Maury
Nichols sent this warning: “Rather
frighteningly as more and more doctor’s offices are going to video
conferencing (as opposed to having patients come into the office) one
of the top solutions they are selecting is --Zoom.”
Zoom
Hires Security Heavyweights to Fix Flaws
Correcting
EU ‘guidance?’
Lack
of Vision: A Comment on the EU’s White Paper on Artificial
Intelligence
In
February 2020 the EU published its white paper on ‘Artificial
Intelligence: A European approach to excellence and trust’. This
is likely to form the core of future policy and legislation within
the EU and as such will have global impact on standards and norms.
In this comment piece we survey the five sections of the white paper
and then critically examine three themes, namely, i. regulatory
signalling, ii. the risk-based approach, and, iii. the auditing
styles. The key takeaway is that the
white paper, and the EU’s strategy at large, is ambiguous and lacks
vision, which, if unchecked, is likely to have a negative
impact on EU competitiveness in the development of AI solutions and
services.
Garbage
in, garbage out – the AI version.
A
Legal Framework for AI Training Data
Building
on the recently published White Paper of the EU Commission on
Artificial Intelligence (AI), this article shows that training
data for AI do not only play a key role in the development of AI
applications, but are currently only inadequately captured by EU law.
In this, I focus on three central risks of AI training data: risks
of data quality, discrimination and innovation. Existing EU law,
with the new copyright exception for text and data mining, only
addresses a part of this risk profile adequately. Therefore, the
article develops the foundations for a discrimination-sensitive
quality regime for data sets and AI training, which emancipates
itself from the controversial question of the applicability of data
protection law to AI training data. Furthermore, it spells out
concrete guidelines for the re-use of personal data for AI training
purposes under the GDPR.
Another
‘spare time; option.
Why
learning Python is now essential for all data scientists
… The
advancement of technologies like machine learning, artificial
intelligence, and predictive analysis, data science is gaining even
more pace with each passing day. It is becoming a popular career
choice among people. While it is beneficial for data scientists to
know more than one programming language, they must start by grasping
at least one language with clarity. Furthermore, data scientists
point out that obtaining and cleaning the data forms 80 percent of
their job. The data can be messy, has missing values, inconsistent
formatting, malformed records and nonsensical outliers in practice.
While there might be multiple tools out there to assist in this job,
Python is the most preferred. There are more than a few reasons
behind it.
(Related)
PyCaret:
An open source low-code machine learning library in Python |
MarkTechPost
If
you are looking for a Python library to train and deploy supervised
and unsupervised machine learning models in a low-code environment,
then you should try PyCaret. From data preparation to model
deployment, PyCaret allows all these processes in minimum time using
your choice of notebook environment.
PyCaret
enables data scientists and data engineers to perform end-to-end
experiments quickly and efficiently. While most of the open-source
machine learning libraries require complex lines of codes, PyCaret is
a useful low-code library that can increase the performance in
complex machine learning tasks with only a few lines of code.
PyCaret
is essentially a Python wrapper around several machine learning
libraries and frameworks such as
scikit-learn,
XGBoost,
Microsoft
LightGBM,
spaCy,
and many more.
Will
this become the new normal, post-virus?
Cuomo
signs order allowing New Yorkers to obtain marriage licenses and
perform ceremonies remotely
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