Ensuring fairness and safety in artificial intelligence(AI) applications is considered by many the biggest challenge in the space. As AI systems match or surpass human intelligence in many areas, it is essential that we establish a guideline to align this new form of intelligence with human values. The challenge is that, as humans, we understand very little about how our values are represented in the brain or we can't even formulate specific rules to describe a specific value. While AI operates in a data universe, human values are a byproduct of our evolution as social beings. We don't describe human values like fairness or justice using neuroscientific terms but using arguments from social sciences like psychology, ethics or sociology Recently, researchers from OpenAI published a paper describing the importance of social sciences to improve the safety and fairness or AI algorithms in processes that require human intervention.

In this book, the first four chapters are provided as a guide for teachers who want to use the book for teacher training and development. Using the tools, tips and activities provided in these first chapters a teacher with some basic experience of using technology in the classroom should be able to create motivating hands-on edtech training for their peers or for pre-service trainee teachers.

LAST week, the OpenAI research group announced it had created an artificial intelligence capable of generating hundreds of words of convincing text on almost any topic (see Fears of OpenAI's super-trolling artificial intelligence are overblown). But the group said it wouldn't be releasing the AI, because of its potential to be used as a fake news generator. Fear over the power of fake news is widespread. Damian Collins, who heads a committee of UK MPs looking into the matter, this week proclaimed that "democracy is at risk from the malicious and relentless targeting of citizens with …

Elon Musk, recently busying himself with calling people "pedo" on Twitter and potentially violating US securities law with what was perhaps just a joke about weed – both perfectly normal activities – is now involved in a move to terrify us all. The non-profit he backs, OpenAI, has developed an AI system so good it had me quaking in my trainers when it was fed an article of mine and wrote an extension of it that was a perfect act of journalistic ventriloquism. As my colleague Alex Hern wrote yesterday: "The system [GPT2] is pushing the boundaries of what was thought possible, both in terms of the quality of the output, and the wide variety of potential uses." GPT2 is so efficient that the full research is not being released publicly yet because of the risk of misuse. And that's the thing – this AI has the potential to absolutely devastate.

A storm is brewing over a new language model, built by non-profit artificial intelligence research company OpenAI, which it says is so good at generating convincing, well-written text that it's worried about potential abuse. That's angered some in the community, who have accused the company of reneging on a promise not to close off its research. OpenAI said its new natural language model, GPT-2, was trained to predict the next word in a sample of 40 gigabytes of internet text. The end result was the system generating text that "adapts to the style and content of the conditioning text," allowing the user to "generate realistic and coherent continuations about a topic of their choosing." The model is a vast improvement on the first version by producing longer text with greater coherence.

To address the challenges in learning deep generative models (e.g.,the blurriness of variational auto-encoder and the instability of training generative adversarial networks, we propose a novel deep generative model, named Wasserstein-Wasserstein auto-encoders (WWAE). We formulate WWAE as minimization of the penalized optimal transport between the target distribution and the generated distribution. By noticing that both the prior $P_Z$ and the aggregated posterior $Q_Z$ of the latent code Z can be well captured by Gaussians, the proposed WWAE utilizes the closed-form of the squared Wasserstein-2 distance for two Gaussians in the optimization process. As a result, WWAE does not suffer from the sampling burden and it is computationally efficient by leveraging the reparameterization trick. Numerical results evaluated on multiple benchmark datasets including MNIST, fashion- MNIST and CelebA show that WWAE learns better latent structures than VAEs and generates samples of better visual quality and higher FID scores than VAEs and GANs.

A recent decision by research lab OpenAI to limit the release of a new algorithm has caused controversy in the AI community. The nonprofit said it decided not to share the full version of the program, a text-generation algorithm named GPT-2, due to concerns over "malicious applications." But many AI researchers have criticized the decision, accusing the lab of exaggerating the danger posed by the work and inadvertently stoking "mass hysteria" about AI in the process. The debate has been wide-ranging and sometimes contentious. It even turned into a bit of a meme among AI researchers, who joked that they've had an amazing breakthrough in the lab, but the results were too dangerous to share at the moment.

We've written a paper arguing that long-term AI safety research needs social scientists to ensure AI alignment algorithms succeed when actual humans are involved. Properly aligning advanced AI systems with human values requires resolving many uncertainties related to the psychology of human rationality, emotion, and biases. The aim of this paper is to spark further collaboration between machine learning and social science researchers, and we plan to hire social scientists to work on this full time at OpenAI. The goal of long-term artificial intelligence (AI) safety is to ensure that advanced AI systems are aligned with human values -- that they reliably do things that people want them to do. At OpenAI we hope to achieve this by asking people questions about what they want, training machine learning (ML) models on this data, and optimizing AI systems to do well according to these learned models.

The future took another step forward. But it was stopped right in its tracks by its inventor, OpenAI. According to its website, OpenAI is a non-profit AI research company, discovering and enacting the path to safe artificial general intelligence. Elon Musk is one of the backers. In a recent blog post, OpenAI's technology, called GPT2, was shown to craft written passages that mimic the style and content of a given sample.

OpenAI, the non-profit organization that researches artificial intelligence, co-founded by Elon Musk in 2016, has been making big advancements -- even after Musk parted ways amid disagreements about its direction. Researchers have developed systems that can play games, write news articles, and move physical objects with groundbreaking levels of dexterity. OpenAI has caused controversy with its research. Last week, it announced the development of a language model, GTP2, that can generate texts with limited prompts. Given the human-written prompt "Miley Cyrus was caught shoplifting from Abercrombie and Fitch on Hollywood Boulevard today," the system produced a believable complete story that continued with "the 19-year-old singer was caught on camera being escorted out of the store by security guards."