Can I read your mind? How close are we to mind-reading technologies?
Technology nowadays is already heavily progressing in artificial intelligence,
so it doesn’t seem too farfetched. Humans have already developed
brain-computer interface (BCI) technologies that can safely be used on humans.
... How would the government play a role in these mind-reading technologies?
How would it effect the eligibility of use of the technology? Don’t you think
some unethical play would be prevalent, because I sure do. I’m not very
ethically inclined to believe these companies aren’t sending our data to other
companies without our consent. I found this term “Neurorights” in a Vox
article, “Brain-reading tech is coming. The law is not ready to protect us”
written by Sigal Samuel. It’s a good read, and I think she demonstrates well
into the depth of how this would impact society from a privacy concern
standpoint. She discusses having 4 core new rights protected within the law:
The right to your cognitive library, mental privacy, mental integrity, and
psychological continuity. She mentions, “brain data is the ultimate refuge of
privacy”. Once it’s collected, I believe you can’t get it back. There needs to
be strict laws enforced if this were to become a ubiquitous technology.
It's The End Of Infrastructure-As-A-Service As We Know It: Here's What's Next
Containers are the next step in the abstraction trend. Multiple containers can
run on a single OS kernel, which means they use resources more efficiently than
VMs. In fact, on the infrastructure required for one VM, you could run a dozen
containers. However, containers do have their downsides. While they're more
space efficient than VMs, they still take up infrastructure capacity when idle,
running up unnecessary costs. To reduce these costs to the absolute minimum,
companies have another choice: Go serverless. The serverless model works best
with event-driven applications — applications where a finite event, like a user
accessing a web app, triggers the need for compute. With serverless, the company
never has to pay for idle time, only for the milliseconds of compute time used
in processing a request. This makes serverless very inexpensive when a company
is getting started at a small volume while also reducing operational overhead as
applications grow in scale. Transitioning to containerization or a serverless
model requires major changes to your IT teams' processes and structure and
thoughtful choices about how to carry out the transition itself.
9 Future of Work Trends Post-COVID-19
Before COVID-19, critical roles were viewed as roles with critical skills,
or the capabilities an organization needed to meet its strategic goals. Now,
employers are realizing that there is another category of critical roles —
roles that are critical to the success of essential workflows. To build the
workforce you’ll need post-pandemic, focus less on roles — which group
unrelated skills — than on the skills needed to drive the organization’s
competitive advantage and the workflows that fuel that advantage. Encourage
employees to develop critical skills that potentially open up multiple
opportunities for their career development, rather than preparing for a
specific next role. Offer greater career development support to employees in
critical roles who lack critical skills. ... After the global financial
crisis, global M&A activity accelerated, and many companies were
nationalized to avoid failure. As the pandemic subsides, there will be a
similar acceleration of M&A and nationalization of companies. Companies
will focus on expanding their geographic diversification and investment in
secondary markets to mitigate and manage risk in times of disruption. This
rise in complexity of size and organizational management will create
challenges for leaders as operating models evolve.
South African bank to replace 12m cards after employees stole master key
"According to the report, it seems that corrupt employees have had access to
the Host Master Key (HMK) or lower level keys," the security researcher
behind Bank Security, a Twitter account dedicated to banking fraud, told
ZDNet today in an interview. "The HMK is the key that protects all the keys,
which, in a mainframe architecture, could access the ATM pins, home banking
access codes, customer data, credit cards, etc.," the researcher told ZDNet.
"Access to this type of data depends on the architecture, servers and
database configurations. This key is then used by mainframes or servers that
have access to the different internal applications and databases with stored
customer data, as mentioned above. "The way in which this key and all the
others lower-level keys are exchanged with third party systems has different
implementations that vary from bank to bank," the researcher said. The
Postbank incident is one of a kind as bank master keys are a bank's most
sensitive secret and guarded accordingly, and are very rarely compromised,
let alone outright stolen.
What matters most in an Agile organizational structure
An Agile organizational strategy that works for one organization won't
necessarily work for another. The chapter excerpt includes a Spotify org
chart, which the authors describe as, "Probably the most frequently emulated
agile organizational model of all." But an Agile model that serves as a
standard of success won't necessarily replicate to another organization
well. Agile software developers aim to better meet customer needs. To do so,
they need to prioritize, release and adapt software products more easily.
Unlike the Spotify-inspired tribe structure, Agile teams should remain
located closely to the operations teams that will ultimately support and
scale their work, according to the authors. This model, they argue in Doing
Agile Right, promotes accountability for change, and willingness to innovate
on the business side. Any Agile initiative should follow the sequence of
"test, learn, and scale." People at the top levels must accept new ideas,
which will drive others to accept them as well. Then, innovation comes from
the opposite direction. "Agile works best when decisions are pushed down the
organization as far as possible, so long as people have appropriate
guidelines and expectations about when to escalate a decision to a higher
level."
What is process mining? Refining business processes with data analytics
Process mining is a methodology by which organizations collect data from
existing systems to objectively visualize how business processes operate and
how they can be improved. Analytical insights derived from process mining
can help optimize digital transformation initiatives across the
organization. In the past, process mining was most widely used in
manufacturing to reduce errors and physical labor. Today, as companies
increasingly adopt emerging automation and AI technologies, process mining
has become a priority for organizations across every industry. Process
mining is an important tool for organizations that are committed to
continuously improving IT and business processes. Process mining begins by
evaluating established IT or business processes to find repetitive tasks
that can by automated using technologies such as robotic process automation
(RPA), artificial intelligence and machine learning. By automating
repetitive or mundane tasks, organizations can increase efficiency and
productivity — and free up workers to spend more time on creative or complex
projects. Automation also helps reduce inconsistencies and errors in process
outcomes by minimizing variances. Once an IT or business process is
developed, it’s important to consistently check back to ensure the process
is delivering appropriate outcomes — and that’s where process mining comes
in.
How to improve cybersecurity for artificial intelligence
One of the major security risks to AI systems is the potential for
adversaries to compromise the integrity of their decision-making processes
so that they do not make choices in the manner that their designers would
expect or desire. One way to achieve this would be for adversaries to
directly take control of an AI system so that they can decide what outputs
the system generates and what decisions it makes. Alternatively, an attacker
might try to influence those decisions more subtly and indirectly by
delivering malicious inputs or training data to an AI model. For
instance, an adversary who wants to compromise an autonomous vehicle so that
it will be more likely to get into an accident might exploit vulnerabilities
in the car’s software to make driving decisions themselves. However,
remotely accessing and exploiting the software operating a vehicle could
prove difficult, so instead an adversary might try to make the car ignore
stop signs by defacing them in the area with graffiti. Therefore, the
computer vision algorithm would not be able to recognize them as stop signs.
This process by which adversaries can cause AI systems to make mistakes by
manipulating inputs is called adversarial machine learning.
Using a DDD Approach for Validating Business Rules
For modeling commands that can be executed by clients, we need to identify
them by assigning them names. For example, it can be something like
MakeReservation. Notice that we are moving these design definitions towards
a middle point between software design and business design. It may sound
trivial, but when it’s specified, it helps us to understand a system design
more efficiently. The idea connects with the HCI (human-computer
interaction) concept of designing systems with a task in mind; the command
helps designers to think about the specific task that the system needs to
support. The command may have additional parameters, such as date, resource
name, and description of the usage. ... Production rules are the heart of
the system. So far, the command has traveled through different stages which
should ensure that the provided request can be processed. Production rules
specified the actions the system must perform to achieve the desired state.
They deal with the task a client is trying to accomplish. Using the
MakeReservation command as a reference, they make the necessary changes to
register the requested resource as reserved.
7 Ways to Reduce Cloud Data Costs While Continuing to Innovate
This is a difficult time for enterprises, which need to tightly control
costs amid the threat of a recession while still investing sufficiently in
technology to remain competitive. ... This is especially true of analytics
and machine learning projects. Data lakes, ideally suited for machine
learning and streaming analytics, are a powerful way for businesses to
develop new products and better serve their customers. But with data teams
able to spin up new projects in the cloud easily, infrastructure must be
managed closely to ensure every resource is optimized for cost and every
dollar spent is justified. In the current economic climate, no business can
tolerate waste. But enterprises aren’t powerless. Strong financial
governance practices allow data teams to control and even reduce their cloud
costs while still allowing innovation to happen. Creating appropriate
guardrails that prevent teams from using more resources than they need and
ensuring workloads are matched with the correct instance types to optimize
savings will go a long way to reducing waste while ensuring that critical
SLAs are met.
Who Should Lead AI Development: Data Scientists or Domain Experts?
To lead these efforts ethically and effectively, Chraibi suggested data
scientists such as himself should be the driving force. “The data scientists
will be able to give you an insight into how bad it will be using a
machine-learning model” if ethical considerations are not taken into
account, he said. But Paul Moxon, senior vice president for data
architecture at Denodo Technologies, said his experience working with AI
development in the financial sector has given him a different perspective.
“The people who raised the ethics issues with banks—the original ones—were
the legal and compliance team, not the technologists,” he said. “The
technologists want to push the boundaries; they want to do what they’re
really, really good at. But they don’t always think of the inadvertent
consequences of what they’re doing.” In Moxon’s opinion, data scientists and
other technology-focused roles should stay focused on the technology, while
risk-centric roles like lawyers and compliance officers are better suited to
considering broader, unintended effects. “Sometimes the data scientists
don’t always have the vision into how something could be abused. Not how it
should be used but how it could be abused,” he said.
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