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.
By convention, the rare class is usually positive, so this means the True Positive (TP) rate is 0.78, and the False Negative rate (1 – True Positive rate) is 0.22. The Non-Large Loss recognition rate is 0.79, so the True Negative rate is 0.79 and the False Positive (FP) rate is 0.21. They don't report a False Positive rate (or True Negative rate, from which we could have calculated it). This result means that, using their Neural network, they must process 28 uninteresting Non-Large Loss customers (false alarms) for each Large-Loss customer they want.
At its iPhone X event last week, Apple devoted a lot of time to the A11 processor's new neural engine that powers facial recognition and other features. The week before, at IFA in Berlin, Huawei announced its latest flagship processor, the Kirin 970, equipped with a Neural Processing Unit capable of processing images 20 times faster than the CPU alone. The company also has math libraries for neural networks including QSML (Qualcomm Snapdragon Math Library) and nnlib for Hexagon DSP developers. The closest thing that Qualcomm currently has to specialized hardware is the HvX modules added to the Hexagon DSP to accelerate 8-bit fixed operations for inferencing, but Brotman said that eventually mobile SoCs will need specialized processors with tightly-coupled memory and an efficient dataflow (fabric interconnects) for neural networks.
Tech giants and venture capitalists are making serious investments in AI and machine learning. Because the two technologies not only have the potential to automate huge amounts of work currently done by humans, they also present new opportunities for engaging and servicing customers. Find out in this report.
For data prone to noise and anomalies (most data, if we're being honest), a Long Short Term Memory network (LSTM), preserves the long term memory capabilities of the RNN, while filtering out irrelevant data points that are not part of the pattern. Mechanically speaking, the LSTM adds an extra operation to nodes on the map, the outcome of which determines whether the data point will be remembered as part of a potential pattern, used to update the weight matrix, or forgotten and cast aside as noise. For example, to train the HR network, the first input to the network is the number of homers the player hit in his first game, the second input to the network is the number the player hit in his second game and so on. With a network to train and data to train it with, we can now look at a test case where the network attempted to learn Manny Machado's performance patterns and then made some predictions.
SAE International has created the now-standard definitions for the six distinct levels of autonomy, from Level 1 representing only minor driver assistance (like today's cruise control) to Level 6 being the utopian dream of full automation: naps and movie-watching permitted. Many of the features of AI-assisted driving center around increased safety, like automatic braking, collision avoidance systems, pedestrian and cyclists alerts, cross-traffic alerts, and intelligent cruise control. A connected vehicle could also share performance data directly with the manufacturer (called "cognitive predictive maintenance"), allowing for diagnosis and even correction of performance issues without a stop at the dealer. Although it may not at first appear directly tied to automotive AI, the health and medical industry stands to experience some significant disruptions as well.
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.
Deep Learning algorithms mimic human brains using artificial neural networks and progressively learn to accurately solve a given problem. Training a data set for a Deep Learning solution requires a lot of data. Industry level Deep Learning systems require high-end data centers while smart devices such as drones, robots other mobile devices require small but efficient processing units. Deep Learning models, once trained, can deliver tremendously efficient and accurate solution to a specific problem.
Vigilent, a company that uses IoT, machine learning and prescriptive analytics in mission-critical environments, reduces datacenter cooling capacity by employing real-time monitoring and machine language software to match cooling needs with the exact cooling capacity. Reduces Opex: Off-the-shelf smart management and monitoring solutions can be embedded with AI systems to reduce and control datacenter operating expenses. Google reduced the overall datacenter power utilization by 15 percent by using a custom AI smart management and monitoring solution that employs machine learning to control about 120 datacenter variables from fan speeds to windows. Another company called Wave2Wave has developed a rack-mounted robot called ROME (Robotic Optical Switch for Datacenters) for making physical optical connections in a few seconds.
As part of Intel Scalable System Framework (Intel SSF), Intel OPA is designed to tackle the compute- and data-intensive workloads of deep learning and other HPC applications. For example, Intel Xeon Scalable processors and Intel Xeon Phi processors are available with integrated Intel OPA controllers to reduce the cost associated with separate fabric cards. Intel also developed and tested Intel OPA in combination with our full HPC software stack, including Intel HPC Orchestrator, Intel MPI, the Intel Math Kernel Library for Deep Neural Networks (Intel MKL-DNN), and the Intel Machine Learning Scaling Library (Intel MLSL). Learn more about Intel SSF benefits for AI and other HPC workloads at each level of the solution stack: compute, memory, storage, fabric, and software.AI is still in its infancy.