Category: Computing & AI

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Gods, Interns and Cogs

Drew Breunig brings some much-needed clarity to the discussion of AI by suggesting that there are three core use cases.

  1. Gods: Super-intelligent, artificial entities that do things autonomously.
  2. Interns: Supervised copilots that collaborate with experts, focusing on grunt work.
  3. Cogs: Functions optimized to perform a single task extremely well, usually as part of a pipeline or interface.

The Gods use case is where most of the angst is focused. At present, it is mainly hype. Who knows how it will turn out?

Interns are tools desgned for use in particular domains by experts in that domain, for example Github Copilot for programmers, Grammarly for writers, and NotebookLM for researchers. I have been using NotebookLM in recent weeks and can vouch for its rapidly growing usefulness and capability. Drew posits that Interns are providing most of the value obtained from AI at present.

Cogs (aka Agents) are essentially components that perform one function correctly and reliably. They are being used to build systems in ways resonant of the software intensive systems we are already accustomed to. It seems certain that these systems will in due course become a driver of technological change.

Azeem Azhar suggests that the Interns category “can be split into two distinct types of AI assistance: “True Interns” and “Copilots”. True Interns can take on multi-step projects autonomously – imagine telling them “build a guest list for our AI conference” and they’ll research speakers, check social media presence, cross-reference past events, and return with a curated list. This is what Claude’s computer use promises to do. Then there are Copilots – AI that works alongside you in real-time, suggesting code as you type or refining your writing on the fly. They’re more like having a knowledgeable colleague looking over your shoulder than an intern working independently.“

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AI as a Partner in Learning

Although some see artificial intelligence (AI) as our potential nemesis, there is great scope for it to become a transformative ally. In the field of education, AI is already beginning to reshape roles, enhance capabilities, and pose new challenges for educators, students, administrators and support staff. Arguably, we are heading towards a future in which AI’s role is that of a collaborative partner in the educational journey. How might that play out in a university context?

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Long-term memory for Generative AI

Large language models (LLMs) such as ChatGPT have embedded within them and can make use of the huge amount of information they were fed during training. A user is able to access that embedded knowledge by giving the LLM instructions during the course of a conversation with it. At present, however, the LLM has a limited capacity to remember the details of a conversation; that capacity is determined by the size of its context window.

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The growth in ChatGPT’s capability

The capabilities of ChatGPT are increasing at pace. The latest upgrade turns it into a multimodal AI. Instead of being restricted to text-only input and output, ChatGPT can now accept prompts with images or voice as well as text and can output its responses in one of five AI-generated voices. A user can switch seamlessly between text, image and voice prompts within the same conversation.

Browse with Bing enables ChatGPT to search the internet to help answer questions that benefit from recent information.

Advanced Data Analysis (formerly called Code Interpreter) enables ChatGPT to upload and download files, analyze data, do maths, and create and interpret Python code. These are powerful capabilities but there are restrictions which include: no internet access; a limited set of preinstalled packages; maximum upload and runtime limits; state is cleared (along with any generated files or links) when the environment dies.

Open Interpreter is an open source project which seeks to overcome the restrictions of Advanced Data Analysis. Open Interpreter runs in your local computer and interacts with ChatGPT. It has full access to the internet, is not restricted by time or file size, and can utilize any code package or library. Thus Open Interpreter combines the power of GPT-4’s Advanced Data Analysis with the flexibility of your local development environment.

Plugins enable ChatGPT to interact with functionality provided by other systems. Examples are:
Wolfram Plugin for ChatGPT gives it access to powerful computation, accurate maths, curated knowledge, real-time data and visualization through Wolfram|Alpha and Wolfram Language.
Show Me ChatGPT Plugin allows users to create and edit diagrams directly within a conversation in ChatGPT. 
There is a growing number of plugins; some are shown here.

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Plugins expand ChatGPT’s capability

ChatGPT has the ability to make use of third-party plugins which give it access to external sources of information. This is useful because it enables to AI to apply its impressive language capabilities to information that was not in its training data and, unlike the training data which is now two years old, that information can be current.

ScholarAI is a ChatGPT plugin “designed to provide users with access to a database of peer-reviewed articles and academic research“. In this conversation with ChatGPT, I explore a little of what the AI can do when the ScholarAI plugin has been installed. I found that it was able to search for papers, on a given subject, summarise the content of a paper, and answer questions about that content. I have not yet investigated the quality of the answers provided.

Plugins can also provide ChatGPT with additional functionality. In an earlier post, I mentioned the prospect of the AI interfacing with Wolfram Alpha. The Wolfram Alpha plugin is one instance of that, and it enables ChatGPT to give correct answers to prompts that require computation. See below for an example. We can be confident that answers obtained from Wolfram Alpha are of high quality.

There are many plugins to choose from. Websites such as whatplugin.ai can help us to find the ones we need.

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Computational powers for ChatGPT

Being a Large Language Model neural net, ChatGPT cannot by itself do non-trivial computations nor be relied upon to produce correct data. Recent months, however, have seen ChatGPT being linked with Wolfram|Alpha and the Wolfram Language to give it a powerful computational capability. In his blog post ChatGPT Gets Its “Wolfram Superpowers”!, Stephen Wolfram uses some examples to explain the current scope of this combined capability and to hint at the revolutionary power of its future potential.

Steve Blank’s blog post Playing With Fire – ChatGPT looks at that combined capability from another perspective. He highlights that not only is ChatGPT good at what is was designed to do but that it is demonstrating emergent behaviours (things it was not designed to do) which were not seen in its smaller-scale predecessors. He points out, also, that ChatGPT is beginning to interact with a variety of other applications through an application programming interface. These applications can be used by ChatGPT to enhace its own capabilities. Conversely, the applications can harness ChatGPT’s capabilities for their separate, third-party purposes. These increasingly complex systems will display emergent properties, ie properties that the individual parts of the system do not have on their own but which emerge when the parts interact as a whole. Some of the emergent properties will occur by design, but it is inevitable that there will be some which cannot be predicted.

We are still some way from artificial general intelligence, but that is the direction of travel and we should be concerned that the continued development of this technology is driven by for-profit companies, venture capitalists and autocratic governments without any means of control.

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A pocket calculator for ChatGPT

ChatGPT can respond to a question presented to it in natural language and is proving to be good at producing a human-like answer. But the answer is not always correct, and this is especially the case when the question involves quantitative data. In this respect ChatGPT is similar to most humans: we find it easy to write an essay but struggle to include correct facts and figures about the subject where these require us to do complicated calculations. Give us a pocket calculator, however, and we can do very much better. Is there a pocket calculator that ChatGPT could use?

Stephen Wolfram believes there is. In Wolfram|Alpha as the Way to Bring Computational Knowledge Superpowers to ChatGPT, he explains that Wolfram|Alpha is able to accept questions in natural language which it then converts into “precise, symbolic computational language [the Wolfram Language] on which it can apply its computational knowledge power” and then produce an answer in natural language. In other words, because ChatGPT communicates using natural language it is in principle able to use Wolfram|Alpha as its pocket calculator.

A possible next step, which Stephen Wolfram says has already started, is for ChatGPT to learn how to use Wolfram Language directly in the same way that humans do. This could enable ChatGPT to produce computational essays which bring together three elements: text to describe context and motivation; computer input in Wolfram Language for a precise specification of what is being talked about; and computer output for facts and results, often in graphical form. A key point here is that the Wolfram Language enables each piece of computer input to be short, not more than a line or two, and to be understandable both by the computer and by a human reading the essay.

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ChatGPT: an everyday tool for education?

Thomas Rid is Professor of Strategic Studies at and a founding director of the Alperovitch Institute for Cybersecurity Studies at Johns Hopkins University School of Advanced International Studies, Washington DC. Recently, he spent five days as a student in a class studying Malware Analysis and Reverse Engineering. The chat.openai.com/chat/-tab was open on most student machines at all times during the course, and they used it in real time to enhance their learning. Formerly “a hardened skeptic of the artificial intelligence hype“, Professor Rid is now convinced that it will transform higher education.

The class saw that ChatGPT had limitations. “To scale it in the classroom we need to better understand its strengths and weaknesses …. It will hallucinate. It will make mistakes. It will perform more poorly the closer you move to the edge of human knowledge. It appears to be weak on some technical questions.” But Rid wrote that “[by] Saturday evening it felt like we had a new superpower“. Rather than talk about plagiarism and cheating, he urges us to engage in a more inspiring conversation: how can artificial intelligence enable the most creative, ambitious and brilliant students – helped by educators – to “push out the edge of human knowledge through cutting-edge research faster and in new ways“? 

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ChatGPT: an everyday tool for researchers?

In his podcast A Skeptical Take on the A.I. Revolution Ezra Klein talks with Gary Marcus, emeritus professor of psychology and neural science at New York University. Marcus argues that although ChatGPT seems to produce impressive results, in fact it generates a pastiche of true and false information which it is unable to distinguish between. So, is it not to be trusted?

Some commentators such as New York Times tech columnist Kevin Roose are suggesting that ChatGPT could have value as a “teaching aid …. [which] could unlock student creativity, offer personalized tutoring, and better prepare students to work alongside A.I. systems as adults”. James Pethokoukis takes it further, seeing an upside in “the ability of such language models to aid academic research as a sort of “super research assistant” .

That aspect of ChatGPT is the subject of new research from finance professors Michael Dowling (Dublin City University) and Brian Lucie (Trinity College Dublin). In their paper ChatGPT for (Finance) Research: The Bananarama Conjecture, Dowling and Lucie report how ChatGPT’s output can be made impressively good by using domain experts to guide what it does. That opens up the possibility of using ChatGPT as an e-ResearchAssistant and of it becoming an everyday tool for researchers. It also, of course, opens up debate about authorship and copyright of papers co-authored with ChatGPT.

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Conversational interfaces to the web

“Today, hardly anyone questions whether to build a mobile-optimized website. A decade from now, we might be saying the same thing about optimizing digital experiences for voice or chat commands. The convenience of a customer experience will be a critical key differentiator. As a result, no one will think twice about optimizing their websites for multiple interaction patterns, including conversational interfaces like voice and chat.”

Dries Buytaert expands on this proposition here.