Most of us are hardwired to hate not speaking directly to a human when we have a problem—following years of irritating voicemail systems. However, perhaps the only thing worse is being on hold for an uncertain amount of time due to overwhelmed call centres.

Chatbots have come a long way and can now quickly handle most queries within minutes. Where a human is required, the reduced demand through using virtual agent technology (VAT) means customers can get the assistance they need more quickly.

The COVID-19 pandemic has greatly increased the adoption of VAT as businesses seek to maintain customer service through such a challenging time.

According to IBM’s study, 99 percent of organisations reported increased customer satisfaction by integrating virtual agents. Human agents also report increased satisfaction and IBM says those “who feel valued and empowered with the proper tools and support are more likely to deliver a better experience to customers.”

68 percent of leaders cite improving the human agent experience as being among their key reasons for adopting VAT. There’s also economic incentive, with the cost of replacing a dissatisfied agent who leaves a business estimated at as much as 33 percent of the exiting employee’s salary.

IBM claims that VAT performance in the past has only been studied through individual case studies. The company set out, alongside Oxford Economics, to change that by surveying 1,005 respondents from companies using VAT daily.

Businesses wondering whether virtual assistants are worth the investment may be interested to know that 96 percent of the respondents “exceeded, achieved, or expect to achieve” their anticipated return.

On average, companies which have implemented VAT have increased their revenue by three percent.

IBM is one of the leading providers of chatbots through its Watson Assistant solution. While there’s little reason to doubt the claims made in the report, it’s worth keeping in mind that it’s not entirely unbiased.

Watson Assistant has gone from strength-to-strength and appears to have been among the few things which benefited from the pandemic. Between February and August, Watson Assistant usage increased by 65 percent.

You can download a full copy of IBM’s report here.

(Photo by Volodymyr Hryshchenko on Unsplash)

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Anadolu Insurance is using IBM technology for assessing its auto claims – specifically, the IBM Watson Visual Recognition service.

On average, Anadolu reviews around 1200 vehicle damage files each day. Manually checking each file was a slow and tedious process, but – with 63 percent contradicting the amount of described damage – it’s a vital operation.

Mehmet Abacı, Deputy Chief Executive Officer of Anadolu Insurance, said:

“Insurance is one of the primary industries that are affected by technological developments the most. As the largest and long-established company of Turkey, we are also a leader in the use of technology.

We want to reduce the analysis and repair processes to a few hours by using artificial intelligence in analysis of minor damages in our customers’ vehicles. Before that, we also performed such work and managed to pay our customers for minor housing damages such as glass breakage within five seconds. Now we have also started to use artificial intelligence technology in auto insurance more efficiently.

IBM Watson is helping Anadolu’s customers take optimal photos of their damaged vehicle. Affected parts are identified along with the scope of the damage for the AI to determine whether the parts need repair, replacement, or further expert consultation.

Volkan Sözmen, IBM Turkey Country General Manager, comments:

“We are proud that our long-standing collaboration with Anadolu Insurance has acquired a new dimension with this project. IBM Watson’s visual recognition capabilities will greatly contribute to making Anadolu Insurance’s processes more efficient and hassle-free for its customers.

We believe that this project will not only help the insurance industry gain new momentum, but also change the course of the digital transformation journey.”

Experts from Anadolu Insurance are working alongside IBM’s data scientists to train Watson with the language associated with damages and auto parts; such as buffer, door and mud-guards.

The insurance claims solution is expected to launch for contracted auto repair shops in the second half of this year.

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The current ISS commander, German astronaut Alexander Gerst, was first to speak with CIMON. Gerst said “Wake up, CIMON” which prompted the robot to respond “What can I do for you?”

CIMON and Gerst’s first assignment was to perform a student-designed experiment with crystals. The robot, after recognising Gerst’s face and positioning itself autonomously, provided instructions on how to conduct the experiment.

On Earth, CIMON weight just five kilograms and was designed by Airbus. The orb-like robot has a large screen on the front which can either display cartoonish faces or information required for a particular task.

Till Eisenberg, CIMON Project Manager at Airbus, said:

“CIMON represents the realisation of an Airbus vision. It is a huge step forward for human space flight, achieved by working in cooperation with our partners.

With CIMON, we have laid the foundations for social assistance systems that are designed to be used under extreme conditions.”

IBM Watson provides the robot’s AI smarts for understanding questions. CIMON cannot process data itself and requires communication with a computer on the ground.

Matthias Biniok, IBM project lead on CIMON, commented:

“If CIMON is asked a question or addressed, the Watson AI firstly converts this audio signal into text, which is understood, or interpreted, by the AI.

IBM Watson not only understands content in context, it can also understand the intention behind it.

The result is a tailored answer, which is converted into speech and then sent back to the ISS, enabling a natural, dynamic spoken dialogue.”

Data is beamed to and from the ISS using the station’s WiFi network which uses satellites to communicate with the ground.

Bernd Rattenbacher, team leader of the ground control centre, explained:

“The data link to Earth is established via satellite to NASA/ESA and to the DLR’s Columbus control centre in Oberpfaffenhofen, Germany.

From there, the signal is transmitted to us, the CIMON ground station at BIOTESC in Lucerne [University], the Swiss User Support and Operations Center, which is connected to the IBM Cloud in Frankfurt over the internet.

The time needed for the signal to be transmitted via satellite is 0.4 seconds each way. A number of firewalls and VPN tunnels are in place to ensure data security.”

Despite the distance, CIMON is said to respond within a couple of seconds after a question is asked.

CIMON is considered to be a demonstration of what future human-robot interactions could look like on space missions – improving safety, and the ability to conduct more complex tasks and experiments.

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IBM has already cemented its place as a leader in AI with Watson and its deep learning capabilities. Deep learning is a subset of AI which mimics the human brain and is therefore a focus of researchers.

Companies including Microsoft, Facebook, Amazon, and Google have made advancements in deep learning, but IBM’s could be the most groundbreaking. Today’s deep learning systems can take days to analyse data, but IBM has managed to reduce this to just hours.

IBM has achieved this by overcoming a core hurdle of deep learning systems. Most current projects are run on a single server due to how much data is involved and the difficulty of keeping it all synchronised. New software developed by IBM, however, claims to split deep learning tasks across 64 servers running up to 256 processors.

This is available to customers using IBM Power System servers or anyone interested in testing its capabilities. For its tests, IBM used 64 of their Power 8 servers which feature Intel microprocessors and NVIDIA GPUs linked with an NVLink connection. This configuration allows for rapid data flow between the chips.

Using this method, IBM claims performance can be boosted 50-60 times over a single server typically used today. However, it requires some complex work under-the-hood to take full advantage of the extra servers and processing power.

Managing traffic is the main priority to ensure all the processors are working and not sitting idle. Each processor has a dataset to work on but also requires data from other processors to understand the complete picture. Optimising this management of traffic is an ongoing process to achieve peak levels of performance.

A record breaker

In an ideal scenario, each processor added would deliver a 100 percent improvement to performance, but this is unrealistic. IBM claims its system achieved 95 percent across 256 processors, which is both impressive and sets a new record. The previous record was held by Facebook who achieved 89 percent.

This isn’t the only record IBM has claimed to have broken with their latest advancement. For image recognition, IBM says it observed a 33.8 percent accuracy rate analysing 7.5 million images over seven hours. The prior record was held by Microsoft with 29.8 percent accuracy, and that effort took 10 days to complete.

Are you impressed with IBM’s machine learning advancement? Share your thoughts in the comments.

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