If people are worried that Chat-GPT could be taking their jobs, they haven’t seen Auto-GPT yet.
Auto-GPT is an AI chatbot similar to ChatGPT and others. It is based on the GPT-4 language model of OpenAI, the same LLM that powers the ChatGPT. But, as the name implies, “Autonomous Artificial Intelligence Chat Generative Pre-trained Transformer,” a step further, but what exactly is it? Let us go through what Auto-GPT is and how it works.
What is Auto-GPT
Essentially, Auto-GPT is a chatbot. You ask it the questions it answers smartly. But, unlike ChatGPT and other GPT-based chatbots, which need a prompt every single time, Auto-GPT can automate the whole task, so you do not need to prompt it. Once given a task, Auto-GPT will figure out the steps on its own to reach the goal.
The world’s preeminent linguist has spoken — and he seems mighty tired of everyone’s whining about artificial intelligence as it stands today.
In an op-ed for the New York Times, Noam Chomsky said that although the current spate of AI chatbots such as OpenAI’s ChatGPT and Microsoft’s Bing AI “have been hailed as the first glimmers on the horizon of artificial general intelligence” — the point at which AIs are able to think and act in ways superior to humans — we absolutely are not anywhere near that level yet.
“That day may come, but its dawn is not yet breaking, contrary to what can be read in hyperbolic headlines and reckoned by injudicious investments,” the Massachusetts Institute of Technology cognitive scientist mused.
The yacht No Stress has five operating modes using two sources of power.
As the world goes ga-ga over the wonders of artificial intelligence (AI) and how it can improve our daily use apps and make them more intuitive, Italian ship maker Rossinavi has deployed the technology to ease the crew’s workload onboard its latest yacht, No Stress.
The nearly 164 feet (50 m) long super yacht has a sporty design and looks set to cruise the ocean waters with its fast-displacement hull form. However, the ship is making headlines because, under the hood, a computer system has been tasked to monitor and manage power consumption while the passengers look to de-stress away from their hectic lives.
The open-source project is based on a dataset of up to 180,000 annotated amateur drawings.
In the face of increasing competition, it appears that Meta is significantly upping its artificial intelligence (AI) game.
This open-source project is based on a dataset of up to 180,000 annotated amateur drawings. The company has made this dataset and animation code available for AI researchers and creators to use and innovate further.
Dusan Stankovic/iStock.
Following the release of the “Segment Anything Model,” the tech giant has unveiled yet another interesting and fun AI-based project. The project, called Animated Drawings, allows you to turn your doodles into animations. And it could be the next big thing.
Bedrock allows its users to build and scale generative AI applications like chatbots.
Becoming the latest actor to join the generative AI space race, Amazon Web Services (AWS) has launched Bedrock. Not to be confused with OpenAI’s ChatGPT or Google’s Bard which are AI-powered chatbots.
What Bedrock does is allowing users to build and scale generative AI applications such as chatbots, text generation, image generation using language prompts, etc. There’s a range of pre-trained models that the users can customize and embed their own data into and then integrate and deploy in applications using the AWS tools.
Michael Levin’s 2019 paper “The Computational Boundary of a Self” is discussed. The main topics of conversation include Scale-Free Cognition, Surprise & Stress, and the Morphogenetic Field. Michael Levin is a scientist at Tufts University; his lab studies anatomical and behavioral decision-making at multiple scales of biological, artificial, and hybrid systems. He works at the intersection of developmental biology, artificial life, bioengineering, synthetic morphology, and cognitive science.
❶ Scale-Free Cognition. 3:05 Ultimate question of the embodied mind. 5:50 The most difficult interview to prepare for. 6:55 One of my favorite papers of all time (screenshare) 7:40 The Computational Boundary of a Self. 9:25 Defining intelligence (cybernetics) 10:30 Cognitive light cones. 16:50 All intelligence is collective intelligence. 17:35 Nested selves vs. one integrated self (Not Integrated Information Theory) 21:10 The same dynamics in the brain occur in every tissue of the body. 22:50 Why scale “free” cognition?
❷ Stress & Surprise. 27:22 Stress = Surprise? 30:30 Intelligence within a salamander example (homeostatic capability of collective intelligence) 33:35 The scale-free importance of stress. 37:30 Stress is an exported error signal. 40:45 Stress means your problem becomes everyone’s problem (cooperation without altruism) 42:25 Stress has no ownership metadata (gap junctions permit mind meld)
Many current computational models that aim to simulate cortical and hippocampal modules of the brain depend on artificial neural networks. However, such classical or even deep neural networks are very slow, sometimes taking thousands of trials to obtain the final response with a considerable amount of error. The need for a large number of trials at learning and the inaccurate output responses are due to the complexity of the input cue and the biological processes being simulated. This article proposes a computational model for an intact and a lesioned cortico-hippocampal system using quantum-inspired neural networks. This cortico-hippocampal computational quantum-inspired (CHCQI) model simulates cortical and hippocampal modules by using adaptively updated neural networks entangled with quantum circuits. The proposed model is used to simulate various classical conditioning tasks related to biological processes. The output of the simulated tasks yielded the desired responses quickly and efficiently compared with other computational models, including the recently published Green model.
Several researchers have proposed models that combine artificial neural networks (ANNs) or quantum neural networks (QNNs) with various other ingredients. For example, Haykin (1999) and Bishop (1995) developed multilevel activation function QNNs using the quantum linear superposition feature (Bonnell and Papini, 1997).
The prime factorization algorithm of Shor was used to illustrate the basic workings of QNNs (Shor, 1994). Shor’s algorithm uses quantum computations by quantum gates to provide the potential power for quantum computers (Bocharov et al., 2017; Dridi and Alghassi, 2017; Demirci et al., 2018; Jiang et al., 2018). Meanwhile, the work of Kak (1995) focused on the relationship between quantum mechanics principles and ANNs. Kak introduced the first quantum network based on the principles of neural networks, combining quantum computation with convolutional neural networks to produce quantum neural computation (Kak, 1995; Zhou, 2010). Since then, a myriad of QNN models have been proposed, such as those of Zhou (2010) and Schuld et al. (2014).
OpenAI’s breakthrough consistency model will lead into image understanding to make GPT4 multimodal, providing next generation improvements with human-computer interaction, human-robot interaction, and even helping the disabled. Microsoft has already released a predecessor to GPT4 image understanding by with Visual ChatGPT, which is much more limited in its abilities.
AI news timestamps: 0:00 Multimodal artificial intelligence. 0:35 OpenAI consistency models. 1:35 GPT4 and computers. 3:04 GPT4 and robotics. 4:28 GPT4 and the disabled. 5:36 Microsoft Visual ChatGPT
