Tesla AI might play a role in AGI, given that it trains against the outside world, especially with the advent of Optimus -- Elon Musk (@elonmusk) January 19, 2022 When we think about OpenAI's GPT-3's ability to write original code or create cogent text articles, for example, it starts to feel like AI is becoming incredibly human-like. To put this into perspective, let's talk about Tesla's current AI capabilities and what it would actually mean for it or any other company to successfully solve and build an AGI. When you put the media hyperbole surrounding Dojo and Elon's hype together, you get a bunch of people who are convinced that Tesla's AI system are becoming more intelligent at an exponential rate. Many of the people responsible for creating the foundational algorithms and neural networks that Tesla is using are currently working for Facebook, IBM, Google, Nvidia, and Apple. Where IBM and Google have thousands of AI products on the market that help businesses and governments do everything from automating transportation and shipping logistics to providing real-time battlefield analysis and command and control capabilities, Tesla and SpaceX have a handful of narrow systems.

Synthesia is changing how my clients enter the global marketplace by delivering a hyper-local impact. In my world where language is so crucial it makes huge difference. The process itself is so advanced you can't tell your actor is not a native speaker!

Did you miss a session from the Future of Work Summit? In 2019, San Francisco-based AI research lab OpenAI held a tournament to tout the prowess of OpenAI Five, a system designed to play the multiplayer battle arena game Dota 2. OpenAI Five defeated a team of professional players -- twice. And when made publicly available, OpenAI Five managed to win against 99.4% of people who played against it online. OpenAI has invested heavily in games for research, developing libraries like CoinRun and Neural MMO, a simulator that plops AI in the middle of an RPG-like world. But that approach is changing.

Early last year, artificial intelligence company OpenAI unveiled software with the surprising ability to create accurate images from text captions – even obscure inventions such as "an armchair in the shape of an avocado". The company has now released a new AI model that is smaller but capable of producing even better results. Last year's program – called DALL-E – was a large, 12-billion-parameter AI model that was trained on a huge set of images with associated captions.

We present our submission to the Extreme Value Analysis 2021 Data Challenge in which teams were asked to accurately predict distributions of wildfire frequency and size within spatio-temporal regions of missing data. For the purpose of this competition we developed a variant of the powerful variational autoencoder models dubbed the Conditional Missing data Importance-Weighted Autoencoder (CMIWAE). Our deep latent variable generative model requires little to no feature engineering and does not necessarily rely on the specifics of scoring in the Data Challenge. It is fully trained on incomplete data, with the single objective to maximize log-likelihood of the observed wildfire information. We mitigate the effects of the relatively low number of training samples by stochastic sampling from a variational latent variable distribution, as well as by ensembling a set of CMIWAE models trained and validated on different splits of the provided data. The presented approach is not domain-specific and is amenable to application in other missing data recovery tasks with tabular or image-like information conditioned on auxiliary information.

Class imbalance can often degrade predictive performance of supervised learning algorithms. Balanced classes can be obtained by oversampling exact copies, with noise, or interpolation between nearest neighbours (as in traditional SMOTE methods). Oversampling tabular data using augmentation, as is typical in computer vision tasks, can be achieved with deep generative models. Deep generative models are effective data synthesisers due to their ability to capture complex underlying distributions. Synthetic data in healthcare can enhance interoperability between healthcare providers by ensuring patient privacy. Equipped with large synthetic datasets which do well to represent small patient groups, machine learning in healthcare can address the current challenges of bias and generalisability. This paper evaluates synthetic data generators ability to synthesise patient electronic health records. We test the utility of synthetic data for patient outcome classification, observing increased predictive performance when augmenting imbalanced datasets with synthetic data.

Natural language processing (NLP) has been one of the hottest sectors in AI over the past two years. Will the string of big data breakthroughs continue into 2022? We checked in with industry experts to find out. There's been a veritable arms race to develop large transformer models over the past couple of years. It started in 2020 with OpenAI's GPT-3 with 175 billion parameters.

Ethics is at the center of AI research more than ever. We have a better understanding of the risks of harm language models entail -- companies keep improving language models making them not just bigger but smarter and more efficient, multimodal systems are more common (e.g. Google's MUM and OpenAI's DALL·E), and real-world AI is taking leaps forward -- and backward. All in all, AI has maintained or even accelerated the pace of progress we've seen throughout the last decade. The AI community will bring new promising developments and impressive breakthroughs, some of which we can foresee.

Reinforcement Learning is an aspect of Machine learning where an agent learns to behave in an environment, by performing certain actions and observing the rewards/results which it get from those actions. With the advancements in Robotics Arm Manipulation, Google Deep Mind beating a professional Alpha Go Player, and recently the OpenAI team beating a professional DOTA player, the field of reinforcement learning has really exploded in recent years. Applications of reinforcement learning were in the past limited by weak computer infrastructure. However, as Gerard Tesauro's backgamon AI superplayer developed in 1990's shows, progress did happen. That early progress is now rapidly changing with powerful new computational technologies opening the way to completely new inspiring applications. Training the models that control autonomous cars is an excellent example of a potential application of reinforcement learning.

In 2020, the American poet Andrew Brown gave a student the following assignment: write a poem from the point of view of a cloud looking down on two warring cities. "I think I'll start to rain, Because I don't think I can stand the pain, Well, Brown's'student' turned out to be a computer program, not a human. The program, called GPT-3, is one of the most powerful AI language models ever made. Created in 2020 by the research firm OpenAI, its development has cost tens of millions of dollars. Trained on 200 billion words from books, articles, and websites, GPT-3 can generate fluent streams of text on any topic you can imagine. Companies like Amazon, Netflix, Spotify, and LinkedIn feed our personal preferences into them to create targeted recommendations.