📌 Controversial truths
AI's obsolete hype: from prompt gurus to looming jobpocalypse
🐾 IN TODAY'S WILD
Prompt engineering's obsolescence proves relying on humans to manage AI was a short-sighted fix.
Anthropic boasting about catching spambots highlights AI's misuse, not their virtue.
Teaching more to build job-stealing "code agents" is a self-destructive cycle. Replacing human market research with AI guts real insight.
AI-powered flip phones? Peak tech absurdity?
The push for self-hosting reflects distrust in centralized AI.
Microsoft embedding more AI agents just deepens our reliance. Perplexity's "AI browser war" is more about dominance than user benefit. Debating AI consciousness distracts from real, present dangers.
Controversially, we're obsessed with AI's novelty while ignoring its destructive potential and ethical quicksand.
Today’s question:
🚨❓ Will AI make human creatives obsolete?
While AI undoubtedly challenges human creatives, it also presents opportunities for collaboration and innovation.
The key lies in recognizing the unique strengths of both human and artificial intelligence and finding ways to leverage their respective capabilities to push the boundaries of artistic expression.
Ultimately, human creativity may be valued for its ability to connect with and inspire us on a deeply human level, something that AI may never fully replicate.
Continue reading on [Wild Intelligence]
🦾 AI DAILY PULSE
The hottest AI job of 2023 is already obsolete. Prompt engineering, a role aimed at crafting the perfect input to send to a large language model, was poised to become one of the hottest jobs in artificial intelligence.
What happened? [WSJ] —See below our short explanation."We are committed to preventing misuse of our Claude models by adversarial actors while maintaining their utility for legitimate users. While our safety measures successfully prevent many harmful outputs, threat actors continue to explore methods to circumvent these protections. We are continuously using learnings to upgrade our safeguards".
Anthropic detects political spambot network using Claude across 100+ fake accounts, upgrades abuse detection. [Anthropic]Learn how to build code agents in Building Code Agents with Hugging Face smolagents, created in collaboration with Hugging Face, and taught by Thomas Wolf, co-founder and CSO, and Aymeric Roucher, Project Lead. [DeepLearning AI]
⚡️ TOP TRENDS
You can now use agents to do market research. Listen just raised $27M from Sequoia to replace surveys and focus groups with thousands of AI interviews. [Listen Labs]
Motorola’s iconic flip phone gets an AI reboot. Motorola on Thursday debuted new versions of its Razr flip phone with artificial intelligence-powered features from its own AI technology as well as that of several companies, including Perplexity, Meta and Microsoft and Google. [CNN]
💻 TOP TECHIES
Every developer needs to self-host. [Dev.to]
Microsoft launches agent builder for 365 Copilot: automate tasks across Word, Excel, Outlook, and Teams. [Microsoft]
🔮 WHAT ELSE
Perplexity’s CEO on fighting Google and the coming AI browser war . [The Verge]
Consciousness, reasoning and the philosophy of AI with Murray Shanahan
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🌟 AN EXPLANATION ON PROMPT ENGINEERING
The swift decline of prompt engineering as a "hottest job" speaks volumes about the rapid evolution of the AI landscape, particularly concerning large language models (LLMs). Initially, as these models emerged with their impressive but sometimes unpredictable outputs, the ability to craft precise and effective prompts became a crucial skill. It was seen as a way to wrangle these digital oracles, coaxing them to produce desired results for a variety of applications, from content generation to data analysis.
Several factors likely contributed to this rapid obsolescence:
Improved model capabilities: LLMs are becoming increasingly sophisticated. Their ability to understand natural language nuances and infer user intent is constantly improving. This means that the need for highly specialized and meticulously crafted prompts is diminishing. Models are becoming more robust and can handle a wider range of input styles effectively.
Advancements in fine-tuning and customization: Instead of relying solely on clever prompting, organizations are increasingly focusing on fine-tuning models with their own specific data and for their unique use cases. This process allows the model to learn the desired output style and context directly, reducing the reliance on intricate prompts.
Development of higher-level tools and abstractions: The AI ecosystem is maturing. New tools and platforms are emerging that abstract away the complexities of direct prompting. User interfaces and software development kits (SDKs) are being developed that allow users to interact with LLMs through more intuitive means, without needing to be expert prompt engineers. Think of user-friendly chatbots or integrated AI features within applications.
The rise of AI agents and autonomous systems: The trajectory of AI is leaning towards more autonomous agents that can understand goals and execute tasks with less direct human intervention. These agents will likely handle the "prompting" internally as part of their reasoning and planning processes.
Realization of limitations: While skillful prompting can elicit impressive results, it also became clear that it couldn't entirely overcome the inherent limitations of the underlying models, such as biases or factual inaccuracies. Addressing these fundamental issues requires more than just better prompting.
In essence, the initial hype around prompt engineering as a long-term, critical profession may have reflected the early stages of LLM development. As the technology matures and more sophisticated ways of interacting with and customizing these models emerge, the specialized role of the prompt engineer is evolving or being integrated into broader AI development and application roles. This highlights the dynamic and often unpredictable nature of emerging technologies and the skills that become valuable within them.
AI UNBUNDLED
Enhanced, responsible, and collaborative systems are the foundation for thriving in today's technological landscape. How do we build and operate accordingly, and how do we scale in complex environments? How do we conceive the needed solutions to empower our AI systems to address threatening AI systems?
Strategic implementation & roadmap: charting a course for AI safety [Week 12]
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