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DarwinAI Generative Synthesis* Platform and Intel® Optimizations for TensorFlow* Accelerate Neural Networks

DarwinAI, a Waterloo, Canada startup creating next-generation technologies for Artificial Intelligence development, announced that the company’s Generative Synthesis platform – when used with Intel technology and optimizations – generated neural networks with a 16.3X improvement in image classification inference performance. Intel shared the optimization results in this recently published solution brief. The complexity of deep […]

Best of arXiv.org for AI, Machine Learning, and Deep Learning – June 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

The insideBIGDATA IMPACT 50 List for Q3 2019

The team here at insideBIGDATA is deeply entrenched in following the big data ecosystem of companies from around the globe. We’re in close contact with most of the firms making waves in the technology areas of big data, data science, machine learning, AI and deep learning. Our in-box is filled each day with new announcements, commentaries, and insights about what’s driving the success of our industry so we’re in a unique position to publish our quarterly IMPACT 50 List of the most important movers and shakers in our industry. These companies have proven their relevance by the way they’re impacting the enterprise through leading edge products and services. We’re happy to publish this evolving list of the industry’s most impactful companies!

“Above the Trend Line” – Your Industry Rumor Central for 6/12/2019

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

NVIDIA Launches Edge Computing Platform to Bring Real-Time AI to Global Industries

NVIDIA announced NVIDIA EGX, an accelerated computing platform that enables companies to perform low-latency AI at the edge — to perceive, understand and act in real time on continuous streaming data between 5G base stations, warehouses, retail stores, factories and beyond. NVIDIA EGX was created to meet the growing demand to perform instantaneous, high-throughput AI at the edge — where data is created – with guaranteed response times, while reducing the amount of data that must be sent to the cloud.

Using Deep Learning for On-demand Expert Service Cloud for Analytics

Rumblings in the industry indicate there is a new on-demand expert service cloud for analytics. BI teams can now tap thousands of experts around the globe to quickly handle ad-hoc queries and eliminate backlog. The new technology creates a pool of analytics experts to add BI team capacity for ad-hoc analytics on data warehouses.

Best of arXiv.org for AI, Machine Learning, and Deep Learning – April 2019

In this recurring monthly feature, we will filter all the recent research papers appearing in the arXiv.org preprint server for subjects relating to AI, machine learning and deep learning – from disciplines including statistics, mathematics and computer science – and provide you with a useful “best of” list for the month.

UC Berkeley Graduate Receives ACM Doctoral Dissertation Award

ACM, the Association for Computing Machinery, announced that Chelsea Finn receives the 2018 ACM Doctoral Dissertation Award for her dissertation, “Learning to Learn with Gradients.” In her thesis, Finn introduced algorithms for meta-learning that enable deep networks to solve new tasks from small data sets, and demonstrated how her algorithms can be applied in areas including computer vision, reinforcement learning and robotics.

Accelerating Training for AI Deep Learning Networks with “Chunking”

At the International Conference on Learning Representations on May 6, IBM Research will share a deeper look around how chunk-based accumulation can speed the training for deep learning networks used for artificial intelligence (AI).

Advanced Performance and Massive Scaling Driven by AI and DL

In this contributed article, Kurt Kuckein, Director of Marketing for DDN Storage, discusses how current enterprise and research data center IT infrastructures are woefully inadequate in handling the demanding needs of AI and DL. Designed to handle modest workloads, minimal scalability, limited performance needs and small data volumes, these platforms are highly bottlenecked and lack the fundamental capabilities needed for AI-enabled deployments.