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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The AI is designed to be non-invasive and uses a short language sample from a verbal cognitive test given to a patient. Using this sample, the AI model is able to predict the onset of Alzheimer’s with around 71 percent accuracy.

For comparison, standard clinical tests are correct approximately 59 percent of the time and take much longer to diagnose. Current tests analyse the descriptive abilities of people as they age for potential warning signs.

In a paper detailing IBM’s model, the company says it used data from the Framingham Heart Study.

The study first began in 1948 and spans the multiple generations required for building an AI to predict Alzheimer’s in healthy individuals with no other risk factors. 5,000 participants from Massachusetts and their families have been studied.

703 samples from 270 of the study’s participants were collected and analysed to create a dataset consisting of a single sample from 80 participants—half of whom developed Alzheimer’s symptoms before they reached 85.

The AI was trained on this dataset to spot Alzheimer’s signals such as the repetition of words and using short sentences with poor grammatical structures. IBM’s AI was able to correctly predict the onset of Alzheimer’s in every seven of ten cases.

IBM intends to expand the training of their model using more data to better reflect society including socioeconomic, racial, and geographic factors. The Alzheimer’s research is part of a broader IBM effort to better understand neurological health and chronic illnesses through biomarkers and signals in speech and language.

Around 5.5 million people in America alone are estimated to have Alzheimer’s, and some studies suggest it’s the third leading cause of death behind heart disease and cancer.

While there is no cure or prevention for Alzheimer’s yet, earlier diagnosis helps to prepare individuals and their families as much as possible. If treatments become available, Alzheimer’s will almost certainly be more effectively treated when caught earlier.

IBM published its research in The Lancet’s science journal EClinicalMedicine. Pfizer was disclosed as providing funding to obtain data from the Framingham Heart Study Consortium and supporting IBM Research’s involvement.

(Image: Jeff Rogers, global research lead for IBM Research’s Digital Health platform, at work in the IBM Home Health Lab.)

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Humans have travelled the sea in some form for tens of thousands of years—with the earliest crossings occurring around 53,000 to 65,000 years ago (when Australo-Melanesian populations migrated into the Sahul landmass – known today as Australia and New Guinea – from what used to be the Sundaland peninsula.)

It’s often said how we know more about the moon than our oceans, with around 95 percent still unexplored. Arguably, the last major ocean research expedition was between 1872 and 1876 when a converted Royal Navy gunship known as the Challenger travelled close to 70,000 nautical miles and catalogued over 4,000 previously unknown species.

Inspired by the Challenger’s story, IBM has teamed up with non-profit ProMare to make a similarly large impact on ocean research.

The autonomous ship, Mayflower, is named after the ship which carried pilgrim settlers from Plymouth, England to Plymouth, Massachusetts in 1620. On its 400th anniversary, it was decided that a Mayflower for the 21st century should be built.

Brett Phaneuf, a Founding Board Member of ProMare and Co-Director of the Mayflower Autonomous Ship project, said:

“Putting a research ship to sea can cost tens of thousands of dollars or pounds a day and is limited by how much time people can spend onboard – a prohibitive factor for many of today’s marine scientific missions.

With this project, we are pioneering a cost-effective and flexible platform for gathering data that will help safeguard the health of the ocean and the industries it supports.”

Naturally, there are more than a few differences between the original ship and the Mayflower Autonomous Ship (MAS).

Mayflower 2.0 no longer relies solely on wind power and will use a wind/solar hybrid propulsion system with a backup diesel generator. The new ship also trades in a compass and nautical charts for navigation in favour of a state-of-the-art GNSS positioning system with SATCOM, RADAR, and LIDAR.

IBM’s deep learning technology is on-board to help the ship traverse the harsh and rapidly-changing environment of the ocean.

Donald Scott, Director of Engineering at Marine AI (which partnered with ProMare on the project), explained:

“In the middle of the ocean, communications are severely limited. Conditions can change very suddenly, and you don’t have the option to stop and power down.

With MAS, we needed to go beyond the existing technology for unmanned ships, creating a vessel that isn’t just operated remotely and doesn’t simply react to the environment, but learns and adapts independently.

To do this, we had to develop state-of-the-art capabilities around navigation, collision avoidance, communications and more.”

The training of AI models for the MAS began in October 2019. The actual hull for the ship arrived in Plymouth in March and sea trials began. Over the next few months, the ship was fitted with its advanced navigation and research equipment.

Andy Stanford-Clark, CTO of IBM UK & Ireland, added:

“IBM helped put man on the moon and is excited by the challenge of using advanced technologies to cross and research our deepest oceans.

By providing the brains for the Mayflower Autonomous Ship, we are pushing the boundaries of science and autonomous technologies to address critical environmental issues.”

MAS’ voyage couldn’t arrive at a more needed time with humans causing huge amounts of damage to the health of our oceans. A UN report found our oceans are now warmer, more polluted, more depleted, and more acidic than ever before.

Rising sea levels are among the key concerns about the impact on humans, but another is the increasing number of plastics in the sea which is simultaneously causing harm to sealife and ending up in the food we eat.

Professor Richard Thompson, OBE, Director of the Marine Institute, University of Plymouth, commented:

“Microplastics present a substantial challenge to our oceans. Over 700 species come into contact with marine litter which is found from the poles to the equator, and estimates are that the quantity of plastic in the oceans will triple in the decade to 2025.”

However, armed with the right data, it’s not too late to change course and heal our oceans.

MAS is fitted with a range of sensors including acoustic, nutrient, temperature, and water and air samplers. Edge devices will store and analyse all data locally until connectivity is available. When a link has been established, the data will be uploaded to edge nodes onshore.

Unless there are any last-minute delays, MAS is set to depart on its voyage this month. The ship is due to arrive in Plymouth, Massachusetts around two weeks later. Where required, updated deep learning models can be pushed out to the ship.

MAS’ virtual crew will be based in Plymouth, UK but IBM says millions of virtual “pilgrims” will be able to experience the voyage online.

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COVID-19 knows no borders and has resulted in around 75,896 deaths as of writing. People around the world are hoping AI can deliver solutions for tackling the coronavirus which has wrecked social and economic havoc.

AI News has already covered work from predominantly Asian tech firms including Alibaba, Baidu, Tencent, Daergen, and Insilico Medicine, but more and more Western companies are stepping up with solutions.

Nvidia

Nvidia was among the first Western tech giants to offer up some of its substantial resources towards fighting COVID-19. Last month, it gave COVID-19 researchers free access to its Parabricks genome-sequencing software.

27,000 of Nvidia’s GPUs are also powering IBM’s Summit supercomputer which researchers were able to use to simulate 8,000 compounds in a matter of days and identify 77 small-molecule compounds, such as medications and natural compounds, that have shown the potential to impair COVID-19’s ability to dock with and infect host cells.

This week, Nvidia announced that it’s joined the COVID-19 High-Performance Computing Consortium. The consortium brings together leaders from the US government, industry, and academia, and supports researchers by providing access to 30 supercomputers with over 400 petaflops of compute performance.

Nvidia, for its part, will contribute its expertise to the consortium in areas such as AI,  supercomputing, drug discovery, molecular dynamics, genomics, medical imaging, and data analytics.

“The COVID-19 HPC Consortium is the Apollo Program of our time,” said Ian Buck, VP and GM of Accelerated Computing at Nvidia. “Not a race to the moon, this is a race for humanity. The rocket ships are GPU supercomputers, and their fuel is scientific knowledge. NVIDIA is going to help by making these rockets travel as fast as they can.”

Nvidia has also packaged COVID-19 tools on NGC, the company’s hub for GPU-accelerated software. All of the COVID-19 tools on NGC are publicly available.

IBM

IBM has also been active in providing tools and expertise in the fight against COVID-19 and helped to launch the aforementioned COVID-19 HPC Consortium.

Last month, IBM launched COVID-19 data in its Weather Channel app to provide access to detailed virus tracking. The company also merged its Micromedex online medication reference database with EBSCO’s DynaMed peer-reviewed clinical content to form a single comprehensive resource with the goal of improving clinical decision-making.

Our sister publication Developer reported on IBM’s Call for Code initiative expanding to include COVID-19 solutions last week. By tapping into IBM’s vast developer community and offering “starter kits” consisting of relevant tools, innovators from around the world can quickly get to work building potentially lifesaving solutions.

IBM has made two new COVID-19 announcements over the past week.

The first is an AI deep search tool which takes reputable data from the White House, a coalition of research groups, and licensed databases from the DrugBank, Clinicaltrials.gov and GenBank. Researchers can use IBM’s tool to quickly extract critical knowledge regarding COVID-19 from the large collection of papers.

IBM will also make its Functional Genomics Platform available for free during the COVID-19 pandemic. The platform is a cloud-based repository and research tool which includes genes, proteins, and other molecular targets, and is built to discover the molecular features of viral and bacterial genomes. Using the platform, researchers can accelerate the discovery of molecular targets required for drug design, test development, and treatment.

So there we have some of the latest AI solutions from Nvidia and IBM designed to help tackle the COVID-19 pandemic we’re all facing. We’ll keep you posted on further tools and developments that may be of assistance.

Stay safe, stay home where possible, and thank you to all key workers and researchers helping to get us out the other side of this faster.

(Photo by CDC 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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Back in January, AI News reported on findings by Algorithmic Justice League founder Joy Buolamwini who researched some of the world’s most popular facial recognition algorithms.

Buolamwini found most of the algorithms were biased and misidentified subjects with darker skin colours and/or females more often.

Here were the results in descending order of accuracy:

Microsoft

• Lighter Males (100 percent)
• Lighter Females (98.3 percent)
• Darker Males (94 percent)
• Darker Females (79.2 percent)

Face++

• Darker Males (99.3 percent)
• Lighter Males (99.2 percent)
• Lighter Females (94 percent)
• Darker Females (65.5 percent)

IBM

• Lighter Males (99.7 percent)
• Lighter Females (92.9 percent)
• Darker Males (88 percent)
• Darker Females (65.3 percent)

Amazon executives rebuked the findings and claimed a lower level of accuracy was used than what they recommend for law enforcement use.

“The answer to anxieties over new technology is not to run ‘tests’ inconsistent with how the service is designed to be used, and to amplify the test’s false and misleading conclusions through the news media,” Matt Wood, GM of AI for Amazon’s cloud-computing division, wrote in a January blog post.

Signatories of the open letter came to Buolamwini’s defense, including AI pioneer Yoshua Bengio who is a recent winner of the Turing Award.

“In contrast to Dr. Wood’s claims, bias found in one system is cause for concern in the other, particularly in use cases that could severely impact people’s lives, such as law enforcement applications,” they wrote.

Despite having the most accurate facial recognition, Microsoft has rightly not been content at that and has further improved its accuracy since Buolamwini’s work. The firm supports a policy requiring signs to be visible wherever facial recognition is used.

IBM has also made huge strides in levelling the accuracy of their algorithms to represent all parts of society. Earlier this year, the company unveiled a new one million image dataset more representative of the diversity in society.

When Buolamwini reassessed IBM’s algorithm, the accuracy when assessing darker males jumped from 88 percent to 99.4 percent, for darker females from 65.3 percent to 83.5 percent, for lighter females from 92.9 percent to 97.6 percent, and for lighter males it stayed the same at 97 percent.

Buolamwini commented: “So for everybody who watched my TED Talk and said: ‘Isn’t the reason you weren’t detected because of, you know, physics? Your skin reflectance, contrast, et cetera,’ — the laws of physics did not change between December 2017, when I did the study, and 2018, when they launched the new results.”

“What did change is they made it a priority.”

Aside from potentially automating societal problems like racial profiling, inaccurate facial recognition could be the difference between life and death. For example, a recent study (PDF) found that driverless cars observing the road for pedestrians had a more difficult time detecting individuals with darker skin colours.

Everyone, not just AI experts, should be pressuring companies to ensure biases are kept well away from algorithms.

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The computing giant was technically within its rights to obtain and use the photos as they were posted by users under a Creative Commons license allowing free use.

Flickr CEO Don MacAskill sent a couple of tweets on Tuesday about IBM’s use of the photos:

“We love & support photographers and their right to choose their own licenses for their work. By default, they reserve all of their rights, and have the option to loosen them if they’d like.”

“People didn’t have to opt-in to the dataset because they had already opted into the Creative Commons license. They took action. This is the way licensing works. It’s also the magic that enables artists & scientists all over the world to create & invent using CC-licensed work.”

Of course, those posting the photos – which may contain family and friends – likely never thought they’d be used for training AI.

“None of the people I photographed had any idea their images were being used in this way…It seems a little sketchy that IBM can use these pictures without saying anything to anybody,” Greg Peverill-Conti, an exec at PR firm SharpOrange, told NBC News.

IBM’s legal team authorised the use of the photos, according to a company representative.

The collection has over a million photos; including 700 from Peverill-Conti. Some of the photographers claim to have faced difficulties getting IBM to remove their photos.

Each of the photos in the ‘Diversity in Faces’ dataset is annotated with things such as the person’s gender, age, and geometric measurements. The dataset is offered only to academic researchers.

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Alzheimer’s is a brutal disease not just for sufferers, but their loved ones too. The disease currently has no cure and causes an increasing loss of memory, confusion, and difficulty completing once familiar tasks.

IBM Australia published a paper today providing details of how machine learning and AI can be used to predict the severity of the disease and help to slow its progression.

Ben Goudey, Staff Researcher of the Genomics Research Team at IBM Research, wrote:

“Neurodegenerative diseases such as Parkinson’s, Alzheimer’s and Huntington’s are affecting millions of people around the world. While these mysterious and crippling diseases do not yet have a cure, the answer to slowing their growth may lie in prevention.

At IBM Research, our mission is to use AI and technology to understand how to help clinicians better detect and ultimately prevent these diseases in their early stages.

Whether that’s through retinal imaging, blood biomarkers or minor changes in speech, we envision a future in which health professionals have a wide array of easily accessible data available to more clearly identify and track the onset and acceleration of these conditions.”

Early diagnosis helps to prepare the sufferer and their loved ones as much as possible before degeneration takes hold. Official diagnosis also helps to make the patient available for medical trials with the hope of one day finding a full cure.

Hundreds of Alzheimer’s medical trials have been conducted since the early 2000s but with a high failure rate. Some believe this failure rate is due to late detection of the disease when there’s already significant brain tissue loss.

Research suggests a peptide called amyloid-beta changes long before memory loss occurs. Analysing the concentration of this peptide from an individual’s spinal fluid could highlight the risk decades in advance.

Accessing spinal fluid is an invasive and expensive procedure. In his post, Goudey wrote: “Hence, there is a strong effort in the research community to develop a less invasive test, such as a blood test, that can yield information about Alzheimer’s disease risk.”

Using their model, IBM predicts they could help clinicians to predict the risk of Alzheimer’s with an accuracy of up to 77 percent. Goudey said his team’s approach can be extended to other spinal fluid-based biomarkers.

The full paper is published in the science journal Nature here.

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Computers typically have a vast amount of information at their disposal but IBM’s was unable to persuade a live audience of its viewpoint.

IBM’s debating computer was tasked with taking on Harish Natarajan, a grand finalist in 2016’s World Debating Championships.

The live debate was broadcasted live on Monday and held in San Francisco’s famous Yerba Buena Center. It was organised by a non-profit firm called Intelligence Squared US which specialise in hosting debates.

IBM’s system, known as ‘Project Debater’, has been in development since 2012. Project Debater uses newspaper and magazine articles to formulate its points rather than online sources like Wikipedia, which could explain why Natarajan got the better of it.

Debating AIs could provide various benefits. Politicians, for example, could use them for training ahead of live debates against opponents. They could also help with general decisions to determine whether an idea is flawed.

Monday’s debate focused on the question of whether preschool should be subsidised. Both the human and AI participant were not alerted to the subject until 15 minutes before.

The AI was on the side of subsidising preschool. It came up with strong arguments such as how subsidisation helps to break the poverty cycle. During the debate, it highlighted and drew information from various studies.

Natarajan argued that subsidies would not ensure that all children would be able to attend, while also highlighting that it consumes resources that could be used for other things.

Both sides gave a two-minute closing argument. While awaiting the final verdict, Natarajan noted how combining Project Debater’s skills with a human “could be incredibly powerful.”

Prior to the debate, the audience was asked how they felt about the issue. 79 percent said preschool should be subsidised, while 13 percent disagreed.

The winner is decided by which side gained the most points.

Following the debate, 62 percent of the crowd agreed and 30 percent disagreed. By converting 17 percent of the crowd, Natarajan was crowned the winner.

You can watch the full debate here.

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McCormick & Company has been around for 130 years and makes a wide range of seasonings, spices, and condiments. However, after that many years, it seems the firm’s human minds are running out of ideas.

Lawrence Kurzius, CEO of McCormick, said:

“McCormick’s use of artificial intelligence highlights our commitment to insight-driven innovation and the application of the most forward-looking technologies to continually enhance our products and bring new flavours to market.

This is one of several projects in our pipeline where we’ve embraced new and emerging technologies.”

The new line created by an AI will be called ‘One’ and will come to market later this year.

Focused on popular one-dish meals, the line will include flavours like Bourbon Pork Tenderloin and Tuscan Chicken.

McCormick partnered with IBM to provide the AI smarts behind the project.

Kathryn Guarini, VP of Industry Research at IBM, commented:

“IBM Research’s collaboration with McCormick illustrates our commitment to helping our clients and partners drive innovation across industries.

By combining McCormick’s deep data and expertise in science and taste, with IBM’s AI capabilities, we are working together to unlock the bounds of creativity and transform the food and flavour development process.”

The AI was trained on data about raw ingredients, seasoning formulas, sales, trend forecasts, and consumer tests of products. Based on this information, the AI can formulate what it thinks will be successful flavours.

Currently, the firm goes through around 50-150 iterations of a new product. Following this process, it’s then tested with sensory experts and consumers which can take up to six months.

McCormick believes the use of AI will help to slash product development time by around two-thirds.

I don’t know about you, but my stomach is grumbling.

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