Monthly Archives: May 2022

May 11 - The notion that modern technologies can help resolve some of the largest sustainability challenges has a long track record, not least among tech-minded brands. Many solutions just require a tweak of existing applications, like Googles decision to add methane analysers to cars that were already collecting data for its street view service, for example. Others are more targeted. Think, the super-successful Global Forest Watch app, a free deforestation tracking service.

Enthusiasm for so-called "tech-for-good" solutions shows no signs of abating, as shown by the continued flood of brand-led competitions, accelerators and hackathons. Typical is the XPRIZE, set up by Tesla chief executive Elon Musks charitable foundation, which recently named 15 finalists for an $80 million payout. The prize money, due to be dispersed in 2025, will go to the venture with the most compelling solution for removing carbon from the atmosphere.

Similar in ethos (if not in budget) is the new innovation connections initiative from Tesco, the UKs biggest supermarket. Designed to accelerate the growth of eco-minded startups in the food sector, the scheme sees eight early-stage firms compete to have their solution rolled out in the UK supermarkets supply chain.

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All the finalists have a strong tech profile, from the inventor of a bio-acoustics system for monitoring on-farm pest levels through to a fish-feed producer that manufacturers its product from waste-based microalgae.

The Global Cement and Concrete Association also picked out six promising startups this month for its Innovandi Open Challenge, all of which boast ground-breaking technologies geared towards achieving net zero.

The tech-for-good lens is being increasingly turned to solving sustainability-related management challenges within brands own operations. As the scope of social and environmental issues grows, so does the complexity of managing them. A sharp growth in consumer and investor scrutiny also means brands are more under the spotlight than ever to get it right. Claiming to have a relevant sustainability policy is no longer enough; brands need to provide evidence of a marked improvement in practices and performance.

Step forward the bots. According to recent research by U.S. tech giant Oracle, more than 93% of business leaders would trust artificial intelligence more than a human to make a sustainability decision. Underlying the finding is an assumption that bots make fewer mistakes when collecting data (43%), show less bias (42%), and predict future outcomes with greater accuracy (41%).

Digital technologies, especially artificial intelligence and machine learning, remain a fairly new space for many business practitioners, says Elena Avesani, global sustainability director at Oracle. But the potential for their application in the field of sustainability management is considerable.

She cites UK electricity and gas utility National Grid, which now uses cloud-based machine-learning models to calculate the volume of renewable electricity in the grid at any one time. The solution, which analyses data at a speed and breadth that would be impossible for humans to achieve, has increased the accuracy of the companys estimations by 40%, according to Avesani.

The pressure is mounting to really change the way you run your businesses. You need to be able to make strategic decisions that look at ESG (environment, social, and governance) issues as part of the mix of variables that are part of running your business. (This) is bringing innovation on multiple levels, she states.

The initial phase of management-focused tech applications centres around delivering efficiency and data-quality gains. Before brands consider providing evidence of improved performance externally, they need to obtain a clear and accurate picture of where they currently stand on multiple different issues, from employee diversity to greenhouse gas emissions. Smart software systems that can collate, organise, store and assess sustainability statistics from multiple data points make this task of internal stocktaking quicker and more reliable.

Once a clear baseline is established, sustainability practitioners have a working platform to set priorities, determine a strategic direction and chart progress. Given the relatively small size of most corporate sustainability departments, much of this analytical legwork is habitually outsourced to the growing crop of sustainability service providers and consultants.

A pioneer in the emerging software-as-a-service (SaaS) field for sustainability is Manifest Climate. Co-founded by environmental lawyer Laura Zizzo, the Toronto-based tech startup uses a machine-learning model to inform companies the extent to which their climate policies and governance systems are aligned with the 11 recommendations of the Taskforce on Climate-related Financial Disclosures (TCFD).

With Canada expected to follow the UK in making TCFD-aligned financial reporting mandatory, clients include the Canadian financial services firm Scotiabank, insurance company Manulife and mining company Teck Resources.

Zizzo says the ultimate purpose of a SaaS model is to free up management time to make more informed strategic decisions. We organise this information (about climate risk and disclosure) in a way that they can better understand so that they can then prioritise climate in a way that makes more sense. This central claim saw Manifest Climate recently win $30 million Canadian dollars ($23.98 million) in a successful funding round.

The next frontier for digital management solutions lies outside a brands own operations, a key challenge given the wider context in which macro-sustainability issues such as water management, carbon emissions and human rights play out. One example of how digital technology is being adapted for sustainable management is the application of blockchain in corporate supply chains. Startups such as Circulor (in mining, plastic, and construction), Retraced (in fashion), and Peer Ledger (food) are using the distributed database technology to track products from source to sale, thus answering a growing public demand for product-based chains of custody.

The challenge here is that many suppliers, especially smaller companies, are often privately owned and thus not subject to similar disclosure requirements as listed companies. In response, ESG data providers are building ever more sophisticated software systems to determine credible estimates from dispersed public data sets.

At the heart of all management tech is data. As Jon Sykes, chairman of Carbon Intelligence, a London-based climate information provider, puts it: Data is at the root of impact. Data is at the root of change. Data is at the root of transition. Thats not because numbers in and of themselves carry weight, but rather because if you get them right, they can provide a stable launchpad for impactful action.

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Opinions expressed are those of the author. They do not reflect the views of Reuters News, which, under the Trust Principles, is committed to integrity, independence, and freedom from bias. Sustainable Business Review, a part of Reuters Professional, is owned by Thomson Reuters and operates independently of Reuters News.

Oliver Balch is an independent journalist and writer, specialising on businesss role in society. He has been a regular contributor to The Ethical Corporation since 2004. He also writes for a range of UK and international media. Oliver holds a PhD in Anthropology / Latin American Studies from Cambridge University.

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Brand Watch: Under the spotlight as never before, companies turn to AI to get their numbers right - Reuters

SANTA CLARA, Calif.--(BUSINESS WIRE)--Metas VP of Infrastructure Hardware, Alexis Black Bjorlin, will open the flagship AI Hardware Summit with a keynote, while her colleague Vikas Chandra, Metas Director of AI Research will open Edge AI Summit. Other notable keynotes include Microsoft Azures CTO, Mark Russinovich, plus Wells Fargos EVP of Model Risk, Agus Sudjianto; Synopsys President & COO, Sassine Ghazi; Cadences Executive Chairman, Lip-Bu Tan; and Siemens EVP, IC EDA, Joseph Sawicki, among many others

Machine learning and deep learning are fast becoming major line items on agendas in board rooms in every organization across the globe. The technology stack needed to support these workloads, and to execute them quickly, efficiently, and affordably, is fast developing in both the datacenter and in client systems at the edge.

In 2018, a new Silicon Valley event called the AI Hardware Summit launched to provide a platform to discuss innovations in hardware necessary for supporting machine learning both at the very large scale, and in small resource-constrained environments. The event attracted enormous interest from the semiconductor and systems sectors, welcomed Habana Labs into the industry in its inaugural year, and subsequently hosted Alphabet Inc.s Chairman and Turing Award Winner, John L. Hennessy, as a keynote speaker in 2019. Shortly after, the Edge AI Summit was launched to focus specifically on deploying machine learning in commercial use cases in client systems.

Hennessy said of the AI Hardware Summit: Its a great place where lots of people interested in AI Hardware are coming together and exchanging ideas, and together we make the technology better. Theres a synergistic effect at these summits which is really amazing and powers the entire industry.

Fast forward a few years of virtual shows and the events are back in-person with a fresh angle. An all-star cast of tech visionary speakers will address optimizing and accelerating machine learning hardware and software, focusing on the intersection between systems design and ML development. Developer workshops with HuggingFace are a new feature this year focused on helping bring new hardware innovation into leading enterprises.

The co-location of the two industry-leading summits combines the proposition to focus on building, optimizing and unifying software-defined ML platforms across the cloud-edge continuum. Attendees of the AI Hardware Summit can expect content spanning from hardware and infrastructure up to models/applications, whereas the Edge AI Summit has a much tighter focus on case studies of ML in enterprise.

This years audience will consist of machine learning practitioners and technology builders from various engineering disciplines, discussing topics such as systems-first ML, AI acceleration as a full-stack endeavour, software defined systems co-design, boosting developer efficiency, optimizing applications across diverse ML platforms and bringing state of the art production performance into the enterprise.

While the AI Hardware Summit has broadened its scope beyond focusing purely on hardware, there will still be plenty for hardware-focused attendees to explore. The event website, http://www.aihardwaresummit.com, gives accessible information on why a software-focused or hardware-focused attendee should register.

The Edge AI Summit features more end user use cases than any other event of its kind, and is a must attend for anyone moving ML workloads to the edge. The event website, http://www.edgeaisummit.com, gives more information.

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Tech Visionaries to Address Accelerating Machine Learning, Unifying AI Platforms and Taking Intelligence to the Edge, at the Fifth Annual AI Hardware...

This might sound like a hot take but its not: In 50 years, when historians look back on the crazy 2020s, they might point to advances in artificial intelligence as the most important long-term development of our time. We are building machines that can mimic human language, human creativity, and human thought. What will that mean for the future of work, morality, and economics? Bestselling author Steven Johnson joins the podcast to talk about the most exciting and scary ideas in artificial intelligence and an article he wrote for The New York Times Magazine about the frontier of AI. Part of their conversation is excerpted below.

Derek Thompson: What I wanna do in the next 30 minutes to an hour is offer people a tour of the horizon of AI and ask some big, hard questions about what is exciting about this horizon, what is scary about this horizon, and how we can move the frontier of this technology toward the less dystopian implications of it. And I wanna start off with a very specific example of AI and that is GPT-3. Steven, what is GPT-3 and why are you excited about it?

Steven Johnson: Well, GPT-3 is a kind of a subset of AI. Its a specific implementation of a category known as large language models and it also belongs to the family of neural nets and the family of deep learning. So those are a bunch of buzzwords right there that will be meaningful.

DT: Were gonna unpack those buzzwords in just a second.

SJ: Yeah. But it is basically a neural net that is modeled very vaguely on the structure of the human brain, but we should not take that kind of biological analogy too far. That is, it goes through a process thats called a training process, where it is shown a massive corpus of text, basically a kind of a curated version of the open worldwide web, Wikipedia, a body of digitized books, that are part of the public domain. And it basically ingests all of that information. And this training process is really kind of fascinating. We can get into the details of it, but basically it learns to associate connections between all the words in that body of text. And through that training process, it is able then, when you give it promptsinitially it was in the form of heres a sentence or heres a paragraph, continue writing in this mode for another paragraph or another five paragraphs.

And if you have a big enough corpus of text and a deep enough neural network, it turns out that computers over the last couple of years have gotten quite good at continuing human-authored text. And it was initially kind of a little bit of a parlor trick in that you would write a paragraph and earlier versions of this software would kind of continue on and you would look at it and youd be like, Yeah, that sounds vaguely like a human could have written it, but obviously it was also nonsensical in all these ways. And it wasnt particularly interesting. For most users, you see this technology and things like autocomplete when youre using Gmail and you write a sentence and it suggests, you know, a little word at the end, thats basically built on top.

DT: Oh, right. It was great to see you last And then Gmail suggests in light-gray font, night.

SJ: Its the same idea.

DT: It is using its understanding of millions and millions of emails already sent to predict the next word in the email that you are sending. And just to add a little bit of sort of 101 context to your first answer, neural nets, were not gonna get into the full definition here, but basically this is a set of algorithms that mimic a human brain, that learn to identify patterns or solve problems through repeated cycles of trial and error. Its a domain of AI that is very popular, shows a lot of promise, and is behind the large language models that you just talked about. One of these large language models thats very exciting is GPT-3. And the reason I think GPT-3 is so interesting is that its not just the sort of technology that can add the word night when you type in Gmail, It was great to see you last. It can go much further than that. It can summarize books, it can summarize papers, it can write entire essays in response to very complicated prompts. Can you give us some examples of some of the implications of GPT-3 that are most thrilling to you?

SJ: Yeah. So let me say one more thing about the structure of it, which I think is kind of fascinating. And I agree we dont wanna get too far down into the rabbit hole of how it actually works, but on some fundamental level, it is trained on this very elemental act of next-word prediction. And to me this is one of the things that I find kind of mind-blowing about it. I mean, theres a lot of complexity to whats going on in the neural net, but fundamentally the training process is, you know, ingest all the history of the web and Wikipedia. And then its given endlessly a series of training examples where its shown a real-world paragraph that some human has written and then one word is missing. And basically in the initial stages of the training process, the software is instructed like, Come up with the missing word. Come up with a statistically ranked list of the most likely words in this particular paragraph.

And in the initial pass, itll give you, you know, whatever, 30,000 words that might be that missing word. And itll be terrible at it. Itll be awful. But somewhere at the bottom of the list, like word no. 29,000 will be the right word. And so the training process is saying, OK, whatever set of neural connections led you to make guess no. 29,000, strengthen all of those connections and weaken all of the other connections in your neural net. And it just plays that game a trillion times. And eventually it gets incredibly good at predicting the next wordand, in fact, predicting whole sentences or paragraphs. What seems to have happened over the last three or four yearsthere was an earlier version of GPT-3, which was called GPT-2, that came out a couple years ago. Over this period, the software is now much better, as you say, at constructing larger thoughts and making arguments and summarizing and doing things like that. So to me, its just mind-blowing that it really fundamentally comes out of this act of next-word prediction, that thats the kind of fundamental unit of the whole exercise.

This excerpt was lightly edited for clarity.

Host: Derek ThompsonGuest: Steven JohnsonProducer: Devon Manze

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The Future of AI Is Thrilling, Terrifying, Confusing, and Fascinating - The Ringer

It may sound like a sci-fi plot, but an artificial intelligence algorithm recently played a major part in the downfall of an elected government.

In January 2021, the government of the Netherlands decided to step down. The scandal thatset their resignation in motion began in 2012, with the introduction of a self-learning algorithm to seek signs of benefits fraud. The tax authority believed that the algorithm would flag up potential fraud cases, which humans could then assess to weed out any errors.

What played out, as IEEE Spectrum reports, was that the algorithm began to develop patterns of bias, disproportionately labeling those from lower-income backgrounds, immigrants, and ethnic minorities as having committed fraud. The humans tasked with vetting the cases perhaps too trusting of the algorithm accepted many of the false flags, and demanded that money be returned.

The consequences of the new system (and the humans in government who trusted it) cannot be overstated. Tens of thousands of people were left in poverty, while some died by suicide following giant tax bills which sometimes went into six figures.

WhenChermaine Leysner received a bill totaling100,000, she believed that the government had made a mistake in asking her to pay back the child allowance she had received since 2008.

I was working like crazy so I could still do something for my children like give them some nice things to eat or buy candy," Leysner told Politico. "But I had times that my little boy had to go to school with a hole in his shoe."

Following the stressfrom the bill, and other personal circumstances, she separated from herchildren's father. She was far from alone.

TheMinistry of Justice and Security Statistics commissioned an approximation of the number of children who had been placed in care following a false fraud flag. They found that more than 1,100 children had been separated from their families,placed in care, and kept from their families for years as a result of the scheme.

Around 70,000 children were affected by the bills, which were wrongly given to around 30,000 parents. The figure could be far higher, as the estimate was based on data after 2015.

Compensation of at least 30,000is to be paid to victims of the algorithm and the humans thatenabled its false assumptions, but it was not enough to save the government from the scandal, known in the country askinderopvangtoeslagaffaire.

"Innocent people have been criminalised and their lives ruined,"Prime Minister Mark Rutte told reporters as his government took the unanimous decision to step down in January as a result of the scandal."The buck stops here."

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The AI That Led To Children Being Re-homed And The Fall Of An Elected Government - IFLScience

The Department for Work and Pension (DWP) has been trialling new technology over the last few months to connect job seekers with local roles.

As part of a 1.3m investment in new technologies for job seekers, the government has partnered with three firms specialising in job-matching artificial intelligence (AI) FutureFitAI, Bayes Impact and UK jobs board Adzuna.

The technology being used works by asking people looking for work a series of questions, building an online profile. According to the DWP, the software from the three firms makes intelligent suggestions based on this information and live data about the local jobs market.

The new system, which is being tested by 20 jobcentres, involves partnering with FutureFitAI, Bayes Impact and Adzuna using AI to target areas with the highest ratio of vacancies to unemployed.

Future Fit provides AI-powered tools that act as a GPS for careers designed to support individuals from career path exploration, to reskilling, to job placement. AI career coach Bob, from Bayes Impact, provides personalised action plans for job seekers to overcome barriers that it identifies.

The DWP said that for the trial, Bayes Impact is using additional labour market information to allow job seekers to identify jobs in their area that are sustainable and future-proof.

The third application, Adzuna Career Paths, uses data drawn from CVs and the jobs market and is designed to help people explore new career development opportunities based on their current skills and experience

Speaking at the start of the trial, employment minister Mims Davies said: Our Plan for Jobs is delivering in the digital age, and were supporting our work coaches with the smartest technology out there to help get every job seeker at any age or stage into work faster.

Were investing over 1m into improving our services as we push to help get half a million people into work by the summer, helping them boost their income and progress.

In 2019, the DWP developed a prototype skills recommendation engine, inspired by e-commerce sites such as Amazon, designed to help job seekers find similar roles based on their skills, experience and salary.

Dai Hillier, job match project manager, UC product space at the DWP, told Computer Weekly: We dont share any data from our systems into the suppliers. They all have their own way to generate the information they need.

A random group of claimants are selected from a control group and referred to the suppliers. The loose relationship with the three suppliers means the DWP is able to see how many people are getting through the job vacancies process.

The three firms can analyse how job vacancy journeys work end-to-end and provide the DWP with this data, said Hillier. Eventually, we hope we can track how well someone is doing and whether they are moving closer to the labour market.

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DWP partners with AI-powered career and job services - ComputerWeekly.com

SEATTLE--(BUSINESS WIRE)--Lyssn.io, Inc. announced today a new five-year contract with the State of Utah Department of Human Services (Utah DHS). The department will use Lyssns Artificial Intelligence (AI) platform to assess the quality of prevention services offered under the Family First Prevention Services Act (Family First) and evaluate the use of evidence-based practices in Utahs child welfare and juvenile justice systems.

This contract marks the first-ever statewide implementation of an AI platform for quality improvement in a social services program.

Family First passed in 2018 and offers states an amazing opportunity to expand their social services reach and help more families than ever before, said Jenny Cheng, Family First Coordinator at Lyssn. But to access the new funding, states need to implement, monitor and demonstrate the use and quality of approved evidence-based practices something that is new to many, and exactly where Lyssn can help.

Nearly all quality monitoring in social services, mental health and health coaching today is done manually by trained individuals either by reviewing charts or by sitting in and listening in on sessions. While effective for assessing a small sample set, this manual method is expensive, time-consuming, and impossible to do for every single interaction. To qualify for Family First funding, states are required to perform much more extensive monitoring than ever before.

In my background working in public health and child welfare Ive seen firsthand what a big challenge it can be to meet federal reporting requirements, said Cheng. Most agencies simply dont have the staff, funds, or a way to collect this kind of data. Utah is on the leading edge for implementation of their Family First plan, and breaking ground in the use of AI assessment in social services. With Lyssn, Utah DHS will be able to demonstrate quality for all interactions across the state in a reliable and affordable way.

Lyssns AI platform built on the largest database of its kind is in extensive use in both commercial and non-profit coaching, with behavioral health providers in the United States and United Kingdom, and in university training programs across the U.S. The automated AI-platform tracks more than 50 externally validated metrics, including Motivational Interviewing (MI), Cognitive Behavioral Therapy (CBT), engagement in a conversation, empathy, use of open-ended questions, and more.

Many states, like Utah, are including Motivational Interviewing in their Title IV-E Prevention Plan for Family First. Lyssn AI has been specifically trained over a decade of academic research to accurately measure and rate the application of MI, said Lyssn co-founder and CEO David Atkins, Ph.D. All our metrics are based on established and proven rating systems and our MI metric has a 92% accuracy rate. That means that Lyssn gives agencies and providers reliable feedback that is on-par with human raters just at a much larger scale and a lot faster.

In addition to the AI platform, Lyssn will work closely with contracted service providers and Utah state employees to establish guidelines for best practices and offer direct training for up to 35 locations. Training can be one of the biggest issues, said Cheng. To handle the regular turnover that occurs in every social service setting, and address case workers needs to apply new practices and skills, regular and ongoing training is crucial to a successful Family First plan. The Lyssn AI platform will offer ongoing opportunities for case workers to learn and practice skills and can help supervisors train and onboard new hires.

About Lyssn

Lyssn offers a comprehensive AI platform to improve the quality of both clinical and non-clinical interactions, leading to better engagement and outcomes. With Lyssn, secure recording, session assessment, fidelity monitoring against more than 50 independently validated metrics, and training of evidence-based clinical practices has never been easier. Lyssn was created by trained clinicians, academic researchers, and machine learning experts who believe in leveraging the power of science and technology to change health care for the better. For more information about Lyssn, visit http://www.lyssn.io. And follow Lyssn on LinkedIn, Facebook, Instagram, and Twitter at @lyssn.io.

About the Family First Prevention Services Act

The Family First Prevention Services Act (FFPSA) of 2018 is one of the most significant and historic reforms to the Federal child welfare policy in decades. The Act calls on states to radically rethink their approach to serving families who are at risk of entering the child welfare system, and it offers more reimbursement funding for prevention services than ever before.

This significant expansion of prevention services could make an impact for countless families at risk for having children removed from their care specifically families in which mental health, substance abuse or other abuse or neglect has occurred. One of the goals of Family First is to keep children safely with their families, when possible, by providing supportive services to parents. For more information on the Act, visit http://www.FamilyFirstAct.org.

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Lyssn AI to Help Assess the Quality of Prevention Services Offered Under Family First Act - Business Wire

Image: VectorMine/Adobe Stock

In the midst of unprecedented volumes of e-commerce since 2020, the number of digital payments made every day around the planet has exploded hitting about $6.6 trillion in value last year, a 40 percent jump in two years. With all that money flowing through the worlds payments rails, theres even more reason for cybercriminals to innovate ways to nab it.

To help ensure payments security today requires advanced game theory skills to outthink and outmaneuver highly sophisticated criminal networks that are on track to steal up to $10.5 trillion in booty via cybersecurity damages, according to a recent Argus Research report. Payment processors around the globe are constantly playing against fraudsters and improving upon their game to protect customers money. The target invariably moves, and scammers become ever more sophisticated. Staying ahead of fraud means companies must keep shifting security models and techniques, and theres never an endgame.

SEE: Password breach: Why pop culture and passwords dont mix (free PDF) (TechRepublic)

The truth of the matter remains: There is no foolproof way to bring fraud down to zero, short of halting online business altogether. Nevertheless, the key to reducing fraud lies in maintaining a careful balance between applying intelligent business rules, supplementing them with machine learning, defining and refining the data models, and recruiting an intellectually curious staff that consistently questions the efficacy of current security measures.

As new, powerful computer-based methods evolve and iterate based on more advanced tools, such as deep learning and neural networks, so do their plethora of uses both benevolent and malicious. One practice that makes its way across recent mass-media headlines is the concept of deepfakes, a portmanteau of deep learning and fake. Its implications for potential breaches in security and losses for both the banking and payments industries have become a hot topic. Deepfakes, which can be hard to detect, now rank as the most dangerous crime of the future, according to researchers at University College London.

Deepfakes are artificially manipulated images, videos and audio in which the subject is convincingly replaced with someone elses likeness, leading to a high potential to deceive.

These deepfakes terrify some with their near-perfect replication of the subject.

Two stunning deepfakes that have been broadly covered include a deepfake of Tom Cruise, birthed into the world by Chris Ume (VFX and AI artist) and Miles Fisher (famed Tom Cruise impersonator), and deepfake young Luke Skywalker, created by Shamook (deepfake artist and YouTuber) and Graham Hamilton (actor), in a recent episode of The Book of Boba Fett.

While these examples mimic the intended subject with alarming accuracy, its important to note that with current technology, a skilled impersonator, trained in the subjects inflections and mannerisms, is still required to pull off a convincing fake.

Without a similar bone structure and the subjects trademark movements and turns of phrase, even todays most advanced AI would be hard-pressed to make the deepfake perform credibly.

For example, in the case of Luke Skywalker, the AI used to replicate Lukes 1980s voice, Respeecher, utilized hours of recordings of the original actor Mark Hamills voice at the time the movie was filmed, and fans still found the speech an example of the Siri-like hollow recreations that should inspire fear.

On the other hand, without prior knowledge of these important nuances of the person being replicated, most humans would find it difficult to distinguish these deepfakes from a real person.

Luckily, machine learning and modern AI work on both sides of this game and are powerful tools in the fight against fraud.

While deepfakes pose a significant threat to authentication technologies, including facial recognition, from a payments-processing standpoint there are fewer opportunities for fraudsters to pull off a scam today. Because payment processors have their own implementations of machine learning, business rules and models to protect customers from fraud, cybercriminals must work hard to find potential gaps in payment rails defenses and these gaps get smaller as each merchant creates more relationship history with customers.

The ability for financial companies and platforms to know their customers has become even more paramount in the wake of cybercrimes rise. The more a payments processor knows about past transactions and behaviors, the easier it is for automated systems to validate that the next transaction fits an appropriate pattern and is likely authentic.

Automatically identifying fraud in these cases keys off of a large number of variables, including history of transactions, value of transactions, location and past chargebacks and it doesnt look at the persons identity in a way that deepfakes might come into play.

The highest risk of fraud from deepfakes for payments processors rests in the operation of manual review, particularly in cases where the transaction value is high.

In manual review, fraudsters can take advantage of the chance to use social-engineering techniques to dupe the human reviewers into believing, by way of digitally manipulated media, that the transactor has the authority to make the transaction.

And, as covered by The Wall Street Journal, these types of attacks can be unfortunately very effective, with fraudsters even using deepfaked audio to impersonate a CEO to scam one U.K.-based company out of nearly a quarter-million dollars.

As the stakes are high, there are several ways to limit the gaps for fraud in general and stay ahead of fraudsters attempts at deepfake hacks at the same time.

Sophisticated methods of debunking deepfakes exist, utilizing a number of varied checks to identify mistakes.

For example, since the average person doesnt keep photos of themselves with their eyes closed, selection bias in the source imagery used to train AI creating the deepfake might cause the fabricated subject to either not blink, not blink at a normal rate or to simply get the composite facial expression for the blink wrong. This bias may impact other deepfake aspects such as negative expressions because people tend not to post these types of emotions on social media a common source for AI-training materials.

Other ways to identify the deepfakes of today include spotting lighting problems, differences in the weather outside relative to the subjects supposed location, the timecode of the media in question or even variances in the artifacts created by the filming, recording or encoding of the video or audio when compared to the type of camera, recording equipment or codecs utilized.

While these techniques work now, deepfake technology and techniques are quickly approaching a point where they may even fool these types of validation.

Until deepfakes can fool other AIs, the best current options to fight them are to:

In addition to these methods, several security practices should help immediately:

The key to reducing fraud from deepfakes today is primarily won by limiting the circumstances under which manipulated media can play a role in the validation of a transaction. This is accomplished by evolving fraud-fighting tools to curtail manual reviews and by constant testing and refinement of toolsets to stay ahead of well-funded, global cybercriminal syndicates, one day at a time.

Rahm Rajaram, VP of operations and data at EBANX, is an experienced, financial services professional, with extensive expertise in security and analytic topics following executive roles at companies including American Express, Grab and Klarna.

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Protecting payments in an era of deepfakes and advanced AI - TechRepublic

As flexible work continues and customer expectations continue to rise, artificial intelligence (AI) can be a powerful ally in delivering successful customer and employee experience strategies. On one hand, this technology allows employees to be more productive in an all-digital, work-from-anywhere world and on the other, it frees up employees from repetitive processes, enabling them to slow down and focus on customers with empathy where they need it most.

About the authors

Gautam Vasudev is VP of Digital Engagement and Omni Product Management for Service Cloud at Salesforce & John Kucera is Senior VP of Product Management at Salesforce.

The International Data Corp (IDC) predicts that global spending on AI will double over the next four years reaching $110 billion in 2024 (up from $50 billion in 2020). AI is helping us to save time and boost productivity, freeing employees from repetitive work. With automation and AI - intelligent automation - we can solve numerous problems that neither companies or these technologies can tackle on their own.

In an all-digital world businesses need to adapt quickly to decentralized teams and changing customer behaviors. Where meal delivery services, for example, during the pandemic faced a significant rise in case volume, with AI-powered chatbots they managed to scale their customer services, helping customers track their orders, report issues and receive credits or refunds.

When entire workforces shifted to working from home, AI-powered recommendations helped IT departments support requests from teams, like requesting new equipment. Efficiently analyzing historical data allows IT teams to predict which type of equipment to deploy based on a user's parameters and needs. By using an automated workflow, items can be quickly shipped while the inventory system is updated.

AI is also giving businesses a competitive advantage. Take service teams, for instance. With the ability to see insights, key moments and trends highlighted in conversational and chat data, theyre better able to understand common and repetitive issues. In real-time, customer service agents can see suggested next best actions to facilitate solutions faster, and leaders can better understand trending areas that can be better handled by self-service articles or bots. For example, agents may be overwhelmed by address or billing change requests, so a team can decide to publish a self-service article or create a bot to handle these time consuming but simple issues. Integrating insights into action saves teams time and helps them make better decisions. It also allows them to use their skills more effectively to focus on more complicated cases, and to empathize with customers and build rapport.

Work has shifted from a place you go, to what you do. Today, every company must create its own digital HQ which connects its employees, customers, and partners. Automation is the key to enabling this work-from-anywhere operating model, automating how remote teams work together and how they interact with customers.

As a result, for business leaders, breaking down data management silos and point solutions are top of mind. Theyre looking to AI and automation to scale and simplify data management across their organization.

Together, collaboration and data capabilities are making teams more agile and effective, helping them deliver greater value for customers and grow the business. With a single source of truth, service teams can better route issues to the right agent, understand the customers entire journey with the company, and hopefully solve the customers problem on the first call, without making the customer repeat their address, email, and problem to three different agents. If theyre lacking detail in a certain area, they can do that research directly from the platform.

Leveraging the power of automation speeds processes up further. Whether its time to pass off a customer case to a more experienced agent, or agents see in real-time a mass incident affecting a group of customers, like an outage, employees from various departments can automatically come together in one communicative channel. Automation can help gather the right contacts from legal, engineering, support and sales to all swarm on an issue and preemptively alert customers that theyre working on it to reduce additional tickets for a known problem.

All employees want to know theyre making a difference. They dont want to make their way through tens of systems and applications just to uncover whats relevant for their work. At a time when employees are busier than ever - 35% of employees working remotely since the pandemic report working later than usual - simplifying and curating appropriate tools and making them readily accessible in one secure platform is crucial to ensuring teams are focusing on high value, high impact work.

Automation can play an integral role in helping to reduce the work of work that teams and individuals grapple with on a daily basis - essentially removing the paper cuts in manual work that slow down organizations. By removing mundane, repetitive processes and tasks automation can support every line of business in driving productivity as well as revenue.

Service teams in particular can see some of the greatest benefits of automation investments. Driving revenue both directly through cross-sells and upsells, and indirectly by increasing customer loyalty, these technologies are helping companies improve service levels while aligning to the increased customer expectations from the past couple of years. This includes automating scheduling resources, especially for field service teams, to optimize service and minimize travel times.

More strategically for organizations, with AI and automation freeing up their workforce from the repetitive work, leaders can help redefine retention strategies, by enabling employees to focus on personal and professional growth while still effectively carrying out their roles. Opening up their employees to act as ambassadors for the company, and provide great service to our customers, builds loyalty.

The biggest misconception about AI and automation implementation is that it needs to be a top-down exercise involving big projects with big budgets. In reality, a bottom-up, empowered automation strategy can be just as transformational. Data and AI are no longer just for data scientists and data-savvy analysts, it is a team sport.

To build and deploy AI and automation with confidence in the eyes of employees and customers, businesses must have an ethical foundation. Prioritizing only productivity or measuring agents by metrics (such as customer sentiment) which they have little control over, will lead to burn out. By focusing on inclusive measures and ethical intent - such as augmenting agents and growing their personal skills, companies can implement AI in ways that benefit employees and in turn benefit customers.

As the digital economy evolves at pace, now is the time to invest in customer relationships, while empowering employees and increasing their workplace satisfaction. Together, intelligent automation frees up employees to do what humans do best make decisions and build relationships. With consumer uncertainty at an all-time high, by ensuring accountability, transparency, and fairness in the ways they develop and deploy these technologies organizations can also earn trust.

We've featured the best customer experience tools.

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Reducing the 'work of work' with AI and automation - TechRadar

[Image from GE Healthcare/Alliance Medical]GE Healthcare (NYSE:GE) announced today that it signed an agreement with Alliance Medical to create a new digital health solution.

Under the collaboration, the companies will use advanced data analytics and artificial intelligence (AI) to bring together tools that streamline daily management and enable problem-solving, specifically for diagnostics in radiology departments.

Alliance Medical, a provider of imaging services in Europe, will use GE Healthcares analytics offerings, remote collaboration tools and AI to bolster its platforms. Included in that is GEs Imaging Growth Tile AI app, which will harmonize real-time data from across Alliance Medical sites to predict equipment, utilization and suggest opportunities to optimize scheduling.

The companies said in a news release that the partnership aims to improve patient outcomes by standardizing protocols, minimizing radiation dosage required for imaging and delivering consistent, improved care across multiple sites. The initial agreement centers in the UK, but GE Healthcare and Alliance Medical aim to expand the model into other geographies.

Radiology departments are facing significant challenges, with severe staff shortages being exacerbated by lengthening patient backlogs, Alliance Medical Managing Director Richard Evans said in the release. The result is demand exceeding the capacity to deliver. There is no solution to this problem without innovation, which is exactly what this partnership with GE Healthcare is all about.

The companies said the new agreement extends an existing partnership that includes a $55.6 million (45 million) agreement to supply more than 70 medical imaging systems over three years. They have also collaborated on developing a model for rapid diagnostics to speed up cancer diagnosis.

Our software delivers a brand-new level of 360 visibility, allowing radiology departments to manage complexity and optimise productivity in a way that couldnt be done before, GE Healthcare Northern Europe GM Simon McGuire said. Most important of all, we hope it will unburden clinicians and empower them to focus on what matters most: providing the best patient care possible.

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GE Healthcare partners with Alliance Medical to improve healthcare with AI in the UK - Mass Device

DUBLIN--(BUSINESS WIRE)--The "Workforce Management for the Enterprise Report 2022-2023" report has been added to ResearchAndMarkets.com's offering.

The 2022-2023 Workforce Management for the Enterprise report reflects the growing benefits of WFM throughout the enterprise, beyond the contact centre.

The Workforce Management for the Enterprise report presents an in-depth analysis of the contact center WFM market, the competitive landscape, vendors, product suites, technology and innovation. The Report examines the business, market and technology trends and challenges confronting contact centers in the midst of the Great Resignation. The Report analyzes WFM market activity and provides 5-year projections. It also presents customer satisfaction survey results that rate the vendors and their products.

The report features 5 WFM vendors: Alvaria, Calabrio, NICE, Puzzel and Verint. It also provides a high-level overview of two new competitors in the WFM arena: Authority Software and Playvox. This report is intended to help contact centre, back-office and branch operations leaders and chief operating officers (COOs) in companies of all sizes select a WFM solution and partner that best meet their unique requirements.

Contact centres and other people-intensive enterprise departments are looking to their vendors to help them manage their complex workforce scheduling requirements, including hybrid, on-site and work-at-home staffing. Managers need enhanced analytics to track productivity and performance, and the vendors are delivering new capabilities to properly handle changing workplace dynamics.

AI-enabled new-gen WFM is revolutionizing the staffing paradigm

New-gen workforce management (WFM) solutions perform the classic functions of a WFM application; however, the notable and exciting element in new-gen WFM is that employees have direct input and participate in every step of the planning process. Employees can even make changes after a schedule is generated, without penalty.

Workforce management administrators/supervisors also benefit from new-gen WFM, as these solutions greatly reduce the time required to enter agent schedules and change manually into the system, freeing them to focus their efforts on optimizing departmental performance. This drives positive impacts on the customer experience (CX) and benefits the culture and performance of the contact centre or other departments utilizing the WFM solution, which improves the company's bottom line.

Artificial intelligence (AI) is an essential enabler of many of the advancements in WFM solutions. AI makes it possible to manage the complexities associated with forecasting and scheduling digital channels - concurrency, asynchronous transactions, lapsed time, cross-channel interactions, etc. It increases the accuracy of forecasts and enables the application to auto-select the optimal algorithm for each situation. AI is used for real-time adaptive scheduling, which improves accuracy, flexibility and fairness.

It also facilitates the handling of vacation planning, paid time off (PTO), voluntary time off (VTO), overtime (OT), shrinkage projections, and much more. In the future, the publisher expects to see predictive analytics used to align WFM recommendations and schedules with contact centres' core routing and queuing engines to improve the customer and agent experience while increasing productivity and reducing costs.

This report includes:

Key Topics Covered:

1. Executive Summary

2. Introduction

3. DMG Consulting Research Methodology

3.1 Report Participation Criteria

4. Workforce Management Suites Defined

4.1 Workforce Management Vendor Suite Overview

4.2 High-Level Functional Overview

5. Workforce Management Trends and Challenges

5.1 Workforce Management Trends

5.2 Workforce Management Challenges

6. Workforce Management Market Innovation

6.1 New Product Features

6.2 Future Enhancements

7. New-Gen WFM

7.1 Omni-Channel Requirements

7.2 Omni-Channel Forecasting and Scheduling

7.3 Real-Time Intraday Management and Intelligent Adaptive Scheduling

7.4 Real-Time Adherence

7.5 Shrinkage

7.6 Long-Term Planning

7.7 The Work-at-Home/Hybrid Staffing Model

7.8 Workspace Allocation

7.9 Hiring Management

8. The Agent Experience

8.1 Agent Self-Service

8.2 Gamification

8.3 eLearning/Meeting Management

8.4 Dashboards, Reporting and KPIs

9. AI: The "Brains" of the Operation

9.1 Artificial Intelligence in WFM Solutions

10. WFM for the Enterprise: Back-Office, Branch and Beyond

10.1 Back-Office/Branch WFM

10.2 Leveraging WFM Across the Enterprise

11. Workforce Management Market Activity Analysis

11.1 Validating Market Numbers

11.2 WFM Market Share Analysis

12. WFM Adoption Rate

13. WFM Market Projections

14. WFM Competitive Landscape

14.1 Company Snapshot

15. Workforce Management Vendor Satisfaction Analysis

15.1 Summary of Survey Findings and Analysis: Vendor Categories

15.1.1 Vendor Satisfaction by Category and Customer

15.2 Summary of Survey Findings and Analysis: WFM Suite Modules

15.2.1 WFM Modules Satisfaction, by Category and Customer

15.3 Summary of Survey Findings and Analysis: WFM Product Capabilities

15.3.1 WFM Product Capabilities Satisfaction, by Category and Customer

15.4 Customer Background and Insights

15.4.1 Channels Supported by the WFM Solution

15.4.2 Other Enterprise Departments Using the WFM Solution

15.4.3 Top 3-5 WFM Challenges

15.4.4 Additional Comments

16. Pricing

16.1 Pricing for a 250-Seat Premise-Based WFM Solution

16.2 Pricing for a 250-Seat Cloud-Based WFM Solution

17. Company Reports

17.1 Alvaria

17.2 Authority Software

17.3 Calabrio

17.4 NICE

17.5 Playvox

17.6 Puzzel Ltd.

17.7 Verint Systems

18. Appendix: Workforce Management Vendor Directory

For more information about this report visit https://www.researchandmarkets.com/r/l9hstx

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World Enterprise Workforce Management Market Analysis Report 2022-2023 - AI-enabled New-gen WFM is Revolutionizing the Staffing Paradigm -...