Summary Analytics

AI financial and environmental costs

With larger and larger datasets and more complex algorithms, the compute, storage, and energy costs of AI are increasing to unsustainable levels — unsustainable for your budget and for the planet!

The computing power needed to train AI is now rising seven times faster than ever before

An updated analysis from OpenAI shows how dramatically the need for computational resources has increased to reach each new AI breakthrough.

November 11, 2019

Prepare for Artificial Intelligence to Produce Less Wizardry

A new paper argues that the computing demands of deep learning are so great that progress on tasks like translation and self-driving is likely to slow.

July 11, 2020

OpenAI's GPT-3 Language Model: A Technical Overview

OpenAI recently published GPT-3, the largest language model ever trained. GPT-3 has 175 billion parameters and would require 355 years and $4,600,000 to train - even with the lowest priced GPU cloud on the market.

June 03, 2020

Researchers Are Developing Tools to Calculate AI’s Carbon Footprint

The researchers’ goal is to encourage the energy-efficient development of AI models.

July 17, 2020

Training a single AI model can emit as much carbon as five cars in their lifetimes

Deep learning has a terrible carbon footprint.

June 6, 2019

Failure to Scale Artificial Intelligence Could Put 75% of Organizations Out of Business

Companies that shift from AI experimentation to execution achieve lasting ROI and competitive agility.

November 14, 2019

Why 85% of AI projects fail

Despite increased interest in and adoption of artificial intelligence (AI) in the enterprise, 85% of AI projects ultimately fail to deliver on their intended promises to business, according to a Thursday report from Pactera Technologies.

June 20, 2019

We’re not prepared for the end of Moore’s Law

It has fueled prosperity of the last 50 years. But the end is now in sight.

February 24, 2020

Alleged Prices of TSMC Silicon Wafers Appear

September 17, 2020

Data labeling

Data labeling is the bane of most AI projects, often devouring 25% or more of the total project timeframe. And if you don’t do it right? Garbage in, garbage out!

Data labeling for AI is set to become a billion-dollar market by 2023

Once crowdsourced for pennies on platforms like Amazon Mechanical Turk, labeling data for AI is swiftly becoming a hugely lucrative market — with much of the work done in places with cheap labor like China, India and Malaysia.

March 25, 2019

The Ultimate Guide to Data Labeling for Machine Learning

Everything you need to know before engaging a data labeling service. Act strategically, build high quality datasets, and reclaim valuable time to focus on innovation.

September 30, 2020

Human analyst alert and decision fatigue

Whether the monotony of labeling many redundant records or dealing with the plethora of false positive alerts common in many applications, the result is low productivity and human fatigue errors.

Crying wolf: Combatting cybersecurity alert fatigue

Not only must security pros contend with ever-increasing attacks to their networks, they also must finagle the tool sets guarding their systems to make certain settings are as they should be, reports Greg Masters.

June 9, 2017

What Causes IT Alert Fatigue and How to Avoid It

A recent survey by the Cloud Security Alliance found, among other things, that out of a sampling of 2,542 anomalous event alerts, only 23.2 percent were actual threats — the rest were false positives. This is one reason “alert fatigue” persists, plaguing security teams in many organizations and across industries. To fully understand the implications of alert fatigue and the role it plays in failing to prevent damaging breaches, it’s important to dig a little deeper into its causes.

April 21, 2017

Data bias

Are you certain that your training data is unbiased? How do you know? Government regulation is coming and your company’s reputation is at risk.

House Lawmakers Discuss How to Curb Bias in AI

Auditing by third parties, self-testing of algorithms and expanding regulatory authority are floated as ideas for keeping AI fair.

February 13, 2020

Amazon scraps secret AI recruiting tool that showed bias against women

Amazon.com Inc's AMZN.O machine-learning specialists uncovered a big problem: their new recruiting engine did not like women.

October 10, 2018

News About the Problems We Address