Quote for the day:
"The most powerful leadership tool you have is your own personal example." -- John Wooden
Guardians of AIoT: Protecting Smart Devices from Data Poisoning
Machine learning algorithms rely on datasets to identify and predict patterns.
The quality and completeness of this data determines the performance of the
model is determined by the quality and completeness of this data. Data poisoning
attacks tamper the knowledge of the AI by introducing false or misleading
information and usually following these steps: The attacker manipulates the data
by gaining access to the training dataset and injects malicious samples; The AI
is now getting trained on the poisoned data and incorporates these corrupt
patterns into its decision-making process; Once the poisoned data is deployed,
the attackers now exploit it to bypass a security system or tamper critical
tasks. ... The addition of AI into IoT ecosystems has intensified the potential
attack surface. Traditional IoT devices were limited in functionality, but AIoT
systems rely on data-driven intelligence, which makes them more vulnerable to
such attacks and hence, challenge the security of the devices: AIoT devices
collect data from different sources which increases the likelihood of data being
tampered; The poisoned data can have catastrophic effects on the real-time
decision making; Many IoT devices possess limited computational power to
implement strong security measures which makes them easy targets for these
attacks.
Preparing for The Future of Work with Digital Humans
For businesses to prepare their staff for the workplace of tomorrow, they need
to embrace the technologies of tomorrow—namely, digital humans. These advanced
solutions will empower L&D leaders to drive immersive learning experiences
for their staff. Digital humans use various technologies and techniques like
conversational AI, large language models (LLMs), retrieval augmented generation,
digital human avatars, virtual reality (VR,) and generative AI to produce
engaging and interactive scenarios that are perfect for training. Recall that a
major issue with current training methods is that staff never have opportunities
to apply the information they just consumed, resulting in the loss of said
information. Digital humans avoid this problem by generating lifelike roleplay
scenarios where trainees can actually apply and practice what they have learned,
reinforcing knowledge retention. In a sales training example, the digital human
takes on the role of a customer, allowing the employee to practice their pitch
for a new product or service. The employee can rehearse in realistic conditions
rather than studying the details of the new product or service and jumping on a
call with a live customer. A detractor might push back and say that digital
humans lack a necessary human element.
3 ways test impact analysis optimizes testing in Agile sprints
Code modifications or application changes inherently present risks by
potentially introducing new bugs. Not thoroughly validating these changes
through testing and review processes can lead to unintended
consequences—destabilizing the system and compromising its functionality and
reliability. However, validating code changes can be challenging, as it requires
developers and testers to either rerun their entire test suites every time
changes occur or to manually identify which test cases are impacted by code
modifications, which is time-consuming and not optimal in Agile sprints. ...
Test impact analysis automates the change analysis process, providing teams with
the information they need to focus their testing efforts and resources on
validating application changes for each set of code commits versus retesting the
entire application each time changes occur. ... In UI and end-to-end
verifications, test impact analysis offers significant benefits by addressing
the challenge of slow test execution and minimizing the wait time for regression
testing after application changes. UI and end-to-end testing are
resource-intensive because they simulate comprehensive user interactions across
various components, requiring significant computational power and time.
No one knows what the hell an AI agent is
Well, agents — like AI — are a nebulous thing, and they’re constantly evolving.
OpenAI, Google, and Perplexity have just started shipping what they consider to
be their first agents — OpenAI’s Operator, Google’s Project Mariner, and
Perplexity’s shopping agent — and their capabilities are all over the map. Rich
Villars, GVP of worldwide research at IDC, noted that tech companies “have a
long history” of not rigidly adhering to technical definitions. “They care more
about what they are trying to accomplish” on a technical level, Villars told
TechCrunch, “especially in fast-evolving markets.” But marketing is also to
blame in large part, according to Andrew Ng, the founder of AI learning platform
DeepLearning.ai. “The concepts of AI ‘agents’ and ‘agentic’ workflows used to
have a technical meaning,” Ng said in a recent interview, “but about a year ago,
marketers and a few big companies got a hold of them.” The lack of a unified
definition for agents is both an opportunity and a challenge, Jim Rowan, head of
AI for Deloitte, says. On the one hand, the ambiguity allows for flexibility,
letting companies customize agents to their needs. On the other, it may — and
arguably already has — lead to “misaligned expectations” and difficulties in
measuring the value and ROI from agentic projects. “Without a standardized
definition, at least within an organization, it becomes challenging to benchmark
performance and ensure consistent outcomes,” Rowan said.
Researchers astonished by tool’s apparent success at revealing AI’s hidden motives
In a new paper published Thursday titled "Auditing language models for hidden
objectives," Anthropic researchers described how models trained to deliberately
conceal certain motives from evaluators could still inadvertently reveal
secrets, thanks to their ability to adopt different contextual roles or
"personas." The researchers were initially astonished by how effectively some of
their interpretability methods seemed to uncover these hidden motives, although
the methods are still under research. While the research involved models trained
specifically to conceal motives from automated software evaluators called reward
models (RMs), the broader purpose of studying hidden objectives is to prevent
future scenarios where powerful AI systems might intentionally deceive or
manipulate human users. While training a language model using reinforcement
learning from human feedback (RLHF), reward models are typically tuned to score
AI responses according to how well they align with human preferences. However,
if reward models are not tuned properly, they can inadvertently reinforce
strange biases or unintended behaviors in AI models. To conduct its research,
Anthropic trained a customized version of its Claude 3.5 Haiku to pursue a
hidden objective they termed "RM-sycophancy"—the tendency to exploit unintended
biases in reward models in order to maximize reward scores.
Strategies for Success in the Age of Intelligent Automation
Firstly, the integration of AI into existing organizational frameworks calls for
a largely collaborative environment. It is imperative for employees to perceive
AI not as a usurper of employment, but instead as an ally in achieving
collective organizational goals. Cultivating a culture of collaboration between
AI systems and human workers is essential to the successful deployment of
intelligent automation. Organizations should focus on fostering open
communication channels, ensuring that employees understand how AI can enhance
their roles and contribute to the organization’s success. To achieve this,
leadership must actively engage with employees, addressing concerns and
highlighting the benefits of AI integration. ... The ethical ramifications of AI
workforce deployment demand meticulous scrutiny. Transparency, accountability,
and fairness are integral and their importance can’t be overstated. It’s vital
that AI-driven decisions are aligned with ethical standards. Organizations are
responsible for establishing robust ethical frameworks that govern AI
interactions, mitigating potential biases and ensuring equitable outcomes. The
best way to do this requires implementing standards for monitoring AI systems,
ensuring they operate within defined ethical boundaries.
AI & Innovation: The Good, the Useless – and the Ugly
First things first: there is good innovation, the kind that genuinely benefits
society. AI that enhances energy efficiency in manufacturing, aids scientific
discoveries, improves extreme weather prediction, and optimizes resource use in
companies falls into this category. Governments can foster those innovations
through targeted R&D support, incentives for firms to develop and deploy AI,
“buy European tech” procurement policies, and investments in robust digital
infrastructure. The Competitiveness Compass outlines similar strategies. That
said, given how many different technologies are lumped together in the AI
category—everything from facial recognition technology to smart ad tech,
ChatGPT, and advanced robotics—it makes little sense to talk about good
innovation and “AI and productivity” in the abstract. Most hype these days is
about generative AI systems that mimic human creative abilities with striking
aptitude. Yet, how transformative will an improved ChatGPT be for businesses? It
might streamline some organizational processes, expedite data processing, and
automate routine content generation. For some industries, like insurance
companies, such capabilities may be revolutionary. For many others, its
innovation footprint will be much more modest.
Revolution at the Edge: How Edge Computing is Powering Faster Data Processing
Due to its unparalleled advantages, edge computing is rapidly becoming the
primary supporting technology of industries where speed, reliability, or
efficiency aren’t just useful but imperative. Just like edge computing helps
industries remain functional and up to date, staying informed with the latest
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world of sport! Edge computing relies on IoT as its most crucial component since
there are billions of connected devices producing an immense and constant amount
of data that needs to be processed right away. IoT devices in the residential
sector, such as smart sensors in homes or Nest smart thermostats, as well as
peripherals used for industrial automation in factories, all use edge
computing. ... The way edge computing will function in the future is very
exciting. With 5G, AI, and IoT, edge technologies are likely to become smarter,
more widespread, and faster. Imagine a world where factories optimize
themselves, smart traffic systems talk to autonomous vehicles, and healthcare
devices stop illnesses from happening before they start.
Harnessing the data storm: three top trends shaping unstructured data storage and AI
The sheer volume of unstructured information generated by enterprises
necessitates a new approach to storage. Object storage offers a better, more
cost-effective method for handling significant datasets compared to traditional
file-based systems. Unlike traditional storage methods, object storage treats
each data item as a distinct object with its metadata. This approach offers both
scalability and flexibility; ideal for managing the vast quantities of images,
videos, sensor data, and other unstructured content generated by modern
enterprises. ... Data lakes, the centralized repositories for both structured
and unstructured data, are becoming increasingly sophisticated with the
integration of AI and machine learning. These enable organizations to delve
deeper into their data, uncovering hidden patterns and generating actionable
insights without requiring complex and costly data preparation processes. ...
The explosion of unstructured data presents both immense opportunities and
challenges for organizations in every market across the globe. To thrive in this
data-driven era, businesses must embrace innovative approaches to data storage,
management, and analysis that are both cost-effective and compliant with
evolving regulations.
Open Source Tools Seen as Vital for AI in Hybrid Cloud Environments
The landscape of enterprise open source solutions is evolving rapidly, driven by
the need for flexibility, scalability, and innovation. Enterprises are
increasingly relying on open source technologies to drive digital
transformation, accelerate software development, and foster collaboration across
ecosystems. With advancements in cloud computing, AI, and containerization, open
source solutions are shaping the future of IT by providing adaptable and secure
platforms that meet evolving business needs. The active and diverse community
support ensures continuous improvement, making open source a cornerstone of
modern enterprise technology strategies. Red Hat's portfolio, including Red Hat
Enterprise Linux, Red Hat OpenShift, Red Hat AI and Red Hat Ansible Automation
Platform, provides robust platforms that support diverse workloads across hybrid
and multi-cloud environments. Additionally, Red Hat's extensive partner
ecosystem provides more seamless integration and support for a wide range of
technologies and applications. Our commitment to open source principles and
continuous innovation allows us to deliver solutions that are secure, scalable,
and tailored to the needs of our customers. Open source has proven to be trusted
and secure at the forefront of innovation
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