Over the last five years, deep learning-based methods have revolutionised a wide range of applications, for example those requiring understanding of pictures, speech and natural language. For computer scientists, a naturally arising question is whether computers learn to understand source code? It appears to be a trivial question at first glance because programming languages indeed are designed to be understood by computers. However, many software bugs are in fact instances of Do what I mean, not what I wrote. In other words, small typos can have big consequences.

Recorded May 2nd, 2018 at ICLR2018 Koray Kavukcuoglu is the Director of Research at DeepMind, where previously he was a research scientist and led the deep learning team. Before joining DeepMind, he was a research staff member at NEC Labs America in the machine learning department.

At the heart of many successful businesses is a deep understanding of their customers. In a previous blog post I explained how a customer 360o initiative could be enhanced by using Amazon Redshift Spectrum as part of an AWS data lake strategy. In this blog post, I want to continue the theme of demonstrating agility, cost efficiency, and how AWS can help you innovate through your customer analytics practice. Many of you are exploring how AI can enhance their customer 360o initiatives. I'll demonstrate how targeted campaigns can be driven by machine learning (ML) through solutions that leverage Amazon SageMaker and Amazon Pinpoint.

At Microsoft's Build Developers Conference, The Company Is Announcing A Preview Of Project Brainwave Integrated With Azure Machine Learning, Which The Company Says Will Make Azure The Most Efficient Cloud Computing Platform For AI Project Brainwave is a hardware architecture designed to accelerate real-time AI calculations. The Project Brainwave architecture is deployed on a type of computer chip from Intel called a field programmable gate array, or FPGA, to make real-time AI calculations at competitive cost and with the industry's lowest latency, or lag time. This is based on internal performance measurements and comparisons to other organization's publicly posted information. At Microsoft's Build developers conference in Seattle this week, the company is announcing a preview of Project Brainwave integrated with Azure Machine Learning, which the company says will make Azure the most efficient cloud computing platform for AI. According to Allison Linn, senior content manager at Microsoft – in her blog – Mark Russinovich, chief technical officer for Microsoft's Azure cloud computing platform, said the preview of Project Brainwave marks the start of Microsoft's efforts to bring the power of FPGAs to customers for a variety of purposes.

Google's Deep Mind software took just 40 days to become the best ever player of the ancient game of Go, and commentators heralded it as a major milestone for deep learning, a field of artificial intelligence (AI). The achievement highlighted how computers equipped with the right algorithms can now quickly teach themselves to achieve a specific goal. This approach, known as unsupervised learning, works well within a well-defined set of parameters, such as a board game. But what about when something unexpected happens, such as a change in the rules of the game? In the work environment, unexpected things happen all the time, limiting the usefulness of today's AI systems.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research.

D-Wave announced "Quadrant," a new business unit that will provide machine learning services powered by both CPU/GPUs and its quantum annealing computer. D-Wave's Quadrant algorithms will be able to more efficiently provide accurate results with less training data compared to classical deep learning solutions that require significant amounts of labeled data. One of the promises of quantum computers has been that they are so much better at calculating multiple possibilities at the same time and finding the "optimum" result for a variety of problems. D-Wave's quantum annealing computer (a more specialized kind of quantum computer) has already been used in real-world applications such as optimizing traffic flow. Most of the machine learning (ML) or artificial intelligence (AI) solutions out there currently need millions and millions of data points in order to come up with an accurate model that can then be used in the learn world effectively.

We are in the midst of a global transformation and it is touching every aspect of our world, our lives and our businesses. Many in the industry believe artificial intelligence (AI) is the key to fundamentally changing how organizations will derive insights from data. We know that AI will be a game-changer for our clients, but we also know that there is no "magic bullet" when it comes to AI. For most of our clients, AI will be a journey. This is demonstrated by the fact that most organizations are still in the early phases of AI adoption.

Tuesday, May 08, 2018 Top 10 Datasets for Deep Learning The strength and robustness of a machine learning algorithm often lies in the quality of the dataset used to train it. Therefore, it would suffice to say that to gain true mastery within these fields,... Friday, August 25, 2017 Data Scientist Interview: A Perspective On Artificial Intelligence (AI), Ethics & Healthcare Recently HealthTech Women, a national non-profit, sat down with our Data Science instructor, Lesley Cordero to get an inside look at Artificial Intelligence, or, "AI" and how it is impacting society, ... Wednesday, August 09, 2017 Overview of Natural Language Generation (NLG) NLG (Natural Language Generation), a subfield of Artificial Intelligence, is a hot topic in the technology news today. We hear a lot about AI that can soon replace writers and journalists beginning th... Tuesday, May 08, 2018 Top 10 Datasets for Deep Learning The strength and robustness of a machine learning algorithm often lies in the quality of the dataset used to train it. Therefore, it would suffice to say that to gain true mastery within these fields,... Friday, August 25, 2017 Data Scientist Interview: A Perspective On Artificial Intelligence (AI), Ethics & Healthcare Recently HealthTech Women, a national non-profit, sat down with our Data Science instructor, Lesley Cordero to get an inside look at Artificial Intelligence, or, "AI" and how it is impacting society, ... Wednesday, August 09, 2017 Overview of Natural Language Generation (NLG) NLG (Natural Language Generation), a subfield of Artificial Intelligence, is a hot topic in the technology news today. We hear a lot about AI that can soon replace writers and journalists beginning th...

The Artificial Intelligence (AI) wave is rolling across the industry and, for CMOs who intend to stay two steps ahead of their competition, now is the time to get started with this new capability. As the focus of the Chief Marketing Officer shifts toward customer-centric, data-driven and performance-led campaign initiatives, the perception is that AI is yet another technology area that must be grappled with and understood. The reality, however, is that AI will not add complexity but instead should make it easier for the CMO to access insights and automation that influence sales, boost operational efficiency and increase customer lifetime value. The role of the CMO has changed, and it is only the bold and brave who have the traits that will lead their organizations into the next wave of a marketing revolution led by AI. To be successful, it is essential to mine data, uncover insights and apply the learnings in the most efficient and effective manner possible.