Deep learning is one of the most influential and fastest growing fields in artificial intelligence. However, getting an intuitive understanding of deep learning can be difficult because the term deep learning covers a variety of different algorithms and techniques. Deep learning is also a subdiscipline of machine learning in general, so it's important to understand what machine learning is in order to understand deep learning. Deep learning is an extension of some of the concepts originating from machine learning, so for that reason, let's take a minute to explain what machine learning is. Put simply, machine learning is a method of enabling computers to carry out specific tasks without explicitly coding every line of the algorithms used to accomplish those tasks.

Our multiple tracks offer a diverse selection of trending topics, including Artificial Intelligence & Automation, Big Data and Infrastructure, Machine Learning and Deep Learning, Data Visualizations, Data Analytics, Healthcare & IOT, Business Practice, and Data Security. The topics covered will involve many industries, including Healthcare, FinTech, Retail, Media, Manufacturing, Insurance, Education, E-Commerce, and more. Both industry-leading companies and startup firms will present at the conference and demonstrate innovative data analytics technologies. Previous featured speakers included those from IBM, Intel, Capital One, City of Los Angeles, Caltech, Alibaba, Accenture AI, and several other industry-leading companies.

Artificial intelligence research organization OpenAI has achieved a new milestone in its quest to build general purpose, self-learning robots. The group's robotics division says Dactyl, its humanoid robotic hand first developed last year, has learned to solve a Rubik's cube one-handed. OpenAI sees the feat as a leap forward both for the dexterity of robotic appendages and its own AI software, which allows Dactyl to learn new tasks using virtual simulations before it is presented with a real, physical challenge to overcome. In a demonstration video showcasing Dactyl's new talent, we can see the robotic hand fumble its way toward a complete cube solve with clumsy yet accurate maneuvers. It takes many minutes, but Dactyl is eventually able to solve the puzzle.

Japan's health ministry has approved the use of a revolutionary piece of artificial intelligence software to help identify brain aneurysms. It is the first time they have approved a deep learning program for use as medical equipment. The system was developed by a Tokyo venture firm called LPIXEL which develops AI software to analyze images. Their EIRL program can process MRI images and flag suspected aneurysms. It also has the ability to learn by itself and keep improving. LPIXEL says the software has outperformed trained radiologists and neurosurgeons in tests.

At least, that is how I imagine it. I don't have a Ph.D., so I couldn't say for sure, but it seems likely. That is it -- the secret to building great AI systems -- have great data. I could end the article here, but let's take a look at some supporting facts: First, accuracy improvements from growing data set size and scaling computation are empirically predictable. This was part of the conclusion of the paper Deep Learning Scaling is Predictable, Empirically from a group at Baidu.

GANs have achieved state-of-the-art results in image and video generation, and have been successfully applied for unsupervised feature learning among many other applications. Generative adversarial networks have seen rapid development in recent years, however, their audio generation prowess has largely gone unnoticed. In an attempt to explore the audio generation abilities of GANs, a team of DeepMind researchers published a work where they introduce a new model called GAN-TTS. Text-to-Speech (TTS) is a process for converting text into a humanlike voice output. Many audio generation models operate in the waveform domain.

Sign in to report inappropriate content. A full solve of the Rubik's Cube. This video plays at real-time and was not edited in any way.

Sign in to report inappropriate content. Perturbations that we apply to the real robot hand while it solves the Rubik's Cube. All videos play at real-time.

We've trained a pair of neural networks to solve the Rubik's Cube with a human-like robot hand. The neural networks are trained entirely in simulation, using the same reinforcement learning code as OpenAI Five paired with a new technique called Automatic Domain Randomization (ADR). The system can handle situations it never saw during training, such as being prodded by a stuffed giraffe. This shows that reinforcement learning isn't just a tool for virtual tasks, but can solve physical-world problems requiring unprecedented dexterity. Human hands let us solve a wide variety of tasks. For the past 60 years of robotics, hard tasks which humans accomplish with their fixed pair of hands have required designing a custom robot for each task.

Sign in to report inappropriate content. We've trained a pair of neural networks to solve the Rubik's Cube with a human-like robot hand.