Category Archives: Ai

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

[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

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

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 -...

Meta recently revealed a new tool built for the purposes of developing AI programs quickly and efficiently. Just one catch, though: the tools apparently got racist tendencies to it.

Its almost expected that AI, or even AI development systems that are built by humans with inherent bias would ultimately come to reflect some form of them. Its the ultimate fallacy of machines: no system in the world can be truly free of error and bias, especially since the quote unquote unnatural ones such as technological devices are ultimately made by imperfect, natural beings. And yes, this is as philosophical as I intend on getting with the subject matter of technology; now, back to our regularly scheduled programming. It is interesting to note, however, that this is the second time in the past few years that weve come across racist AI being employed by social media platforms, which in and of itself feels like a phenomenon that either shouldnt have happened twice or should have happened many, many more times than that.

The example that I have in mind is one of Twitters image resizing AI. The short-form text platform (thats an indie band name if Ive ever heard one) decided to employ an algorithm that would automatically resize photos that dont fit Twitters basic display, sparing users the effort of editing photos ahead of time. However, users quickly figured out that if photos of a larger group of individuals was posted, minorities such as black people kept getting cropped out of the photo. Some users even ran tests with this, to conclusively agree that the AI straight up started ignoring users that werent white. So, lets be real: theres little chance that developers were actively attempting to make their technology racist, personal beliefs notwithstanding. However, this does display just how effectively racial bias seeps into every social crevice; the AI was probably trained on a database of photos for referencing, and those photos probably just had a ton of white people in them since media channels arent super hip on showing other minorities except for scoring diversity points every now and then. This is, of course, speculative, and Im willing to be educated on the actual reason. My point, however, still stands.

Metas new system, named OPT-175B, was funnily enough outed for its less than scrupulous tendencies by the companys own researchers. In a report accompanying the systems test release, it was elaborated upon that OPT-175B had a tendency to generate toxic language that reinforced harmful stereotypes about individuals and races. I guess Meta wanted to stay ahead of the curve on this, and the researchers are still at work undoing the new AI generators kinks.

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Meta Developed A New AI That Has A Propensity Towards Racist Language - Digital Information World

Systecon, a company that specializes in predictive analytics and performance optimization technologies, has been tapped by the U.S. Army to provide real-time updates on maintenance needs for military vehicles and weaponry.

To do so, Systecon has programmed and utilized artificial intelligence algorithms developed with its collaborator 4042 AI that are designed to identify system failures ahead of time and determine the hardwares remaining useful life, the company said Tuesday.

The project is in support of the Army Prognostic and Predictive Maintenance initiative and is aimed to furnish the Army with the capability to know when vehicles and weapons are in a compromised state. The apparatus created by Systecon is intended to only notify when a technologys disrepair will affect a missions successful completion. If it is a minor malfunction, the algorithm is built to defer the issue.

Ultimately, the predictive analysis tools are slated to enable fleet-readiness in a time-sensitive and efficient manner that properly informs the supply chain. They do this by harnessing operational tempo impact data and actual mean time between failure rates as well as observing effectiveness of existing service schedules, precision of supply forecasting and depot production sufficiency, among other factors.

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Systecon Deploys AI Algorithms to Predict Army Hardware Maintenance - Executive Gov

When tasked with creating the art for the fourth annual Forbes AI 50 list, it immediately struck me that we should use Artificial Intelligence to generate our deliverable. AI is making considerable strides not only in commercial applications, but in visual art as well. Its artistic endeavors are being auctioned at Sothebys, generating NFT collections, and expediting traditional production processes across media. What does this mean for the future of art, and its reception by the general public?

In most cases for artificially intelligent visual work, such as the main art for AI 50, a General Adversarial Network, or GAN, is trained on a large dataset and compares the individual relationships between each instance of data to understand what belongs and what doesnt. The GAN slowly learns to filter out noise in that data, revealing the similarities, and finally achieving the ability to recreate the material received or to determine whether or not a new input matches it. Its the same machine learning process that goes into loan approval automation or public health diagnostics, the main differences being the end goal and the dataset used. Once trained, the GANs knowledge is contained in a matrix of vectors, referred to as a latent space. The art you are viewing is a composite of 4 separate latent spacewalks, trained using two ready-made datasets provided by Runway ML, and two that were hand-curated by Forbes staff. We walked through the matrix, each step resulting in a frame of a video.

I find this process enjoyable because it renders any single output of the GAN less interesting than a sequence of them. My goal is to engage audiences with art in a dynamic format that is reliant on time and systemic relationships beyond the static perfection of a framed painting or statue. Rather than objects made to create an aesthetic experience and hold value, I like to think of art as a momentary output of artists practices, which are living, breathing, often focused not just on form but on investigation of the world. Its more than giving the viewer a feel-something moment, though thats great. Its also meant to spur thought, influence opinion, and ultimately affect change. This activity is dynamic, purposely fuzzy, ill-defined, loose and inviting serendipitous meanderings and forking pursuits. It generates turbulence and optimistically criticizes, inviting you to do the same.

In the turbulence of this dynamism, theres something else to realize: the so-called author of a work of AI art is no longer a lone creator. The AI, plus the sources of its training material, is her (often unpredictable) partner. Designers and artists have long discussed the idea of programmatic co-creation, see Sol Lewitts Wall Drawings or the Conditional Design Manifesto. But the impending widespread utility of artificial intelligence is going to bring this spirit of collaboration further into the mainstream. Everyone and no one can be a creator, and thats great! It comes at a moment historically pivotal for other reasons that bear the need for collaborative spirit, such as climate change and the reframing of peaceful globalism in the face of a resurging Cold War. The idea of domination has to go away: it is time to rewild, reunite, and return to traditions of communal and ecological reciprocity that we have lost sight of. Although AI is high-tech, and can certainly be used for evil, it has the potential to reinvigorate organic relationships that are essential to a sustainable future. By no means do I see what weve made as very pretty or refined, but maybe that shouldnt be the point anymore.

Lets also take a moment to explore the idea of refinement in AI as it relates to kitsch and ingenuity. Researchers measure the accuracy of a GAN using a metric called the Frchet Inception Distance, or FID, which basically quantifies the accuracy of the GANs output in relation to the data it was trained with. If you want to make a GAN that generates, say, leaf blowers, the lower the FID, the more realistic the leaf blower. If we tried to do this in art, we would immediately arrive at kitsch; its just a thoughtless reiteration of something preexistent. Thats the difficulty in creating something meaningful with AIif its too accurate it's meaningless, and if its too ambiguous it's meaningless again (and yes, swirling images that make buildings look like dogs, Van Goghs or cheese balls almost always fall into the first extreme of this meaninglessness spectrum).

Click here for full coverage of the 2022 AI 50 list.

Not to mention, its pretty hard to find 5000 images of something, and harder still to curate those images in a way that isn't betrayed by the limitations, and underlying discriminations, of humanitys image-making apparatus. For example, we used a series of images capturing well-designed industrial products for one component of the illustration, and I question if it only results in a kitschy pastiche that reinforces the heuristics of that field, as opposed to investigating the universal structures governing them, and profoundly winding the world into a moment. Its critical that as GANs are adopted by the public we avoid setting off on the wrong path. Look around you, what meaning is embedded in the built environment? How are you interacting with the cumulative expression of society?

Ultimately, we will forever continue fusing disciplines into others and discovering the intersectionality of our existence. Art will become a tool and tools will become art and hopefully, at some point soon, decentralization will shift the responsibility of creation and design away from a technocratic few to the cooperative many. When that happens, it will be more important than ever for us to define whats essential to sustaining our lives. I would like to think they will be filled with individuality, curiosity, accountability and optimism.

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Explaining The Art Behind The Forbes AI 50 Design - Forbes

This years inductees reflect the booming VC interest as well as the growing variability in AI-focused startups making unique uses of existing technologies, others developing their own and many simply enabling other companies to add AI to their business model.

The mad scramble to adopt Artificial Intelligence amid the Covid-19 crisis is officially old news. We interact with AI as seamlessly as we do our smartphones, through voice assistants, customer service, automated tasks, self-checkout, fraud detection, in healthcare decisions and infinitely more invisible applications that affect our daily lives. Investments in AI research and applications are set to hit $500 billion by 2024, according to research firm IDC. And PwC predicts AI will contribute $15.7 trillion to the global economy by 2030. With all that money flowing, it can be hard to figure out what the coming thing is, but certain trends do emerge.

Our fourth annual AI 50 list, produced in partnership with Sequoia Capital, recognizes standouts in privately-held North American companies making the most interesting and effective use of artificial technology. This years list launches with new AI-generated design and and multiple funding round announcements that came about after our esteemed panel of judges laid down their metaphorical pencils. Inductees reflect the booming VC interest as well as the growing variability in AI-focused startups making unique uses of existing technologies, others developing their own and many simply enabling other companies to add AI to their business model.

Click here for full coverage of the 2022 AI 50 list.

Hugging Face makes its AI 50 premier as the low-key developer darling turned $2 billion unicorn. The open-source platform (named for the autological emoji) hosts the closest thing to plug-and-play machine learning models, which are used by developers to build features like search, text moderation, image segmentation powered by machine learning and other tools for their own organizations. Hugging Face is also proving an important linchpin in the major leagues, partnering on projects with Qualcomm and Amazon, among others.

The fourth year of AI 50 also heralds the fourth appearance on the list for three startups that also provide AI architecture to major companies. Its no surprise to see Forbes Under 30 alum Alex Wang and his company Scale AI, back with a $7.2 billion valuation and a fresh deal with the Department of Defenses Joint Artificial Intelligence Center. Automated support platform Moveworks marks its AI 50 streak with a $2.1 billion valuation and a breakthrough with conversational AI now able to understand nuance in six languages. And Domino Data Lab returns with an $800 million valuation.

These AI 50 hat tricks the companies whove been on the list three years in a row illustrate the breadth and depth of artificial intelligence and include Abnormal Security (cybersecurity), AMP Robotics (recycling robots), ASAPP (customer service), Cresta (sales support), Databricks (analytics) and Genesis Therapeutics (drug discovery).

But enough about the old dogs the 2022 AI 50 list also features some fasinating new companies. Overjet emerged from stealth in 2021 to become the first-ever dental AI product cleared by FDA. Cofounder and CEO Dr. Wardah Inam, who did her post-doctorate work in biomedical sensing in MITs computer science and artificial intelligence lab, got the idea for Overjet when her new dentist prescribed a treatment plan very different from those she received before. Waabi, founded by AI pioneer and computer scientist Raquel Urtasun, believes its taking a new approach to creating self-driving technology for long-haul trucking. And Aurora Solar cofounders Chris Hopper and Sam Adeyemo were introduced to the inefficiency of solar sales as Stanford Students when they installed panels at a school in East Africa. That frustrating experience inspired them to develop Aurora Solars proprietary measurement and modeling technologies to help speed up and lower the cost of solar power installations.

The Forbes AI 50 list was compiled through a submission process open to any company based in North America, privately held and developing technology that enables machines to learn from experience or new data or perform human-like tasks such as recognizing speech or images, classifying information and predicting outcomes. The application asked companies to provide details on their technology, business model, customers and financials like funding, valuation and revenue history (companies had the option to submit information confidentially, to encourage greater transparency). In total, Forbes received more than 400 entries. From there, our VC partners applied an algorithm to identify more than 120 with the highest quantitative scores and then a panel of 12 expert AI judges in academia, new IPO executives, venture capital and international technology companies identified the 50 most compelling companies.

Private AI companies that were incubated at, largely funded or acquired by large tech, manufacturing or industrial firmsincluding some of the leading autonomous vehicle developerswere not eligible for consideration.

Forbes

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AI 50 2022: North America's Top AI Companies Shaping The Future - Forbes