Mind Matters News and Analysis on Natural and Artificial Intelligence

CategoryMachine Learning

Clouds Michael Weidner-h-rP5KSC2W0-unsplash

Improve Your Job Chances by Scaling the Cloud

WBC Fellow Releases introductory book on Building Scalable PHP web applications using the cloud

One new issue that the cloud creates is that programmers are more often required to be “full stack” developers,” Jonathan Bartlett explains. “Unfortunately, most programmers coming out of college have little to no system administration experience. That’s why this book is based on the ‘full stack’ concept, showing how system administration and programming relate to each other.”

Read More ›
Red sports car Josh Rinard Unsplash

What Vehicle Would Bob Buy?

Both empirical generalized information (EGI) and the Gini metric can generate useful information

Contrary to traditional Fisherian hypothesis testing, it is possible to create models after viewing the data and still quantify the generality of the model.

Read More ›
Black balls white balls Adobe Stock

Machine learning: Harnessing the Power of Empirical Generalized Information (EGI)

We can calculate a probability bound from entropy. Entropy also happens to be an upper bound on the binomial distribution

We want our calculation to demonstrate the notion that if we have high accuracy and a small model, then we have high confidence of generalizing. Intuitively, then, we add the model size to the accuracy and subtract this quantity from the entropy of having absolutely no information about the problem.

Read More ›
Missing piece

Machine Learning: Decision Trees Can Solve Murders

Decision trees increase our chance at a right answer. We can see how that works in a mystery board game like Clue

Entropy is a concept in information theory that characterizes the number of choices available when a probability distribution is involved. Understanding how it works helps us make better guesses.

Read More ›
Playing cards for poker and gambling, isolated on white background with clipping path

Machine Learning: Using Occam’s Razor to Generalize

A simple thought experiment shows us how

This approach is contrary to Fisher's method where we formulate all theories before encountering the data. We came up with the model after we saw the cards we drew.

Read More ›
Boeing 777

Boeing’s Sidelined Fuselage Robots: What Went Wrong?

It’s not what we learn, it’s what we forget

By all means, let’s build machines that enhance our abilities. But let’s not forget that the really amazing thing is not the tool, but the tool builder.

Read More ›
Burnout

Boeing Workers, Please Don’t Kick the Robot on Its Way Out

The jetliner manufacturer’s decision to give the robots’ job back to machinists underlines the hard realities of automation. For example, it doesn’t always work

Robot error turned out to be a bigger problem than human error.

Read More ›
3D Rendering of abstract binary data in glowing blue and red color. For deep machine learning, crypto currency, hi tech product uses. Big data visualization, artificial intelligence. With copy space

The Greatest Threat We Face From AI—and What We Can Do

Here’s a list of things that have really happened with artificial intelligence (AI), in order of increasing severity.

When we get to the end of the list, we will see that it is like beads connected by a string—revealing the most dangerous threat.

Read More ›
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.

Read More ›
Brush and razor for shaving beard. Concept background of hair salon men, barber shop

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.

Read More ›
jockey

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.

Read More ›
COSM-3887

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.

Read More ›
COSM-3128

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.

Read More ›
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.

Read More ›
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.

Read More ›
iot machine learning with human and object recognition which use artificial intelligence to measurements ,analytic and identical concept, it invents to classification,estimate,prediction, database

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 ›
ke-wen-623575-unsplash
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 ›