As the capabilities of deep learning algorithms have improved exponentially over the last few years, there has been increasing awareness of the ethical considerations in deploying technology that can autonomously capture the underlying patterns of data and make decisions based upon it with precision and nuance unthinkable even a few years ago. This rapid developmental pace is enabling deep learning applications that push the boundaries of computational decision-making, from today's facial recognition algorithms to tomorrow's driverless cars to future autonomous "killer robots." At the same time, the impact of algorithmic bias is becoming more visible as AI systems are being deployed into ever more influential roles. As society reacts to AI adoption in sensitive areas like military, judicial and surveillance use, governments and companies have responded by arguing that so long as their algorithms perform as intended, they are ethical, suggesting there is considerable confusion about the difference between AI correctness and AI ethics. Like all computer code, deep learning algorithms and the data-driven models that power them are designed to perform specific tasks within specific operating constraints with a guaranteed accuracy rate.

If you already know some TensorFlow and are looking for something with a little more dynamism, you no longer have to switch all the way to PyTorch, thanks to some substantial changes coming as part of TensorFlow 2.0. In fact, many of the changes in 2.0 specifically address the alleged shortcomings of TensorFlow. With eager execution by default, you no longer have to pre-define a static graph, initialize sessions, or worry about tensors falling outside of the proper scope when you get over-zealous in your object-oriented principles. TensorFlow still has about 3 times the user base of PyTorch (judging from the repositories on GitHub referencing each framework), and that means more extensions, more tutorials, and more developers collaboratively exploring the space of all possible code errors on Stack Overflow. You'll also find that, despite the major changes starting with TensorFlow 2.0, the project developers have taken many steps to ensure that backward compatibility can be maintained.

The Tesla and SpaceX founder has notoriously urged for Artificial Intelligence to be respected and potentially regulated. Speaking at MIT in 2014, he called AI humanity's "biggest existential threat" and compared it to "summoning the demon". Four years on and Musk, who is usually far from a technological pessimist, reignited those fears.

Learn why a custom setup and tuning of a high-performance system requires large resources and a team of experts. In contrast, NVIDIA's deep learning expertise protects your investment with a system already fully integrated with GPU-optimized deep learning frameworks, tools, and libraries paired with the simplicity of DGX Cloud Services for fast and easy performance that scales.

Artificial intelligence (AI) may help develop safe, clean and virtually limitless fusion energy for generating electricity, scientists say. A team, including researchers from Princeton University and Harvard University, are applying deep learning to forecast sudden disruptions that can halt fusion reactions and damage the doughnut-shaped tokamaks or apparatus that house the reactions. Deep learning is a powerful new version of the machine learning form of AI, according to the findings published in the journal Nature magazine. "This research opens a promising new chapter in the effort to bring unlimited energy to Earth," Steven Cowley, director of US Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL). "Artificial intelligence is exploding across the sciences and now it's beginning to contribute to the worldwide quest for fusion power," Cowley said in a statement.

At Uber, we take advanced research work and use it to solve real world problems. In our Science at Uber video series, Uber employees talk about how we apply data science, artificial intelligence, machine learning, and other innovative technologies in our daily work. Zoubin Ghahramani, Chief Scientist at Uber, understands that movement requires intelligence, and draws a parallel between biological and artificial systems. His organization, Uber AI, develops artificial intelligence to advance Uber's core business needs. Research into reinforcement learning, deep learning, probabilistic modeling, and evolutionary algorithms makes Uber's products work more efficiently.

While so-called'deep learning' methods, that is machine learning techniques that use layered, artificial neural networks, to perform learning of unstructured data, has gained great popularity in research in the last few years, its integration within GIS is not always easy without specialist knowledge. New tools, however, are being and have been developed that potentially will allow a wider field of researchers to better take advantage of machine learning and deep learning specifically. So far, two clear leaders have emerged. ArcGIS has spent significantly in recent years to develop better tools for deep learning. They have used a variety of platforms that have been developed for imagery analysis, such as Keras and Tensorflow which are popular deep learning platforms, and have integrated them with ArcGIS Pro tools, such as ArcGIS Image Server.

Roughly three months ago, Facebook launched calls for research proposals in three subfields of natural language processing (NLP), the cross-disciplinary study of linguistics and AI concerned with computer-language interactions. It specifically sought "robust" deep learning approaches for NLP and computationally efficient NLP in addition to neural machine translation for low-resource dialects, ultimately in the pursuit of advancing cutting-edge research in machine translation. That was just the start, it would seem. In a blog post today announcing 11 winning proposals among the 115 submitted from 35 countries, Facebook announced the AI Language Research Consortium, a community of partners it says will "work together to advance priority research areas" in NLP. Details were tough to come by at press time, but Facebook says the newly formed group will foster collaboration to tackle challenging tasks like representation learning, content understanding, dialog systems, information extraction, sentiment analysis, summarization, data collection and cleaning, and speech translation.