Although the concept and definition of artificial intelligence (AI) is still morphing as the technology matures, generally it is the idea of building machines that can think like humans. The term machine learning (ML) is used to describe the idea of teaching computers to learn in the same way humans do. It represents the leading edge of AI.
AI is not a new concept, and this concept is in use since last many decades. For instance, think about a company which used to develop industrial control systems. They could be as primary industrial IoT systems. It used to take readings from sensors located inside the machines, parse them through simple processing scripts, and then send the outputs over a wire to control equipment on the factory floor.
How times have changed! Todayโs industrial IoT systems are far more capable on some dimensions:
- Sensors are much more accurate and measure different types of inputs, including pressure, friction, vibration, and appearance
- Communications have higher capacities and reliability, enabling wired and wireless connectivity over longer distances
- The ability to handle big data flowing in from the edge
This last point not only refers to faster processing, larger storage volumes, and more bandwidth - it also refers to the increased ability to derive insights and take immediate action based on them automatically with Artificial intelligence (AI). This underlying technology trend has been re-ignited by the explosion of sensorsโ data โ Industrial IoT data - and has started to transform operations at the edge, leading to new and exciting possibilities.
Impact of AI in different verticals
AI in Health
- Self-testing leading to diet, supplement and exercise advice
- Hospital re-admission prediction
- Personalised drugs
- Background scanning of digital footprints to predict disease
- Home monitoring, e.g., fall monitoring, habit monitoring
AI in Retail
- AI robots continuously track inventory on shelves
- Computer vision + deep learning for auto-checkout
- Machine learning to detect sources of counterfeits
- Machine-learning personalization
- Auto-login and facial recognition
- Real-time store pricing
AI in Electric Utilities
- Predictive maintenance of field gear
- Machine learning-based forecasting maximises different energy sources
- Self-generation use or selling of power
- Drones and on-site robots look for faults
- Sensors and machine learning micro adjust generation efficiency
- Virtual agents automate call centers
AI in Farming
- Drones and hi-res cameras to identify โprecisionโ farming needs
- Robots for precision farming โ Drones, robots, and cameras for physical security
- Machine learning to determine optimum lighting and nutrition for vertical farming
- Robots for crop picking and processing
- Self-driving farm machinery
- Multi-variable prediction for optimum plant and harvest timing
AI in Insurance
- Insurance advice and customer service
- Transaction and claims processing
How to get started with AI and ML in Industrial IoT
Hewlett Packard Enterprise (HPE) has established partnerships with companies that can bring platform and application know-how. They include GE Predix, a platform for developing industrial applications and analytics, as well as PTCโs software and agents for anomaly detection, predictive analytics, and IoT connectivity. HPEโs service arm, HPE Pointnext, can also advise companies interested in implementing ML solutions.
Many organizations lack several fundamental requirements to implement deep learning, including expertise and resources; sophisticated and tailored hardware and software infrastructure; and the integration capabilities required to assimilate different pieces of hardware and software to scale AI systems.
To help customers overcome these challenges and realize the potential of AI, HPE has announced the following offerings:
- HPE Rapid Software Installation for AI: HPE introduced an integrated hardware and software solution, purpose-built for high-performance computing and deep learning applications.
- HPE Deep Learning Cookbook: Built by the AI Research team at Hewlett Packard Labs, the profound 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 the performance of various hardware platforms, characterize the most popular broad 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.
- HPE AI Innovation Center: The innovation center serves as a platform for research collaboration between universities, enterprises on the cutting edge of AI research and HPE researchers.
- Enhanced HPE Center of Excellence (CoE): Designed to assist IT departments and data scientists in accelerating their deep learning applications and realizing better ROI from their deep learning deployments in the near term, the HPE CoE offer select customersโ access to the latest technology and expertise including the latest NVIDIA GPUs on HPE systems.
New Offerings, announced on March 21st:
- HPE Digital Prescriptive Maintenance Services, the first in a series of AI-enabled industry offerings from HPE Pointnext, which automates problem prevention and increases productivity of industrial equipment
- HPE Artificial Intelligence Transformation Workshop, providing consulting expertise from HPE Pointnext to help customers get started with AI, evolve their strategic data and analytics initiatives and prioritize AI use cases
- HPE Apollo 6500 Gen10 System, a next-generation high performance computing system purpose-built for deep learning that delivers a 3x faster model training than previous generations(1)
- HPE has also extended its AI partner ecosystem through a reseller agreement with WekaIO to deliver optimized storage performance in AI environments
Featured articles:
- AI soon to be your BFF and mental health therapist?
- Is AI the magic bullet for your company's data glut?
- What does it take to build a machine learning capacity? Less than you think
- Want to know the future of technology? Sign up for weekly insights and resources
Be on the lookout for future OEM blogs and collateral about HPE AI capabilities & solutions.
Audrey Cox
WW OEM Communications & Brand Awareness
Hewlett Packard Enterprise
