Monthly Archives: September 2021

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!

You could say that when it comes to AI, companies today are engaged in a competition reminiscent of the 60s space race. So it should be no surprise that OODA, an old pilots acronym for observe, orient, decide and act, has been co-opted by those wanting to amass business advantages through the use of data and machine learning.

The OODA loop for AI updates the language, but the intent is just the same. The more data you have, the better your models get. The better your models are, the better your service becomes. This leads to more usage and, subsequently, more data. Thus the cycle continues.

Following this model, youd think most companies would be rushing to adopt AI. In more cases than youd think, its the opposite. And this hesitancy could have massive repercussions.

According to Boston Consulting Group (BCG) research from 2020, one in three public companies will cease to exist in its current form by 2025 a rate six times higher than it was 40 years ago. Furthermore, 44% of todays leading companies have only held their position for at least five years, down from 77% from 1970.

This opportunity shows AI doesnt just have the potential to be an equalizer, it can be an advantage. Thats because the AI OODA loop has a flywheel effect. The more times a business cycles through it, the greater the competitive distance. Companies that have operationalized this model are simply going to be harder to catch up with.

In a word, leadership. Many executives, who subscribe to methodologies like Six Sigma, dont want to think about probabilistic methods and uncertainty. They just dont recognize the need for AI. Even if they did, theyd probably be dismayed by their technical debt and how their workforce lacks those with enough experience to connect AI to business use cases.

This take is supported by a 2019 OReilly Media survey conducted by my frequent collaborator Paco Nathan. In the below chart, he plotted the percentage of responses he received when asking companies at different stages about their AI adoption challenges.

As you can see, those whove advanced to what Paco calls the Evaluating phase are no longer in denial and recognize whats preventing them from embracing AI. Their identified problems are a data crunch, a hiring gap and having execs who are facing challenges from multiple departments. These companies dont yet have the solutions, but they arent daunted by them like the first group.

Interestingly, by the time a company has entered the Mature phase, their problems arent really problems anymore. Companies in this group are making money with AI and are working on ways to further increase their profits.

A key insight from a joint BCG-MIT Sloan Management Review research project makes a compelling case for adopting AI to gain a competitive edge. This data shows the spread in profitability between top- and bottom-quartile companies has nearly doubled over the past 30 years.

In my previous article Deadline 2024: Why you only have 3 years left to adopt AI, I explored the opportunities AI can unlock and the sense of urgency required. So how can companies get unstuck and proceed through those Evaluation and Maturity phases? It really requires a culture shift within a company and, of course, that starts with the person at the top.

This is reinforced by McKinsey & Companys State Of AI in 2020, where respondents at AI high performers were 2.3X more likely to consider their C-suite leaders very effective. This same group was also more likely to say AI initiatives have an engaged and knowledgeable champion in the C-suite.

In Nancy Giordanos new book Leadering, she delves into the future of company stewardship. The gist: There has to be a transition from leadership to leadering. Nancy who also advises my company defines the former as a static, closed, hierarchical, organizational approach designed to scale efficiently for consistent, short-term growth. She goes on the say the latter differs as it cultivates a dynamic, adaptive, caring, inclusive mindset which supports continuous innovation for long-term, sustainable value.

Once the concept of leadership is re-framed, it becomes easier to achieve what needs to be done to begin AI utilization (as it should be led from the top down). This includes:

Devising a plan for how AI will transform. Its critical to have a vision for how AI will impact your business over the next three years. Consider how itll steer data acquisition, digital spend, and use case exploration in a practical manner that de-risks and accelerates the time to outcome. The BCG-MIT research found that companies with the right data, tech, and talent but no strategy only have a 21% chance of achieving significant benefits.

Allowing disparate teams to work together. A legacy business practice like siloing business units (and their data) to minimize risk is now a liability. A company that wants to succeed with AI needs to tear down those walls and empower a network of teams to explore new ways of working together. This will help improve agility and innovation.

Leaning into diversity. This isnt just about making sure teams have a mix of genders and ethnicities. Its also about inviting employees with different professional experiences. Companies that hope to thrive with AI should welcome a wide variety of perspectives. This means being open to dissent as well.

Rethinking how people interact with machines (and vice versa). BCG research shows when you create feedback loops, theres a greater chance of success. To seize upon this, youll want AI learning from human feedback, humans learning from AI, and AI learning autonomously. Doing all three of these things gives a company a 53% chance of significant financial benefit (versus the 5% chance that comes from doing nothing).

Soldiering ahead with AI doesnt just require a change in technology, it also demands a change in process, culture, and collaboration. Those that will prosper from AI are the ones investing in strong cultures and better communication structures.

Employees at AI high performers tend to agree. In McKinseys 2020 survey, 52% of these employees said their team leaders feel empowered to move AI initiatives forward in collaboration with peers across business units and functions. 42% also believe a strong, centralized coordination of AI initiatives should be balanced with close connectivity to business end users.

If youre serious about using AI to gain and hold a market edge, ask your employees about the changes theyd like to see in how theyre led and how they interact. A feedback loop is just as crucial to success as the OODA loop. By institutionalizing both, youll be able to amass an advantage or at least stop falling behind.

Steve Meier is a co-founder and Head of Growth at AI services firm KUNGFU.AI.

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Want to win with AI? Focus on your leadership, not the competition. - VentureBeat

Issued on: 20/09/2021 - 15:16

In this edition, we tell you how AI-driven software used by companies to help them recruit talents is actuallyhaving the opposite effect,increasing the number of "hidden workers". Wespeak to Harvard Professor JosephFuller about how these algorithms are eliminating candidates based on criteria disconnected from reality.

Nearly 75 percentof companies in the United States rely on some degree of automation to fill vacancies. They deploy AI-driven software to sourcecandidates and managethe application process or performbackground checks. The same trend is gaining ground in China, where first-time applicants are finding the process quite impersonal.

But according to a new Harvard Business School study, in the US, these AI-driven algorithms that sort through applicants are excluding as many as 10 million candidates from consideration, increasing the number of so-called "hidden workers". We speak to the author of the report, Harvard Business School management professor Joseph Fuller, about what can be done to correct AI bias.

Our tech editor Peter O'Brien also tells uswhy AI systems replicate and even exaggerate human biases.

In Test 24,we bring you a selection of gadgets designed in Benin, highlighting the city of Cotonou as it looks to become one of Africa's leading tech hubs.

We also show you three devicesdesigned by Richard Odjrado, a tech entrepreneur and the creator of the AsWatch.

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Tech 24 - The 4.0 HR revolution: Are AI-driven algorithms excluding qualified workers? - FRANCE 24

In a post-pandemic world, businesses continue to adopt newer technologies to accelerate digital transformation and unlock new avenues for growth and stability. Businesses have started to re-engineer digital strategies like never before. From streamlining work from home to offer seamless customer experience offline and online, organizations continue to adapt powerful enablers of digital transformation, such as Business Intelligence (BI) and analytics across multiple operations. As organizations continue to change their business models to become more data-driven, they are also seeking newer ways of leveraging Artificial Intelligence (AI) and Machine Learning (ML) for their BI and analytics initiatives. Lets look at how these robust technologies are reimagining the world digitally in the new normal.

There is an increasing volume of complex business data which demands organizations to integrate AI and ML in day-to-day analytics. These cutting-edge technologies help companies to pull out accurate, actionable insights by identifying trends and patterns and spotting anomalies. Businesses are now investing in creating automated data workflows to ensure that the most current data is available in the system for analytics, always.

The world has inevitably moved from offline to online. Consumers are working and shopping online from remote locations. As a result, businesses are adapting location analytics, or geo-analytics, for precise location information. It is a combination of factors including behavior, demographics, mobility, among others. Now businesses are churning out location-based insights to customize their product offerings across different geographies. They are applying location analytics to spot trends, analyze risks, and predict demand at specific locations. Delivering in-depth analysis of GIS-based data, location intelligence is further empowering business owners to assess and map actions accurately to a granular level like a pin code.

With the work from the home model in place, the HR teams are integrating AI-ML capabilities for streamlining employee management and hiring processes. This includes analyzing resumes, identifying the right candidates, monitoring employees, and tracking their performances. For example, companies are using AI for designing more user-friendly job application forms. A job applicant is more likely to fill the entire form if its simple, thus, reducing the number of abandoned ones. Moreover, these technologies are helping in candidate rediscovery by analyzing the existing pool of applicants and identifying those who best fit for the current opening.

Equipped with such insights, the HR teams add massive value to the business. At the same time, businesses are able to optimize resource costs and enhance efficiency as they continue to operate with remote employees.

Predictive analytics has become an essential aspect for businesses in todays digital world. Organizations are enhancing their real-time reporting using AI-driven analytics to understand the changing customer behavior. This is helping organizations to prepare for the unexpected and rethink their business approach to take advantage of the positive trends.

The use of technologies like AI and ML has is also helping businesses unravel new growth opportunities. A 2019 global survey conducted by McKinsey & Company has shown a 25% YoY increase in the adoption of AI across multiple businesses. According to the survey, 44% of executives said that AI is helping them to reduce cost leakages while many reiterated that the adoption of AI-ML-driven analytics is opening new avenues for increasing revenue.

Automating business processes is enabling informed decision-making and is helping business leaders to improve the overall outcomes. Technologies like business intelligence, AI, ML, IoT, etc. Have evolved from being nice to have initiatives to becoming a necessity. These new-age technologies are not only adding immense value to business workflows but also in improving the overall customer experience.

Technologies like AI, BI, and ML have triggered a transformation, and this is just the beginning.

Rajesh Murthy, Founder Architect & Vice President Engineering, Intellicus Technologies

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Analytics Insight is an influential platform dedicated to insights, trends, and opinions from the world of data-driven technologies. It monitors developments, recognition, and achievements made by Artificial Intelligence, Big Data and Analytics companies across the globe.

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How BI & AI are Reimagining the World Digitally in the New Normal? - Analytics Insight

The past year demonstrated the power of digital to overcome the challenges seen in the physical world. When customer relationships were threatened by pandemic restrictions, many firms stepped outside their comfort zones to respond with new virtual experiences, services, and conveniences to maintain or grow those vital customer relationships.

Could that response have been faster? For many organizations, the answer is yes. If there was one key learning for data and analytic professionals in the past year, its that our data and AI foundations werent as ready for this challenge as they could have been. Nearly every client conversation I have today focuses on the acceleration of investment and modernization or the deployment of new operating model plans.

Were also seeing an unprecedented number of client questions about embedding data and AI into event-driven and real-time capabilities. Connecting with customers at scale is increasingly about personalization, intelligent automation, and in-moment adaptation based on where the customer is. For many organizations, thats led to the realization that a cloud data and data science platform to build models is only one part of the puzzle. Data and AI need to be at the edge of business in the applications, mobile devices, and machines where customers engage and interact with the business. This is the new world of connected intelligence, and its not just for the big tech companies its the required state for any modern enterprise.

Its time to envision AI as more than a churn model, chatbot, and language processor. Organizations embracing connected intelligence use AI to bridge business silos and generate holistic experiences where all touchpoints and channels capture, share and combine intelligence. Having a clear framework to orient the data and AI operating model with technology is the right way to move forward.

With all of this in mind, here is what business, technology, and analytic leaders can expect as they push ahead with connected intelligence strategies:

The original article by Michele Goetz, vice president and principal analyst at Forrester, is here.

The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.Image credit: iStockphoto/Thomas-Soellner

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How To Join the Connected World With Data and AI - CDOTrends

When Iranian nuclear scientist,Mohsen Fakhrizadeh, was assassinated last November, the reports of the operation from the Iranian investigation agencies raised many eyebrows and were deemed ridiculous. But a recently published report in New York Times details the sequence of events leading up to that day and how Iranian reports were not just flights of fantasy.

Following Fakhrizadeh's death, the Iranian investigators gave a series of explanations ranging from a gunfight to an 'explosion' and even artificial intelligence. Almost a month later, Open Source Intelligence (OSINT), a group of analysts shared a model that confirmed that the multiple rounds at the scene were all fired from a single gun. Intriguingly, Fakhrizadeh's wife, who was traveling with him in the car and seated just inches away, was unharmed in the incident, further increasing the possibility that machine expertise was involved.

The NYT report confirmed this after speaking to intelligence officials in Iran as well as Israel and the United States, who carried out the attack. According to the report, Fakhrizadeh, who had been on Israel's hit list for many years, had evaded previous assassination attempts. This time around, backed by the high-ranking officers in the U.S., such as then-President Donald Trump, former Secretary of State Mike Pompeo, and CIA Director Gina Haspel, Israeli's intelligence agency, Mossad, decided to use a robotic apparatus to remotely operate a machine gun.

Since the entire apparatus would together weigh more than a ton, it was disassembled and smuggled into Iran, piece by piece, where it was put together again on the bed of a Nissan Zamyad, a locally made pickup truck. In addition to the robot that would control the Belgian-made FN MAG, 7.62mm machine gun, capable of firing 600 rounds per minute, multiple cameras were also mounted to provide a detailed picture to the control room, which was located thousands of miles away at an undisclosed location, the report said.

To positively identify that it was Fakhrizadeh who was traveling in the car, a decoy car was deployed to force his convoy to slow down and take a U-turn. When the scientist was identified, the sniper, who was sitting miles away, took his shot and fired. A burst of bullets left the pickup truck after the AI system on the robotic apparatus took into account the car speeds of the convoy and the signal delays and corrected for them, the report said.

After the car swerved following the initial fire, the sniper readjusted the gun and fired once again at the car's windshield, targeting the scientist. Fakhrizadeh who is believed to have been hit on the shoulder at this point, took refuge behind an open front door of the car, where he was fired at again, three bullets hitting his spine, killing him. His wife was unhurt.

The entire sequence of events played out in less than a minute during which fifteen rounds were fired. The pickup truck, packed with explosives, blew up to erase any trace of what had transpired there, but most of the robotic equipment survived, the report said.

However, an Israeli outlethas reported that Iran has denied the New York Times report. The Jerusalem Post reported that an Iranian foreign ministry spokesperson denied NYT's claims, further stating that Iranian intelligence had all the details of the incident, including "all people involved."

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Iranian Nuclear Scientist Confirmed to Have Been Killed by AI-Controlled Gun - Interesting Engineering

PROMO You can have all the data in the world, but without teasing out the connections hidden within, its still just a pile of random information.

Graph algorithms are the key to finding the connections, structures and patterns that are essential to modern analytics, AI and machine learning, and which underpin applications such as recommendation engines, fraud detection, cyber security and IOT, to name just a few.

And its why you will want to connect, both physically and virtually, with some of the smartest brains in the field at the Graph + AI Summit, the industrys largest and only open conference dedicated to accelerating analytics and AI with graph algorithms.

This autumns event, brought to you by TigerGraph, kicks off in-person in San Franciso on October 5, and will include opening remarks from Rita Sallam, distinguished VP analyst and Gartner Fellow at Gartner Research. A second in-person event will follow in New York City on October 19.

The two in person events will be bridged by a series of online sessions. Throughout this two week deep dive into graph algorithms and their application, you can expect speakers from top organizations in finance, automotive, as well as academia, software, the cloud, and the cutting edge hardware organizations making all this possible. And of course, top engineers and researchers from TigerGraph.

This is more than a theoretical tour. Youll also have the chance to get hands on with workshops and breakout sessions. To make this all easier to navigate, the event will be organized along three industry tracks: Banking, Insurance, and Fintech; Technology, Advertising, Media, and Entertainment; and Healthcare, Life Sciences and Government.

Whatever your interests, you can secure complimentary registration for the online sessions by heading here. Attendance at the inperson events is by invitation or nomination, and you can nominate yourself here.

And if you have something to contribute, why not respond to the call for speakers and ensure youre on the agenda yourself.

The secret of data, as with humanity, is to only connect. The Graph+AI Summit will help you do just that, on a data and a human level.

Sponsored by TigerGraph

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If youre plotting AI success, you need graph algorithms - The Register

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Register now!

The gaps and limitations of applicant tracking systems (ATSs) and recruiting management systems (RMSs) are driving the development of new AI-infused approaches to modern talent management. Lessons learned from ATS and RTS limitations form the basis for new kinds of talent management platforms and systems at the same time as labor shortages surface in the wake of COVID-19.

But, while AI-based platforms are essential to new styles of talent management, their implementation is a matter of no small concern.

This was driven home by a recent study by Harvard Business School (HBS) and Accenture that found over 10 million workers are excluded from consideration due to the way ATSs are wired. The study, Hidden Workers: Untapped Talent, finds that inflexibly configured ATSs and RMSs completely miss qualified workers. Making matters worse, 90% of companies rely on ATS and RTS alone to screen for middle-skill and high-skill candidates.

According to a recent Wall Street Journal story, current talent tracking systems are working more or less as designed, screening millions of resumes on keywords and phrases that match job descriptions but leaving many capable applicants out.

And this is not the sole challenge to responsible talent management strategy. While outside job applicants may be mistakenly overlooked, the same may be true for existing staff people who are capable of helping in new positions, but who are filtered out from consideration.

Still, augmenting ATS and RMS with an AI-based platform approach to talent management is seeing accelerating adoption across enterprises. Hiring on capabilities is proving to deliver better business performance gains, according to many customer presentations at Cultivate 21, a recent user conference hosted by Eightfold.ai, maker of the Talent Intelligence Platform tool.

Talent mobility dominated sessions and discussions at Cultivate. It is clear organizations are turning to AI and machine learning for new insights into closing talent gaps and managing upskilling and reskilling more effectively.

Just ahead of Cultivate 21,Eightfold.ai reported results of the survey HRs Future State Report 2021: The Impact of Artificial Intelligence on Talent Processes. This and the summit together provided a candid, pragmatic look at where AI is transforming the nature of work and what needs to improve. The following are key insights from the survey.

The two leading uses of AI in hiring and recruiting are

Eightfolds survey finds that most HR leaders in enterprises first rely on AI to improve candidate filtering based on their potential, capabilities, learnability, fit for a role, and likelihood to succeed. The goal is answering the question of what the next best position for the candidate is. This technique goes far beyond the keyword-matching approach of existing ATS and RMS platforms.

HR teams also rely on AI to create positive candidate experiences through chatbots and self-service systems. Matt Hill, director of talent acquisition at Dexcom, explained at the Cultivate 21 Summit that AI technology streamlines the companys recruiting process by finding the most qualified candidates for positions. One way it does this is by helping candidates quickly identify positions they are most qualified for and auto-populating work experiences for them in their applications. As a result of these efficiencies, the company has seen a 40% conversion of website visitors into unique applicants.

Achieving greater efficiency and scale is the most significant benefit HR teams say AI provides today. AI also enables companies to reduce turnover because it allows them to build employee career paths and present growth opportunities. When internal mobility is high and turnover is low, HR teams can focus their time and resources on scaling the organization. That is a key benefit identified by survey respondents.

Another significant benefit is that technology and humans perform better in combination than they do working alone, a key point made in the recent Harvard Business Review article Why AI Will Never Replace Managers. AI cant solve all the problems HR faces; however, it can provide contextual data and intelligence to help reframe a problem, so HR teams know what needs to be solved. Contextual intelligence is the goal, with AI supporting HR teams experience, insights, and intuition.

About 57% of HR teams are currently using AI-related tools to manage workforces and hiring processes. In addition, the survey found that, through AI-powered tech stacks, HR teams are streamlining parts of the recruiting, hiring, and onboarding processes, with a high priority placed on creating positive candidate experiences.

Similarly, HBS and Accentures study on Hidden Workers emphasizes the need for HR teams to take a customer experience mindset in designing recruitment and onboarding processes. AI is how we lead, says Diane Gherson, former chief HR officer for IBM and current Harvard Business School faculty member. IBM relies on AI in HR to achieve various outcomes, including more personalized experiences for employees, positive chatbot interactions, accurate skills inferences for workforce management, and improved productivity for HR team members.

Nearly 82% of enterprise HR teams plan to adopt more AI tools in five years. Enterprise customers presenting at Eightfolds Cultivate 21 Summit reinforced this finding with the AI and talent transformation roadmaps they referenced during the virtual event.

Bayers Bijoy Sagar, EVP and chief IT and digital transformation officer, and Holly Quincey, global head of talent acquisition and HR, discussed the essential role of AI in their talent transformation initiatives, including unleashing the potential of Bayers Employee Entrepreneurs program. In addition, Jolen Anderson, global head of human resources at BNY Mellon, shared how AI is helping to bring greater scale to diversity and inclusion efforts company-wide, helping to create a foundation for changing corporate culture.

Eightfold.ais Cultivate 21 Talent Summit provided a series of insights that create a strong case for how AI and machine learning can help enterprises improve talent management. Bayer, Dexcom, Ericsson, Micron Technology, Nationwide, Prudential, and Tata Communications were a few of the Eightfold.ai customers who shared the results they achieved using Eightfolds Talent Intelligence Platform and their plans for the future. According to Kamal Ahluwalia, Eightfolds president, the company has grown to serve customers on four continents, 110 countries, and over 15 languages since 2018.

The summits two days of customer interviews provided insights into how Eightfold is helping enterprises take a data-driven approach to solving their most challenging problems using AI, machine learning, and neural networks. A replay of all the sessions is available online, and together they provide a glimpse into how enterprises are getting results from their AI strategies.

Talent mobility, diversity, equity and inclusion, talent acquisition, talent management, and governance were the leading topics covered in the 33 sessions. Based on customer presentations, its clear Eightfold is concentrating on helping their customers accelerate and improve talent acquisition. Customers including Dexcom and Micron explained how theyre relying on Eightfold for each stage of talent acquisition, including sourcing, screening, interview scheduling, diversity hiring, candidate experience, candidate relationship management, and on-campus hiring.

Talent mobility dominated many of the sessions and discussions at the event. By definition, talent mobility relies on a companys current employees to meet current and future talent needs through reskilling and redeployment across the organization. Eightfolds approach to talent mobility uses algorithms to identify and match open positions to provide internal employees with work that best matches their innate capabilities and skills. The data needed to train and fine-tune predictive models based on employees innate capabilities and skills are currently available, yet scattered across enterprise HR systems and external sources.

Eightfold looks to solve this challenge by integrating public sources and global data sets with HR systems to identify a candidates potential, capabilities, learnability, fit for a role, and likelihood to succeed. Sachit Kamat, chief product officer, provided an overview of the Eightfold Deep Learning AI architecture as part of his roadmap presentation. Central to this is Eightfolds use of neural-network-based deep learning able to learn from 1-billion-plus profiles, billions of global data points, and over 1 million unique skills to deliver what the company says are bias-free, data-driven insights.

Systems that improve talent mobility can open possibilities for workers and organizations alike, according to Betsy Summers, principal analyst for future of work and HCM at Forrester. Summers provided an insightful presentation that included an excellent analysis of talent mobility and the role of talent marketplaces at Eightfolds event.

Her presentation, Future-Fit Talent Mobility and Talent Marketplaces,explained the measurable benefits of talent mobility and talent marketplaces. Organizations that adopt talent mobility initiatives internally find that retention increases, costs to fill roles versus external hires drop, and the quality of hires increases, she said.

She also pointed out that providing employees with career planning improves employee experiences and long-term retention.

Artificial intelligence itself helps enable scalability, diversity and inclusion, and adoption because you can reduce the manual effort required by all employees by proactively suggesting matches across the pool. So imagine what you could unlock in terms of employee potential and employee experience by harnessing AI to help do that work for you, she told event attendees.

Three principles guide Eightfolds product strategy and ongoing investment:

At the companys event, Sachit Kamat, Eightfolds chief product officer, presented the Eightfold Product Vision & Roadmap, highlighted by these features:

For many organizations, talent mobility is becoming the most reliable strategy for closing the talent gap and staffing positions pivotal to a companys growth. By using AI and machine learning, organizations can factor in whats best for a candidate from a skills and capabilities perspective and still meet their talent management needs.

Data-driven approaches to improving every aspect of talent acquisition and management prove the most successful because they simultaneously bring greater personalization for candidates and scale for organizations.

How filters are applied will help determine the future, as AI moves deeper into talent management system operations. For their parts, HBS and Accenture both say hiring companies should shift from negative to affirmative filters in configuring their ATS and RMS, as well as emphasize finding talent within the corporation.

Their study on hidden talent points out that today, [An] ATS/RMS largely relies on negative logic to winnow the applicant pool [and] most use proxies (such as a college degree or possession of precisely described skills) for attributes such as skills, work ethic, and self-efficacy. Most also use a failure to meet certain criteria (such as a gap in full-time employment) as a basis for excluding a candidate from consideration irrespective of their other qualifications.

AI-based platforms are essential to improving every aspect of talent management, starting with recruiting, according to HBS and Accenture. But the power of AI will be best harnessed only when companies gain an understanding of the background of current employees that correlate to their success.

That data can then be translated into a new and powerful framework hiring on the basis of skills and demonstrated competencies, not credentials, according to the report.

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Celebrating AI-infused talent management at the Eightfold conference - VentureBeat

Aberdeen-based Wood and National Energy Resources Australia (NERA) today announced that the pair have teamed up to deliver an artificial intelligence (AI) software solution for offshore asset inspections that could create savings of A$2.8 billion ($2 billion) per year.

The Augmented Machine Vision Solution (AMVS) has been developed through a 12-month, $288,600 partnership, between Wood and NERA, the pair said today in joint statement. NERA is an independent, federally funded, industry growth centre, established to drive expansion in the Australian energy resources sector to develop a solution for the inspection of critical industrial equipment, particularly for subsea oil and gas infrastructure.

By combining Woods deep domain knowledge with cutting-edge AI technology, the AMVS will deliver a safer and faster inspection approach which can provide operators with more accurate and up-to-date information to help maximise the output of their assets. Its a game-changer for inspections that we know are susceptible to human error and inconsistencies, said Azad Hessamodini, president of growth & development at Wood.

Historically, inspections have required technicians to travel offshore to manually review numerous hours of footage recorded by inspection devices. The pair said the new solution uses an AI engine to watch the footage, searching for potential faults and flaws that need to be further inspected or have repairs undertaken. Not only does the new technology flag up any anomalies but it also eliminates the need for technicians to travel to hazardous, offshore sites and is faster and more accurate.

With the reduction in safety risks and the associated process improvements, the solution has the potential to create savings of A$2.8 billion per year for the offshore energy industry. Improvements from better-connected operations can also be realised through faster turnaround times and reduced costs for crew and vessels, said the pair.

NERA chief executive Miranda Taylor said shes delighted NERA is part of this exciting and revolutionary project, developing new skills and solutions in Australia that are already being used around the world to improve safety and reduce costs.

This project is improving the inspection of infrastructure thats long been a highly labour intensive and dangerous activity. Through this project were helping to reduce the need for technicians to spend long hours offshore examining footage of equipment by using software developed by Wood to examine the footage under the control of technicians who can remain safely onshore, she added.

Were excited to see potential opportunities emerging for this solution to be deployed into a number of other fields.

This project is another great example of what can be achieved when Australian companies are provided with the support they need to accelerate the development of technologies growing exports, growing jobs and improving the safety of the workforce, she added.

This is the latest collaboration between Wood and NERA who have previously worked together on the Transforming Australia Subsea Equipment Reliability (TASER) project, which aims to improve subsea equipment design and reduce the requirement for costly and time-consuming interventions in Australias challenging offshore warm water environment. As part of the project, living laboratories were created to assess the effectiveness of innovative coatings, materials and technologies against calcareous deposition and marine organism growth on subsea equipment.

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Wood and NERA celebrate AI tech that could save billions of dollars offshore Australia - News for the Energy Sector - Energy Voice

As schools welcome back students, many educators and administrators are depending on bots to alert them of kids who are at risk of harming themselves or others.

School districts use artificial-intelligence software that can scan student communications and web searches on school-issued devicesand even devices that are logged in via school networksfor signs of suicidal ideation, violence against fellow students, bullying and more. Included in the scans are emails and chats between friends, as well as student musings composed in Google Docs or Microsoft Word.

When the AI recognizes certain key phrases, these systems typically send an alert to school administrators and counselors, who then determine whether an intervention with the student and parents is warranted.

Many school districts have used monitoring software over the past three years to prevent school shootings, but it has evolved to become a tool to spot a range of mental-health issues, including anxiety, depression and eating disorders. School administrators say such surveillance is more important than ever as students return to the classroom after 18 months of pandemic-related stress, uncertainty and loss. Critics say it raises questions about privacy, misuse and students ability to express feelings freely or search for answers.

I asked an expert in childrens mental health to provide some ways to talk to your kids about this before you get a call from the school. Ive compiled them below (in case you just want to skip down for the parenting advice).

Surveillance of student communications and web searches appears to be fairly widespread. In June, the consumer-advocacy nonprofit Center for Democracy and Technology conducted online surveys of more than 1,000 third- through 10th-grade teachers, more than 1,600 parents and more than 400 high-school students across the country. The topic was student-monitoring software. According to the results, which the organization plans to release Tuesday, 81% of the teachers surveyed said their school uses some form of monitoring software, and 77% of the students said the same. Of those students, 80% said knowing they were being monitored made them more careful about what they searched online.

In some cases, its unclear whether students understand they are being monitored. Some schools disclose it in tech-use policies or codes of conduct, but how many kids actually read those? The Springfield School District, in Oregon, mentions monitoring in the policy students must sign when checking out school-issued laptops, but the district doesnt trumpet it.

I dont want to be sneaky about it, but if we were really obvious about it, students might not use their school devices," said Brian Megert, special programs director for the district, which this fall began using school online-safety company Lightspeed Systems to monitor student communications. Lightspeeds monitoring software is used in nearly 32,000 U.S. schools at an annual cost of about $2 a student.

From a public-sector perspective, there is no presumed anonymity in anything you do on a school device, on a school network or in a school setting," Dr. Megert added. I have mixed feelings about it, but if were going to err on one side it has to be on the side of safety."

Just last weekend, Lightspeed flagged a Google chat that a student in Dr. Megerts district had with a suicide hotline, as well as chats another student had with peers about plans for self-harm. In both cases, the school contacted the students families and arranged for mental-health services.

The San Marcos Unified School District near San Diego received five legitimate alerts of self-harm from Lightspeed in the first two weeks of school. The districts assistant superintendent, Tiffany Campbell, said that is an unusually high number this early in the school year.

Intellectually we knew how much support our students would need, but its a hard reality to see," Dr. Campbell said.

She said the majority of alerts the district receives are false alarms, in which a problematic phrase is part of an assignment, or just jokes among friends. However, she estimates that 20% to 30% of the alerts indicate potential crises.

Dr. Campbell described a recent case involving a student who kept a personal diary in a Google Doc, stating a desire to die. An administrator was immediately able to call the parent, and the parent immediately got their child help," she said. Thats the type of situation that makes the program worth it."

False alarms might decrease now that Lightspeed has added human reviewers to look at flagged communications and assign them a threat level.

Bark, another online monitoring company, said it detected 5,000 credible self-harm or suicide situations in the second week of September alone. Bark, available to schools at no charge, is used by more than 2,900 school districts and doesnt typically include human review.

Its hard to argue against efforts to save kids lives. But privacy and mental-health experts say such surveillance can be a slippery slope, especially if it ends up being used for reasons other than harm prevention.

Beila Lugo, mental health coordinator for Charles County Public Schools in Maryland, which uses Barks software, said she has had to tell some administrators to back off when they were planning to confront students for inappropriate language or content in some flagged communications. Were not using this for discipline, were using it for monitoring," she said.

Privacy advocates and mental-health experts say this kind of monitoring might take away the only safe space that some kids, especially in poorer families, have to search for help and to communicate with friends.

The school Chromebook is the only device some kids have, and the school Wi-Fi is the only internet connection some have," said Sophia Cope, a senior staff attorney with the Electronic Frontier Foundation.

What You Can Do

Privacy is a developmental milestone for teens," said Hina Talib, an associate professor of pediatrics and an adolescent-medicine specialist at Childrens Hospital at Montefiore in New York. By acknowledging that, she added, we give them choices and respect." Still, she said, there are ways to talk to kids about mental health before you get a call from the school.

Start early. Even very young kids can have thoughts of self-harm. Bark has detected increased expressions of suicidal ideation among elementary-school students so far this school year. Dr. Talib suggests checking in with kids about mental health starting in fifth grade.

Dont be afraid to talk about suicide. Dr. Talib said some parents worry that bringing up the topic of self-harm or suicide could inspire kids to act, but she said that isnt true; kids usually feel relieved to have someone to talk to.

Find a conversation starter. Rather than directly asking children how theyre doing, it can be helpful to find a reason to broach the topic. Kids might be aware that September is Suicide Prevention Awareness Month. You can talk about that, and ask what kinds of preventive steps they think would be helpful. Parents can also raise the topic in the context of a schools surveillance tech. When kids are asked their opinion, it helps them open up, Dr. Talib said.

Ask about their peers. Instead of making the conversation about them, a good way to get into a discussion is to ask about others. Dr. Talib suggests saying something like, Have you ever heard of anyone who cut themselves and you werent sure what that was about? Im happy to talk to you about it."

Make helplines available. Along with numbers of neighbors or relatives on the fridge, Dr. Talib said, you can post the number to the National Suicide Prevention Lifeline (800-273-8255) or the Crisis Text Line (Text HOME to 741741) so that your child knows theres someone to call or text if they need help.

Bring in a neutral party. If you suspect your child is struggling but you dont think your child will open up to you, Dr. Talib suggests asking a trusted third party to check in with your childit could be a coach, a teacher or a relative.

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How to detect your childs emotional distress before the schools AI does - Livemint

The pharmaceuticals firm GSK has struck a five-year partnership with Kings College London to use artificial intelligence to develop personalised treatments for cancer by investigating the role played by genetics in the disease.

The tie-up, which involves 10 of the drug makers artificial intelligence experts working with 10 oncology specialists from Kings across their labs, will use computing to play chess with cancer, working out why only a fifth of patients respond well to immuno-oncology treatments.

Dr Kim Branson, the global head of artificial intelligence and machine learning at GSK, said only 20% of patients respond well to the new oncology drugs that harness the bodys immune system to fight cancer.

Sometimes it works like a game buster and it wipes out the cancer. Wed like that to work all the time. This could be transformative, Branson said.

The partnership will use GSKs cancer drugs to start with and initially focus on solid cancers such as thoracic malignancies, gastrointestinal and womens cancers. Hopefully well create a framework that other people can contribute to, Branson said.

GSK and other large drug makers have been investing in AI to mine the vast quantities of data available to develop new medicines, pinpoint why some people are susceptible to certain diseases, and improve and personalise patient care.

AI uses algorithms to carry out tasks, with computers learning through repetitive processes rather than instruction from humans. The team will use a 3D cellular model of a patients disease to study how tumour cells from the patients undergoing treatment interact with immune cells.

What if we could play chess with the cancer? Branson said. Cancer is a tricky thing. You treat with X, then you see resistance. The tumour says, You do that, Im going to respond with this. Were using the predictive power of AI to think of potential strategies to outmanoeuvre disease. Our partnership with Kings can make this a possibility.

The team will monitor for dynamic biomarkers molecules found in blood, other body fluids or tissues that are a sign of disease that can predict resistance during treatment or a later relapse. The research partnership is based on a novel machine learning model that integrates multimodal data, genetic and molecular traits, tumour location, images and biomarker blood tests.

Prof Tony Ng, head of the comprehensive cancer centre at Kings, said that in general half of cancer patients who were clinically diagnosed to have advanced but operable cancers came back within one to two years after treatment, such as chemotherapy, when it was discovered that the cancer had spread to other parts of the body.

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To identify those at high risk, the team will create a digital biological twin of the patient, to test multiple drugs, and multiple doses, at multiple time points.

We are linking up the patient with the twin and can immediately feed back info to the clinical trial or clinical management algorithms, Ng said. The biological twin will not only tell us this person has a high risk, but also what we as oncologists do about it.

Ng added that different parameters besides genomics can be looked at within the twin, such as whether the immune system is suppressed through contact with cancer cells (quantified by new imaging methods), to develop a multimodal monitoring tool. Over the five years, the team hope to create specialist equipment.

Branson said the partnership could, if necessary, use the UKs most powerful supercomputer, developed by the US-based firm Nvidia, which became operational in July. The Cambridge-1 deploys AI methods and is available to a range of organisations, including GSK and Kings.

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GSK teams with Kings College to use AI to fight cancer - The Guardian