Instead of hiring a larger team, Hearst Newspapers is solving the problem with Google Cloud AI. Using Google Cloud Natural Language API to enable content classification with powerful machine learning models in an easy-to-use REST API, Hearst Newspapers can understand what its content is about, regardless of how it is structured and presented on the company's many websites. Although Hearst Newspapers previously used a legacy system that attempted to automate the classification process, it was not as fast or as accurate. "Google Cloud Natural Language API is unmatched in its accuracy for content classification," says Naveed Ahmad, Senior Director of Data at Hearst Newspapers, who is responsible for data centralization and business intelligence using Google Cloud Platform. At Hearst Newspapers, we publish several thousand articles a day across more than 30 properties.
Graphcore has today announced a $50 million Series C funding round by Sequoia Capital as the machine intelligence company prepares to ship its first Intelligence Processing Unit (IPU) products to early access customers at the start of 2018. The Series C round enables Graphcore to significantly accelerate growth to meet the expected global demand for its machine intelligence processor. The funding will be dedicated to scaling up production, building a community of developers around the Poplar software platform, driving Graphcore's extended product roadmap, and investing in its Palo Alto-based US team to help support customers. Nigel Toon, CEO at Graphcore said: "Efficient AI processing power is rapidly becoming the most sought-after resource in the technological world. We believe our IPU technology will become the worldwide standard for machine intelligence compute.
HPE Rapid Software Installation for AI: HPE introduced an integrated hardware and software solution, purpose-built for high performance computing and deep learning applications. Based on the HPE Apollo 6500 system in collaboration with Bright Computing to enable rapid deep learning application development, this solution includes pre-configured deep learning software frameworks, libraries, automated software updates and cluster management optimized for deep learning and supports NVIDIA Tesla V100 GPUs. HPE Deep Learning Cookbook: Built by the AI Research team at Hewlett Packard Labs, the deep learning cookbook is a set of tools to guide customers in selecting the best hardware and software environment for different deep learning tasks. These tools help enterprises estimate performance of various hardware platforms, characterize the most popular deep learning frameworks, and select the ideal hardware and software stacks to fit their individual needs. The Deep Learning Cookbook can also be used to validate the performance and tune the configuration of already purchased hardware and software stacks.
By modeling human testers, including manual and test automation tasks such as scripting, Appvance has developed algorithms and expert systems to take on those tasks, similar to how driverless vehicle software models what a human driver does. The Appvance AI technology learns from various existing data sources, including learning to map an application fully on its own, various server logs, Splunk or Sumo Logic production data, form input data, valid headers and requests, expected responses, changes in each build and others. The resulting test execution represented real user flows, data driven, with near 100% code coverage. Built from the ground up with DevOps, agile and cloud services in mind, Appvance offers true beginning-to-end data-driven functional, performance, compatibility, security and synthetic APM test automation and execution, enabling dev and QA teams to quickly identify issues in a fraction of the time of other test automation products.
At Intel, we have an optimistic and pragmatic view of artificial intelligence's (AI) impact on society, jobs and daily life that will mimic other profound transformations – from the industrial to the PC revolutions. To drive AI innovation, Intel is making strategic investments spanning technology, R&D and partnerships with business, government, academia and community groups. We have also invested in startups like Mighty AI*, Data Robot* and Lumiata* through our Intel Capital portfolio and have invested more than $1 billion in companies that are helping to advance artificial intelligence. To support the sheer breadth of future AI workloads, businesses will need unmatched flexibility and infrastructure optimization so that both highly specialized and general purpose AI functions can run alongside other critical business workloads.
Today, that future is here with the unveiling of the Myriad X, the world's first vision processing unit (VPU) to ship with a dedicated Neural Compute Engine to deliver artificial intelligence (AI) capabilities to the edge in an incredibly low-power, high-performance package. In the coming years, we'll see a huge range of new products emerge that are made more autonomous by embedding real-time intelligence capabilities in devices – from drones and smart cameras to augmented reality and more – to give them the ability to see, understand, interact with and learn from rapidly changing environments. Myriad X combines dedicated imaging, computer vision processing and – thanks to the industry-first Neural Compute Engine – high-performance deep learning inference within the same chip, and the results are opening up new realms of possibility. I invite you to learn more about how Myriad X is accelerating the possibilities of computer vision intelligence at the edge by visiting "Intel Unveils Neural Compute Engine in Movidius Myriad X VPU to Unleash AI at the Edge."
We then examined the model's performance, from its estimated errors in classifying the training data. This output shows that overall, the estimated classification error rate was 3.7%. However for the target surveil class, representing likely surveillance aircraft, the estimated error rate was 20.6%. The output shows that the model classified 69 planes as likely surveillance aircraft.
Our social media guy David Dhannoo remains in Africa, following on from his article last week about the rise of FinTech on the continent. Dave's technology in Africa series now looks at how a startup in Southern Africa is using artificial intelligence to source news articles. Controvert Media from Zimbabwe has implemented the use of artificial intelligence to automatically write and post news articles. They caught my attention from when they trialled their AI idea at the recent African Cup of Nations which took place Gabon. The startup used an AI bot that puts together basic reports on final match reports, taking into account the match scores, player performance data, and so on.
For example, software algorithms are being used to generate scores that evaluate teacher effectiveness. Rating teachers is a laudable goal and could theoretically eliminate human bias in evaluation, but these algorithms have faced criticism because reducing human behavior to mathematical formulas is very hard. Days after making the shift, Facebook was criticized again when the algorithms published a fake news story. We expect human editors to act with journalistic integrity, but replacing people with algorithms doesn't always solve the problem.
"The Silicon Review 50 Smartest Companies of the Year 2016 program identifies the companies transforming the way we work via cutting-edge technology. We selected WorkCompass because of its unique application of artificial intelligence to improve performance appraisal, its revenue growth, customer reviews and domain influence," said Manish Pandey, Editor-in-Chief of The Silicon Review Magazine. "We are honored to be recognized by The Silicon Review Magazine as the one of the 50 Smartest Companies of the Year 2016," said Denis Coleman, Founder and CEO at WorkCompass. I wanted to transform performance appraisal into an ongoing process about coaching and mentoring staff to achieve their full potential.