The Bay Area is famed for nurturing speculative investments like flying cars, floating cities, and the notion that a ride hailing service can turn a profit. A new utopian investment opportunity arrived Monday: Shovel dollars into a San Francisco artificial intelligence lab cofounded by Elon Musk and you'll receive a share of the profits when (or if) it figures out how to create machines smarter than humans. That pitch comes from OpenAI, an independent AI research lab cofounded as a nonprofit in 2015 by Musk and Sam Altman, the president of startup incubator YCombinator. Its stated mission was to safely create software as capable as people, which it terms artificial general intelligence or AGI, and share the benefits with the world. The founders argued society shouldn't have to hope that profit-seeking tech giants would do that.

A threat to democracy (manipulation of public opinion, aggravation of social or community-based tensions, etc.), invasion of a person's privacy or violation of their dignity, a risk of fraud or scams, or still a headache for future researchers seeking the truth, etc. Deep fakes, technologies that make it possible to replace someone's face with that of another person in a video thanks to deep learning, are worrying. Ever more sophisticated, today they are pretty much available to all thanks to relatively easy-to-use tools. A chat about these "weapons of mass falsification" with Vincent Nozick, a teacher and researcher at the LIGM and co-author of a publication presenting an efficient method to detect deep fakes (MesoNet: a Compact Facial Video Forgery Detection Network, Darius Afchar, Vincent Nozick, Junichi Yamagishi, Isao Echizen, 2018). There are several methods for tampering with faces, some of which do use deep learning, like Deepfake, which is one of the most well-known. Deepfake is a program that belongs to the GAN family, Generative Adversarial Networks, and makes it possible to transfer facial expressions onto video. It is based on an autoencoder, which is made up of an encoder and a decoder.

Computer vision is perhaps one area that has been most impacted by developments in deep learning. It can be difficult to both develop and to demonstrate competence with deep learning for problems in the field of computer vision. It is not clear how to get started, what the most important techniques are, and the types of problems and projects that can best highlight the value that deep learning can bring to the field. On approach is to systematically develop, and at the same time demonstrate competence with, data handling, modeling techniques, and application domains and present your results in a public portfolio of completed projects. This approach allows you to compound your skills from project to project.

We've created OpenAI LP, a new "capped-profit" company that allows us to rapidly increase our investments in compute and talent while including checks and balances to actualize our mission. Our mission is to ensure that artificial general intelligence (AGI) benefits all of humanity, primarily by attempting to build safe AGI and share the benefits with the world. We've experienced firsthand that the most dramatic AI systems use the most computational power in addition to algorithmic innovations, and decided to scale much faster than we'd planned when starting OpenAI. We'll need to invest billions of dollars in upcoming years into large-scale cloud compute, attracting and retaining talented people, and building AI supercomputers. We want to increase our ability to raise capital while still serving our mission, and no pre-existing legal structure we know of strikes the right balance.

Machine learning and artificial intelligence are set to transform the banking industry, using vast amounts of data to build models that improve decision making, tailor services, and improve risk management. According to the McKinsey Global Institute, this could generate value of more than $250 billion in the banking industry.1 1.For the purposes of this article machine learning is broadly defined to include algorithms that learn from data without being explicitly programmed, including, for example, random forests, boosted decision trees, support-vector machines, deep learning, and reinforcement learning. The definition includes both supervised and unsupervised algorithms. For a full primer on the applications of artificial intelligence, we refer the reader to "An executive's guide to AI." But there is a downside, since machine-learning models amplify some elements of model risk.

Marcus du Sautoy is the kind of science writer who cares more about questions than answers. In his books he tackles "unsolved problems", "number mysteries" and "the great unknown", topics at the edge of human understanding. They are subtitled with words such as "odyssey", "exploration" and "journey". But Du Sautoy is a flaneur: his trips are not motivated by destinations. This is both the main strength and flaw of The Creativity Code, a wide-ranging and fact-packed tour d'horizon of current applications of artificial intelligence in mathematics and the arts.

He will investigate the performance of the more recently developed deep learning algorithms in various detection tasks such as real-life sound event detection, rare event detection and bird audio detection. He will also propose a feature learning algorithm based on the combination of neural networks and the domain knowledge from human auditory perception. The Tech Talk, which will take place at Audio Analytic's office in Quayside, Cambridge on Wednesday 13th March, is free to attend and doors open at 18:00, with the talk starting promptly at 18:30. The talk will be followed by Q&A and networking around nibbles. Audio Analytic is recruiting so this is also a fantastic occasion to come and chat with its staff.

What does the world look like to AI? Researchers have puzzled over this for decades, but in recent years, the question has become more pressing. Machine vision systems are being deployed in more and more areas of life, from health care to self-driving cars, but "seeing" through the eyes of a machine -- understanding why it classified that person as a pedestrian but that one as a signpost -- is still a challenge. Our inability to do so could have serious, even fatal, consequences. Some would say it already has due to the deaths involving self-driving cars. New research from Google and nonprofit lab OpenAI hopes to further pry open the black box of AI vision by mapping the visual data these systems use to understand the world.

In recent years, the real-world impact of machine learning (ML) has grown in leaps and bounds. In large part, this is due to the advent of deep learning models, which allow practitioners to get state-of-the-art scores on benchmark datasets without any hand-engineered features. Given the availability of multiple open-source ML frameworks like TensorFlow and PyTorch, and an abundance of available state-of-the-art models, it can be argued that high-quality ML models are almost a commoditized resource now. There is a hidden catch, however: the reliance of these models on massive sets of hand-labeled training data. These hand-labeled training sets are expensive and time-consuming to create -- often requiring person-months or years to assemble, clean, and debug -- especially when domain expertise is required.