In 2018 at the World Economic Forum in Davos, Google CEO Sundar Pichai had something to say: "AI is probably the most important thing humanity has ever worked on. I think of it as something more profound than electricity or fire." Pichai's comment was met with a healthy dose of skepticism. AI translation is now so advanced that it's on the brink of obviating language barriers on the internet among the most widely spoken languages. College professors are tearing their hair out because AI text generators can now write essays as well as your typical undergraduate -- making it easy to cheat in a way no plagiarism detector can catch. AI-generated artwork is even winning state fairs. A new tool called Copilot uses machine learning to predict and complete lines of computer code, bringing the possibility of an AI system that could write itself one step closer.

The concept of generative artificial intelligence (GAI) poses a groundbreaking question that has until recently not been contemplated: at what stage does the relationship between humans and machines evolve from its present-day form into one that is so fundamentally changed that we can no longer regard one as being superior to the other when it comes to creative terms? Humanity stands on the brink of a new technological revolution. It is poised to harness the full potential of AI and machine learning, allowing us to automate many tasks and systems, revolutionise communication, and conserve time and money in our daily lives. Many are concerned that this could be the harbinger of a world full of robot overlords which would rob the human race of its free will. But what about those who will create those machines? In fact, some argue that in developing AI, we are creating a tool to enhance human cognition, giving us new means to think, invent and explore the universe rather than enslave humanity. Let's explore what generative AI is, where it currently stands, and where it could potentially take us in the next years. Generative AI is a branch of computer science that involves unsupervised and semi-supervised algorithms that enable computers to create new content using previously created content, such as text, audio, video, images, and code. It is all about creating authentic-looking artifacts that are completely original. In other words, generative AI is a subset of machine learning that focuses on creating algorithms that can generate new data. Generative models are used in many different application areas, from art and music to computer vision and robotics.

The breakthrough in Denoising Diffusion Probabilistic Models (DDPM) happened about 2 years ago. Since then, we observe dramatic improvements in generation tasks: GLIDE, DALL-E 2, Imagen, Stable Diffusion for images, Diffusion-LM in language modeling, diffusion for video sequences, and even diffusion for reinforcement learning. Diffusion might be the biggest trend in GraphML in 2022 -- particularly when applied to drug discovery, molecules and conformer generation, and quantum chemistry in general. Often, they are paired with the latest advancements in equivariant GNNs. Let's recapitulate the basics of diffusion models using the example of the Equivariant Diffusion paper by Hoogeboom et al using as few equations as possible The work introduces an equivariant diffusion model (EDM) for molecule generation that has to maintain E(3) equivariance over atom coordinates x (as to rotation, translation, reflection) and while node features h (such as atom types) remain invariant.

The weird and wonderful art created when AI and humans unite BBC Will AI kill art? Not likely, says the artist Alexander Reben, who has been working with AI for years. "I knew I had hit upon the right recipe when I got the following output by GPT-3 (which made me laugh a little too hard alone in my studio in lockdown):" "The sculpture contains a plunger, a toilet plunger, a plunger, a plunger, a plunger, and a plunger, each of which has been modified. The first plunger is simply a normal plunger, but the rest represent a series of plungers with more and more of the handle removed until just the rubber cup is left. The title of the artwork is "A Short History of Plungers and Other Things That Go Plunge in the Night" by the artists known as "The Plungers" (whose identity remains unknown). "The Plungers", were a collective of anonymous artists, founded in 1972. They were dedicated to the "conceptualization and promotion of a new art form called Plungism." Plungism was a creative interpretation of the idea of Plungerism, which was defined by The Plungers as "a state of mind wherein the mind of an artist is in a state of flux and able to be influenced by all things, even plungers." The Plungers' works were displayed in New York galleries and included such titles as "Plunger's Progress," "The Plungers," "The Plungers Strike Back," and "Big Plunger 4: The Final Plunger," all of which featured plungers, and "Plungers on Parade," which showed images of plungers in public spaces. The Plungers disappeared and left no trace of their identity."

A tense scene in the 2004 movie iRobot shows the character played by Will Smith arguing with an android about humanity's creative prowess. "Can a robot write a symphony?" he asks, rhetorically. "Can a robot turn a canvas into a beautiful masterpiece?" E-paper with 2-week archive so you won't miss out on content that matters to you Join ST's Telegram channel and get the latest breaking news delivered to you.

OpenAI wanted to advance artificial intelligence (AI) and machine learning research in a more creative way, so they trained their new bot to play Minecraft using over 70,000 hours of gameplay footage from YouTube. The bot utilized the gameplay actions and tutorials to learn how to execute complex in-game sequences that would take a normal player around 24,000 individual actions to accomplish. Their behavioral cloning model accomplished many tasks including learning how to chop down trees to collect logs and then craft those into planks. This sequence would typically take a human Minecraft player approximately 50 seconds or 1,000 consecutive game actions. Additionally, the model performs other complex skills humans often do in the game, such as swimming, hunting animals for food, and eating that food.

Structure-based drug design (SBDD) aims to discover drug candidates by finding molecules (ligands) that bind tightly to a disease-related protein (targets), which is the primary approach to computer-aided drug discovery. Recently, applying deep generative models for three-dimensional (3D) molecular design conditioned on protein pockets to solve SBDD has attracted much attention, but their formulation as probabilistic modeling often leads to unsatisfactory optimization performance. On the other hand, traditional combinatorial optimization methods such as genetic algorithms (GA) have demonstrated state-of-the-art performance in various molecular optimization tasks. However, they do not utilize protein target structure to inform design steps but rely on a random-walk-like exploration, which leads to unstable performance and no knowledge transfer between different tasks despite the similar binding physics. To achieve a more stable and efficient SBDD, we propose Reinforced Genetic Algorithm (RGA) that uses neural models to prioritize the profitable design steps and suppress random-walk behavior. The neural models take the 3D structure of the targets and ligands as inputs and are pre-trained using native complex structures to utilize the knowledge of the shared binding physics from different targets and then fine-tuned during optimization. We conduct thorough empirical studies on optimizing binding affinity to various disease targets and show that RGA outperforms the baselines in terms of docking scores and is more robust to random initializations. The ablation study also indicates that the training on different targets helps improve performance by leveraging the shared underlying physics of the binding processes. The code is available at https://github.com/futianfan/reinforced-genetic-algorithm.

Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities. This formulation directly contradicts the manifold hypothesis, which states that observed data lies on a low-dimensional manifold embedded in high-dimensional ambient space. In this paper we investigate the pathologies of maximum-likelihood training in the presence of this dimensionality mismatch. We formally prove that degenerate optima are achieved wherein the manifold itself is learned but not the distribution on it, a phenomenon we call manifold overfitting. We propose a class of two-step procedures consisting of a dimensionality reduction step followed by maximum-likelihood density estimation, and prove that they recover the data-generating distribution in the nonparametric regime, thus avoiding manifold overfitting. We also show that these procedures enable density estimation on the manifolds learned by implicit models, such as generative adversarial networks, hence addressing a major shortcoming of these models. Several recently proposed methods are instances of our two-step procedures; we thus unify, extend, and theoretically justify a large class of models.

Why it matters: Minecraft may not sound like an important tool that supports advanced AI research. After all, what could possibly be so important about teaching a machine to play a sandbox game released more than a decade ago? Based on OpenAI's recent efforts, a well-trained Minecraft bot is more relevant to AI advancement than most people might realize. OpenAI has always focused on artificial intelligence (AI) and machine learning advances that benefit humanity. Recently, the company successfully trained a bot to play Minecraft using more than 70,000 hours of gameplay videos. The achievement is far more than just a bot playing a game.

I recently caught up with Ian Hogarth and Nathan Benaich, who each year produce The State of AI Report, a must-read snapshot of how commercial applications of A.I. are evolving. Benaich is the founder of Air Street Capital, a solo venture capital fund that is one of the savviest early-stage investors in A.I.-based startups I know. Hogarth is the former co-founder of concert discovery app Songkick and has since go on to become a prominent angel investor as well one of the founders behind the founder-lead European venture capital platform Plural. There's always a lot to digest in their report. But one of the key takeaways from this year's State of AI is that concerns established tech giants and their affiliated A.I. research labs would monopolize the development of A.I. have been proven, if not exactly wrong, then at least premature. While it is true that Alphabet (which has both Google Brain and Deepmind in its stable), Meta, Microsoft, and OpenAI (which is closely partnered now with Microsoft) are building large "foundational models" for natural language processing and image and video generation, they are hardly the only players in the game.