Mind Matters News and Analysis on Natural and Artificial Intelligence

TagWatson

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Has Aristo broken bounds for thinking computers?

The Grade 8 graduate improves on Watson but we must still think for ourselves at school. Here’s why
Aristo combines questions and answers on a multiple-choice test to decide on the best answer without understanding any of the information. Read More ›
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Why Was IBM Watson a Flop in Medicine?

Robert J. Marks and Gary S. Smith discuss how the AI couldn’t identify which information in the tsunami of medical literature actually MATTERED

Last year, the IBM Health Initiative laid off a number of people, seemingly due to market disillusionment with the product.

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The Holy Grail of Artificial Intelligence

Gary N. Smith wonders whether AI will ever achieve common sense

Gary N. Smith and Robert J. Marks continue their discussion of IBM’s Watson and its grim future in health and medicine. The problem, they say, is that Watson amounts to a real world instance of John Searle’s “Chinese Room”. Computers don’t understand Chinese, English, or numbers for that matter. With reference to many of the leading thinkers in AI research, Read More ›

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Why did Watson think Toronto was in the U.S.A.?

How that happened tells us a lot about what AI can and can’t do, to this day

Strictly speaking, the answer Watson spit out was "What is Toronto?????", which does sound distinctly less than certain. But the programmers had chosen not to program in the option of saying, “I don’t know.”

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Why an AI pioneer thinks Watson is a “fraud”

The famous Jeopardy contest in 2011 worked around the fact that Watson could not grasp the meaning of anything

Gary N. Smith explains that a computer’s inability to understand what “it” means in a sentence is because it doesn’t understand what any of the words in the sentence mean.

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Time Passes, Love Fades, But What Does “It” All Mean?

Gary Smith and Bob Marks on AI's incomprehension, from IBM's Watson to the Clinton campaign's Ada

Gary N. Smith and Robert J. Marks discuss the inability of AI to understand puns, lyrics, context, or anything at all. From trading futures, predicting political outcomes, and parsing lyrics, the fundamental incomprehension of artificial intelligence is a key to understanding its limitations. Show Notes 02:30 | The AI Delusion by Oxford University Press 03:00 | The importance of knowing Read More ›

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Gary Smith: The AI Delusion

“When I Nod My Head, Hit It!” And Other Commands that Confuse AI.

Pablo Picasso said “Computers Are Useless. They Can Only Give You Answers.”  Picasso didn’t go far enough. The answers that computers give must themselves be questioned. This is especially true of AI. Questioning AI is the topic today on Mind Matters. Show Notes 01:27 | Introduction to Gary Smith 02:40 | The AI Delusion 04:50 | Stocks and Data 07:00 Read More ›

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1: IBM’s Watson Is NOT Our New Computer Overlord

AI help, not hype: It won at Jeopardy (with specially chosen “softball” questions) but is not the hoped-for aid to cancer specialists
One problem that has dogged Watson has nothing to do with AI or medicine. The journalism around the introduction of projects like Watson is long on the Gee Whiz! An Electronic Brain! It Won at Jeopardy! And it is short on systematic inquiry as to outcomes versus goals. Read More ›
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AI Winter Is Coming

Roughly every decade since the late 1960s has experienced a promising wave of AI that later crashed on real-world problems, leading to collapses in research funding.
Nearly all of AI’s recent gains have been realized due to massive increases in data and computing power that enable old algorithms to suddenly become useful. For example, researchers first conceived neural networks—the core idea powering much machine learning and AI’s notable advances—in the late 1950s. The worries of an impending winter arise because we’re approaching the limits of what massive data combined with hordes of computers can do. Read More ›
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GIGO alert: AI can be racist and sexist, researchers complain

Can the bias problem be addressed? Yes, but usually after someone gets upset about a specific instance.

From James Zou and Londa Ziebinger at Nature: When Google Translate converts news articles written in Spanish into English, phrases referring to women often become ‘he said’ or ‘he wrote’. Software designed to warn people using Nikon cameras when the person they are photographing seems to be blinking tends to interpret Asians as always blinking. Word embedding, a popular algorithm used to process and analyse large amounts of natural-language data, characterizes European American names as pleasant and African American ones as unpleasant. Now where, we wonder, would a mathematical formula have learned that? Maybe it was listening to the wrong instructions back when it was just a tiny bit? Seriously, machine learning, we are told, depends on  absorbing datasets of Read More ›