Are you afraid of spiders? 🕷️🕸️
Because that’s what autonomous agents will be like.
Spiders are like AI autonomous agents in that they have specific tasks to perform and can operate independently. They make decisions based on their environment, such as where to spin a web, when to repair it, or how to catch prey. Spiders are goal-oriented creatures—whether it's catching food, reproducing, or surviving. Similarly, AI autonomous agents have objectives like optimizing a process, solving a problem, or assisting users.
The spider web represents the interconnected system or network within which the AI autonomous agent operates. Just as a spider's web is carefully designed to catch prey, the system is designed to fulfill specific functions, such as data collection, user engagement, or task automation. For instance, the web could be an IoT system, a data pipeline, or a customer service platform.
Individual web threads are analogous to the various data streams and algorithms the AI utilizes. Spiders use different kinds of silk for different purposes—some for structural support some for capturing prey. Likewise, an AI system might use different algorithms for natural language processing, decision-making, and data analysis.
Just as spiders rely on the web to catch prey, AI agents rely on the system to provide input (data), which they can act upon and generate output (decisions, actions). The effectiveness of catching the prey could be likened to how accurately and efficiently the AI performs its tasks.
Both spiders and AI agents can adapt to changing conditions. If a spider notices that a particular part of its web isn't effective in catching prey, it may modify it. Similarly, advanced AI autonomous agents can adapt and optimize their behavior based on the effectiveness of their actions or any new data they might acquire (though this feature isn't present in current versions of ChatGPT).
Finally, just as spiders are part of a larger ecosystem that includes other creatures, plants, and environmental factors, AI agents exist within a broader technological and human context. Their effectiveness isn't just determined by their capabilities but also by how they interact with other systems, technologies, and people.
🗝️ Quick Bytes:
Stack Overflow Announces Workforce Cut Amid Shift to Profitability
Stack Overflow is cutting 28% of its workforce, primarily affecting its go-to-market sales team, as part of a move towards profitability. The layoffs come a year after the company doubled its staff to over 500, with almost 45% of those hires in sales.
The rise of generative AI, particularly AI-powered coding assistance, challenges Stack Overflow's core service as a coding help forum. The company had temporarily banned users from generating answers via AI chatbots, a policy which led to a months-long strike among moderators.
Stack Overflow plans to charge AI companies for training their models on its site in a new revenue-generating strategy. This follows a period of under-enforcement of its AI chatbot ban, and how this will impact the forum's user engagement and profitability remains to be seen.
Majority of Academics Yet to Feel AI's Day-to-Day Impact
AI chatbots like ChatGPT are becoming increasingly common in academic settings, offering a range of functionalities from refining text to generating code. According to Nature's global postdoc survey, 31% of employed respondents use chatbots, but only 17% do so daily. In specific fields, engineering and social sciences have higher usage rates of 44% and 41%, respectively.
Despite the growing adoption, many researchers remain skeptical or uninformed about AI’s role in academia. About 67% of the survey's respondents felt that AI had not changed their day-to-day work, attributed partly to institutional inertia and lack of formal guidelines on responsible AI usage.
Language barriers are being broken down as chatbots aid in formal written communication for non-native speakers. Rafael Bretas, a postdoc in Japan, uses ChatGPT to write formal Japanese emails, saving time and reducing misunderstandings. The technology mainly benefits early-career researchers and postdocs who aren't native English speakers, enhancing the quality of their written work.
Google's SGE Adds Draft Writing Feature Directly from Search Bar
Google has introduced its Search Generative Experience (SGE), a feature allowing users to generate images and written drafts directly from the Google search bar. The tool uses the Imagen family of AI models for image generation and various LLMs for text drafts.
SGE has strict guidelines for responsible use, such as watermarking and metadata labeling for AI-generated images. It also limits image generation to users 18 or older and prohibits creating images that violate Google's policy on generative AI.
Although initially launched in May with criticisms about speed and user experience, Google has continually improved SGE with added functionalities like more video options and better links. Microsoft's Bing Chat also offers a similar image-generation feature using OpenAI's DALL-E model since March.
🎛️ ChatGPT Command Line
I will expand your thinking in 5 minutes.
And you will get the content ideas for months.
This is the thing that I have many questions about - how to overcome writer's block and get a new perspective or inspiration for creating content. The method is easy, even for beginners, and you can implement it in minutes.
I also use it a lot in different variations (but that's a story for another video or even a lot of videos).
So, let me know if you find this valuable and want to watch more of these tips and tricks.
I am still learning how to do a video, and this is an entirely new world for me - please don't judge 😅 And for the end… are you satisfied with the results of your enhanced prompt?
💡Explained
What Are LLM Agents?
LLM agents have gained popularity not only in technical and scientific communities but also in everyday discussions. But what exactly is an LLM agent?
First, let's start by understanding LLM chains.
⛓️ LLM Chains
An LLM chain is like a pipeline that shows how data flows through the LLM system. The basic chain is inputting text and getting output text. However, chains can have additional steps, like querying a vector database or using an external API.
An example of a chain that connects LLM with our newsletter would be: sending a request (input text e.g. "Can you summarize the contents of the latest The Keygen news?") → Calling the calendar API to check the date → Downloading relevant text through designated API and combining it with the input → Passing the concatenated text to the LLM (prompt + newsletter content) → Getting the prediction (summary).
But, instead of downloading a text (or using a vector knowledge base), we could also employ an external API for a different purpose, e.g. to talk about weather. Then we could use an external weather API to check the current weather mentioned in the prompt.
If we want to design an LLM app that will allow a user to ask both for the weather and to retrieve information depending on the prompt, we possibly would not want to unnecessarily send requests to weather API. Especially when we would only want to retrieve data from the knowledge base. This is when agents come in handy.
🔍 Agents
Agents are autonomous LLMs that can query external APIs or models when needed, in addition to having a memory (e.g. vector knowledge base). The LLM first analyzes the input and plans the necessary steps to achieve the desired outcome. Then, it executes the plan step by step. This flexible and robust approach allows the agent to break the problem into smaller actionable steps instead of following a predefined chain of actions like in the example above.
This planning process is often repeated after every action, successful or not, allowing for self-reflection on its own actions.
🥡 Takeaway
In my opinion, using LLMs to build agents has great potential. However, it can be challenging to determine what an agent should be capable of and how to perfect it. Without a very strong LLM, utilizing it effectively remains a challenge.
➡️ Reading: We've covered the definition of chains and agents in LLMs, but if you're interested in learning more, check out Lilian Weng's blog post on LLM-powered autonomous agents.
➡️ Libraries: Common Agent libraries include LangChain, Agents, AutoGen, or Haystack, but there are many others. Check Awesome AI Agents repository to learn more.
Let us know if you are interested in learning more about agents!
🗞️ Longreads
- A ‘Godfather of AI’ Calls for an Organization to Defend Humanity (read)
- Huberman husbands and the rise of self-optimization (read)
🎫 Events
Hey there, fellow no-code and low-code enthusiasts! It's Aleksander here, and I'm thrilled to inform you about the upcoming No Code Days 2023 conference this autumn. If you're passionate about creating applications without diving deep into coding, this event is for you! 🤖 Here's what's in store:
💡 Two days jam-packed with insightful lectures.
💡 18 hours of workshops that will inspire and motivate.
💡 3 exciting thematic tracks: No Code & AI / Low Code / Change Path.
💡 Hands-on with the latest No Code / Low Code tools.
📅 Mark your calendars for November 20-21, 2023. 📍 We'll be gathering at Expo XXI, Warsaw.
And here's a little treat for you, my lovely readers: use the promo code KEYGEN to grab your tickets at a 15% discount!
I can't wait to see you there! Let's catch up, exchange high fives, and dive deep into some tech talks. Let me know if you're coming, and we can plan to meet up.