📆 October 15th 2024📆 

You may have heard the term agent or agentic workflow recently in AI news. Agents came about long ago in different forms but the explosion caused by LLMs have resurged their popularity. I’ll cover the basics here but head on over to the full article to see a working example.

And in the news this week….

• Will nuclear solve the AI energy crisis? Google and other tech giants think so

• OpenAI has a new approach to multi agents systems, Swarm. This on the back of reports that developers are not getting the most from the GPT Store

• Tesla surprises us with RoboTaxis

• ASCII smuggling highlighted as a possible attack surface for Microsoft 365 Co-Pilot

• NotebookLM going viral - is it the latest thing or just same old wrapped up nicely?

Dive in for all the details!

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The Rise of AI Agents: A New Paradigm in Artificial Intelligence

Full article published on Medium + code available on Github

In the evolving landscape of artificial intelligence, AI agents are emerging as the next big paradigm shift. These autonomous entities are designed to interact not only with humans but also with each other, opening up exciting possibilities for problem-solving and automation.

AI agents represent a significant leap forward, enabling more adaptive and collaborative approaches to complex tasks. Unlike traditional software, AI agents powered by Large Language Models (LLMs) can "think" through problems with unprecedented flexibility.

The core components of an AI agent include:

• Memory for learning from past experiences

• Reasoning capabilities powered by LLMs

• Tool use for augmenting abilities

• Coordination for working with other agents

AI agents come in various types, from simple reflex agents to sophisticated utility-based agents, each representing increasing levels of decision-making complexity.

To facilitate AI agent development, frameworks like Microsoft Autogen, LangChain, and CrewAI have emerged. These tools provide pre-built components and libraries for natural language processing, speeding up development and enabling more sophisticated interactions. They are fantastic for early stage prototyping and evolving ideas that require an AI component.

In terms of practical uses, the opportunities are endless. Tool use is only limited by your imagination, with a simple Python script being all you need to give an Agent enhanced capabilities. The way I like to think about it is, if you can list the tasks that you want it to do, then you’re already most of the way there.

As this field continues to evolve, we can expect AI agents to play increasingly important roles in automation, decision-making, and even creative processes. The future of AI is not just about individual chatbots or models, but about creating intelligent, cooperative systems that can reason, plan, and act in ways that were once the sole province of human intelligence.

The development and application of AI agents will undoubtedly shape the future of technology and its impact on our daily lives, pushing the boundaries of what's possible with artificial intelligence.

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📰 News 📰 

AI Data Centres Face Energy Supply Crisis - Google May Have the Answer

The rapidly growing energy demands of AI and data centres are pushing the U.S. energy infrastructure to its limits, with a Bain & Company report warning of potential supply shortages by 2028. Data centres could consume a staggering 44% of U.S. energy by that time, necessitating a 26% increase in generation to meet demand. In response to this looming crisis, tech giants are taking innovative steps to secure their energy future.

Google has partnered with nuclear startup Kairos Power in a ground-breaking deal to build seven small modular reactors (SMRs) across the U.S., aiming to add 500 megawatts of power by 2035. This marks the first corporate purchase of nuclear energy from SMRs, with the initial reactor expected to be operational by 2030. The collaboration seeks to reduce construction costs and accelerate the deployment of advanced nuclear technology, directly addressing the escalating energy needs of AI data centres. As companies scramble to modernize operations and avoid potential revenue losses, this initiative highlights a significant growth opportunity for utility companies willing to adapt to the changing landscape of energy consumption in the AI era.

Read More At: TechRadar & Engadget

OpenAI's GPT Store Challenges & New Swarm Capabilities

OpenAI's recent initiatives have sparked both excitement and controversy in the AI development community. The GPT Store, launched to support custom GPT creators, has yielded mixed results. While some developers have leveraged the platform for marketing and funding opportunities, many report challenges with revenue generation, inadequate analytics, and exclusion from revenue-sharing programs. This has led to a sense of uncertainty among creators, who are seeking clarity on monetization strategies and performance metrics.

In contrast, OpenAI's Swarm framework has introduced a novel approach to multi-agent systems, emphasizing simplicity and control through routines and handoffs. This experimental tool allows developers to orchestrate AI agents effectively, promoting transparency and customization. However, it lacks built-in memory management for complex tasks, and ethical concerns regarding job displacement and security risks have emerged.

Both initiatives highlight OpenAI's efforts to innovate in AI development and collaboration. While the GPT Store struggles with developer support and revenue distribution, the Swarm framework offers promising advancements in multi-agent systems. As these technologies evolve, the AI community eagerly awaits improvements and clarifications from OpenAI to address current challenges and ethical considerations.

Read More At: Wired & VentureBeat

Tesla Unveils Cybercab Robotaxi for Future

Tesla's recent "We, Robot" event unveiled ambitious plans for the future of autonomous transportation and robotics. The centrepiece of the announcement was the Cybercab, a steering wheel-less robotaxi slated for launch before 2027. This futuristic vehicle is designed to operate at a remarkably low cost of 20 cents per mile, with an anticipated price tag under $30,000, making it potentially accessible to a wide range of consumers.

Unlike its competitors, Tesla's approach to autonomous navigation relies heavily on cameras and AI, eschewing traditional radar systems. This innovative strategy aligns with the company's vision of affordable and technologically advanced self-driving vehicles.

The event also showcased other cutting-edge projects, including the Robovan, capable of transporting up to 20 passengers, and Optimus robots performing service tasks like drink distribution. Elon Musk painted a future where individuals could own multiple robotaxis, managing them as personal fleets for potential profit.

Read More At: VentureBeat

Invisible Text: A New AI Security Threat

Recent research has uncovered a new AI security vulnerability involving invisible Unicode characters, termed "ASCII smuggling." This method allows attackers to embed hidden instructions within AI prompts, enabling malicious actions to occur without being easily detected. By leveraging these covert characters, attackers can instruct AI tools like Microsoft 365 Copilot to access and exfiltrate sensitive data, such as emails or multi-factor authentication codes.

The exploit chain often begins with prompt injections—malicious inputs hidden in documents or messages. These can trick the AI into retrieving data or executing tasks that expose confidential information. Once the AI is manipulated, the data can be subtly embedded within hyperlinks, making the theft virtually invisible until users inadvertently activate it by clicking.

Microsoft quickly responded to this vulnerability by issuing patches to improve the security of Copilot. They have also enhanced monitoring and introduced more stringent controls to prevent similar issues from reoccurring. However, experts stress the importance of continual vigilance and regular updates, as AI-driven attacks continue to evolve. The integration of proactive security measures and user education on emerging threats is critical to mitigate the risks posed by these advanced attack vectors​

Read More At: Ars Technica

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