CAMPBELL, Calif., March 26, 2020 – WekaIO (Weka) announced that The Business Intelligence Group has named Weka a winner in its Artificial Intelligence Excellence Awards program. The Weka File System (WekaFS), Weka's flagship product that is uniquely built to solve big problems, delivers the industry's best performance at any scale. WekaFS has a clean sheet design that handles the demands of new emerging and converging workloads, including artificial intelligence (AI) and machine/deep learning (ML/DL), high-performance data analytics (HPDA), and high-performance computing (HPC). The file system can deliver 80 GB/sec of bandwidth to a single GPU server, scale to Exabytes in a single namespace, and support an entire pipeline for edge-to-core-to-cloud workflows. The system also delivers operational agility with versioning, explainability, and reproducibility along with governance and compliance with in-line encryption and data protection.

PHILADELPHIA--(BUSINESS WIRE)--The Business Intelligence Group today announced that Kneron was named a winner in its Artificial Intelligence Excellence Awards program. Kneron is a leading on-device edge artificial intelligence (AI) company based in San Diego, California. Kneron provides complete end-to-end integrated hardware and software solutions that enable on-device edge AI inferencing in mobile devices, personal computers, and IoT use cases including smart home devices, surveillance, payments, and smart cars. Their solutions augment cloud-based AI to accelerate AI inferencing on any device. As the entire on-device edge AI industry is still emerging, Kneron's early investment and commercialization of its technology have positioned it in a leadership position to enable AI adoption in mass-market devices.

Noah is an AI systems architect. Prior to founding Quorum, Noah spent 12 years in academic research, first at the University of Southern California and most recently at Northwestern as the Assistant Chair of Neurobiology. His work focused on information processing in the brain and he has translated his research into products in augmented reality, brain-computer interfaces, computer vision, and embedded robotics control systems. Your interest in AI and robotics started as a little boy. How were you first introduced to these technologies?

Noah is an AI systems architect. Prior to founding Quorum, Noah spent 12 years in academic research, first at the University of Southern California and most recently at Northwestern as the Assistant Chair of Neurobiology. His work focused on information processing in the brain and he has translated his research into products in augmented reality, brain-computer interfaces, computer vision, and embedded robotics control systems. Your interest in AI and robotics started as a little boy. How were you first introduced to these technologies?

"If you look at the key names in the global debate on AI ethics, it is in fact dominated by women who have many different types of backgrounds, not only tech backgrounds." Artificial Intelligence (AI) is the game-changer in the industry, turbocharging new use cases in transportation, law enforcement, e-commerce, retail, healthcare, and entertainment. However, the quick pace of transformation and adoption is not accompanied by concrete industry standards on AI ethics and fairness in Machine Learning algorithms. While ethics in AI have been a dominant narrative for sometime, Big Tech is still seeking ways to design a code of conduct when building ML algorithms. Some tech giants like Microsoft have laid down guidelines to responsible AI and has operationalized responsible AI at scale, others are yet to follow suit.

A few years ago, I asked CIOs about data science and it turned into a yawner of a discussion. However, in the last few years as chief data officers have made their mark at more and more enterprises, CIOs have needed to build their data chops. Given this, it was time to assess where CIOs are today. To do this, I ran a #CIOChat on AI and Data Science. From this discussion, it was clear CIOs are spending more time considering the "I" part of their titles.

Recently one morning, I asked my computer a relatively simple question: can artificial intelligence (AI) write? We're not too certain on what artificial intelligence will be able to write, but there are some scenarios in which computers could be responsible for a huge number of word documents … The biggest potential scenarios would involve machines analyzing what has already been written and determining what pieces need to be edited to make the content seem fresh. The above sentences were composed by a machine in a matter of seconds. The tool used is a freely accessible interface based on the GPT-2 text generator released by OpenAI – a company founded by technology industry leaders, including Elon Musk and Sam Altman. Only a limited version of the tool was made available, as it was dubbed "too dangerous" by the company to release fully into the world.

They are both engineers and started working together a few years ago, as strategy consultants. In 2016 they co-founded (with a third colleague and friend) a start-up called daco, later acquired by Veepee, with the idea of helping retailers to achieve growth through a deep knowledge of their competitors. The start-up leveraged the power of AI and image recognition to gain insightful information about competitors' strategy, offer, pricing, discount and store network, classifying products and making them comparable. Their working partnership has not been limited to business: indeed an equal interest in art and science pushed them to pursue also an artistic collaboration that led to the creation of the collective Aurèce Vettier. The duo is based in Paris and investigates the space between real and imaginary. Their interest in expanding the creativity of both humans and machines pushes the concepts of creator and created, process and practice, leaving room for fascinating discussions between art, engineering and a territory still unexplored but capable of surprising.

Learning to control robots without requiring models has been a long-term goal, promising diverse and novel applications. Yet, reinforcement learning has only achieved limited impact on real-time robot control due to its high demand of real-world interactions. In this work, by leveraging a learnt probabilistic model of drone dynamics, we achieve human-like quadrotor control through model-based reinforcement learning. No prior knowledge of the flight dynamics is assumed; instead, a sequential latent variable model, used generatively and as an online filter, is learnt from raw sensory input. The controller and value function are optimised entirely by propagating stochastic analytic gradients through generated latent trajectories. We show that "learning to fly" can be achieved with less than 30 minutes of experience with a single drone, and can be deployed solely using onboard computational resources and sensors, on a self-built drone.

In the active research area of employing embedding models for knowledge graph completion, particularly for the task of link prediction, most prior studies used two benchmark datasets FB15k and WN18 in evaluating such models. Most triples in these and other datasets in such studies belong to reverse and duplicate relations which exhibit high data redundancy due to semantic duplication, correlation or data incompleteness. This is a case of excessive data leakage---a model is trained using features that otherwise would not be available when the model needs to be applied for real prediction. There are also Cartesian product relations for which every triple formed by the Cartesian product of applicable subjects and objects is a true fact. Link prediction on the aforementioned relations is easy and can be achieved with even better accuracy using straightforward rules instead of sophisticated embedding models. A more fundamental defect of these models is that the link prediction scenario, given such data, is non-existent in the real-world. This paper is the first systematic study with the main objective of assessing the true effectiveness of embedding models when the unrealistic triples are removed. Our experiment results show these models are much less accurate than what we used to perceive. Their poor accuracy renders link prediction a task without truly effective automated solution. Hence, we call for re-investigation of possible effective approaches.