Micron Technology, Inc., today announced a powerful new set of high-performance hardware and software tools for deep learning applications with the acquisition of FWDNXT, a software and hardware startup. When combined with advanced Micron memory, FWDNXT's (pronounced "forward next") artificial intelligence (AI) hardware and software technology enables Micron to explore deep learning solutions required for data analytics, particularly in IoT and edge computing. With this acquisition, Micron is integrating compute, memory, tools and software into a comprehensive AI development platform. This platform in turn provides the key building blocks required to explore innovative memory optimized for AI workloads. "FWDNXT is an architecture designed to create fast-time-to-market edge AI solutions through an extremely easy to use software framework with broad modeling support and flexibility," said Micron Executive Vice President and Chief Business Officer Sumit Sadana.

There is an unreasonable amount of information that can be extracted from what people publicly say on the internet. At Heuritech we use this information to better understand what people want, which products they like and why. This post explains from a scientific point of view what is Knowledge extraction and details a few recent methods on how to do it. Highly structured databases make it easy to reason with and can be used for inference. For example in WikiData or YAGO, entities are isolated and linked together with relations.

If you've followed the latest advancements in Natural Language Processing (NLP), you'll know that Transformer Models are all the latest craze. These language models are currently the state of the art for many tasks including article completion, question answering, and dialog systems. The two famous transformers that come to mind are BERT, and the infamous GPT-2, which demonstrated such architectures. Having said that, it was only a matter of time before NVIDIA researchers pushed the limits of the technology, enter Megatron-LM. Megatron is a 8.3 billion parameter transformer language model with 8-way model parallelism and 64-way data parallelism trained on 512 GPUs (NVIDIA Tesla V100), making it the largest transformer model ever trained.

DeepFakes are created by a deep learning technique known as Generative Adversarial Networks (GANs), where two machine learning models are used to make the counterfeits more believable. By studying the images and videos of a person, in the form of training data, the first model creates a video, while the second model attempts to detect its flaws. These two models work hand-in-hand until they create a video that is believable. DeepFake opens up a whole new world when it comes to unsupervised learning, which is a sub-field of machine learning where machines can learn to teach themselves, and it has been argued to hold great promise when it comes to self-driving vehicles' to detect and recognize obstacles on the road and virtual assistants such as Siri, Cortana and Alexa learning to be more conversational. The real question is, what potential does it have of being misused, like any other technology.

"If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake." Reinforcement learning is an intriguing and complex field. We at Analytics Vidhya are strongly behind the incredible potential of this domain and the breakthroughs and research by behemoths like DeepMind support our thought process. Reinforcement learning (RL) has been around for a while now. However, there is a general perception in the community that RL's use cases are limited to computer simulations.

The challenges associated with operating AI workloads at scale have been reduced in the cloud, and it's never been easier or more cost-effective to collect data, store it, and build custom machine learning and deep learning models. AWS C5 instances, with the computational power of Intel Xeon Scalable processors, enable customers to create intelligent and innovative new products and experiences, powered by machine learning. AWS gives users access to Intel-optimized images for TensorFlow and Intel Performance Libraries to enhance application performance. Intel and AWS are working together to make AI simple, accessible and fast.

RL algorithm learns how to act best through many attempts and failures. Trial-and-error learning is connected with the so-called long-term reward. This reward is the ultimate goal the agent learns while interacting with an environment through numerous trials and errors. The algorithm gets short-term rewards that together lead to the cumulative, long-term one. So, the key goal of reinforcement learning used today is to define the best sequence of decisions that allow the agent to solve a problem while maximizing a long-term reward. And that set of coherent actions is learned through the interaction with environment and observation of rewards in every state. Reinforcement learning is distinguished from other training styles, including supervised and unsupervised learning, by its goal and, consequently, the learning approach. Three ML training styles compared.

This is the second installment in a two-part series about generative adversarial networks. For the full story, be sure to also read part one. Now that we've described the origin and general functionality of generative adversarial networks (GANs), let's explore the role of this exciting new development in artificial intelligence (AI) as it pertains to cybersecurity. Perhaps the most famous application of this technology is described in a paper by researchers Briland Hitaj, Paolo Gasti, Giuseppe Ateniese and Fernando Perez-Cruz titled "PassGAN: A Deep Learning Approach for Password Guessing," the code for which is available on GitHub. In this project, the researchers first used a GAN to test against password cracking tools John the Ripper and HashCat, and then to augment the guessing rules of HashCat.

Earlier today, Andrew Ng joined us onstage at TWIMLcon - as the Founder and CEO of Landing AI and founding lead of Google Brain, Andrew is no stranger to knowing what it takes for AI and machine learning to be successful. Hear about the work that Landing AI is doing to help organizations adopt modern AI, his experience in overcoming challenges for large companies, how enterprises can get the most value for their ML investment as well as addressing the'essential complexity' of software engineering.

If you think your data is clean, perhaps you have not looked into it yet; if you think your data is messy, it's even messier. Nobody cares how you did it; just do it correctly. People do not care how much you know until they know how much you care (about them and their business). In 2-3 years, nobody will talk about Big Data anymore. It always pays off to be damn good at numbers, Excel, and PowerPoint (and yes, presentation skills); Tableau is a big plus. Downloading some code and data and running them does not make you a data scientist. The same is true for doing data science courses. Participating in Kaggle competitions does not make you a data scientist, although it can help you learn from others. Winning Kaggle competitions does not necessarily make you a good data scientist. ETL is always needed - be good at it and learn a good tool for it (Talend is a good one). Also, learn scripting languages for ETL. Deep learning is cool, but it's still cool if you don't use it when you don't need it, and in 99% of cases you don't need it. Algorithms are commodities, your data is not. Ideas are commodities, execution is not. Deep learning expertise will soon become a commodity; problem-solving skills won't.