The Future of Data and Artificial Intelligence: What’s Coming Next?

Industries and consumers tend to look at the leading edge of data and artificial intelligence as the representation of the future, but innovators across sectors see things differently. The future for them is now and the rest of the “laggards” have yet to develop a plan for implementation. An example would be how the focus is on autonomous vehicles when the present future is autonomous last-mile delivery.

The Present Future of Autonomous System Design

Everything from small and regular-size vehicles to aerial delivery drones is showing how data analytics and AI are affecting autonomous system design. Statistics show that aerial delivery drones will hold 61% of the global autonomous last-mile delivery market share, according to market research firm Fact.MR. This represents the triumph over large data set challenges and data scientist shortages via new approaches to artificial intelligence and machine learning algorithms.

MIT researchers have developed a novel approach to machine-learning model training that uses a special type of machine-learning model rather than using a dataset. The model generates realistic synthetic data that can train another model for downstream vision tasks, according to MIT News.

This ‘generative model’ requires a fraction of the memory needed for storage and sharing than a typical dataset. These and other approaches such as the Techolution AutoAI are the foundation of future innovation that is more accessible, actionable, and affordable for industry sectors.

Autonomous systems from vehicles and robots to warehouses, factor systems, and drones result from decades of innovation that are fueled by future processes put into action today. The broadening uses of IoT sensors are driven by adaptive algorithms, ML, and machine vision. This ability to meet present and future business outcomes requires a platform approach to innovation that relies upon present cloud and app development, which includes:

• DevOps and Continuous Delivery
• Cloud Native Development
• Architecture model-based engineering
• Automatic code generation

Innovation via autonomous system design, computer vision, robotics, and indispensable hardware fabrication starts with data and physical systems. These components interpret and carry out the roles that are modified with updated algorithms. Software is the means for linking systems for this behavior modification that guides hardware fabrication and operation. Businesses can apply this in countless ways across every sector including manufacturing, retail, and banking

Data, Artificial Intelligence, and Robotics in Manufacturing and Retail

The future of connected manufacturing innovation trends will rely on data and artificial intelligence and analysis to deliver real-time autonomous system functionality. The sector will also need the architecture of cloud, edge, applications, and APIs to make new functionalities and services a reality. For example, edge computing enables moving the technology and computing power closer to where manufacturers need it on the factory floor or remote industrial sites.

This enables autonomous systems to use manufacturing execution systems to track and document the transformation of things like raw ingredients into finished products. Integrating computer vision robotics, AI/ML, applications, cloud, and edge computing enables autonomous systems to monitor and control production quality. They can also tie into SAP, ERP, and other databases to support and learn from the supply chain and production output needs based on real-time data.

Retail uses intelligent automation and autonomous systems in everything from merchandising, planning, supply chain, and store operations to deal with rapidly changing market conditions.

Real-time vision systems can monitor shopping behavior, inventory, and customer behavior to integrate the physical store with eCommerce in profound ways.

As autonomous system applications expand, many will also use computer vision to perceive tasks coherently. The future of computer vision and robotics shows how data, artificial intelligence and data analytics are all tied to cloud and edge computing.

Data Analytics, Robotics, and Computer Vision

Tomorrow’s computer vision platforms for manufacturing must be capable of combining, aggregating, and analyzing data by leveraging a federated model.

This approach means that the analysis of data happens in real time coming from autonomous robots:

• On the factory floor
• In remote industrial sites such as for oil and gas or the energy sector
• Aerospace production and inspection

By using AI/ML models at the edge, robots and autonomous systems no longer need to push data to the cloud for analysis. The systems can then send the results back to the cloud for model training and updates. The cloud application can then send the updates back to the autonomous systems and robots in the field or on the factory floor. These manufacturers and industrial sector businesses can apply computer vision, autonomous system design, robotics, and its related hardware fabrication to affect every production and operational outcomes such as:

• Personal and facility safety
• Production control and monitoring
• Massive data set collation and analysis
• Supply chain and production integration
• Improved customer experience
• Operational efficiencies
• Sustainability and waste reduction
• Revenue generation

More financial institutions are using computer vision technology for improving customer experiences and back-office procedures like service requests or biometric authentication and authorization. This is based on data and artificial intelligence to develop machine learning algorithms to:

• Identify fraud trends and cybersecurity threats
• Document scanning and processing
• Real-time transaction analysis for risk management

Computer vision technologies in retail can fuel computer vision prediction engines to support customer choices and experiences. This can include associating product bundles with visual data derived from smart mirrors based on shopping behavior and visual data from product purchases. These retailers can deliver a complete shopping and purchasing experience that bridges the gap between brick-and-mortar and ecommerce.

The result might include ecommerce visual product bundle suggestions based on in-store purchases or in-store bundle suggestions from ecommerce searches. This could free customers from long searches across a catalog to match outfits and accessories or products connected to other purchases.

Solving the Challenges of Data and Artificial Intelligence Costs and Expertise

Today’s ML models can take advantage of turnkey AI with MLOps using fractional AI expert teams to overcome traditional time, cost, and personnel issues associated with these projects. Businesses can replace the need for on-site data scientists to perform analyses with fractional services of specialists that can work on a per-project basis. The result can be a process of innovation that is affordable, yields real time results, and drives innovation possibilities for the future.

Robotics and autonomous system Hardware fabrication encompasses designing, validating, and troubleshooting all aspects of the electrical and physical system design including components, PCB, and sensor systems. Today and tomorrow’s innovators will fully integrate these hardware fabrication processes into a holistic approach to system design and implementation.

The approach would be to enlist a co-creation partner that could supplement all needs for AI, cloud, edge AppDev and hardware fabrication with in-house personnel. This will mean lower manufacturer project costs and greater control in meeting present business outcomes and operational functionality while driving future innovation possibilities.

This cross functional approach to autonomous system design, robotics and computer vision will define the future of data and artificial intelligence use across sectors. The goal is to help businesses minimize the proof-of-concept stage and move more quickly into full operational use with systems that they can update and modify in real time for future innovation.

To learn more about how data, AI/ML Cloud, Edge, AppDev can affect your business’s ability to innovate, read our IT Innovation Guide.