AI-powered robots will be the next big work revolution in warehouses

For the bot’s creators, Californian AI and robotics startup Covariant, the installation in Germany is a big step forward, and one that shows the firm has made great strides with a challenge that’s plagued engineers for decades: teaching robots to pick things up.

Google has run a stable of robot arms in an attempt to learn how to reliably grasp things, while Amazon holds an annual competition challenging startups to stock shelves with robots in the hope of finding a machine good enough for its warehouses.

This is particularly true in the world of warehouses and logistics, where experts say it’s difficult to find human workers and they need all the robots they can get.

Covariant uses a variety of AI methods to train its robots, including reinforcement learning: a trial and error process where the robot has a set goal and has to solve it itself.

“Non-AI robots can pick around 10 percent of the products used by our customers, but the AI robot can pick around 95 to 99 percent,” says Puchwein.

In Germany, Covariant’s picking robot is packing electronics components for a firm named Obeta, but the company says it’s eager for more robots to compensate for a staff shortage – a situation common in logistics.

What about the employees that Covariant’s robots now operate alongside – do they mind the change? According to Pultke, they don’t see it as a threat, but an opportunity to learn how to maintain the robots and get a better type of job.

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Meena is Google’s attempt at making true conversational AI

Talk to any of the best-known AI assistants today – Alexa, Siri, Google Assistant – and they’re not exactly conversational.

To share progress towards deep learning designed to carry a conversation, Google today introduced Meena, a neural network with 2.6 billion parameters.

Meena can handle multiturn dialogue, and Google claims it’s better than other AI agents built for conversation and available online today.

Google today also released Sensibleness and Specificity Average, a metric created by Google researchers to measure the ability of a conversational agent to maintain responses in conversation that make sense and are specific.

The SSA standard Google proposes is different than the metric other AI assistants have set for assessing a truly conversational AI. Now in its third year, the Alexa Prize is a challenge for teams of student developers to create AI that can hold a conversation for up to 20 minutes.

Conversations is a feature that packages voice app recommendations in conversational multiturn dialogue.

AI assistants that can maintain a conversation may be able to secure closer bonds with humans and do things like provide emotional support to people, or cure the loneliness epidemic, as former Alexa Prize head and current Google Research director Ashwin Ram put it in 2017.

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Get ready for the emergence of AI-as-a-Service

AIaaS is gaining momentum precisely because AI-based solutions can be economically used as a service by many companies for many purposes.

As these companies continue to grow and mature, expect to see AIaaS surge, particularly as vertical markets become more comfortable with the AI value proposition.

An AIaaS provider with knowledge of a specific vertical understands how to leverage the data to get to those meaningful insights, making data far more manageable for people like claims adjusters, case managers, or financial advisors.

If we extend the claims adjuster example from above, he could use AIaaS for much more than predictive analysis.

Once flagged, the adjuster could take immediate action, as guided by an AI system, to intervene and prevent the claim from heading off the rails.

While these examples are specific to insurance claims, it’s not hard to see how AIaaS could be tailored to meet other verticals’ needs by applying specific information to solve for a defined need.

The place for AIaaS. AIaaS models will be essential for AI adoption.

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