AI comes alive: From bartenders to surgical aides to puppies, tomorrow’s robots are on their way
The current generation of robots face three key challenges: processing visual
information quickly enough to react in real-time; understanding the subtle
cues in human behavior; and adapting to unexpected changes in their
environment. Most humanoid robots today are dependent on cloud computing and
the resulting network latency can make simple tasks like picking up an object
difficult. ... Gen AI powers spatial intelligence by helping robots map their
surroundings in real-time, much like humans do, predicting how objects might
move or change. Such advancements are crucial for creating autonomous humanoid
robots capable of navigating complex, real-world scenarios with the
adaptability and decision-making skills needed for success. While spatial
intelligence relies on real-time data to build mental maps of the environment,
another approach is to help the humanoid robot infer the real world from a
single still image. As explained in a pre-published paper, Generative World
Explorer (GenEx) uses AI to create a detailed virtual world from a single
image, mimicking how humans make inferences about their surroundings. ...
Beyond the purely technical obstacles, potential societal objections must be
overcome.
Why some companies are backing away from the public cloud
Technical debt may be the root of many moves back to on-premise environments.
"Normally this is a self-inflicted thing," Linthicum said. "They didn't
refactor the applications to make them more efficient in running on the public
cloud providers. So the public cloud providers, much like if we're pulling too
much electricity off the grid, just hit them with huge bills to support the
computational and storage needs of those under-optimized applications." Rather
than spending more money to optimize or refactor applications, these same
enterprises put them back on-premise, said Linthicum. Security and compliance
are also an issue. Enterprises "realize that it's too expensive to remain
compliant in the cloud, with data and sovereignty rules. So, they just make a
decision to push it back on-premise." The perceived high costs of cloud
operations "often stem from lift-and-shift migrations that in some cases
didn't optimize applications for cloud environments," said Miha Kralj, global
senior partner for hybrid cloud service at IBM Consulting. "These direct
transfers typically maintain existing architectures that don't leverage
cloud-native capabilities, resulting in inefficient resource utilization and
unexpectedly high expenses." However, the solution to this problem "isn't
necessarily repatriation to on-premises infrastructure," said Kralj.
7 Common Pitfalls in Data Science Projects — and How to Avoid Them
It's worth noting, too, that just because data is of low quality at the start
of a project doesn't mean the project is bound to fail. There are many
effective techniques for improving data quality, such as data cleansing and
standardization. When projects fail, it's typically because they failed to
assess data quality and improve it as needed, not because the data was so poor
in quality that there was no saving it. ... There are two key stakeholders in
any data science project — the IT department, which is responsible for
managing data assets, and business users, who determine what the data science
project should achieve. Unfortunately, poor collaboration between these groups
can cause projects to fail. For example, IT departments might decide to impose
access restrictions on data without consulting business users, leading to
situations where the business can't actually use the data in the way it
intends. Or lack of input from business stakeholders about what they want to
do may cause the IT team to struggle to determine how to deliver the data
resources necessary to support a project. ... 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.
Facial recognition for borders and travel: 2025 trends and insights
Seamless and secure border crossings are crucial for a thriving travel
industry. However, border control processes that still rely on traditional
manual checks pose unnecessary risks to both national security and traveler
satisfaction. Slow and cumbersome identity verification conducted by humans
leads to long lines and frustrated travelers. This is where biometrics come
in. Biometric technologies, particularly facial recognition, are
revolutionizing border security by providing a faster, more secure and more
efficient approach to verifying traveler identities. As passenger volumes
continue to rise globally, transportation authorities and immigration agencies
quickly realize the value of onboarding facial recognition technology to
streamline busy and mission-critical border crossings — helping improve
throughput, reduce wait times and enhance the overall traveler experience. ...
By adopting advanced facial recognition technologies, immigration authorities
can: Improve traveler experience. Self-service authentication shortens wait
times and delivers a satisfying, hassle-free journey. Deliver fast and
reliable authentication. The entire process to authenticate an individual is
now accomplished in seconds.
Enhance border security.
AI-Driven Microservices: The Future of Cloud Scalability
Even with modern auto-scaling in cloud platforms, the limitations are clear.
Scaling remains largely reactive, with additional servers spinning up only
after demand spikes are detected. This lag leads to temporary throttling and
performance degradation. During peak times, over-provisioning results in
wasted CPU and server utilization during subsequent low-traffic periods. The
inadequacy of threshold-based auto-scaling becomes particularly apparent
during high-traffic events like holiday sales. Engineers often find themselves
on-call to handle performance issues manually, adding operational overhead and
delaying service recovery. These systems lack predictive capabilities and
struggle to optimize cost and performance simultaneously. ... AI offers a
solution to these challenges. Through my experience with cloud-native
platforms, I have seen how AI can transform scaling capabilities by
incorporating predictive analytics. Instead of waiting for problems to occur,
AI-driven systems can analyze historical patterns, current trends and multiple
data points to anticipate resource needs in advance. This innovation has
particular significance for smaller enterprises, enabling them to compete
effectively with larger organizations that have traditionally dominated due to
superior infrastructure capabilities.
More AI, More Problems for Software Developers in 2025
Using AI to generate code can leave users — especially more junior developers —
without the context the code was written with and who it was written for, making
it harder to figure out what’s gone wrong. The risk is generally higher for
junior developers. Senior developers tend to have a much better awareness and
quicker understanding of the code that’s generated,” Reynolds observed. “Junior
developers are under a lot of pressure to get the job done. They want to move
fast, and they don’t necessarily have that contextual awareness of the code
change.” Without quality and governance controls — like security scans and
dependency checks, and unit, systems and integration testing — deployed
throughout the software development lifecycle, he warned, the wrong thing is
often merged. ... Shadow IT has developers looking to engineer their way out of
a problem by adopting — and often even paying for — tools that aren’t among
those officially approved by their employers. Shadow AI is an extension that
sees, the report found, 52% of developers using AI tools that aren’t provided by
or explicitly approved by IT. It’s not like developers are behaving
insubordinately. The reality is, three years into widespread adoption of
generative AI, most organizations still don’t have GenAI policies.
7 top cybersecurity projects for 2025
To effectively secure AI workloads, security teams should first gain an
understanding of AI use within their enterprise, as well as the data and models
used to power their business. “Next, assemble a cross-functional team to assess
risks and develop a comprehensive security strategy,” Ramamoorthy advises.
“Following best practices and adopting a secure AI framework will help to enable
a strong security foundation and ensure that when AI models are implemented,
they are secure by default.” ... With a successful TPRM project, your enterprise
will have a better security posture, with fewer vulnerabilities and proactive
control over outside hazards, Saine says. TPRM, backed by real-time monitoring
and the ability to quickly respond to developing hazards, can also ensure
compliance with pertinent laws, reducing the risk of fines and legal headaches.
“Compliance will also help your enterprise project credibility and dependability
to clients and partners,” he says. ... When implementing trust-by-design
principles with AI-powered systems, security leaders should align their goals
with overall enterprise objectives while obtaining buy-in from key executives
and stakeholders. Additionally, conducting thorough assessments of the
development processes can help identify vulnerabilities while prioritizing
remediation and controls.
The Tech Blanket: Building a Seamless Tech Ecosystem
Traditionally, organizations have built their technology strategies around “tech
stacks”—discrete tools for solving specific problems. While effective in the
short term, this approach often creates silos, with each department operating
within its own set of platforms. Knowledge and data are trapped, preventing the
organization from realizing its full potential. In 2024, many companies
recognized the limitations of this approach and began prioritizing integration.
This trend will deepen in 2025 as businesses build interconnected ecosystems
where tools work together harmoniously. According to Deloitte, 58% of companies
are shifting their focus toward integrating their platforms into unified
ecosystems rather than continuing to invest in standalone tools. ... One
of the biggest challenges in building a seamless tech ecosystem is ensuring that
tools communicate effectively. Selecting platforms that support open APIs is
essential for facilitating easy integration. Open APIs allow different systems
to share data and work together, eliminating friction and enabling better
collaboration. In practical terms, this means teams can pull insights from a
centralized knowledge management platform into other tools, such as CRM systems
or analytics dashboards, without additional manual effort. The result? A more
connected organization that can move at the speed of business.
AI Poised to Deliver Value, Innovation to Software Industry in 2025
“IoT technology has created a new level of visibility into complex, live systems
and enables vital insights. By providing real-time data streams for millions of
devices, IoT enables them to be monitored for issues and controlled from a
distance. This will lead to ever-increasing safety, security, and efficiency in
their operation. Smart buildings, transportation systems, logistics networks,
and countless other applications all benefit from using IoT to provide essential
services at reasonable cost. ... “The demand for faster software development has
become a serious industry threat, increasing code vulnerabilities and leading to
avoidable security risks. This relentless development pace is unsustainable and
only being accelerated by Generative AI. The more we speed up development and
release cycles with GenAI and otherwise, the more code vulnerabilities are
introduced, giving attackers more opportunities to execute their missions. ...
“AI is poised to become a foundational business tool, joining virtualization,
cloud computing, and containerization as essential layers of modern
infrastructure. By 2025, startups and enterprises will routinely leverage AI for
tasks like security, audits, and cost management.
AI and cybersecurity: A double-edged sword
How exactly is AI tipping the scales in favor of cybersecurity professionals?
For starters, it’s revolutionizing threat detection and response. AI systems can
analyze vast amounts of data in real time, identifying potential threats with
speed and accuracy. Companies like CrowdStrike have documented that their
AI-driven systems can detect threats in under one second. But AI’s capabilities
don’t stop at detection. When it comes to incident response, AI is proving to be
a game-changer. Imagine a security system that doesn’t just alert you to a
threat but takes immediate action to neutralize it. That’s the potential of
AI-driven automated incident response. From isolating compromised systems to
blocking malicious IP addresses, AI can execute these critical tasks swiftly and
without human input, dramatically reducing response times and minimizing
potential damage. ... AI is not just changing the skill set required for
cybersecurity professionals, it’s augmenting it for the better. The ability to
work alongside AI systems, interpret their outputs, and make strategic decisions
based on AI-generated insights will be paramount for both users and experts.
While AI is improving at its cybersecurity capabilities, a human paired with an
AI tool will outperform AI by itself ten-fold.
Quote for the day:
"Your present circumstances don’t
determine where you can go; they merely determine where you start." --
Nido Qubein
No comments:
Post a Comment