The era of Artificial Intelligence is here, and boy are people freaking out.
Fortunately, I am here to bring the good news: AI will not destroy the world, and in fact may save it.
First, a short description of what AI is: The application of mathematics and software code to teach computers how to understand, synthesize, and generate knowledge in ways similar to how people do it. AI is a computer program like any other – it runs, takes input, processes, and generates output. AI’s output is useful across a wide range of fields, ranging from coding to medicine to law to the creative arts. It is owned by people and controlled by people, like any other technology.
A shorter description of what AI isn’t: Killer software and robots that will spring to life and decide to murder the human race or otherwise ruin everything, like you see in the movies.
An even shorter description of what AI could be: A way to make everything we care about better. — Read More
Recent Updates Page 191
What ChatGPT Can and Can’t Do for Intelligence
In November 2022, ChatGPT emerged as a front-runner among artificial intelligence (AI) large language models (LLMs), capturing the attention of the CIA and other U.S. defense agencies. General artificial intelligence—AI with flexible reasoning like that of humans—is still beyond the technological horizon and might never happen. But most experts agree that LLMs are a major technological step forward. The ability of LLMs to produce useful results in some tasks, and entirely miss the mark on others, offers a glimpse into the capabilities and constraints of AI in the coming decade.
The prospects of ChatGPT for intelligence are mixed. On the one hand, the technology appears “impressive,” and “scarily intelligent,” but on the other hand, its own creators warned that “it can create a misleading impression of greatness.” In the absence of an expert consensus, researchers and practitioners must explore the potential and downsides of the technology for intelligence. — Read More
Welcome to the new surreal. How AI-generated video is changing film
The Frost nails its uncanny, disconcerting vibe in its first few shots. Vast icy mountains, a makeshift camp of military-style tents, a group of people huddled around a fire, barking dogs. It’s familiar stuff, yet weird enough to plant a growing seed of dread. There’s something wrong here.
“Pass me the tail,” someone says. Cut to a close-up of a man by the fire gnawing on a pink piece of jerky. It’s grotesque. The way his lips are moving isn’t quite right. For a beat it looks as if he’s chewing on his own frozen tongue.
Welcome to the unsettling world of AI moviemaking. “We kind of hit a point where we just stopped fighting the desire for photographic accuracy and started leaning into the weirdness that is DALL-E,” says Stephen Parker at Waymark, the Detroit-based video creation company behind The Frost.
The Frost is a 12-minute movie in which every shot is generated by an image-making AI. It’s one of the most impressive—and bizarre—examples yet of this strange new genre. You can watch the film below in an exclusive reveal from MIT Technology Review. — Read More
The Falcon has landed in the Hugging Face ecosystem
Falcon is a new family of state-of-the-art language models created by the Technology Innovation Institute in Abu Dhabi, and released under the Apache 2.0 license. Notably, Falcon-40B is the first “truly open” model with capabilities rivaling many current closed-source models. This is fantastic news for practitioners, enthusiasts, and industry, as it opens the door for many exciting use cases.
In this blog, we will be taking a deep dive into the Falcon models: first discussing what makes them unique and then showcasing how easy it is to build on top of them (inference, quantization, finetuning, and more) with tools from the Hugging Face ecosystem. — Read More
Ex-Google Officer Finally Speaks Out On The Dangers Of AI! – Mo Gawdat
The Illusion of China’s AI Prowess
Regulating AI Will Not Set America Back in the Technology Race
The artificial intelligence revolution has reached Congress. The staggering potential of powerful AI systems, such as OpenAI’s text-based ChatGPT, has alarmed legislators, who worry about how advances in this fast-moving technology might remake economic and social life. Recent months have seen a flurry of hearings and behind-the-scenes negotiations on Capitol Hill as lawmakers and regulators try to determine how best to impose limits on the technology. But some fear that any regulation of the AI industry will incur a geopolitical cost. In a May hearing at the U.S. Senate, Sam Altman, the CEO of OpenAI, warned that “a peril” of AI regulation is that “you slow down American industry in such a way that China or somebody else makes faster progress.” That same month, AI entrepreneur Alexandr Wang insisted that “the United States is in a relatively precarious position, and we have to make sure we move fastest on the technology.” Indeed, the notion that Washington’s propensity for red tape could hurt it in the competition with Beijing has long occupied figures in government and in the private sector. Former Google CEO Eric Schmidt claimed in 2021 that “China is not busy stopping things because of regulation.” According to this thinking, if the United States places guardrails around AI, it could end up surrendering international AI leadership to China. — Read More
StyleDrop: Text-To-Image Generation in Any Style
We present StyleDrop that enables the generation of images that faithfully follow a specific style, powered by Muse, a text-to-image generative vision transformer. StyleDrop is extremely versatile and captures nuances and details of a user-provided style, such as color schemes, shading, design patterns, and local and global effects. StyleDrop works by efficiently learning a new style by fine-tuning very few trainable parameters (less than 1% of total model parameters), and improving the quality via iterative training with either human or automated feedback. Better yet, StyleDrop is able to deliver impressive results even when the user supplies only a single image specifying the desired style. An extensive study shows that, for the task of style tuning text-to-image models, Styledrop on Muse convincingly outperforms other methods, including DreamBooth and Textual Inversion on Imagen or Stable Diffusion. — Read More
Air Force colonel backtracks over his warning about how AI could go rogue and kill its human operators
… An Air Force colonel who oversees AI testing used what he now says is a hypothetical to describe a military AI going rogue and killing its human operator in a simulation in a presentation at a professional conference.
But after reports of the talk emerged Thursday, the colonel said that he misspoke and that the “simulation” he described was a “thought experiment” that never happened. — Read More
Open-Source LLMs
In February, Meta released its large language model: LLaMA. Unlike OpenAI and its ChatGPT, Meta didn’t just give the world a chat window to play with. Instead, it released the code into the open-source community, and shortly thereafter the model itself was leaked. Researchers and programmers immediately started modifying it, improving it, and getting it to do things no one else anticipated. And their results have been immediate, innovative, and an indication of how the future of this technology is going to play out. Training speeds have hugely increased, and the size of the models themselves has shrunk to the point that you can create and run them on a laptop. The world of AI research has dramatically changed.
This development hasn’t made the same splash as other corporate announcements, but its effects will be much greater. It will wrest power from the large tech corporations, resulting in both much more innovation and a much more challenging regulatory landscape. The large corporations that had controlled these models warn that this free-for-all will lead to potentially dangerous developments, and problematic uses of the open technology have already been documented. But those who are working on the open models counter that a more democratic research environment is better than having this powerful technology controlled by a small number of corporations. — Read More
Generating Images with Multimodal Language Models
We propose a method to fuse frozen text-only large language models (LLMs) with pre-trained image encoder and decoder models, by mapping between their embedding spaces. Our model demonstrates a wide suite of multimodal capabilities: image retrieval, novel image generation, and multimodal dialogue. Ours is the first approach capable of conditioning on arbitrarily interleaved image and text inputs to generate coherent image (and text) outputs. To achieve strong performance on image generation, we propose an efficient mapping network to ground the LLM to an off-the-shelf text-to-image generation model. This mapping network translates hidden representations of text into the embedding space of the visual models, enabling us to leverage the strong text representations of the LLM for visual outputs. Our approach outperforms baseline generation models on tasks with longer and more complex language. In addition to novel image generation, our model is also capable of image retrieval from a prespecified dataset, and decides whether to retrieve or generate at inference time. This is done with a learnt decision module which conditions on the hidden representations of the LLM. Our model exhibits a wider range of capabilities compared to prior multimodal language models. It can process image-and-text inputs, and produce retrieved images, generated images, and generated text — outperforming non-LLM based generation models across several text-to-image tasks that measure context dependence. — Read More
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