The idea of artificial general intelligence as we know it today starts with a dot-com blowout on Broadway. Twenty years ago--before Shane Legg clicked with neuroscience postgrad Demis Hassabis over a shared fascination with intelligence; before the pair hooked up with Hassabis's childhood friend Mustafa Suleyman, a progressive activist, to spin that fascination into a company called DeepMind; before Google bought that company for more than half a billion dollars four years later--Legg worked at a startup in New York called Webmind, set up by AI researcher Ben Goertzel. Today the two men represent two very different branches of the future of artificial intelligence, but their roots reach back to common ground. Even for the heady days of the dot-com bubble, Webmind's goals were ambitious. Goertzel wanted to create a digital baby brain and release it onto the internet, where he believed it would grow up to become fully self-aware and far smarter than humans.

AI learns from seen data to make predictions about unseen data. What is utterly remarkable is that prediction can underpin extraordinary creativity and mimicry. These developments have the potential to unleash an explosion of scale creativity -- delivering content design and production tools into the hands of the mass market that have hitherto only been available to large corporations with hefty budgets. Even now -- when we are still in the infancy of AI media generation -- there are demos, apps and subscription-based services to faceswap individuals into movies (see Zao), turn rough sketches into photorealistic images (try the GauGAN demo here), convert one voice into another (see Respeecher), personalise marketing videos (try the Synthesia demo here), age- and emotion-alter images (see Photoshop's new Neural Filters), generate face-synched videos of new or translated scripts (see Canny AI), play a video game with characters speaking any of 10 face-synched languages (see Cyberpunk 2077), and play a text-based adventure game with endless dialogue generated by AI (try out the free version of AI Dungeon here). Moreover, the same AI techniques will spawn new applications in a wide range of fields: advertising, architecture, interior design, gaming, song-writing, web design, education, even software development and pure mathematics -- in fact anywhere where structured or constrained creativity is key.

By leveraging deep learning based technologies, the data-driven based approaches have reached great success with the rapid increase of data generated of Industrial Indernet of Things(IIot). However, security and privacy concerns are obstacles for data providers in many sensitive data-driven industrial scenarios, such as healthcare and auto-driving. Many Federated Learning(FL) approaches have been proposed with DNNs for IIoT applications, these works still suffer from low usability of data due to data incompleteness, low quality, insufficient quantity, sensitivity, etc. Therefore, we propose a ring-topogy based decentralized federated learning(RDFL) scheme for Deep Generative Models(DGMs), where DGMs is a promising solution for solving the aforementioned data usability issues. Compare with existing IIoT FL works, our RDFL schemes provides communication efficiency and maintain training performance to boost DGMs in target IIoT tasks. A novel ring FL topology as well as a map-reduce based synchronizing method are designed in the proposed RDFL to improve decentralized FL performance and bandwidth utilization. In addition, InterPlanetary File System(IPFS) is introduced to further improve communication efficiency and FL security. Extensive experiments have been taken to demonstate the superiority of RDFL with either independent and identically distributed(IID) datasets or non-independent and identically distributed(Non-IID) datasets.

Video game level generation based on machine learning (ML), in particular, deep generative models, has attracted attention as a technique to automate level generation. However, applications of existing ML-based level generations are mostly limited to tile-based level representation. When ML techniques are applied to game domains with non-tile-based level representation, such as Angry Birds, where objects in a level are specified by real-valued parameters, ML often fails to generate playable levels. In this study, we develop a deep-generative-model-based level generation for the game domain of Angry Birds. To overcome these drawbacks, we propose a sequential encoding of a level and process it as text data, whereas existing approaches employ a tile-based encoding and process it as an image. Experiments show that the proposed level generator drastically improves the stability and diversity of generated levels compared with existing approaches. We apply latent variable evolution with the proposed generator to control the feature of a generated level computed through an AI agent's play, while keeping the level stable and natural.

In June last year, GPT-3 released by OpenAI, it is composed of 175 billion parameters, and training cost tens of millions of dollars, it was the largest artificial intelligence language model ever produced. From answering the questions to writing articles and poems, and even writing slang language everything is covered. The full name of GPT-3 is Generative Pretrained Transformer-3 (Generative Pretrained Transformer-3). This is the third series of generating pretraining converters, which is more than 100 times that of GPT-2 in 2019. In GPT-3 there are 175 billion parameters, the second largest language model has 17 billion parameters.

These macromolecules are encoded as linear chains of amino acids, which then fold into dynamic 3-dimensional structures that accomplish a staggering variety of functions. To improve proteins for human purposes, protein engineers have developed a variety of experimental and computational methods for designing sequences that fold to desired structures or perform desired functions [1, 2, 3, 4]. A developing paradigm, machine learning-guided protein engineering, promises to leverage the information obtained from wet-lab experiments with data-driven models to more efficiently find desirable proteins [5, 6, 7]. Much of the early work has focused on incorporating discriminative models trained on measured sequence-fitness pairs to guide protein engineering [5]. However, methods that can take advantage of unlabeled protein sequences are improving the protein engineering paradigm.

In June 2020, the Californian company OpenAI announced GPT-2's upgrade to GPT-3, a language model based on artificial intelligence and deep learning with cognitive capabilities. It is a technology that has generated great expectations and that has been presented as the most important and useful advance in AI in recent years. OpenAI is a non-profit company founded by Elon Musk, co-founder and director of Tesla and SpaceX, which was born with the aim of researching and democratizing access to General Artificial Intelligence. Originally, it was a non-profit organization. However, in 2020, it became a company and partnered with Microsoft in order to achieve new advances, both in the field of language with GPT-3 models, and in the field of robotics and vision.

OpenAI is an artificial intelligence research lab dedicated to promoting and developing friendly AI for the benefit of humanity. OpenAI was founded in 2015 by Elon Musk and Sam Altman. This startup received a $ 1 billion investment from Microsoft in 2019. OpenAI was created in part out of the founders' fear of potential disaster as a result of the negligence and misuse of general-purpose AI. In the long term, the company pays special attention to fundamental advances in AI and its capabilities.

We're releasing an analysis showing that since 2012, the amount of compute used in the largest AI training runs has been increasing exponentially with a 3.4-month doubling time (by comparison, Moore's Law had a 2-year doubling period).[1] Since 2012, this metric has grown by more than 300,000x (a 2-year doubling period would yield only a 7x increase). Improvements in compute have been a key component of AI progress, so as long as this trend continues, it's worth preparing for the implications of systems far outside today's capabilities. The total amount of compute, in petaflop/s-days,[2] used to train selected results that are relatively well known, used a lot of compute for their time, and gave enough information to estimate the compute used. Three factors drive the advance of AI: algorithmic innovation, data (which can be either supervised data or interactive environments), and the amount of compute available for training.

GPT-3 AI Examples - The Good, The Bad and The Ugly AF // Wanna 10x your content creation with GPT 3? GPT 3 is powerful AI for content creation that uses large datasets of text crawled from the internet to create human-sounding, AI generated content. Created by the startup OpenAI, this AI content generator is taking the tech world by storm. But is it really any good, you might be wondering? Today I'm going to answer that question in a GPT 3 demo by showing you GPT 3 AI examples of how I used GPT-3 in my own business to create content snippets. OpenAI GPT 3 was released for public use in June 2020 and now has been used by TONS of data entrepreneurs creating SAS products that run off of GPT-3, such as Copy.ai and Writesonic.