When
will we start taking election security seriously? They talk about
old hardware. They should be concerned about the data!
Two
computers stolen from Atlanta polling site contain statewide voter
data
Two
computers containing statewide voter data were stolen from an Atlanta
polling site on Tuesday ahead of a vote for a city school board
election.
The
Atlanta Journal-Constitution reports the
computers were used to check in voters and contained names,
addresses, birthdates and driver’s license information for every
voter in the state, but not social security numbers, according to
Fulton County elections director Richard Barron.
… Officials
said the burglary did not impact the election Tuesday in any way.
Barron
noted that the stolen equipment can’t be used for other purposes as
they do not connect to the internet.
… “A
Palm Pilot from 2000 is probably more sophisticated than those
things. They’re pretty primitive pieces of equipment.”
(Related)
Experts
Warn of Voting Machine Vulnerabilities in N.C.
At
a recent meeting in Greensboro, N.C., a cybersecurity expert told an
emergency meeting of the NAACP that even the newest era of voting
machines can be vulnerable to reprogramming by hackers.
Least
common denominator level. No excuses for falling below this level.
NIST
Releases Preliminary Draft of Privacy Framework
The
U.S. Department of Commerce’s National Institute of Standards and
Technology (“NIST”) now has released the preliminary draft of the
“NIST
Privacy Framework: A Tool for Improving Privacy through Enterprise
Risk Management.”
NIST is seeking
comments on
the preliminary draft of the Privacy Framework and plans to use these
comments to develop version 1.0 of the Privacy Framework. Comments
are due by 5:00 p.m. ET on October 24, 2019.
Order
from chaos.
The
Seven Patterns Of AI
From
autonomous vehicles, predictive analytics applications, facial
recognition, to chatbots, virtual assistants, cognitive automation,
and fraud detection, the use cases for AI are many. However,
regardless of the application of AI, there is commonality to all
these applications. Those who have implemented hundreds or even
thousands of AI projects realize that despite all this diversity in
application, AI use cases fall into one or more of seven common
patterns.
- The Hyperpersonalization Pattern: Treat each customer as an individual
- Autonomous systems Pattern: Reducing the need for manual labor
- AI powered predictive analytics
- The Conversational Pattern: Machines that can communicate as humans do
- Identifying Patterns and anomalies with AI
- Machines that can recognize the world: The Recognition Pattern
- Solving the Puzzle: The Goal-Driven Systems Pattern
While
these might seem like discrete patterns that are implemented
individually in typical AI projects, in reality, we have seen
organizations combine one or more of these seven patterns to realize
their goals. By companies thinking of AI projects in terms of these
patterns it will help them better approach, plan, and executate AI
projects.
Worth teaching our students to score high?
LinkedIn
launches skills assessments, tests that let you beef up your
credentials for job hunting
LinkedIn will now offer a new feature called
Skills Assessments: short, multiple-choice tests that users can take
to verify their knowledge in areas like computer languages, software
packages and other work-related skills.
… First up are English-language tests covering
some 75 different skills, all free to take
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