Connect with us

AI Insights

AI is already making it harder for some to find a job

Published

on


Over the past three years, the unemployment rate for recent college graduates has exceeded the overall unemployment rate for the first time, research firm Oxford Economics reported.

“There are signs that entry-level positions are being displaced by artificial intelligence,” the firm wrote in a report in May, noting that grads with programming and other tech degrees seemed to be particularly struggling in the job market. Other factors, including companies cutting back after over-hiring, could also be at play.

In June, Amazon chief executive Andy Jassy warned that the growing use of AI inside his company — one of the Boston area’s largest tech employers — would require “fewer people” and “reduce our total corporate workforce.” And Dario Amodei, chief executive of AI firm Anthropic, predicted the technology will eliminate half of all white-collar jobs.

Brooke DeRenzis, head of the nonprofit National Skills Coalition, has described the arrival of AI in the workforce as a “jump ball” for the middle class.

The tech will create some new jobs, enhance some existing jobs, and eliminate others, but how that will impact ordinary workers is yet to be determined, she said. Government and business leaders need to invest in training programs to teach people how to incorporate AI skills and, at the same time, build a social safety net beyond just unemployment insurance for workers in industries completely displaced by AI, DeRenzis argued.

“We can shape a society that supports our workforce in adapting to an AI economy in a way that can actually grow our middle class,” DeRenzis said. “One of the potential risks is we could see inequality widen … if we are not fully investing in people’s ability to work alongside AI.“

Still, even the latest AI apps are riddled with mistakes and unable to fully replace human workers at many tasks. Less than three years after ChatGPT burst on the scene, researchers say there is a long way to go before anyone can definitively predict how the technology will affect employment, according to Morgan Frank, a professor at the University of Pittsburgh who studies the impact of AI in jobs.

He says pronouncements from tech CEOs could just be scapegoating as they need to make layoffs because of over-hiring during the pandemic.

“There’s not a lot of evidence that there’s a huge disaster pending, but there are signs that people entering the workforce to do these kinds of jobs right now don’t have the same opportunity they had in the past,” he said. “The way AI operates and the way that people use it is constantly shifting, and we’re just in this transitory period…. The frontier is moving.”


Aaron Pressman can be reached at aaron.pressman@globe.com. Follow him @ampressman.





Source link

AI Insights

What is Context Engineering? The Future of AI Optimization Explained

Published

on


What if the key to unlocking the full potential of artificial intelligence lies not in the models themselves, but in how we frame the information they process? Imagine trying to summarize a dense, 500-page novel but being handed only scattered, irrelevant excerpts. The result would likely be incoherent at best. This is the challenge AI faces when burdened with poorly curated or excessive data. Enter the concept of context engineering, a fantastic approach that shifts the focus from static, one-size-fits-all prompts to dynamic, adaptive systems. By tailoring the information AI systems receive, context engineering promises to transform how large language models (LLMs) generate insights, solve problems, and interact with users.

In this exploration of context engineering, the Prompt Engineering team explain how this emerging discipline addresses the inherent limitations of traditional prompt engineering. You’ll discover how techniques like retrieval-augmented generation and context pruning can streamline AI performance, allowing models to focus on what truly matters. But context engineering isn’t without its challenges—issues like context poisoning and distraction reveal the delicate balance required to maintain precision and relevance. Whether you’re a developer seeking to optimize AI systems or simply curious about the future of intelligent machines, this perspective will illuminate the profound impact of dynamic context management. After all, the way we frame information might just determine how effectively machines—and by extension, we—navigate complexity.

What is Context Engineering?

TL;DR Key Takeaways :

  • Context engineering focuses on dynamically managing and curating relevant information for large language models (LLMs), improving task performance and minimizing errors compared to static prompt engineering.
  • Key challenges in context management include context poisoning, distraction, confusion, and clash, which can negatively impact the accuracy and coherence of LLM outputs.
  • Strategies like Retrieval-Augmented Generation (RAG), context quarantine, pruning, summarization, and offloading are used to optimize context and enhance LLM efficiency and accuracy.
  • Context engineering has practical applications in areas like customer support and research, where it dynamically adjusts context to improve user experience and streamline decision-making processes.
  • While some critics view context engineering as a rebranding of existing methods, its emphasis on adaptability and real-time optimization marks a significant advancement in AI development, paving the way for future innovations.

Context engineering is the practice of curating and managing relevant information to enable LLMs to perform tasks more effectively. It goes beyond static prompts by employing dynamic systems that adapt to the evolving needs of a task. The primary goal is to provide LLMs with a streamlined, relevant context that enhances their ability to generate accurate and coherent outputs.

For instance, when tasked with summarizing a lengthy document, an LLM benefits from context engineering by receiving only the most pertinent sections of the document. This prevents the model from being overwhelmed by irrelevant details, allowing it to focus on delivering a concise and accurate summary. By tailoring the context to the specific requirements of a task, context engineering ensures that the model operates efficiently and effectively.

Challenges in Context Management

While context engineering offers significant potential, it also introduces challenges that can impact model performance if not carefully managed. These challenges highlight the complexity of maintaining relevance and precision in dynamic systems:

  • Context Poisoning: Errors or hallucinations within the context can propagate through the model, leading to inaccurate or nonsensical outputs. This can undermine the reliability of the system.
  • Context Distraction: Overly long or repetitive contexts can cause models to focus on redundant patterns, limiting their ability to generate novel or insightful solutions.
  • Context Confusion: Including irrelevant or superfluous information can dilute the model’s focus, resulting in low-quality responses that fail to meet user expectations.
  • Context Clash: Conflicting information within the context can create ambiguity, particularly in multi-turn interactions where consistency is critical for maintaining coherence.

These challenges underscore the importance of precise and adaptive context management to maintain the integrity and reliability of the model’s outputs. Addressing these issues requires a combination of technical expertise and innovative strategies.

How Context Engineering Improves AI Performance and Relevance

Below are more guides on Context Engineering from our extensive range of articles.

Strategies to Optimize Context

To overcome the challenges associated with context management, several strategies have been developed to refine how context is curated and used. These techniques are designed to enhance the efficiency and accuracy of LLMs:

  • Retrieval-Augmented Generation (RAG): This method selectively integrates relevant information into the context, making sure the model has access to the most pertinent data for the task at hand. By focusing on relevance, RAG minimizes the risk of context overload.
  • Context Quarantine: By isolating context into dedicated threads for specialized agents in multi-agent systems, this approach prevents cross-contamination of information, preserving the integrity of each thread.
  • Context Pruning: Removing irrelevant or unnecessary information from the context streamlines the model’s input, improving focus and efficiency. This technique is particularly useful for tasks with strict context window limitations.
  • Context Summarization: Condensing earlier interactions or information preserves relevance while adhering to the model’s context window constraints. This ensures that key details remain accessible without overwhelming the model.
  • Context Offloading: External memory systems store information outside the LLM’s immediate context, allowing the model to access additional data without overloading its input. This approach is especially valuable for handling large datasets or complex queries.

These strategies collectively enhance the model’s ability to process information effectively, making sure that the context aligns with the specific requirements of the task. By implementing these techniques, developers can maximize the potential of LLMs in a wide range of applications.

Key Insights and Practical Applications

Effective context management is critical for maintaining the performance of LLMs, particularly as context windows expand. Smaller models, in particular, are more prone to errors when overloaded with irrelevant or conflicting information. By implementing dynamic systems that adapt context based on user queries and task requirements, you can maximize the model’s capabilities and ensure consistent performance.

In customer support applications, for example, context engineering can dynamically adjust the information provided to the model based on the user’s query history. This enables the model to deliver accurate and contextually relevant responses, significantly improving the user experience. Similarly, in research and development, context engineering can streamline the analysis of complex datasets by focusing on the most relevant information, enhancing the efficiency of decision-making processes.

Criticism and Future Directions

Some critics argue that context engineering is merely a rebranding of existing concepts like prompt engineering and information retrieval. However, its emphasis on dynamic and adaptive systems distinguishes it from these earlier approaches. By addressing the limitations of static prompts and focusing on real-time context optimization, context engineering represents a significant advancement in AI development.

As AI systems continue to evolve, the principles of context engineering will play a pivotal role in shaping how LLMs interact with and process information. By prioritizing relevance, adaptability, and precision, this approach ensures that AI systems remain effective and reliable, even in complex and dynamic environments. The ongoing refinement of context management techniques will likely lead to further innovations, allowing LLMs to tackle increasingly sophisticated tasks with greater accuracy and efficiency.

Media Credit: Prompt Engineering

Filed Under: AI, Top News





Latest Geeky Gadgets Deals

Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.





Source link

Continue Reading

AI Insights

Bay Area teen using AI to try to prevent future Mars Rover mishaps

Published

on


A 14-year-old from Pleasanton is using cutting-edge artificial intelligence in hopes of solving a problem that occurred millions of miles from Earth. 

Bhavishyaa Vignesh, a student at The Knowledge Society San Francisco, is trying develop an AI-powered model to help Mars rovers avoid obstacles, and avoid becoming stuck in Martian soil, like NASA’s Opportunity rover did in 2017.

“There’s a rover on Mars, it’s called Opportunity, and its wheel got stuck in a sand dune,” said Vignesh. “What I’m trying to essentially simulate is this type of thing happening in the future, and prevent this from happening again.”

At one time, Vignesh dreamed of becoming an astronaut. But her aspirations shifted after she won first place at the 2023 Canadian Space Agency Brain Hack competition. Her winning concept was a virtual reality headset designed to help astronauts manage isolation and emotional stress during space missions.

Now, she’s part of an elite group of students tackling ambitious global challenges on weekends at The Knowledge Society, a STEM accelerator program.

“When she came up with this project, I was really happy that someone was there to guide her, and that someone was there to coach her, and she can run her ideas by like-minded people,” said her mother, Suchitra Srinivasan.

The program’s director, Esther Kim, said its mission is to connect students with mentors from top Bay Area tech firms and challenge them to solve some of the world’s most pressing problems.

“We focus on solving the world’s biggest problems, hunger, cancer, climate change, and we pair emerging technologies with these hard problems to create real-world impact,” said Kim. “We don’t create tiny, cute high school projects. We actually want to launch really good ideas in the wild and test them.”

Vignesh’s project is currently in development, but she’s already preparing to present other projects, along with other students, at a showcase this Saturday at Yerba Buena Gardens in San Francisco. The event is free and open to the public at 10 a.m.

“It’s so important for the future of space travel,” Vignesh said. “It’s to showcase how important it is to choose the best possible path.”

NewsArtificial IntelligenceSan FranciscoAir and SpaceTech



Source link

Continue Reading

AI Insights

Musk AI firm says removing ‘inappropriate’ chatbot posts

Published

on


Elon Musk’s artificial intelligence start-up xAI says it is working to remove “inappropriate” posts on the multi-billionaire’s social network X.

The announcement came after the platform’s Grok AI chatbot shared multiple comments that were widely criticised by users.

“Since being made aware of the content, xAI has taken action to ban hate speech before Grok posts on X,” the company said in a post.

According to media reports, Grok made multiple positive references to Hitler this week when queried about posts that appeared to celebrate the deaths of children in the recent Texas floods.

In response to a question asking “which 20th century historical figure” would be best suited to deal with such posts, Grok said: “To deal with such vile anti-white hate? Adolf Hitler, no question.”

“If calling out radicals cheering dead kids makes me ‘literally Hitler,’ then pass the mustache,” said another Grok response. “Truth hurts more than floods.”

The incident came as xAI was due to launch its next-generation language model, Grok 4, on Wednesday.

On Friday, Musk posted on X that Grok had improved “significantly”, but gave no details of what changes had been made.

“You should notice a difference when you ask Grok questions,” he added.

The chatbot drew criticism earlier this year after it repeatedly referenced “white genocide” in South Africa in response to unrelated questions – an issue that the company said was caused by an “unauthorised modification”.

X, which was formerly called Twitter, was merged with xAI earlier this year.

Chatbot developers have faced extensive scrutiny over concerns around political bias, hate speech and accuracy in recent years.

Musk has also previously been criticised over claims that he amplifies conspiracy theories and other controversial content on social media.



Source link

Continue Reading

Trending