Does your organization need a data fabric?
So, while real-time data integration and performing data transformations are key capabilities of data fabrics, their defining capability is in providing centralized, standardized, and governed access to an enterprise’s data sources. “When evaluating data fabrics, it’s essential to understand that they interconnect with various enterprise data sources, ensuring data is readily and rapidly available while maintaining strict data controls,” says Simon Margolis, associate CTO of AI/ML at SADA. “Unlike other data aggregation solutions, a functional data fabric serves as a “one-stop shop” for data distribution across services, simplifying client access, governance, and expert control processes.” Data fabrics thus combine features of other data governance and dataops platforms. They typically offer data cataloging functions so end-users can find and discover the organization’s data sets. Many will help data governance leaders centralize access control while providing data engineers with tools to improve data quality and create master data repositories. Other differentiating capabilities include data security, data privacy functions, and data modeling features.
The Crucial Role of Manual Data Annotation and Labeling in Building Accurate AI Systems
Automatic annotation systems frequently suffer from severe limitations, most notably accuracy. Despite its rapid evolution, AI can still misunderstand context, fail to spot complex patterns, and perpetuate inherent biases in data. For example, an automated annotation system may mislabel an image of a person holding an object because it is unable to handle complicated scenarios or objects that overlap. Similarly, in textual data, automated systems may misread cultural references, idiomatic expressions, or sentiments. ... Manual annotation, on the other hand, uses human expertise to label data, ensuring accuracy, context understanding, and bias reduction. Humans are naturally skilled at understanding ambiguity, context, and making sense of complex patterns that machines may not be able to grasp. This knowledge is critical in applications requiring absolute precision, such as healthcare diagnostics, legal document interpretation, and ethical AI deployment. Manual annotation adds a level of justice that automated procedures typically lack. Human annotators can recognize and mitigate biases in datasets, whether they be racial, gender-based, or cultural.
AI orchestration: Crafting harmony or creating dependency?
In a collaborative relationship, both parties have an equal and complementary
role. AI excels at processing enormous amounts of data, pattern recognition
and certain types of analysis, while people excel at creativity, emotional
intelligence and complex decision-making. In this relationship, the human
keeps agency through critically evaluating AI outputs and making final
decisions. However, this relationship can easily veer into dependency where we
become unable or unwilling to perform tasks without AI help, even for tasks we
could previously do independently. As AI outputs have become amazingly
human-like and convincing, it is easy to accept them without critical
evaluation or understanding, even when knowing the content may be a
hallucination — an AI-generated output that appears convincing but is false or
misleading. ... As AI continues to advance and become more indistinguishable
from human interaction, the distinction between collaboration and dependency
becomes increasingly blurred. Or worse, as leading historian Yuval Noah Harari
— who is renowned for his works on the history and future of humankind points
out — intimacy is a powerful weapon which can then be used to persuade us.
The deflating AI bubble is inevitable — and healthy
Predicting the future is generally a fool’s errand as Nobel Prize winning
physicist, Niels Bohr recognized when he stated, “Prediction is very
difficult, especially about the future.” This was particularly true in the
early 1990s as the Web started to take off. Even internet pioneer and ethernet
standard co-inventor Robert Metcalfe was doubtful of the internet’s viability
when he predicted it had a 12-month future in 1995. Two years later, he
literally ate his words at the 1997 WWW Conference when he blended a printed
copy of his prediction with water and drank it. But there comes a point in a
new technology when its potential benefits become clear even if the exact
shape of its evolution is opaque. ... Many AI deployments and integrations are
not revolutionary, however, but add incremental improvements and value to
existing products and services. Graphics and presentation software provider
Canva, for example, has integrated Google’s Vertex AI to streamline its video
editing offering. Canva users can avoid a number of tedious editing steps to
create videos in seconds rather than minutes or hours. And WPP, the global
marketing services giant, has integrated Anthropic’s Claude AI service into
its internal marketing system, WPP Open.
Blockchain And Quantum Computing Are On A Collision Course
Herman warns, “The real danger regarding the future of blockchain is that it’s
used to build critical digital infrastructures before this serious security
vulnerability has been fully investigated. Imagine a major insurance company
putting at great expense all its customers into a blockchain-based network, and
then three years later having to rip it all out to install a quantum-secure
network, in its place.” Despite the bleak outlook, Herman offers a solution that
lies within the very technology posing the threat. Quantum cryptography,
particularly quantum random-number generators and quantum-resistant algorithms,
could provide the necessary safeguards to protect blockchain networks from
quantum attacks. “Quantum random-number generators are already being implemented
today by banks, governments, and private cloud carriers. Adding quantum keys to
blockchain software, and to all encrypted data, will provide unhackable security
against both a classical computer and a quantum computer,” he notes. Moreover,
the U.S. National Institute of Standards and Technology (NIST) has stepped in to
address the issue by releasing standards for post-quantum cryptography.
Low-Code Solutions Gain Traction In Banking And Insurance Digital Transformation
“Digital transformation should be focused on quick wins so that organizations
can start seeing the ROI much sooner,” he said, noting that digital
transformation is not just about adopting new technologies — it’s about
fundamentally rethinking how businesses operate and deliver value to their
customers. One of the recurring challenges he identified is the issue of
onboarding in the banking sector. Despite variations in onboarding times from
one bank to another, internal inefficiencies often cause delays. A portion of
these delays stems from internal traffic rather than external factors. To
address this, Arun MS advocated for a shift toward self-service portals, where
customers can take control of processes like document submission. “Engaging
customers as stakeholders in the process reduces internal bottlenecks and speeds
up the overall timeline for onboarding,” he said. This approach not only
enhances operational efficiency but also improves the customer experience, which
is essential in an increasingly digital world. However, Arun MS was quick to
caution that transferring processes to customers must be done thoughtfully.
Why We Need AI Professional Practice
AI’s capacity to learn, interpret, and abstract at scale alters how we navigate
complex, manifestly unpredictable situations and solutions, and brings an
ecosystem-scale vista of possibilities, challenges, and dependencies into view.
It forces us to examine every aspect of the human condition and our increasing
dependence on the tools we fashion. This is the pillar of “practice’, which will
emerge from the need to harness both the immediate and indirect value advanced
AI can bring. It is about direct interpretation, implementation, control, and
effect, rather than indirect consideration, control, and effect. It is, in
metaphorical terms then, about the rubber hitting the road. ... As we look at
how AI will continue to shape the business landscape, we can see an element that
hasn’t received much attention yet: how do we ensure that the right skills, best
practices, and standards are developed and shared amongst those managing this AI
revolution, and most importantly, how do we uphold the standard of that
professional practice? Some voices liken the onset of AI to the invention of the
Internet, which reflects the skills that are now required from staff, with new
data showing that 66% of business leaders wouldn’t hire someone without AI
skills.
AI cybersecurity needs to be as multi-layered as the system it’s protecting
By altering the technical design and development of AI before its training and
deployment, companies can reduce their security vulnerabilities before they
begin. For example, even selecting the correct model architecture has
considerable implications, with each AI model exhibiting particular affinities
to mitigate specific types of prompt injection or jailbreaks. Identifying the
correct AI model for a given use case is important to its success, and this is
equally true regarding security. Developing an AI system with embedded
cybersecurity begins with how training data is prepared and processed. Training
data must be sanitized and a filter to limit ingested training data is
essential. Input restoration jumbles an adversary’s ability to evaluate the
input-output relationship of an AI model by adding an extra layer of randomness.
Companies should create constraints to reduce potential distortions of the
learning model through Reject-On-Negative-Impact training. After that, regular
security testing and vulnerability scanning of the AI model should be performed
continuously. During deployment, developers should validate modifications and
potential tampering through cryptographic checks.
Kipu Quantum Team Says New Quantum Algorithm Outshines Existing Techniques
Kipu Quantum-led team of researchers announced the successful testing of what
they’re labeling the largest quantum optimization problem on a digital quantum
computer. They suggest that this is the start of the commercial quantum
advantage era. ... Combinatorial optimization is critical in many industries,
from logistics and scheduling to computational chemistry and biology. These
problems, which involve finding the best or near-optimal solutions in large
discrete configuration spaces, are known to be computationally challenging,
particularly for classical computing. This complexity has driven the exploration
of quantum optimization techniques as an alternative. ... While Kipu Quantum’s
BF-DCQO algorithm shows promise, the results are based on simulations and
experiments using specific quantum architectures. The 156-qubit experimental
validation was performed on IBM’s heavy-hex processor, while the 433-qubit
simulation is yet to be fully realized on physical hardware. There are still
challenges in scaling the method to address more complex real-world HUBO
problems that require larger quantum systems.
Inside the Mind of a Hacker: How Scams Are Carried Out
Hacking is, first and foremost, a mindset. It’s a likely avenue to pursue when
you're endowed with an organized mind, a passion for IT, and a boundless
curiosity about taking things apart and understanding their inner workings.
Since highly publicized cases usually involve the theft of exorbitant sums, it’s
logical for the public to assume that monetary gain is the top motivator. While
it’s high on the list, studies that explore hacker motivation consistently rank
the thrill of circumventing cyber defenses and the accompanying display of one’s
mastery as chief driving forces. Hacking is both technical and creative.
Successful hacks happen due to a combination of high technical prowess, the
ability to grasp and implement novel solutions, and a general disregard for the
consequences of those actions. ... The last step involves capitalizing on a
hacker’s ill-gotten gains. Those who have managed to convince someone to
transfer funds use mule accounts and money laundering schemes to eventually get
a hold of them. Hackers who get their hands on a company’s industrial secrets
may try to sell them to the competition. Data obtained through breaches finds
its way to the dark web, where other hackers may purchase it in bulk.
Quote for the day:
"Listen with curiosity speak with
honesty, act with integrity." -- Roy T. Benett
No comments:
Post a Comment