The AI startup Covariant and the industrial robotics company ABB will be partnering to engineer sophisticated robots that can pick up and manipulate a wide variety of objects. These robots will be used in warehouses and other industrial settings.

As Fortune reported, the industrial robotics company ABB is primarily involved in the creation of robotics for car manufacturers, but the company wants to branch out to other sectors. ABB is aiming to become involved in logistics, where its robots will be used in large warehouses, such as those run by Amazon, to manipulate items, package goods, and make shipments.

According to ABB president Sami Atiya, according to Fortune, ABB sought partners that were experienced in the creation of sophisticated computer vision applications. While the company currently uses computer vision algorithms to operate some of its robots, ABB aimed to push the envelope and create reliable, high-dexterity robots capable of maneuvering and manipulating thousands of different objects.

The company examined many different companies before settling on Covariant as its partner. Covariant is a robotics research company whose researchers come from places like OpenAI and the University of California Berekely. Covariant managed to produce the only software examined by ABB that could reliably recognize many different items without the intervention of human operators.

The computer vision and robotics applications developed by Covariant were trained with reinforcement learning. Thanks to deep neural networks and reinforcement learning, Covariant was able to create software that learns through experience and is able to reliably and consistently recognize objects once a pattern has been learned. The CEO of Covariant, Peter Chen, was interviewed by Fortune. Chen explained that as more robotics companies like ABB branch out into new industries and markets, the goal becomes the creation of robots capable of a wider variety of tasks than those currently used in many manufacturing and logistics operations. Most of the robots employed in industrial capacities are only capable of doing a handful of very specific things. Chen explained that the goal is to create robots capable of adaptation.

As a result of the partnership with Covariant, ABB will get insight into the technology that drives Covariant’s AI, and this knowledge could help them better integrate AI into the tech that powers their existing robots. Currently, Covariant is a fairly small operation with only a handful of robots in full-time operational status, spread out across industries like the electronics industry, the pharmaceutical industry, and the apparel industry. However, its collaboration with ABB could cause it to see substantial growth.

The partnership between Covariant and ABB highlights the increasing role of AI startups in the robotics field. Other examples of AI startups collaborating with robotics companies includes the Japanese corporation IHI establishing a partnership with the AI startup Osaro. The joint collaboration also concerned the use of robots to grasp and manipulate objects.

While there is currently a lot of focus on robots automating away human jobs, in some industries there simply aren’t enough humans to do those jobs, to begin with. A recent report about the logistics sector estimates that over half of all logistics companies will face staff shortages over the course of the next five years. There will be a particular shortage of warehouse workers over the next half-decade. The report suggests that causes of the labor shortage within the logistics industry are falling unemployment rates, long hours, tedious work, and low wages.

The post Industrial Robotics Company ABB Joins Up With AI Startup Covariant appeared first on Artificial Intelligence.

One of the key factors in the success of global businesses has been the efficient analysis of past performance data: consumer data to improve products and increase customer satisfaction or operational data to improve efficiency and reduce cost. However, in today’s interconnected digital world with the proliferation of smart mobile devices and the advancements in operations and transport automation, we are seeing a shift towards larger and more diverse real-time data that is revolutionizing the way companies can manage their new-age supply chain networks.

What is Big Data?

Big Data refers to extremely large data sets, from several sources that are often available real-time and that cannot be managed by traditional data processing systems. Advanced statistical programs, machine and deep learning algorithms can process this data and generate patterns, trends and implementable business insights. This has enabled companies to not only make instant decisions to increase efficiency but also to automatically adjust their robotic processes via a continuous feedback and improvement loop powered by big data analytics.

Why is Big Data a good fit for the logistics industry?

Big Data is a perfect fit for logistics as there are millions of packages moving across the world daily that go through multiple touch-points via a complex network of shippers (sellers), consignees (buyers), warehouse personnel, customs agents, transporters, loaders, packers, shipping and air carriers. This creates a multitude of data-points and enormous potential to improve both delivery times and cost and to achieve greater visibility across the network.

How Big Data can be used in the logistics industry?

1)  Optimal Routing:

With the spread of sensors and mobile devices, not only can a customer get uber-type tracking for their consignments but also the trucking companies can collect a range of data from engine performance, fuel consumption, tire wear-and-tear and even external data such as weather and traffic conditions. The data can process and computer algorithms can automatically manage route selection for the driver. The fleet operator will gain from better fleet optimization, thereby reducing cost while also ensuring on-time deliveries to customers.

A great example of this is when UPS used big data analytics to implement a policy where drivers should only turn left when absolutely necessary that saved them 40 million litres of fuel and increased deliveries by approximately 350,000 orders.

Similarly, for international shipping, data on congestion, strikes, weather conditions etc. enable carriers to provide accurate and predictive assessments of potential delays and disruptions to customers and adjust routes and capacity accordingly.

2) Smart Warehousing:

Today, with robotic package handling, sorting and automated forklifts and other warehouse equipment we are nearing the complete mechanization of smart warehouses. While tech companies such as Amazon led the way, now even regular manufacturing companies are starting to automate their warehouse operations. Warehouses offer rich operational metrics on storage and movement of parcels that can provide insight into efficiency gaps.

Big Data analytics and tracking sensors can improve warehouse robotics, which can increase equipment life cycles (via preventive maintenance), accelerate product movement, optimize inventory management (through better predictive models), and also increase warehouse safety. Warehouse managers, using data analytics can make immediate operational decisions, resulting in seamless resource allocation, reduced costs and better warehouse throughput.

3) Customer Satisfaction:

Customer feedback has always received through either anecdotal evidence from sales reps or customer questionnaires in logistics or most other B2B industries. On social media and public websites, users provide open, accurate and current feedback that can be incident specific or generic. New technologies such as semantic analysis and text processing can dissect and group these reactions and analyze customer disposition to eventually create an instantaneous feedback loop.

A DHL study illustrates this point and concludes that “a meticulous review of the internet gives unbiased customer feedback”, thereby enabling product and customer service managers to design solutions to guarantee customer satisfaction and retention, which is crucial in today’s hyper-competitive environment.

Consumer demands are rapidly changing, and businesses can no longer use retrospective data to take strategic decisions to stay relevant. Big Data heeds this call with real-time information that displays patterns and trends, which allow businesses to make intelligent, immediate and most significantly automated operational decisions.

With millions of available data points through sensors and connected devices, robots in warehouses, delivery drones and self-driving vehicles, it is only a matter of time until we see a fully automated intelligent supply chain that will be continually optimized by big data analytics.

The post Big Data: The Driving Force for Logistics Automation appeared first on Artificial Intelligence.