March 15, 2025

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.

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