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Edge to Core AI Futures for OEMs
The ability of computers to autonomously learn, predict, and adapt using massive datasets is driving innovation and competitive advantage across many industries and applications. The artificial intelligence (AI) is budding faster and prompting businesses to hop aboard the next big wave of computing to uncover deeper insight, quickly resolve their most difficult problems, and differentiate their products and services. Whether the goal is to build a smarter city, power an intelligent car, or deliver personalized medicine, we’ve only just begun to understand the real potential of AI.
To highlight the AI strategies, Hewlett Packard Enterprise (HPE) OEM organized a webcast back in May on Artificial Intelligence for OEM partners, presented by Aalap Tripathy, Expert Technologist and Gerald Kleyn, Director of Edgeline Platform R&D & Strategy. The experts described the technology and walked through real-world scenarios. They on the current uses of AI, how to create better customer experiences and deliver efficiently. AI not only help operators and OEMs to analyze data but also predict, recommend and make automated decisions.
For the implementation of AI, HPE OEM has the expertise, edge to core technologies and partner ecosystem to help explore different use cases, experiment with AI and data technologies, and build the solution to be enterprise-ready. HPE OEM will benefit at all stages of the journey from formulating a roadmap through implementation and data migration. They focused on the difference between classical machine learning vs. the method popular today using a deep neural network called Deep Learning (DL).
Inspired by the human brain, deep learning is typically implemented for challenging tasks such as image and facial recognition, image classification and voice recognition. To take advantage of DL, one needs a high performance compute infrastructure to build and train learning models that can manage large volumes of data to recognize patterns in audio, images, videos, text and sensor data.
HPE Artificial Intelligence (AI) and Data-Driven Services will help you every step of the way from significant data foundation to AI-driven automated business outcomes. Let's focus on how do you get beyond the hype and realize the benefits of AI today:
- Focus on real business outcomes- Access to adorable technologies and advanced AI models and libraries make it possible to solve real business problems. Accelerate, predict and automate outcomes and decisions from edge to core by applying AI. Innovate, identify and apply emerging technologies and AI to industry-specific use cases.
- Take an architectural approach-HPE Pointnext helps you understand your choices and offers objective guidance based on your business and HPE’s vision for AI.
- Embrace incremental AI success- Offer stake holder’s novel opportunities, ultimate outcomes, and a clear path forward with HPE Pointnext.
- Explore- Align teams around a shared vision by identifying AI, data, analytics outcomes, and challenges, and exploring use cases and developing high-level plans.
- Experiment- Prove the value of AI in your environment with a preferred use case and model aligned to your business needs. Design analytics scope and load, qualify data and implement analytics functions.
- Evolve solutions by protecting and modernizing your data platforms, deploying new use cases, offering consumption-based models, and managing change with ongoing support and training.
Industries where AI is making a colossal impact
- Healthcare- Artificial Intelligence and Machine Learning are helping in medical imaging, drug discovery, medication management and robotic surgery. The healthcare industry stands to benefit the most from advancements in AI. The ability to continually analyze different types of diseases and potential treatments could eventually lead to a cure for cancer.
- Education Industry- Will robots replace teachers one day? Probably not entirely, but artificial intelligence is undoubtedly playing an ever-increasing role in the field of education. From adaptive learning programs that cater to special needs children to online learning schools that automatically adapt to a student’s native language anywhere in the world, advancements in education technology powered by AI are helping to shrink the knowledge gap.
- Self Driving cars- Companies are building these types of driver-assistance services, as well as full-blown self-driving vehicles like Google’s, need to teach a computer how to take over essential parts (or all) of driving using digital sensor systems instead of a human’s senses. To do that companies generally start out by training algorithms using a significant amount of data.
- Voice Search & Voice-Activated Assistants- One of the most popular usage areas of deep learning is voice search & voice-activated intelligent assistants. With the big tech giants have already made significant investments in this area, voice-activated assistants can be found on nearly every smartphone. Apple’s Siri is on the market since October 2011. Google Now, the voice-activated assistant for Android, was launched less than a year after Siri. The newest of the voice-activated intelligent assistants is Microsoft Cortana.
- Automatically Adding Sounds To SilentMovies- In this task, the system must synthesize sounds to match a silent video. The system is trained using 1000 examples of video with audio of a drumstick striking different surfaces and creating different sounds. A deep learning model associates the video frames with a database of pre-rerecorded sounds to select a sound to play that best matches what is happening in the scene.
- Image Recognition- Another popular area regarding Deep Learning is image recognition. It aims to recognize and identify people and objects in images as well as to understand the content and context. Image recognition is used in several sectors like gaming, social media, retail, and tourism.
- Predicting Earthquakes- Harvard scientists used Deep Learning to teach a computer to perform viscoelastic computations; these are the computations used in predictions of earthquakes. Until their paper, such computations were very processor intensive, but this application of Deep Learning improved calculation time by 50,000%. When it comes to earthquake calculation, timing is essential, and this improvement can be vital in saving a life.
- Neural Networks for Brain Cancer Detection- A team of French researchers noted that spotting invasive brain cancer cells during surgery is difficult, in part because of the effects of lighting in operating rooms. They found that using neural networks in conjunction with Raman spectroscopy during operations allows them to detect the cancerous cells easier and reduce residual cancer post-operation.
An enterprise platform for accelerated AI computing
The iterative process of training an AI model requires high levels of compute performance. For faster time-to-value, it may be necessary to leverage massively parallel, high-performance accelerators (GPUs). With support for eight high-performance GPUs, the HPE Apollo 6500 Gen10 System reduces AI training time by delivering dramatic increases in application performance.
HPE has a comprehensive, purpose-built portfolio for deep learning. HPE AI and Data-Driven Services will help you every step of the way from significant data foundation to AI-driven automated business outcomes. AI and machine learning used as an extension of data analytics – an area that enterprises have been exploiting for some years for churn reduction, marketing ROI, and improved customer experience.
In its mission to help make AI real for its OEM customers, HPE offers flexible consumption services for HPE infrastructure, which avoids over-provisioning, increases cost savings and scales up and down as needed to accommodate the needs of deep learning deployments.
Many people bristle at the thought of automation and robots replacing human workers in the future. However, certain jobs should be automated, especially dangerous ones like bomb-detection, underwater exploration, space exploration, and even military exercises. Any type of artificial intelligence technology that can take a human being out of harm’s way is suitable. Moreover, more and more safety-focused applications and solutions will continue to be developed and advanced in 2018.
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