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 Read More ›
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 Read More ›
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 Read More ›
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.
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.
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).
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 ›
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 ›
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.”
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 ›
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 ›
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 ›
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.
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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 ›
“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 Read More ›
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 ›