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.
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.
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.
Bold (and perhaps impossible to fulfill) promises are a natural result when prefix investing is again profitable - the only difference that the prefix is now "AI-". Just as the tests we give students are rarely reflective of the real world, neither are the tests we give these AI systems. As an example, a standard dataset in natural language processing is the Penn Treebank, composed primarily of articles from the Wall Street Journal in 1989. Not only is time an issue but the Wall Street Journal is primarily composed of financial articles.
Over the past 9 months we have developed a machine learning system – Pythia – that predicts financial markets using media sentiment data set called the Thomson Reuters MarketPsych Indices (TRMI). Since live trading began, Pythia has generated a gain of 18.84% by making a simple buy or sell decision on the SPY (an ETF proxy for the S&P 500) every day, which is in line with the model's historic performance. Pythia Pythia is a machine learning system, that predicts financial markets using media sentiment data set called the Thomson Reuters MarketPsych Indices (TRMI). Since live trading began, Pythia has generated a gain of 18.84% by making a simple buy or sell decision on the SPY (an ETF proxy for the S&P 500) every day, which is in line with the model's historic performance.
By making these moves, Microsoft is hoping to build up a broader community of developers around CNTK. We've seen firsthand the kind of performance CNTK can deliver, and we think it could make an even greater impact within the broader machine learning and AI community. Indeed, in November it released another project, Distributed Machine Learning Toolkit, to Github. "With its ability to run on a single, high-end Graphical Processing Unit (GPU) server or distributed clusters of GPU-based machines--a differentiator, according to Microsoft--CNTK appears to give developers speed and throughput advantages," he adds.
AI allows for the uncovering of insights in data that would take humans too long to achieve to be useful, allowing humans to make decisions on the insight that delivers a benefit (or indeed allowing another machine to make the decision based on what human intelligence has taught it to do). Embedding the'cognitive' capability of a machine into solving specific business issues provides a general ability to be more efficient – faster, better, cheaper.. At Chatterbox Labs we are working with many of the world's leading technology and consulting organizations who are harnessing our Cognitive Engine to solve real world business problems. Use cases are emerging daily as our partners apply their intelligence to build out new cognitive products in days to harness our machine learning algorithms, our speed and scale, our multi-lingual capability to bring artificial intelligence to benefit their clients. Chatterbox Cognitive Engine delivers artificial intelligence, machine learning and natural language processing capability in one to provide mid level business analysts with the ability to create new cognitive products in days.