Quote for the day:
"Hardships often prepare ordinary people for an extraordinary destiny." -- C.S. Lewis
Like it or not, AI is learning how to influence you
We need to consider the psychological impact that will occur when we humans
start to believe that the AI agents giving us advice are smarter than us on
nearly every front. When AI achieves a perceived state of “cognitive supremacy”
with respect to the average person, it will likely cause us to blindly accept
its guidance rather than using our own critical thinking. This deference to a
perceived superior intelligence (whether truly superior or not) will make agent
manipulation that much easier to deploy. I am not a fan of overly aggressive
regulation, but we need smart, narrow restrictions on AI to avoid superhuman
manipulation by conversational agents. Without protections, these agents will
convince us to buy things we don’t need, believe things that are untrue and
accept things that are not in our best interest. It’s easy to tell yourself you
won’t be susceptible, but with AI optimizing every word they say to us, it is
likely we will all be outmatched. One solution is to ban AI agents from
establishing feedback loops in which they optimize their persuasiveness by
analyzing our reactions and repeatedly adjusting their tactics. In addition, AI
agents should be required to inform you of their objectives. If their goal is to
convince you to buy a car, vote for a politician or pressure your family doctor
for a new medication, those objectives should be stated up front.
Leveraging AI for Business Continuity and Disaster Recovery in the Work-From-Home Era
AI-driven tools can monitor the health and performance of hardware and predict
hardware failure before it happens using anomaly detection algorithms. For
example, if a hard drive is starting to fail or there’s unusual network
activity, AI systems can flag the activity/potential problem early and send an
email to alert the WFH user or corporate IT staff, allowing businesses to take
preventative action. ... AI can detect anomalies in network traffic or access
patterns which may indicate a cyberattack (e.g., ransomware, phishing, or data
breach). AI-powered cybersecurity tools, such as intrusion detection systems
(IDS) and endpoint protection software, can respond automatically to threats
by isolating affected systems or rolling back malicious changes. ... Small
businesses may not have reliable or frequent data backups or rely on manual
processes (e.g., external hard drives) that aren’t automated or secure. It may
be difficult to recover without a proper backup strategy if critical data is
lost due to hardware failure, cyber-attacks, or natural disasters. ...
AI-assisted BC and DR solutions offer a range of benefits, particularly for
SOHO and WFH users. These offerings are becoming essential as businesses of
all sizes seek to maintain operational resilience in an ever-changing
technological landscape.
GenAI can make us dumber — even while boosting efficiency
“A key irony of automation is that by mechanizing routine tasks and leaving
exception-handling to the human user, you deprive the user of the routine
opportunities to practice their judgement and strengthen their cognitive
musculature, leaving them atrophied and unprepared when the exceptions do
arise,” the study found. Overall, workers’ confidence in genAI’s abilities
correlates with less effort in critical thinking. The focus of critical
thinking shifts from gathering information to verifying it, from
problem-solving to integrating AI responses, and from executing tasks to
overseeing them. The study suggests that genAI tools should be designed to
better support critical thinking by addressing workers’ awareness, motivation,
and ability barriers. ... As Agentic AI becomes common, people may come to
rely on it for problem-solving — but how will we know it’s doing things
correctly, Gold said. People might accept its results without questioning,
potentially limiting their own skills development by allowing technology to
handle tasks. Lev Tankelevitch, a senior researcher with Microsoft Research,
said not all genAI use is bad. He said there’s clear evidence in education
that it can enhance critical thinking and learning outcomes.
How to harness APIs and AI for intelligent automation
APIs are the steady bridges connecting diverse systems and data sources. This
reliable technology, which emerged in the 1960s and matured during the
noughties ecommerce boom, is bridging today’s next-gen technologies. APIs
allow data transfer to be automated, which is essential for training AI models
efficiently. Rather than building complex integrations from scratch, they
standardize data flow to ensure the data that feeds AI models is accurate and
reliable. ... Data preprocessing is the critical step before training any AI
model. APIs can ensure that AI applications and models only receive
preprocessed data. This minimizes manual errors which smoothes the AI training
pipeline. With a direct interface to standardized data, developers can focus
on refining the model architecture rather than spending excessive time on data
cleanup. Real-time evaluation keeps AI models in check in dynamic
environments. By feeding real-time performance data back into the system,
developers can quickly adjust parameters to improve the model. ... As your
data volumes and transaction rates increase, your APIs must scale accordingly.
Performance issues like latency or downtime can disrupt AI training and
real-time processing. To be responsive under heavy loads, design APIs with
load balancing, caching, and built-in redundancy to maintain consistent
performance during peak use.
Applying Behavioral Economics to Phishing and Social Engineering Attacks
It’s all about deeply and thoroughly understanding human behavior and how
these behaviors are impacted by influences that use cognitive biases,
emotions, social influences, and contextual factors to drive decisions. Bad
actors in the world of cybersecurity also prey upon these human tendencies to
drive actions that put organizations at risk. ... Humans are social creatures
that trust those they believe are authorities. They’re driven by fear, greed,
and curiosity that can cloud their judgement. And they’re prone to cognitive
shortcuts—biases that often drive behaviors. Understanding the power of these
drivers can help organizations put strategies into place to thwart them. ...
Here are some important steps that can help employees make better
decisions:Training employees about the threat of cyberattacks, the form these
attacks generally take, and their role in helping to avert them is an
important first step. Training should be ongoing, not a single instance or
once a year event. Phishing simulations have proven to be a very effective way
to tangibly reduce security breakdowns. These simulations serve to test
employee awareness and identify areas of opportunity for improvement. Strong
authentication measures can help keep accounts secure by requiring two or more
methods of identification and verification—muti-factor authentication—before
allowing access to information or systems.
Why Digital Projects Need Transparency and Accountability
As a CIO, it is easy to underestimate the time it will take to build forward.
In the public sector, this takes longer due to inherent risk aversion. In my
first few months at DWP, I felt I was making a difference, but after the first
few months, the size of the prize began to take its toll and the risk factors
of going forward began to set in. As CIOs, it is our role to persuade,
influence and keep in mind where we are trying to get to. We landed that
vision with the senior team but DWP's size and geographic spread made it
harder to get the spokes of the business to hear the same story and grasp the
same benefits. If I had my time again, I would spend more time with the
business, less at the center and try to build momentum that was unstoppable.
As I completed my first 100 days in the CIO role at Segro, one of the key
takeaways from DWP was making sure the digital leadership team knew how to act
together. In my new role, I am able to replicate that at a faster pace. Brand
identity matters. At Segro, we are not known as the digital team, and I am
striving to change that. The organization will benefit from unifying its
understanding of technology, transformation and data.
Navigating Europe’s AI Code of Practice Before the Clock Runs Out
The Code of Practice for general-purpose AI demonstrates a sincere effort to
get the details right. Yet, in a rush to cover every contingency, it risks
overlooking the bigger picture: spurring the next generation of AI-driven
breakthroughs that can speed up drug discovery, modernize public services, and
let small farmers use new predictive tools for planting and harvesting.
Innovation is a delicate process, especially in emerging areas like
large-scale language models or real-time climate analytics. Europe possesses
the scientific expertise and market size to shape a future where these tools
become transformative assets in every corner of the continent. But that future
hinges on how carefully policymakers, industry players, and civil society
calibrate the rules. ... Europe’s AI revolution will not happen on autopilot.
Real progress demands revamping processes, investing in talent, and scaling up
what works. The public sector must also move faster if Europe is to modernize
healthcare, education, and core government services. Tangled or rigid rules
risk derailing Europe’s ambitions. Europe’s digital regulations already weigh
heavily on businesses. Over the past 25 years, the number of economy-wide laws
doubled, and the EU has rolled out close to 100 tech-focused laws. High-minded
ideals often mix with fragmented enforcement and overlapping rules.
Seven Common Reasons Why Data Science Projects Fail
Large organizations may own hundreds of data assets spread across sprawling,
multi-faceted IT infrastructures. Unless they have a detailed, continuously
updated data catalog in place that tracks all of those assets – which many
don’t – simply finding the data that the team needs to complete a project can
present a major challenge. Here again, however, tools and techniques are
available that can help. The major solution is data discovery software, which
can automatically identify data resources, including those that are not
documented. ... Too often, businesses decide that they want to do something
with their data, but they don’t know exactly what. For example, they might
establish a high-level goal like using data-derived insights to grow revenue,
without determining exactly which types of revenue-related challenges they
want to solve with help from data. Avoiding this pitfall is simple: You need
to articulate precise deliverables and outcomes at the start of your project.
There’s always room to adjust the details a bit once a project is underway,
but you should know from the beginning what the overarching outcomes of the
project should be. ... A final key challenge that can thwart data science
project success is the failure to understand what the goals of data science
are, and which methodologies and resources data science requires.
What’s changing the rules of enterprise AI adoption for IT leaders
As model costs fall and the value from AI migrates up to the application
layer, enterprises are going to have even greater choice in business
solutions, either from third parties or those developed inhouse. For CIOs with
access to the right resources, building applications internally is now a more
realistic proposition. This becomes increasingly attractive in the context of
complex business processes that may be unique to enterprises. As the costs of
running models fall to near zero, the ROI equation shifts dramatically.
According to Forrester Research, the ability to run hyper-efficient models
like DeepSeek locally on PCs opens up a new era of edge intelligence, which
businesses can deploy across organizations. “The real value in AI isn’t just
in building bigger models, but innovating on top of them and in implementing
them efficiently,” says Devesh Mishra, president of CoreAI at digital
transformation specialists Keystone. “Companies that pair foundation model
advancements with deep business and operational expertise will lead the next
phase of AI-driven ROI.” This deep understanding of industry verticals and
their specific issues and needs will define success for many vendors as they
increasingly compete with inhouse development teams.
Rowing in the Same Direction: 6 Tips for Stronger IT and Security Collaboration
Due to market dominance, many software vendors focus on Windows, but IT fleets
today include a mix of Chromebooks, Linux systems and Apple devices. Security
and IT teams must recognize that the weakest endpoint determines the overall
defense posture. By ensuring IT and security teams are aligned on what’s in
the environment, you can break down silos and work together toward shared
security goals, such as zero-trust implementation. ... Security and IT teams
should collaborate to ensure policies protect the overall business mission,
not just the bottom line. For example, if security requires an agent to
collect telemetry for advanced analysis (e.g., CrowdStrike, Halcyon, etc.),
what’s the performance impact on endpoints? If the agent is running AI/ML
workloads, how is it optimized for performance on XPU and non-XPU systems? IT
fleet leaders care about security BUT they also demand top performance and
battery life from devices. Both security and IT teams together can align
solutions that offer best-in-class security without degrading fleet
performance. ... Ownership in IT and security is one of the hardest challenges
to solve. In many cases, responsibility over cloud workloads, applications and
ephemeral systems isn’t always clearly defined.
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