Maluuba, a deep learning company which raised a 9 million series A in January, has opened a research and development lab with a focus on developing proprietary algorithms to solve language problems. The research lab will be led by Maluuba's CTO, Kaheer Suleman, and will be staffed by 13 deep learning research scientists. Maluuba has partnered with Yoshua Bengio, a machine learning and neural computation expert from the Montreal Institute for Learning Algorithms (MILA) and a Canada Research Chair in statistical learning algorithms. Maluuba has also partnered with reinforcement learning expert Richard Sutton, a principal investigator from the Alberta Innovates Centre for Machine Learning and an Association for the Advancement of Artificial Intelligence Fellow. "For a computer to understand humans speaking in natural language and respond appropriately, it needs to capture and represent a large amount of knowledge that is not just words, but also common sense and context about the topic being discussed by the human," said Sam Pasupalak, co-founder and CEO of Maluuba.

As part of its exhibition at GTC 2016, the worlds largest GPU conference, Boston Limited is showcasing Boston ANNA, the worlds fastest deep learning training accelerator. Expert scientists in the field of machine learning have leveraged the power of the GPU to make huge strides in improving a multitude of applications. Deep Learning is the fastest-growing field within this sphere and today's advanced deep neural networks use algorithms, big data, and the computational power of GPUs to reduce time-to-solution or to improve the accuracy of results. Deep learning is used in the research community and in industry to help solve many big data problems such as computer vision, speech recognition, and natural language processing. Models can take days or even weeks to train, forcing data scientists to make compromises between accuracy and time to deployment.

Shares of graphics chip maker Nvidia (NVDA) are down 6 cents at 35.69, following yesterday's annual meeting with analysts. A webcast replay of presentations CEO Jen-Hsun Huang and other executives, and the Q&A, can be viewed from the company's investor relations page. Huang made the pitch that with new frontiers of machine learning and artificial intelligence, Nvidia "are no longer a co-processor," meaning a handmaid to the PC microprocessor. "There is no workload we run," said Huang, such as a video game. Instead, he said, with the company's programming technology, "CUDA," "we run an application that a developer writes on top of it."

NVIDIA has released a new state-of-the-art chip that pushes the limits of machine learning. The Tesla P100 GPU, which CEO Jen-Hsun Huang revealed yesterday at NVIDIA's annual GPU Technology Conference, can perform deep learning neural network tasks 12 times faster than NVIDIA's previous top-end system. The P100 was a huge commitment for NVIDIA, costing over 2 billion in research and development, and it sports a whopping 150 billion transistors on a single chip, making the P100 the world's largest chip, NVIDIA claims. In addition to machine learning, the P100 will work for all sorts of high performance computing tasks -- NVIDIA just wants you to know it's really good at machine learning . To top off the P100's introduction, NVIDIA has packed eight of them into a crazy-powerful 129,000 supercomputer called the DGX-1, which was also announced yesterday.

Baltimore, MD - Alex Zhavoronkov, PhD, CEO of Insilico Medicine will present a range of deep learned biomarkers of ageing and deep learned predictors of biological age at the RE-WORK Deep Learning in Healthcare Summit in London, 7-8th of April. The first such predictor is already available online at http://www.Aging.AI trained on hundreds of thousands of human biochemistry and cell count samples linked to chronological age, gender and health status. Transcriptomic and signalomic ageing markers and predictors of chronological and biological age and cross-species comparison will be discussed. "RE-WORK summits are clearly outperforming most industry conferences in agility, openness, diversity and focus on applications of deep learning in multiple areas and we are happy to be invited to present at their Deep Learning in Healthcare Summit in London. Artificial intelligence will transform biomarker development and drug discovery much sooner than most pharmaceutical companies and regulators expect and we are happy to be at the forefront of this emerging trend", said Alex Zhavoronkov, PhD, CEO or Insilico Medicine, Inc.

This month we will talk about Applications of Deep Learning in this event hosted by our amazing sponsor Boxever (Customer Intelligence Cloud for Travel). Online booking sites for example in the travel industry often use personalised recommendations to drive conversions. The ability to link behavioural clickstream data with transactional order data affords us the opportunity to utilise feedback online with respect to the recommendations made. For example, it is common to deploy a recommendation model, test it as part of an A/B testing framework, and evaluate retrospectively for which customers the model worked and for which ones it didn't. This is often a manual process and as a consequence may fail to exploit the richness in the behavioural and transactional features.

Convolutional Neural Networks (CNNs) have been doing wonders in the field of image recognition in recent times. CNN is a type of deep neural network in which the layers are connected using spatially organized patterns. This is in line with how the human visual cortex processes image data. Researchers have been working on coming up with better architectures over the last few years. In this blog post, we will discuss a particular type of layer that has been used consistently across many famous architectures.

Salesforce has joined hands with the artificial intelligence startup, MetaMind. In a wise move, Salesforce has managed to integrate deep learning with its data science capabilities, beating other leading companies in their pursuit of machine learning and artificial intelligence. Salesforce acquires Palo-Alto-based deep learning company, MetaMind, the companies announced on April 4. Launched in July 2014, MetaMind specializes in artificial intelligence (AI) techniques of data crunching to help businesses arrive at better decisions. While the terms of the deal still remain undisclosed, the AI startup will shut down services on May 4 for their unpaid users, and on June 4 for the monthly recurring users. "[R]eal AI solutions with breakthrough capabilities that further automate and personalize customer support, marketing automation, and many other business processes," says MetaMind CEO Richard Socher, who added that he is "thrilled" with the integration.

What will Google do next? Google's London AI powerhouse has set up a new healthcare division and acquired a medical app called Hark, an article from Business Insider, tells us the latest. DeepMind, Google's artificial intelligence research group, launched a new division recently called DeepMind Health and acquired a healthcare app. The article describes DeepMind Health's new app called Hark, "Hark -- acquired by DeepMind for an undisclosed sum -- is a clinical task management smartphone app that was created by Imperial College London academics Professor Ara Darzi and Dr Dominic King. Lord Darzi, director of the Institute of Global Health Innovation at Imperial College London, said in a statement: "It is incredibly exciting to have DeepMind – the world's most exciting technology company and a true UK success story – working directly with NHS staff.

Nvidia chief executive Jen-Hsun Huang said that deep-learning artificial intelligence has become a new computing platform, and the company is dealing with hundreds of startups in the space that plan to take advantage of the platform. Speaking at the GPUTech conference in San Jose, California, Huang noted that 5 billion was invested last year in A.I. startups, and there are probably a thousand companies working on the technology for applications ranging from face recognition to self-driving cars. "Deep learning is not an industry," he said. "Deep learning is going to be in every industry. Deep learning is going to be in every application."