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Mind Matters Reporting on Natural and Artificial Intelligence

CategoryMachine Learning

Beautiful Male Computer Engineer and Scientists Create Neural Network at His Workstation. Office is Full of Displays Showing 3D Representations of Neural Networks.

How Algorithms Can Seem Racist

Machines don’t think. They work with piles of “data” from many sources. What could go wrong? Good thing someone asked…

Some of the recent conflicts around algorithms and ethnicity are flubs that social media entrepreneurs will regret. Others may endanger life.

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Occam’s Razor Can Shave Away Data Snooping

The greater an object's information content, the lower its probability.

One technique to avoid data snooping is based on the intersection of information theory and probability: An object’s probability is related to its information content. The greater an object’s information content, the lower its probability. We measure a model’s information content as the logarithmic difference between the probability that the data occurred by chance and the number of bits required to store the model. The negative exponential of the difference is the model’s probability of occurring by chance. If the data cannot be compressed, then these two values are equal. Then the model has zero information and we cannot know if the data was generated by chance or not. For a dataset that is incompressible and uninformative, swirl some tea Read More ›

AI face detection,Face Recognition Vendor

Big Tech Tries to Fight Racist and Sexist Data

The trouble is, no machine can be better than its underlying training data. That’s baked in

The problem with machine learning-based AI in police work is not so much its inherent bias (none of us is bias-free) but the delegation to a machine of what should be a human decision.

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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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Former Microsoft Head of Research: Machines Will Soon Know Better Than Your Doctor

Other experts at the COSM Technology Summit were skeptical of Craig Mundie’s claims

Mundie, former Microsoft Chief Research & Strategy Officer, formerly told his audience that Big Data will enable each person to be “completely understood” by machines that can produce a computer facsimile of each detail. It would be far too complex for human physicians to make sense of, he said.

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Students sitting a test in an exam hall in college

The Challenge of Teaching Machines to Generalize

Teaching students simply to pass tests provides a good illustration of the problems

We want the machine learning algorithms to learn general principles from the data, and not merely little tricks and trivia that that score high but ignore problems.

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Microprocessor on girls fingertip

Carver Mead Asks, Where Did AI Come From?

The microprocessor pioneer who was a colleague of Feynman and named Moore’s Law is certainly in a position to know

In 2002, he received the National Medal of Technology for a number of “pioneering contributions to microelectronics,” which underlies cell phones and computer neural networks.

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Machines Are Not Really Learning

A bit of machine learning history helps us see why
Go talk to a neighbor or a friend. You’ve just done something that Deep Learning can’t do. Worse, it can’t even learn because that’s not a narrow, well-defined problem. Read More ›
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Crosswalk with fake car and pedestrians

Does a Western Bias Affect Self-Driving Cars?

How a driver is expected to act varies by culture
Self-driving cars (autonomous vehicles) will need to adapt to different rules and we will, very likely, need to change those rules to make the vehicles work. Read More ›
Man with cardboard box on his head on grey background

The Machine Knows You Are Angry

Okay, it knows if your facial muscles are twisted in a certain way… does the difference matter?
Five accomplished scientists representing different camps reviewed over a thousand studies of machine emotion recognition. Essentially, there seems no clear science basis for the claims made. Read More ›
Chatbot conversation on smartphone screen app interface with artificial intelligence technology providing virtual assistant customer support and information, person hand holding mobile phone

You can build your own chatbot

New tools have made it comparatively easy

Natural Language Interfaces (the technical term for a chatbot) are becoming more and more popular. Many dial-in phone services have switched from numeric interfaces (“Dial 1 for sales, 2 for service, etc.”) to natural language interfaces (“Please say what you are calling about”). Where they have taken off though is with chatbots. Many online help systems at least start with chatbots, which collect basic information about a problem or situation and point to existing solutions before passing the contact off to a human expert. Additionally, the rise of the Generation Text, as well as the proliferation of chat-based groupware such as Slack, means that text-based natural language interfaces are one of the best ways of interacting with young people. Is Read More ›

Modern Red Haired Man Relaxing in Office

Lazy Engineers Treat AI as Magic!

When software engineers mostly use shared code, they save time but risk losing understanding

Building from scratch is different. Knowing when to use a tool and why and knowing the limitations of each tool separates the craftsperson from the novice.

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We Built the Power Big Social Media Have Over Us

Click by click, and the machines learned the patterns. Now we aren’t sure who is in charge

We’re stuck, working for free, training the Web giants’ ML systems to reap benefits for them while enduring (assuming we notice) the downsides.

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Car top taxi sign

Even Uber didn’t believe in Uber’s self-driving taxis

We found that out after Google’s Waymo sued the company

Optimism is not driving the recent collaboration and corporate consolidation in the self-driving car industry. Rather, their retrenchment is protection against an uncertain future.

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Helmeted police officers photographed from behind during a protest

Can AI Predict and Prevent Political Unrest?

The 1996 Democratic Convention tried neural networks but discovered a hidden flaw

The police union’s 1996 objection to fingering specific officers as violence risks without a detailed explanation pinpoints a weakness of neural networks even to this day. The neural network is basically a black box.

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Ruins of Knossos Palace, Crete, GREECE

Could Machine Learning Decipher Lost Languages?

It gives us new search powers, based on perennial facts of language

An endless variety of word games derives from the fact that only certain combinations and orderings of words can be correct. Machine learning applies vast resources to cracking the code.

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Animal clouds shape

How Business Intelligence Can Break the Data Deadlock

Companies today are awash in information. But which patterns are real? Which are cloud bunnies?

Contrary to the dogma of hypothesis testing, it is possible to do after-the-fact pattern analysis while limiting the probability of false positives.

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Abstract Visualisation of data and technology in graph form. 3D Illustration
Abstract Visualization of data and technology in graph form. 3D Illustration

Machine Learning Tip: Set Boundaries for the Problems

We cannot take a giant pile of unorganized data, shove it into a machine, and expect useful results

Humans intrinsically understand causation and, therefore know which pieces of data likely have some correlation. Therefore, when we select data for computers to analyze, we are drastically reducing the size of the problem for computers.

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Just Did It hashtag

Winning Tag Lines Are Hard Enough To Write…

But AI really flops at that

AI tools help us do things better, faster, or more efficiently. But they lack the mind needed to know when “I’m loving’ it” is the winning slogan—and stop there. 

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