Quote for the day:
“Going into business for yourself, becoming an entrepreneur, is the modern-day equivalent of pioneering on the old frontier.” -- Paula Nelson
Hyperlight Wasm points to the future of serverless
WebAssembly support significantly expands the range of supported languages for
Hyperlight, ensuring that compiled languages as well as interpreted ones like
JavaScript can be run on a micro VM. Your image does get more complex here, as
you need to bundle an additional runtime in the Hyperlight image, along with
writing code that loads both runtime and application as part of the launch
process. ... There’s a lot of work going on in the WebAssembly community to
define a specification for a component model. This is intended to be a way to
share binaries and libraries, allowing code to interoperate easily. The
Hyperlight Wasm tool offers the option of compiling a development branch with
support for WebAssembly Components, though it’s not quite ready for prime
time. In practice, this will likely be the basis for any final build of the
platform, as the specification is being driven by the main WebAssembly
platforms. One point that Microsoft makes is that Wasm isn’t only
language-independent, it’s architecture-independent, working against a minimal
virtual machine. So, code written and developed on an x64 architecture system
will run on Arm64 and vice versa, ensuring portability and allowing service
providers to move applications to any spare capacity, no matter the host
virtual machine.
Beyond SIEM: Embracing unified XDR for smarter security
Implementing SIEM solutions can have challenges and has to be managed
proactively. Configuring the SIEM system can be very complex where any error
can lead to false positives or missed threats. Integrating SIEM tools with
existing security tools and systems is not easy. The implementation and
maintenance processes are also resource-intensive and require significant time
and manpower. Alert fatigue can be set with traditional SIEM platforms where
numerous alerts are generated making it rather difficult to identify the
genuine ones. ... For industries with stringent compliance requirements, such
as finance and healthcare, SIEM remains a necessity due to its log retention,
compliance reporting, and event correlation capabilities. Microsoft Sentinel’s
AI-driven analytics help security teams fine-tune alerts, reducing false
positives and increasing threat detection accuracy. Microsoft Defender XDR
platform offers, Unified visibility across attack surfaces, CTEM Exposure
management solution, CIS framework assessment, Zero Trust, EASM, AI-driven
automated response to threats, Integrated security across all Microsoft 365
and third-party platforms, Office, Email, Data, CASB, Endpoint, Identity, and
Reduced complexity by eliminating the need for custom configurations.
Compliance Without Chaos: Build Resilient Digital Operations
A unified platform makes service ownership a no-brainer by directly connecting
critical services to the right responders so there’s no scrambling when things
go sideways. Teams can set up services quickly and at scale, making it easier
to get a real-time pulse on system health and see just how far the damage
spreads when something breaks. Instead of chasing down data across a dozen
monitoring tools, everything is centralized in one place for easy analysis.
... With all data centralized in a unified platform, the classification and
reporting of incidents is far easier with accessible and detailed incident
logs that provide a clear audit trail. Sophisticated platforms also integrate
with IT service management (ITSM) and IT operations (ITOps) tools to simplify
the reporting of incidents based on predefined criteria. ... Every incident,
both real and simulated, should be viewed as a learning opportunity.
Aggregating data from disparate tools into a single location gives teams a
full picture of how their organization’s operations have been affected and
supplies a narrative for reporting. Teams can then uncover patterns across
tools, teams and time to drive continuous learning in post-incident reviews.
Coupled with regular, automated testing of disaster recovery runbooks, teams
can build greater confidence in their system’s resilience.
How Organizations Can Benefit From Intelligent Data Infra
The first is getting your enterprise data AI-ready. Predictive AI has been
around for a long time. But teams still spend a significant amount of time
identifying and cleaning data, which involves handling ETL pipelines,
transformations and loading data into data lakes. This is the most expensive
step. The same process applies to unstructured data in generative AI. But
organizations still need to identify the files and object streams that need to
be a part of the training datasets. Organizations need to securely bring them
together and load them into feature stores. That's our approach to data
management. ... There's a lot of intelligence tied to files and objects.
Without that, they will continue to be seen as simple storage entities. With
embedded intelligence, you get detection capabilities that let you see what's
inside a file and when it was last modified. For instance, if you create
embeddings from a PDF file and vectorize them, imagine doing the same for
millions of files, which is typical in AI training. This consumes significant
computing resources. You don't want to spend compute resources while
recreating embeddings on a million files every time there is a modification to
the files. Metadata allows us to track changes and only reprocess the files
that have been modified. This differential approach optimizes compute
cycles.
Tariff war throws building of data centers into disarray
The potentially biggest variable affecting data center strategy is timing.
Depending on the size of an enterprise data center and its purpose, it could
take as little as six months to build, or as much as three years. Planning for
a location is daunting when ever-changing tariffs and retaliatory tariffs
could send costs soaring. Another critical element is knowing when those
tariffs will take effect, a data point that has also been changing. Some
enterprises are trying to sidestep the tariff issues by purchasing components
in bulk, in enough quantities to potentially last a few years. ... “It’s not
only space, available energy, cooling, and water resources, but it’s also a
question of proximity to where the services are going to be used,” Nguyen
said. Finding data center personnel, Nguyen said, is becoming less of an
issue, thanks to the efficiencies gained through automation. “The level of
automation available means that although personnel costs can be a bit more [in
different countries], the efficiencies used means that [hiring people] won’t
be the drag that it used to be,” he said. Given the vast amount of
uncertainty, enterprise IT leaders wrestling with data center plans have some
difficult decisions to make, mostly because they will have to guess where the
tariff wars will be many months or years in the future, a virtually impossible
task.
The Modern Data Architecture: Unlocking Your Data's Full Potential
If the Data Cloud is your engine, the CDP is your steering wheel—directing that
power where it needs to go, precisely when it needs to get there. True real-time
CDPs have the ability to transform raw data into immediate action across your
entire technology ecosystem, with an event-based architecture that responds to
customer signals in milliseconds rather than minutes. This ensures you can
dynamically personalize experiences as they unfold—whether during a website
visit, mobile app session, or contact center interaction–all while honoring
consent. ... As AI capabilities evolve, this Intelligence Layer becomes
increasingly autonomous—not just providing recommendations but taking
appropriate actions based on pre-defined business rules and learning from
outcomes to continuously improve its performance. ... The Modern Data
Architecture serves as the foundation for truly intelligent customer experiences
by making AI implementations both powerful and practical. By providing clean,
unified data at scale, these architectures enable AI systems to generate more
accurate predictions, more relevant recommendations, and more natural
conversational experiences. Rather than creating isolated AI use cases,
forward-thinking organizations are embedding intelligence throughout the
customer journey.
Why AI therapists could further isolate vulnerable patients instead of easing suffering
While chatbots can be programmed to provide some personalised advice, they may
not be able to adapt as effectively as a human therapist can. Human therapists
tailor their approach to the unique needs and experiences of each person.
Chatbots rely on algorithms to interpret user input, but miscommunication can
happen due to nuances in language or context. For example, chatbots may struggle
to recognise or appropriately respond to cultural differences, which are an
important aspect of therapy. A lack of cultural competence in a chatbot could
alienate and even harm users from different backgrounds. So while chatbot
therapists can be a helpful supplement to traditional therapy, they are not a
complete replacement, especially when it comes to more serious mental health
needs. ... The talking cure in psychotherapy is a process of fostering human
potential for greater self-awareness and personal growth. These apps will never
be able to replace the therapeutic relationship developed as part of human
psychotherapy. Rather, there’s a risk that these apps could limit users’
connections with other humans, potentially exacerbating the suffering of those
with mental health issues – the opposite of what psychotherapy intends to
achieve.
Breaking Barriers in Conversational BI/AI with a Semantic Layer
The push for conversational BI was met with adoption inertia. Two major
challenges have hindered its potential—the accuracy of the data insights and the
speed at which the interface could provide the answers that were sought. This
can be attributed to the inherent complexity of data architecture, which
involves fragmented data in disparate systems with varying definitions, formats,
and contexts. Without a unified structure, even the most advanced AI models risk
delivering contextually irrelevant, inconsistent, or inaccurate results.
Moreover, traditional data pipelines are not designed for instantaneous query
resolution and resolving data from multiple tables, which delays responses. ...
Large language models (LLMs) like GPT excel at interpreting natural language but
lack the domain-specific knowledge of a data set. A semantic layer can resolve
this challenge by acting as an intermediary between raw data and the
conversational interface. It unifies data into a consistent, context-aware model
that is comprehensible to both humans and machines. Retrieval-augmented
generation (RAG) techniques are employed to combine the generative power of LLMs
with the retrieval capabilities of structured data systems.
The rise of AI PCs: How businesses are reshaping their tech to keep up
Companies are discovering that if they want to take full advantage of AI and run
models locally, they need to upgrade their employees' laptops. This realization
has introduced a hardware revolution, with the desire to update tech shifting
from an afterthought to a priority and attracting significant investment from
companies. ... running models locally gives organizations more control over
their information and reduces reliance on third-party services. That setup is
crucial for companies in financial services, healthcare, and other industries
where privacy is a big concern or a regulatory requirement. "For them, on-device
AI computer, it's not a nice to have; it's a need to have for fiduciary and
HIPAA reasons, respectively," said Mike Bechtel, managing director and the chief
futurist at Deloitte Consulting LLP. Another advantage is that local running
reduces lag and creates a smoother user experience, which is especially valuable
for optimizing business applications. ... As more companies get in on the action
and AI-capable computers become ubiquitous, the premium price of AI PCs will
continue to drop. Furthermore, Flower said the potential gains in performance
offset any price differences. "In those high-value professions, the productivity
gain is so significant that whatever small premium you're paying for that
AI-enhanced device, the payback will be nearly immediate," said Flower.
Many CIOs operate within a culture of fear
The culture of fear often stems from a few roots, including a lack of
accountability from employees who don’t understand their roles, and mistrust of
coworkers and management, says Alex Yarotsky, CTO at Hubstaff, vendor of a time
tracking and workforce management tool. In both cases, company leadership is to
blame. Good leaders create a positive culture laid out in a set of rules and
guidelines for employees to follow, and then model those actions themselves,
Yarotsky says. “Any case of misunderstanding or miscommunication is always on
the management because the management is the force in the company that sets the
rules and drives the culture,” he adds. ... Such a culture often starts at the
top, says Jack Allen, CEO and chief Salesforce architect at ITequality, a
Salesforce consulting firm. Allen experienced this scenario in the early days of
building a career, suggesting the problems may be bigger than the survey
respondents indicate. “If the leader is unwilling to admit mistakes or punishes
mistakes in an unfair way, then the next layer of leadership will be afraid to
admit mistakes as well,” Allen says. ... Cultivating a culture of fear leads to
several problems, including an inability to learn from mistakes, Mort says.
“Organizations that do the best are those that value learning and highlight
incidents as valuable learning events,” he says.
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