Mind Matters Natural and Artificial Intelligence News and Analysis

TagMachine Learning

World twitter Connection on Blackboard

Governments Worldwide Pressured Twitter to Censor in 2020

World governments demanded the removal of content from 199 journalist sources

Twitter released its latest Transparency Report on Wednesday, revealing that in the latter half of 2020, there was a 26% increase in requests from international governments to remove posts from verified journalists. The report tracks various data from July 1 to December 31, 2020, including global legal requests and Twitter Rules enforcement. Global legal requests are divided between information requests and removal requests. Twitter received over 14,500 global government information requests, and over 38,500 global legal demands to remove content. According to the report, “94% of the total global volume of legal demands originated from only five countries (in decreasing order): Japan, India, Russia, Turkey, and South Korea.” Of the information requests received, Twitter announced that they “produced some or…

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Robot vs man. Human humanoid robot work with laptops at desk.

Will Humans Ever Be Fully Replaceable by AI? Part 3

Data outlines what can be quantified but does not show the comparison between AI and human performance at the most important points

To get the right answer to the question of whether artificial intelligence will ever become capable of replacing man we must get the ontology, epistemology, and metrology right. Ontology seeks to understand the essential nature of things and the relationships between different things. Epistemology looks at what we can know and how accurately we can know what is knowable. Finally, metrology explores how we make measurements and comparisons. To get the right answer we must measure the right things (ontology), select what we will measure (epistemology), and determine how we make our measurements and comparisons with accuracy, precision, and repeatability (metrology). Mistakes in any of these areas will lead to a bad outcome. A common mistake is to measure what…

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explosion of a filament light bulb

AI Smash Hits 2020 Part I

An ultimate test of a successful technology is whether it has been reduced to practice. Has it made a financial impact on the market? Has it been adopted by the very picky US military? Has it changed lives? We’re going to count down the AI Smash Hits: the top ten AI success stories for 2020. Join Dr. Robert J. Marks as he…

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Machine Learning, Part 2: Supervised Learning

Machine learning isn’t hard to understand; it’s just different. Let’s start with the most common type

The neat thing about machine learning is that the algorithm can extract general principles from the dataset that can then be applied to new problems. It is like the story that Newton observed an apple fall and then derived from it the general law of gravity that applies to the entire universe.

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Is Ray Kurzweil’s Singularity Now Nearer — or Impossible?

In response to Kurzweil’s talk at the COSM Technology Summit, panelists noted that AI achievements are revolutionary in size but limited by their nature in scope

George Montañez, Assistant Professor of Computer Science at Harvey Mudd College, took issue with Kurzweil’s claim that AlphaGoZero needed no instructions to beat humans at the game of Go: “For a system like this to work, a human must define the incentive structure, also encoding the assumptions.” The sheer power of a computing system does not cause it to do anything at all.

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Two computer keyboards, user typing on one of them

AI Is Not a Simple Fix for Plagiarism

The internet speeded up a perennial problem without changing it

If imitation is the sincerest form of flattery, plagiarism amounts to passing ourselves off as experts without tears. It’s not realistic to expect software to detect all of the subtleties.

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Scrabble pieces with numbers and letters

The Numbers Don’t Speak for Themselves

The patterns uncovered by machine learning may reflect a larger reality or just a bias in gathering data

Because Machine Learning is opaque—even experts cannot clearly explain how a system arrived at a conclusion—we treat it as magic. Therefore, we should mistrust the systems until proven innocent (and correct).

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Composite image of image of data
Composite image of image of data

Part 1: Navigating the Machine Learning Landscape

To choose the right type of machine learning model for your project, you need to answer a few specific questions
Most machine learning systems fall into three main categories—supervised learning, unsupervised learning, and reinforcement learning. The choice of system depends first on which category of machine learning best addresses your situation. Read More ›
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Market with hundreds of stalls from above

Will the Free Market Help or Hurt Us in an AI-Empowered World?

We may need new institutions, such as insurance against job obsolescence
If humans are free to experiment with new institutions, I believe we will find an excellent solution. However, there is a great danger that those who benefit from the status quo will use their influence to prevent the adoption of new institutions. Read More ›
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Does AI Art Spell the End of the Artist’s Way of Life?

An AI-produced painting sold at auction for $432,500. But is it a trend or just a novelty?

Rather than announce that human artists are now doomed, software engineer Ben Dixon interviewed a number of them and came away with a rather different picture, that “AI-generated art will improve, but artistic creativity will remain a human discipline.”

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Amoebae move and feed by using pseudopods, which are bulges of cytoplasm formed by the coordinated action of actin microfilaments pushing out the plasma membrane that surrounds the cell.
Amoebae move and feed by using pseudopods, which are bulges of cytoplasm formed by the coordinated action of actin microfilaments pushing out the plasma membrane that surrounds the cell.

Is an Amoeba Smarter Than Your Computer?

Hype aside, the microbe’s math skills ace the Traveling Salesman problem and may help with cybersecurity
When we hear hype about machines that will soon out-think people, we might put it in perspective by recalling that we still struggle to build a machine that can out-think amoebas looking for crumbs. Read More ›
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It’s 2019: Begin the AI Hype Cycle Again!

Media seemingly can’t help portraying today’s high-tech world as a remake of I, Robot (2004), starring you and me.
I have a problem with the possible outcomes when people who don’t know the difference between technology fact and fiction make important decisions based on information from journalists who write as if every computer is a potential personality like HAL from Space Odyssey 2001. Read More ›
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Researchers: Deep Learning Vision Is Very Different from Human Vision

Mistaking a teapot shape for a golf ball, due to surface features, is one striking example from a recent open-access paper
The networks did “a poor job of identifying such items as a butterfly, an airplane and a banana,” according to the researchers. The explanation they propose is that “Humans see the entire object, while the artificial intelligence networks identify fragments of the object.” Read More ›
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AI: Think About Ethics Before Trouble Arises

A machine learning specialist reflects on Micah 6:8 as a guide to developing ethics for the rapidly growing profession
To love mercy sometimes means to give up efficiency. It could mean losing a few points of model accuracy by refusing to take into account features that invade privacy or are proxies for race, leading to discriminatory model behavior. But that’s OK. The merciful are willing to give up some of their rights and advantages so they can help others.   Read More ›
Children using computer in school
Children using computer in school

Can an Algorithm Be Racist?

No, the machine has no opinion. It processes vast tracts of data. And, as a result, the troubling hidden roots of some data are exposed
It’s tempting to assume that a villain lurks behind such a scene when the exact opposite is the problem: A system dominated by machines is all calculations, not thoughts, intentions, or choices. If the input is wrong, so is the output. Read More ›
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2: AI Can Write Novels and Screenplays Better than the Pros!

AI help, not hype: Software can automatically generate word sequences based on material fed in from existing scripts. But with what result?

“AI rites reel gud!” Seriously, the idea is not new. Back in the 1940s, George Orwell (1903–1950) thought that a machine could write popular novels so long as no creative thinking was involved. Thus, in his 1984 police state world, one of the central characters has a job minding a machine that mass produces them. In the 1960s, some film experiments were done along these lines, using Westerns (cowboy stories). At the time, there were masses of formula-based film material to work with in this popular genre. But what does the product look and sound like? In 2016, Ars Technica was proud to sponsor “the first AI-written sci-fi script:” As explained in The Guardian, a recurrent neural network “was fed the…

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4: Making AI Look More Human Makes It More Human-like!

AI help, not hype, with Robert J. Marks: Technicians can do a lot these days with automated lip-syncs and smiles but what’s behind them?
This summer, some were simply agog over “Sophia, the First Robot Citizen” (“unsettling as it is awe-inspiring”) Read More ›
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A host of blue squid

9: Will That Army Robot Squid Ever Be “Self-Aware”?

AI help, not hype: What would it take for a robot to be self-aware?
The thrill of fear invites the reader to accept a metaphorical claim as a literal fact. Read More ›
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Be Choosy About What You Automate!

Having automated many processes, I can assure you that that is the First Rule of Automation
The worst trap that people who are pursuing automation fall into is the desire to automate everything. That’s usually a road to disaster. Automation is supposed to save time and money, but it can wind up costing you both if you don't carefully consider what you automate. Read More ›