm, but is so invasive that it cannot be used inside someone during an operation.

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Deep Learning Market 2020 Detailed Analysis Focusing On Application, Types and Regional Outlook Google, IBM, Microsoft, Qualcomm Technologies

deep learning market 2020 detailed analysis focusing on application, types and regional outlook google, ibm, microsoft, qualcomm technologies

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Latest statistical data titled Deep Learning has been published by Research N Reports. The report offers an overview of various features of recent trends such as Deep Learning which are comprehensively discussed in order to provide an in-depth analysis of the progress of the industries. Effective exploratory techniques such as qualitative and quantitative analysis are also used in order to explore accurate data.
For an effective business outlook, the market study examines various global regions, such as North America, Latin America, Asia-Pacific, Japan, and India by considering different segments such as type, size, as well as applications. SWOT and Porter’s five analyses are also effectively discussed to analyze informative data such as cost, prices, revenue, and end-users.
Ask for Sample Copy of This Report: https://www.researchnreports.com/request_sample.php?id=64529
Top Key Players Included in This Report:
Google, IBM, Microsoft, Qualcomm Technologies, Inc, Skymind, Baidu, Hewlett Packard Enterprise, Sensory Inc., General Vision Inc., Intel, NVIDIA Corporation
Major highlights of this research report:-In-depth analysis of the degree of competition across the globe.-Estimation of Global Deep Learning Market values and volumes.– Global Deep Learning Market analysis through industry analysis tools such as SWOT and Porter’s five …

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