Outsmarting ML Biases: A Checklist
Machine learning algorithms relentlessly search for a solution. In the case of
GANs, the generator and discriminator network somehow finds a way to fool each
other. The result is a Deepfake. Not that deep fakes are harmless but ML is used
in more critical industries such as healthcare. So when a model that is fed with
an underrepresented dataset is used, the chances of misdiagnosis increases.
“Each ML algorithm has a strategy to answer optimally to your question,” warned
Luca. ... The different definitions makes things even more cumbersome for the
data scientist. Citing the work on the impossibility of fairness, Luca also
explained why some notions of fairness are mutually incompatible and cannot be
satisfied simultaneously. “ There is no single universal metric for quantifying
fairness that can be applied to all ML problems,” he added. No matter how fool
proof the data curation process is, loopholes might creep in. So, what are these
loopholes? ... When it comes to ML fairness toolkits, Google’s TensorFlow team
has been on the top. The team has been developing multiple tools to assist niche
areas within the realms of fairness debate. The whole debate around ML fairness
is forcing companies like Google to establish an ecosystem of fairer ML practice
through their tools.
Visual Studio Code comes to Raspberry Pi
There are already some great editors, but nothing of the calibre of VS Code. I
can take my $35 computer, plug it into a keyboard and mouse, connect a monitor
and a TV and code in a wide range of languages from the same place. I see kids
learning Python at school using one tool, then learning web development in an
after-school coding club with a different tool. They can now do both in the same
application, reducing the cognitive load – they only have to learn one tool, one
debugger, one setup. Combine this with the new Raspberry Pi 400 and you have an
all-in-one solution to learning to code, reminiscent of my ZX Spectrum of
decades ago, but so much more powerful. The second reason is to me the most
important — it allows kids to share the same development environment as their
grown-ups. Imagine the joy of a 10-year-old coding Python using VS Code on their
Raspberry Pi plugged into the family TV, then seeing their Mum working from home
coding Python in exactly the same tool on her work laptop as part of her job as
an AI engineer or data scientist. It also makes it easier when Mum has to
inevitably help with unblocking the issues that always come up with learners.
This new open source tool could improve data quality within the enterprise
While Soda SQL is more geared toward data engineers, Soda also offers a hosted
service geared toward the business user and, specifically, the chief data
officer (CDO). Interest in data testing and monitoring might start with the CDO
when they recognize the need to ensure quality data feeding executive
dashboards, machine learning models, and more. At the same time, data
engineers, responsible for building data pipelines (transforming, extracting,
and preparing data for usage), just need to do some minimal checks to ensure
they're not shipping faulty data. Or, you might have a data platform engineer
who just wants hands-off monitoring after connecting to the data platform
warehouse. In this universe, data testing and data monitoring are two distinct
things. In both cases, Baeyens said, "The large majority of people with which we
speak have an uncomfortable feeling that they should be doing more with data
validation, data testing, and monitoring, but they don't know where to start, or
it's just kind of blurry for them." Soda is trying to democratize data
monitoring, in particular, by making it easy for non-technical,
business-oriented people to build the data monitors.
Cybersecurity is still the #1 risk for manufacturers
We see lots of incidents, but there’s no obligation for the owners and operators
to disclose the incident. The incidents that you see in the media are often just
a small percentage of the incidents that you actually see in the public eye. We
know of many serious incidents that you’ll never read in the headlines and for
good reason, really. So, what I would do is say that cybersecurity is still a
priority for many organizations. It’s their number one risk, and it’s something
that they’re dealing with every day. ... Ask the question, “What is the problem
that I’d like to solve, as a result of implementing digital where any other
solution couldn’t?” If you’re already on that journey, I would be looking back
and reviewing and saying, “Does my digital solution so far answer the question?
Is it solving the problem that I want to solve as a result of a digital
solution?” In a recent study, we found that less than 20% of organizations have
more than a third of the employees actually trained in digital, and trained in
their digital strategy as an organization. But, more than 60% of our customers
actually have a digital strategy, so there’s a mismatch between customers in
heading out on the digital journey, but not really taking their employees with
them.
Keeping control of data in the digital supply chain
While organisations will never have as much control over a supplier’s security
as they do their own, they can take steps to minimise risks. Security standards
must be set out within service level agreements (SLAs), for instance, insisting
that the third-party meets ISO 27001 accreditation as a minimum and ensuring
that the supplier has a framework of policies and procedures governing
information risk management processes. Unfortunately, this approach is rare. The
UK Government’s Data Breaches Survey 2019 indicates that less than one in five
businesses (18%) demanded that their suppliers have any form of cybersecurity
standard or good practice guidelines in place. The issue also becomes more
complicated when the sheer scale and intricacy of the average supply chain
network comes into play. A firm may have its data stolen from a company three or
four connections deep into the supply chain. If the breached third-party lacks
the ability to detect an attack itself, a company’s data could be in the hands
of criminals for months before they are finally alerted to the breach. Even if a
security breach originates with a third party, it will carry just as much of a
financial and reputational cost as a direct attack on the organisation’s own
network.
Metaethics, Meta-Intelligence And The Rise Of AI
The notion of ethics has evolved. Decisions around right and wrong always
depended on human cognition and were guided by popular sentiments and socially
acceptable norms. Now, with the rise of AI, machines are slowly taking over
human cognition functions, a phenomenon that author Ray Kurzweil predicts will
increase over time and culminate in the advent of singularity where machines
irrevocably take over humans, possibly at some distant point in the future. This
trend is causing technologists, researchers, policymakers and society at large
to rethink how we interpret and implement ethics in the age of AI. ... To face
the challenges of the future, we also need to develop a new discipline of
meta-intelligence by taking inspiration from the concepts of metadata and
metaethics. Doing so will help us improve the traceability and trustworthiness
of AI-driven insights. The concept of meta-intelligence has been doing the
rounds of thought leadership for the last few years, especially led by people
thinking about and working on singularity. The pace of technological evolution
and the rise of AI has become essential for human progress today. Businesses
around the world are getting impacted by the transformative power of these
technologies.
Qualcomm's new X65 5G modem downloads data at lightning-fast 10Gbps speeds
With the X65, unveiled Tuesday, users will get a bump in speed but also see
better battery life. Coverage will improve, latency will decrease and
applications will be even more responsive than they are with Qualcomm's earlier
X60 modem technology. And capacity will be "massive," letting more people on a
network make reliable and crisp video calls with their doctors and face off
against rivals in streaming games. With the previous-generation X60 modem, just
now arriving in smartphones like Samsung's Galaxy S21, you can download data
over 5G networks at up to 7.5Gbps and upload information as fast as 3Gbps, only
slightly faster than the previous generation of modem. But the X60 also has the
ability to aggregate the slower but more reliable sub-6 networks with the faster
but finicky millimeter-wave spectrum, boosting overall performance and helping
users see faster average speeds. The X65 has the same benefit. While it's
unlikely that you'll regularly -- or maybe even ever -- see 10Gbps download
speeds, you'll consistently see speeds that are magnitudes faster than your
current 4G smartphone.
Using NGINX to Serve .NET Core, Nodejs, or Static Contents
NGINX is a high-performance HTTP server as well as a reverse proxy. Unlike
traditional servers, NGINX follows an event-driven, asynchronous architecture.
As a result, the memory footprint is low and performance is high. If you’re
running a Node.js-based web app or .NET Core Web Application, you should
seriously consider using NGINX as a reverse proxy. NGINX can be very efficient
in serving static assets as well. For all other requests, it will talk to your
Node.js back end or .NET Core Web Application and send the response to the
client. ... Although the focus of this article is NGINX. But we will be dealing
with a little bit of bash commands, NodeJS, and .NET Core. I have written about
all of these topics on DZone, so you check my other articles for background
information on these topics if needed. ... A reverse proxy server is a web
server that accepts requests and sends them to another web server which actually
creates the responses for those requests. The responses are sent back to the
proxy server who forwards them to the clients who issued the corresponding
requests. Nginx is a web server that can act as a reverse proxy for ASP.NET Core
applications and which is also very good at serving static content.
To succeed in an AI world, students must learn the human traits of writing
AI cannot yet plan and does not have a purpose. Students need to hone skills in
purposeful writing that achieves their communication goals. Unfortunately, the
NAPLAN regime has hampered teaching writing as a process that involves planning
and editing. This is because it favours time-limited exam-style writing for no
audience. Students need to practise writing in which they are invested, that
they care about and that they hope will effect change in the world as well as in
their genuine, known readers. This is what machines cannot do. AI is not yet as
complex as the human brain. Humans detect humour and satire. They know words can
have multiple and subtle meanings. Humans are capable of perception and insight;
they can make advanced evaluative judgements about good and bad writing. There
are calls for humans to become expert in sophisticated forms of writing and in
editing writing created by robots as vital future skills. Nor does AI have a
moral compass. It does not care. OpenAI’s managers originally refused to release
GPT-3, ostensibly because they were concerned about the generator being used to
create fake material, such as reviews of products or election-related
commentary.
Living, and Breathing Data Governance, Security, and Regulations
A top-down approach to building data and analytics platforms, based on data
governance best practices and policies, is often the choice. This approach can
provide a cohesive and robust solution that complies well with privacy
regulations, and where all the components interact well, adhering to strict
security policies. Unfortunately, it can often become cumbersome for users and
slow the time-to-value, with data consumers forced to adapt their data usage
and consumption to the strict compliance and security-driven protocols driving
the platform. On the flip side, a bottom-up approach to data analytics is
engineering and design-focused, with the goal of introducing incremental
deliverables that add value to the platform in response to the user’s needs.
... Whether top-down or bottom-up, it’s critical for organizations to start
with documenting privacy, security, data risks, controls, and technology needs
around data access to address topics like culture of federated data ownership,
adoption of self-service or collaboration across teams around critical data
sets, and enterprise-wide technology standards for certain key areas.
Quote for the day:
“Believe in your infinite potential. Your only limitations are those you set upon yourself.” -- Roy T. Bennett
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