Work in the Wake of AI: Adapting to Algorithmic Management and Generative Technologies
Current legal frameworks are struggling to keep pace with the issues arising
from algorithmic management. Traditional employment laws, such as those
concerning unfair dismissal, often do not extend protections to “workers” as a
distinct category. Furthermore, discrimination laws require proof that the
discriminatory behaviour was due or related to the protected characteristic,
which is difficult to ascertain and prove with algorithmic systems. To mitigate
these issues, the researchers recommend a series of measures. These include
ensuring algorithmic systems respect workers’ rights, granting workers the right
to opt out of automated decisions such as job termination, banning excessive
data monitoring and establishing the right to a human explanation for decisions
made by algorithms. ... Despite the rapid deployment of GenAI and the
introduction of policies around its use, concerns about misuse are still
prevalent among nearly 40% of tech leaders. While recognising AI’s potential,
55% of tech leaders have yet to identify clear business applications for GenAI
beyond personal productivity enhancements, and budget constraints remain a
hurdle for some.
The rise of sustainable data centers: Innovations driving change
Data centers contribute significantly to global carbon emissions, making it
essential to adopt measures that reduce their carbon footprint. Carbon usage
effectiveness (CUE) is a metric used to assess a data center's carbon
emissions relative to its energy consumption. By minimizing CUE, data centers
can significantly lower their environmental impact. ... Cooling is one of the
largest energy expenses for data centers. Traditional air cooling systems are
often inefficient, prompting the need for more advanced solutions. Free
cooling, which leverages outside air, is a cost-effective method for data
centers in cooler climates. Liquid cooling, on the other hand, uses water or
other coolants to transfer heat away from servers more efficiently than air.
... Building and retrofitting data centers sustainably involves adhering to
green building certifications like Leadership in Energy and Environmental
Design (LEED) and Building Research Establishment Environmental Assessment
Method (BREEAM). These certifications ensure that buildings meet high
environmental performance standards.
How AIOps Is Poised To Reshape IT Operations
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A meaningfully different, as yet underutilized, high-value data set can be
derived from the rich, complex interactions of information sources and users
on the network, promising to triangulate and correlate with the other data
sets available, elevating their combined value to the use case at hand. The
challenge in leveraging this source is that the raw traffic data is impossibly
massive and too complex for direct ingestion. Further, even compressed into
metadata, without transformation, it becomes a disparate stream of rigid,
high-cardinality data sets due to its inherent diversity and complexity. A new
breed of AIOps solutions is poised to overcome this data deficiency and
transform this still raw data stream into refined collections of organized
data streams that are augmented and edited through intelligent feature
extraction. These solutions use an adaptive AI model and a multi-step
transformation sequence to work as an active member of a larger AIOps
ecosystem by harmonizing data feeds with the workflows running on the target
platform, making it more relevant and less noisy.
Addressing Financial Organizations’ Digital Demands While Avoiding Cyberthreats
The financial industry faces a difficult balancing act, with multiple
conflicting priorities at the forefront. Organizations must continually
strengthen security around their evolving solutions to keep up in an
increasingly competitive and fast-moving landscape. But while strong security
is a requirement, it cannot impact usability for customers or employees in an
industry where accessibility, agility and the overall user experience are key
differentiators. One of the best options to balancing these priorities is the
utilization of secure access service edge (SASE) solutions. This model
integrates several different security features such as secure web gateway
(SWG), zero-trust network access (ZTNA), next-generation firewall (NGFW),
cloud access security broker (CASB), data loss prevention (DLP) and network
management functions, such as SD-WAN, into a single offering delivered via the
cloud. Cloud-based delivery enables financial organizations to easily roll out
SASE services and consistent policies to their entire network infrastructure,
including thousands of remote workers scattered across various locations, or
multiple branch offices to protect private data and users, as well as deployed
IoT devices.
Three Signs You Might Need a Data Fabric
One of the most significant challenges organizations face is data silos and
fragmentation. As businesses grow and adopt new technologies, they often
accumulate disparate data sources across different departments and platforms.
These silos make it tougher to have a holistic view of your organization's
data, resulting in inefficiencies and missed opportunities. ... You understand
that real-time analytics is crucial to your organization’s success. You need
to respond quickly to changing market conditions, customer behavior, and
operational events. Traditional data integration methods, which often rely on
batch processing, can be too slow to meet these demands. You need real-time
analytics to:Manage the customer experience. If enhancing a customer’s
experience through personalized and timely interactions is a priority,
real-time analytics is essential. Operate efficiently. Real-time monitoring
and analytics can help optimize operations, reduce downtime, and improve
overall efficiency. Handle competitive pressure. Staying ahead of competitors
requires quick adaptation to market trends and consumer demands, which is
facilitated by real-time insights.
The Tension Between The CDO & The CISO: The Balancing Act Of Data Exploitation Versus Protection
While data delivers a significant competitive advantage to companies when used
appropriately, without the right data security measures in place it can be
misused. This not only erodes customers’ trust but also puts the company at
risk of having to pay penalties and fines for non-compliance with data
security regulations. As data teams aim to extract and exploit data for the
benefit of the organisation, it is important to note that not all data is
equal. As such a risk-based approach must be in place to limit access to
sensitive data across the organisation. In doing this the IT system will have
access to the full spectrum of data to join and process the information, run
through models and identify patterns, but employees rarely need access to all
this detail. ... To overcome the conflict of data exploitation versus
security and deliver a customer experience that meets customer expectations,
data teams and security teams need to work together to achieve a common
purpose and align on the culture. To achieve this each team needs to listen to
and understand their respective needs and then identify solutions that work
towards helping to make the other team successful.
Content Warfare: Combating Generative AI Influence Operations
Moderating such enormous amounts of content by human beings is impossible.
That is why tech companies now employ artificial intelligence (AI) to moderate
content. However, AI content moderation is not perfect, so tech companies add
a layer of human moderation for quality checks to the AI content moderation
processes. These human moderators, contracted by tech companies, review
user-generated content after it is published on a website or social media
platform to ensure it complies with the “community guidelines” of the
platform. However, generative AI has forced companies to change their approach
to content moderation. ... Countering such content warfare requires
collaboration across generative AI companies, social media platforms,
academia, trust and safety vendors, and governments. AI developers should
build models with detectable and fact-sensitive outputs. Academics should
research the mechanisms of foreign and domestic influence operations emanating
from the use of generative AI. Governments should impose restrictions on data
collection for generative AI, impose controls on AI hardware, and provide
whistleblower protection to staff working in the generative AI
companies.
OpenAI reportedly nears breakthrough with “reasoning” AI, reveals progress framework
OpenAI isn't alone in attempting to quantify levels of AI capabilities. As
Bloomberg notes, OpenAI's system feels similar to levels of autonomous driving
mapped out by automakers. And in November 2023, researchers at Google DeepMind
proposed their own five-level framework for assessing AI advancement, showing
that other AI labs have also been trying to figure out how to rank things that
don't yet exist. OpenAI's classification system also somewhat resembles
Anthropic's "AI Safety Levels" (ASLs) first published by the maker of the
Claude AI assistant in September 2023. Both systems aim to categorize AI
capabilities, though they focus on different aspects. Anthropic's ASLs are
more explicitly focused on safety and catastrophic risks (such as ASL-2, which
refers to "systems that show early signs of dangerous capabilities"), while
OpenAI's levels track general capabilities. However, any AI classification
system raises questions about whether it's possible to meaningfully quantify
AI progress and what constitutes an advancement. The tech industry so far has
a history of overpromising AI capabilities, and linear progression models like
OpenAI's potentially risk fueling unrealistic expectations.
White House Calls for Defending Critical Infrastructure
The memo encourages federal agencies "to consult with regulated entities to
establish baseline cybersecurity requirements that can be applied across
critical infrastructures" while maintaining agility and adaptability to mature
with the evolving cyberthreat landscape. ONCD and OMB also urged agencies and
federal departments to study open-source software initiatives and the benefits
that can be gained by establishing a governance function for open-source
projects modeled after the private sector. Budget submissions should identify
existing departments and roles designed to investigate, disrupt and dismantle
cybercrimes, according to the memo, including interagency task forces focused
on combating ransomware infrastructure and the abuse of virtual currency.
Meanwhile, the administration is continuing its push for agencies to only use
software provided by developers who can attest their compliance with minimum
secure software development practices. The national cyber strategy - as well
as the joint memo - directs agencies to "utilize grant, loan and other federal
government funding mechanisms to ensure minimum security and resilience
requirements" are incorporated into critical infrastructure projects.
Unifying Analytics in an Era of Advanced Tech and Fragmented Data Estates
“Data analytics has a last-mile problem,” according to Alex Gnibus, technical
product marketing manager, architecture at Alteryx. “In shipping and
transportation, you often think of the last-mile problem as that final stage
of getting the passenger or the delivery to its final destination. And it’s
often the most expensive and time-consuming part.” For data, there is a
similar problem; when putting together a data stack, enabling the business at
large to derive value from the data is a key enabler—and challenge—of a modern
enterprise. Achieving business value from data is the last mile, which is made
difficult by complex, numerous technologies that are inaccessible to the final
business user. Gnibus explained that Alteryx solves this problem by acting as
the “truck” that delivers tangible business value from proprietary data,
offering data discovery, use case identification, preparation and analysis,
insight-sharing, and AI-powered capabilities. Acting as the easy-to-use
interface for a business’ data infrastructure, Alteryx is the AI platform for
large-scale enterprise analytics that offers no-code, drag-and-drop
functionality that works with your unique data framework configuration as it
evolves.
Quote for the day:
“Success is most often achieved by
those who don't know that failure is inevitable.” --
Coco Chanel
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