They
keep getting bigger!
Nikkei
worker tricked into transferring $29 million into scammer’s bank
account
…
In
a press
release,
the largest independent business media group in Asia which lends its
name to Japan’s leading stock index, revealed that an employee of
its American subsidiary had been fooled into transferring the money
into a bank account after a fraudster posed as a Nikkei management
executive.
Because
my students missed this one. (Would I get rich if I developed an
“Alexa, create an alabi” skill?)
Police
turn to Alexa in murder case
In
brief: Not
for the first time, police are turning to Amazon’s digital
assistant Alexa in the hope of solving a murder.
The
victim in question was 32-year-old Silvia Galva. Her boyfriend,
43-year-old Reechard Crespo, claims the death was an accident that
occurred as the two engaged in a physical altercation. He says the
pair were having an argument, and Crespo was trying to drag her off
his bed. Galva grabbed a spear with a 12-inch blade in an attempt to
stop herself being pulled, but the spear broke and impaled her in her
chest. Crespo pulled the blade out in an alleged attempt to save
her, but Galva died.
According
to the Sun
Sentinel,
police obtained a search warrant for anything recorded on the two
Alexa-powered devices found in the apartment.
(Related)
Amazon
wants Alexa to run your life. First, it must know everything about
you
(Related)
Not to be outdone, Microsoft is pushing Cortana to new tools.
Microsoft
is building Cortana into Outlook as an AI that helps you stay
productive
This
seems obvious to us computer auditors.
Why
Audits Are the Way Forward for AI Governance
When
organizations use algorithms to make decisions, biases built into the
underlying data create not just challenges but also engender enormous
risk. What should companies do to manage such risks? The way
forward is to conduct artificial intelligence (AI) audits, according
to this opinion piece by Kartik
Hosanagar,
a Wharton professor of operations, information and decisions who
studies technology and the digital economy. This column is based on
ideas from his book, A
Human’s Guide to Machine Intelligence.
… An
audit process would begin with the creation of an inventory of all
machine learning models being employed at a company, the specific
uses of such models, names of the developers and business owners of
models, and risk ratings — measuring, for example, the social or
financial risks that would come into play should a model fail —
which, in turn, might help determine the need for an audit. Were a
model audit to go forward, it would evaluate the inputs (training
data), model, and the outputs of the model. Training data would need
to be evaluated for data quality as well as for potential biases
hidden in the data.
Important
changes, but not revolutionary?
Microsoft
beefs up Word, Excel, and Outlook with machine learning
… A
preview of Ideas in Word for the web is rolling out for Office 365
commercial users. It’s an AI-powered proofreader that taps natural
language processing and machine learning to deliver intelligent,
contextually aware suggestions that could improve a document’s
readability. For instance, Ideas in Word will recommend ways to make
phrases more concise, clear, and inclusive. And when Ideas in Word
comes across a particularly tricky snippet, it will put forward
synonyms and alternative phrasings, like “society” as a
substitute for “society as a whole.”
For
the Reference Shelf.
Blockchain:
What Information Professionals Need to Know
Via
LLRX – Blockchain: What Information Professionals Need to Know –
Anna Irvin, Ph.D. and Janice E. Henderson, Esq. presented this
comprehensive 64 page guide at the LLAGNY Education Committee Program
on October 15, 2019. The guide is an multidisciplinary resource that
includes: articles from law, business and finance journals, CLE
programs/materials, smart contracts, Westlaw and Practical Law
citations, sources on the impact of blockchain on the U.S. government
and the international regulatory landscape, as well as all states
with blockchain and cybersecurity laws (introduced, pending and
failed).
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