Dennis Gross

AI-driven discussions present a unique opportunity for intellectual engagement and growth in today’s dynamic and rapidly changing world. Facilitated or generated by artificial intelligence (AI) systems—such as advanced language models like OpenAI’s GPT series—these discussions can take various forms, including virtual debates among AI entities. AI-driven discussions enable users to engage with diverse topics anytime, anywhere, fostering a flexible learning experience. These discussions broaden users’ understanding and encourage critical thinking by presenting fresh perspectives on controversial or complex issues. Serving as a valuable resource for brainstorming sessions, AI-driven discussions can help researchers and creative professionals generate new ideas and insights. Moreover, they facilitate time efficiency by concisely summarizing vast amounts of information or presenting multiple viewpoints. Importantly, these automated discussions are devoid of personal biases or emotions, which often impede productive debates, allowing for more objective and focused discourse.

I created a Python script enabling multiple AI language models to engage in an AI-moderated discussion on any topic. An additional AI model provides real-time analysis and critique to improve the conversation’s quality further.

The AI Philosopher’s Roundtable Script

The Python script harnesses OpenAI’s GPT-4 to create an interactive setting in which three distinct AI entities, each assigned specific roles, engage in a structured dialogue:

Moderator: This AI model ensures the conversation remains focused, provides guidance, and promotes productive discourse. GPT-based analysis system: This AI model summarizes and assesses the debate in real-time, offering valuable insights and constructive feedback to enhance the conversation’s quality. The script starts by requesting the user to input a discussion topic. Once entered, the conversation begins with the Moderator setting the stage. The AI philosophers, System1 and System2, alternate in contributing to the debate, with the Moderator periodically intervening to maintain focus.

System1 and System2: These AI models represent philosophers celebrated for their critical thinking and capacity to propel discussions forward.

Evaluator: After a predetermined number of iterations, the GPT-based analysis system evaluates and summarizes the discussion.

The script can be found here.

Analysis

Despite the numerous advantages, there are also some drawbacks to AI-driven discussions. One significant limitation is that AI language models are based on existing knowledge and might not be able to provide truly original insights or ideas. They may lack the depth and nuance that human experts can bring to a conversation, resulting in oversimplifications of certain topics. This can be observed in the discussion records. They may also inadvertently reproduce biases present in the data they were trained on. Misinterpretations or inaccuracies may also arise, as AI models might not fully comprehend the context or nuances behind a specific subject. Lastly, the absence of emotions and personal experiences may limit the empathetic understanding and interpersonal connections that can be fostered through human-to-human discussions.

Nevertheless, even with such a simple script, it is already possible with GPT-4 to produce plausible discussions. It will be interesting to see how these discussions involve with more advanced system information prompts, models, and scripts.

Read Further

Quite similar to the topic discussed in this blog post are Auto-GPT and BabyAGI.

These projects attempt to create AI agents that can perform multistep tasks autonomously. While they currently require significant human input and are not yet fully autonomous, they represent early steps towards more complex AI models.

Auto-GPT, created by Toran Bruce Richards, chains together GPT-4 outputs to achieve a set goal. It currently requires user permission for each step and can’t make purchases, but it demonstrates the potential for AI assistants. BabyAGI, created by Yohei Nakajima, is inspired by the idea of using GPT-4 as an AI co-founder for businesses and has a task-oriented approach. Both projects face limitations due to GPT-4’s narrow range of interpretive intelligence and the issue of confabulations.

Read the full article to learn more about Auto-GPT, BabyAGI, and their implications for AI development.

Another self-looping ChatGPT agent system is described in the paper “Generative Agents: Interactive Simulacra of Human Behavior.” Implemented in a sandbox environment inspired by The Sims, these agents exhibit realistic individual and social behaviors. The research emphasizes the significance of observation, planning, and reflection in creating convincing simulations and demonstrates the integration of large language models with interactive agents.

Over the past few years, medical diagnostic apps are on the rise. Rapidly emerging technologies used to diagnose diseases result in more personalized patient care. About 70% of medical decisions are supported by diagnostics [2]. However, short of specialists and relatively low diagnostic accuracy calls for a new way of diagnostic strategy, in which deep learning may play a significant role. Smartphones and wearable devices can play a key role in health monitoring and diagnostics. They already support remote diagnostics and decrease the workload of GPs.

Diagnostic Apps

Modern smartphones are fitted with several sensors. These sensors allow for the sensing of several health parameters and health conditions.

Respiratory sounds are important indicators of respiratory health and respiratory disorders. When a person breathes, the sound emitted is directly related to air movement, changes within lung tissue, and the position of secretions within the lung. Depending on the sound, it is possible to monitor asthma, to record respiratory sounds for snoring and sleep apnea severity, detect respiratory symptoms like sneeze and cough, to detect bronchitis, bronchiolitis, and pertussis, to record wheezes in pediatric populations, and to detect the chronic obstructive pulmonary disease (COPD).

Leukocoria is an abnormal white reflection from ophthalmologists’ retina to detect several different eye diseases. As well as being an early indication of retinoblastoma, a pediatric eye cancer, leukocoria can also be a sign of pediatric cataract, Coats’ disease, amblyopia, strabismus, and other childhood eye disorders [4]. A portable 3d-printed device connected to a smartphone makes precise images of the retina to detect back-of-the-eye (fundus) disease at a far lower cost than conventional methods [3]. It is also possible to analyze images of the eye taken with this retinal camera to detect diabetic retinopathy, one of the leading causes of blindness.

Heart rate variability (HRV) is a measure of variations in the time intervals between your heartbeats and describes how “uneven” your heart beats. This metric can be used for different purposes:

• to measure stress levels
• to diagnose chronic health problems
• to assess the immune system
• to predict the recovery time after a severe illness

Ear and mastoid disease can easily be treated by early detection and appropriate medical care.

Skin diseases are widespread nowadays and spreading widely among people. The resolution of smartphone cameras has such a high resolution nowadays that these kinds of diseases can be detected [5].

In the modern world, psychological health issues like anxiety and depression have become very common among the masses.  Smartphone technology can be used to diagnose depression and anxiety [2].

And this is not the end of the medical usage of smartphones. Future sensors for analyzing sweat, isoline levels, and more are coming and will support more diagnostic applications for smartphones and wearables.

Datasets

The lack of large training data sets is often mentioned as an obstacle. However, this is only partially correct. Nowadays, hospitals are huge data storages, and databases in, e.g., radiology, are filled with millions of images. There are also large public data sets available on the internet.

What ML model for what task?

A survey name ‘A Survey on Deep Learning in Medical Image Analysis’ concluded that the exact deep learning architecture is not the most important determinant in getting a good solution. More important is expert knowledge about the task that can provide advantages beyond adding more layers to a CNN. Novel data preprocessing strategies also contribute to more accurate and more robust neural networks [6].

Problems

There remain still some problems with the accuracy of such systems. 98% accuracy of a diagnostic system can be quite expressing, but when we scale this up to a user base of millions, we could overrun our health systems with wrong diagnosed patients. So it is essential to make these diagnostic systems more and more robust.

References

[1] Majumder, Sumit, and M. Jamal Deen. “Smartphone sensors for health monitoring and diagnosis.” Sensors 19.9 (2019): 2164.

[2] https://www.bupa.com/newsroom/our-views/the-future-of-diagnostics

[3] https://medicalxpress.com/news/2019-06-portable-device-eye-disease-remotely.html

[4] Munson, Micheal C., et al. “Autonomous early detection of eye disease in childhood photographs.” Science advances 5.10 (2019): eaax6363.

[5] Chan, Stephanie, et al. “Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations.” Dermatology and therapy (2020): 1-22.

[6] Litjens, Geert, et al. “A survey on deep learning in medical image analysis.” Medical image analysis 42 (2017): 60-88.

Governments around the world are increasingly investing in autonomous military systems. Many are already developing programs and technologies that they hope will give them an edge over their adversaries.

AI in Warfare

Target Systems Analysis (TSA) and Target Audience Analysis (TAA) are intelligence-related methods to develop a deep understanding of potential areas for operations. Detection of military assets on the ground can be performed by applying deep learning-based object detectors on drone surveillance footage. For military forces on the ground, the challenge is to conceal their presence as much as possible from discovery from the air. A common way of hiding military assets from sight is camouflage, for example, by using camouflage nets.

Algorithmic Targeting: Today autonomous weapon platforms are using computer vision to identify, track targets, and shoot targets. These algorithmic targeting technologies are nowadays so precise, that even the military adds randomness to the targeting process to spread the bullets over the target. Otherwise, the bullet would just fly through the already generated holes.

Problem

These previously mentioned AI systems outperform human experts dramatically. But there are still major issues with these technologies. These systems are vulnerable to adversarial noise. You can imagine this (adversarial) noise as blurry pixels on your Instagram photos, usually, these pixel perturbations are so small that none of your human followers will see them but when Instagram uses an image classification software to check your account for illegal posts, it could happen (in the WORST CASE) that Instagram will block you.

Manipulating these AI systems

So what can you do when you forgot your camouflage net or when you don’t want a huge hole in your battleship? Well, you could try to generate this previously mentioned adversarial noise and stick it on your battleship or airplane. So from the point of mobility, it would be much easier to print a specific pattern on top of your battleship to hide from adversarial drones as carrying camouflage nets.

Summary

In this post, I wanted to describe the current weaknesses of AI systems, especially in warfare. The biggest problem of the current AI technology is (not only in warfare), that they are quite vulnerable against (adversarial) noise. This should be considered before releasing fully autonomous war machines that could make huge damage because of perturbated pixels in input data.