In the past, ad systems relied on basic heuristics, which can be effective for making immediate judgments, but often result in inaccurate conclusions. To really optimize for what advertisers and marketers care about — which is delivering custom marketing campaigns on the open internet that result in a hard ROI on their ad dollars — you need first-party data and a sophisticated machine learning (ML) platform that can optimize for return on ad spend (ROAS). Under the cover of a modern ML-based platform, there are many different ML models doing everything from predicting conversion likelihood to determining the b********* to bid for an individual ad request.

AI ML in Marketing: AI and Big Data Analysis Used to Find Brands’ Emotional Connection

Activating your first-party data is more important than ever given the seismic privacy changes happening in the industry, including Apple’s ATT and Google’s Privacy Sandbox, which are making it incredibly challenging for traditional ad tech systems to adapt. ML-based approaches, however, have a distinct, almost magical ability to adapt to these changes faster and more holistically than what a vigilant technical team can do.

Developing first-party data sets isn’t as easy as it sounds, however. Marketers need to be wary of the quality of the data that goes into machine learning models. These models have the capability to drive accurate and effective results; however, it can have an equal and opposite effect if brands are relying on static third-party data. To mitigate this, businesses need to invest in building and growing first-party datasets that ensure ads are being targeted more precisely and accurately to a relevant audience.

Keys to Developing Quality First-party Datasets

In performance marketing, it’s critical to have confidence in the quality of data being used.

There is a famous saying in the machine learning world – garbage in, garbage out. Marketers should be reassured that there is no fraudulent data in their system and have the ability to remove such data – ensuring the model is being fed with quality inputs. 

ML models make use of quality data that is a mix of contextual and behavioral signals that can help infer an individual’s intent or interest in a particular ad. In general, if that data can help increase engagement for an ad, it is useful.

There are many types of useful data, and quality is largely determined by accuracy — for example exact location versus an inferred metro area; consistency, which requires having the same data available for every user or ad request; and timeliness, which relates to how often the data is refreshed.

Building and Growing First-Party Datasets

The imminent depreciation of third-party cookies and improved privacy for device IDs mean that marketers and advertisers will be challenged to target consumers in a meaningful way. The good news is they have access to first-party data, which can be turned into gold if harnessed and used correctly. 

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First off, it is important to understand what constitutes personally identifiable information (PII) in context of individual users. There are both intuitive and non-obvious ways that data can be PII, so it really requires a lot of thought and an overall strategy. Keep in mind that PII is not just how your product/service uses a piece of customer data, but the downstream potential for it to be combined with other data to identify individuals.

Building a strong first-party dataset starts with having a system for collecting the data around your user journey and engagement activities in your products or services, including how customers shop, the brands they prefer to purchase, their site journey, pages visited, items clicked and navigation sequence, and organizing it into user profiles, segments, and audiences. Like what product managers need to build great products, marketers need to have a thorough understanding of their users, the user journey, and, ultimately, the value users derive from a product or service.

The next step is to integrate the data with other business systems (CRM or data warehouse) so you can gather insights through a mix of analytics, Mobile Measurement Partner (MMP), or business intelligence tools.

With the proliferation of cloud data warehouses, this doesn’t have to be a massive initial effort since these platforms can scale to manage more complex use cases as your data grows.

Unlock the Power of Your Data Through Sophisticated Machine Learning

In the past, marketers had to rely on human intelligence and manual optimization such as daily budget adjustments or pub throttling. With the advent of machine learning, those tactics no longer add value and quite often actually have a negative impact. It is extremely important to “let the machines do the work” and minimize any extraneous human interaction or data throttling.

In addition to human error, there are other factors contributing to the need for modern ML, including an explosion in the amount of data available, particularly as mobile device growth and usage are now at a peak; the sophistication in tools and systems for supporting large scale data processing in the cloud; and the sophistication in ML algorithms, particularly neural network-based ML.

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It’s important to note that not all machine learning systems are created equally. To successfully leverage the power of the technology and achieve performance marketing goals such as ROAS, CPI, CPA, or revenue, the ML platform should include the following:

• Sophisticated ML technology, including deep neural networks (DNN) to assess hundreds of features and their impact on downstream engagement/conversion (ROAS).
• Large-scale data processing, including the ability to listen to and process an incredible amount of data to continue informing and training the ML models.
• Highly performant output, meaning the system should make multiple predictions and optimize immediately, iteratively, and in real time, and continue learning.
• A combination of first-party data with proprietary user data, which allows the system to begin learning about users before launching an advertising campaign. 
• Deep-learning based ML and historical performance data – allows for quickly generalizing with very limited campaign responses. For example, by observing advertising responses for a limited geo, a modern ML platform can precisely predict how a campaign would perform on other geo locations. 

Another important aspect of machine learning is it allows marketers to develop privacy-safe approaches to doing relevant ad targeting, which is critical in today’s privacy-first environment.

Machine learning can be used to construct more advanced behavioral cohorts that make it impossible to accidentally reveal PII information. ML models for targeting can also be run “on the edge“ so that sensitive information never leaves a user’s mobile device.

This is an exciting and innovative time for the industry. Advanced ML-based solutions are enabling advertisers of all sizes to develop privacy-safe or privacy-first approaches that deliver relevant ads, generate ROI, and accelerate their business.

[To share your insights with us, please write to sghosh@martechseries.com]

The post How Machine Learning and First-party Data Work in Harmony for Performance Marketers appeared first on AiThority.

Neuro-Symbolic AI, which is alternatively called composite AI, is a relatively new term for a well-established concept with enormous significance for almost any enterprise application of Artificial Intelligence. By combining AI’s statistical foundation (exemplified by machine learning) with its knowledge foundation (exemplified by knowledge graphs and rules), organizations get the most effective cognitive analytics results with the least amount of headaches—and cost.

Pairing these two historical pillars of AI is essential to maximizing investments in these technologies and in data themselves. Alone, machine learning simply patterns recognition at a massive scale.

By itself, rules-based symbolic reasoning doesn’t improve over time. Together, these AI approaches create total machine intelligence with logic-based systems that get better with each application.

Understanding Neuro-Symbolic AI

The full value of Neuro-Symbolic AI isn’t just in its elimination of the training data or taxonomy building delays that otherwise impede Natural Language Processing applications, cognitive search, or conversational AI. Nor is it only in the ease of generating queries and bettering the results of constraint systems, all of which it inherently does. The real reason for the adoption of composite AI is that, as Marvin Minsky alluded to in his society of mind metaphor, human intelligence is comprised of numerous systems (analogous to diverse society members or machines) working together to produce intelligent behavior. Similarly, AI requires an assortment of approaches and techniques working in conjunction to solve the myriad business problems organizations regularly apply to it.

Natural Language Proficiency

Knowledge graphs are seminal to Neuro-Symbolic AI because they represent enterprise concepts via data so intelligent systems can reason and learn about them. These graphs are the foundation of the unified system AI pioneer Allen Newell who c***** that the layers of mechanisms are required for intelligence. Nearly all the various AI elements—including semantic inferencing, unsupervised learning, supervised learning, and other reasoning and statistical approaches—are readily incorporated or visualized in knowledge graphs containing business logic for enterprise data.

These semantic graphs are integral to the most widely used Neuro-Symbolic AI application: natural language question-answering of textual data (including transcriptions of speech).

Combining AI’s reasoning and learning sides generates profound results in this deployment. It allows users to rapidly construct a knowledge base from a corpus via unsupervised and supervised learning techniques, the former of which drastically reduce the amounts of labeled training data impeding pure supervised learning approaches. Alternatively, combining AI’s statistical and knowledge sides can automate the labels for supervised learning approaches to natural language applications, which also saves money and time. No matter which technique is used, the marketplace is realizing that natural language applications almost always need some reasoning and knowledge-based techniques to deliver the best outcomes at the lowest costs.

Machine Logic

Knowledge graphs are also central to Neuro-Symbolic AI because they provide ideal settings for machine logic. Their heightened relationship detection and intelligent inferences make them complementary for logic-based systems like Prolog, an AI language specializing in first-order logic. Consequently, organizations can write various AI algorithms in this language that’s also useful for creating logic rules, which have a lengthy history in AI via symbolic reasoning. Newell proposed that human cognition could be expressed in a system of symbols that could provide rules-based constraints.

Knowledge graphs can do the same thing for AI, allowing organizations to create rules about different ways to identify a business outcome (like codifying rules for finding out if a customer mentioned a specific timeframe for making a purchase to contact center representatives). This capability is also applicable to constraint system deployments like optimizing delivery routes and the time they take. These systems’ results get better by running machine learning on the outcomes, then inputting the latter findings into the graph as additional knowledge factoring into rules.

Graph Neural Networks

Another compelling use case for composite AI is the predictive accuracy of Graph Neural Networks (GNNs). These machine learning models excel in graph settings that fully depict or visualize all of the dimensions of their statistical analytics on multi-dimensionality data—like considerations across networks for fraud analysis, for example.

More importantly, knowledge graph environments allow organizations to combine traditional knowledge-based techniques like semantic inferencing with the node prediction prowess of GNNs to ascertain the likelihood of customers buying a certain product at a specific time, for example. This combination not only simplifies the query writing process for analyzing customer subsets or micro-segments, but also grants unparalleled insight into graph influencers and how they’ll affect business use cases.

Composite AI

Organizations’ adoption of Neuro-Symbolic AI as a product is still growing. However, the overall utility—and use cases—from the concept of coupling AI’s statistical and knowledge base is too great to ignore and one of the most effective means of creating competitive advantage that these technologies afford.

[To share your insights with us, please write to sghosh@martechseries.com]

The post Neuro-Symbolic AI: The Peak of Artificial Intelligence appeared first on AiThority.

Supply chains are the systems of organizations, people, activities, information, and resources involved in supplying a product or service to a consumer. The proliferation of AI-based video solutions has impacted supply chains in industries all across the globe. Analysts have reported consistent growth in this space, with no end in sight, projecting revenues upwards of $8 billion through 2026. As technology continues to advance, it’s important to understand the impact it already has on supply chain management practices, as well as the ways that it can continue to reshape the global industry. 

Top 10 Countries and Cities by Number of CCTV Cameras

AI-based Video Solutions Ensure Driver and Road Safety 

AI-enabled advanced driver-assistance systems (ADAS), driver monitoring systems (DMS), and cabin monitoring systems have become critical in today’s fleet management practices. AI-based video telematics can assist ADAS with automatic warnings, both to the driver or the fleet manager for incidents such as collisions, lane departure, and object/pedestrian hazard warnings. In addition, AI camera applications in the cabin along with driver monitoring systems can improve cabin and driver safety, generate alerts for drowsy/distracted drivers, reduce insurance costs, and ultimately help resolve vehicle-involved incidents in a more timely fashion. 

While in transit, the use of AI-enabled analytics makes it possible for fleet managers to analyze truck utilization patterns and get a view of loading/unloading activity. This information allows supply chain managers to better understand the overall productivity of their fleet and make changes whenever necessary. Additional capabilities like facial recognition and driver wellness can be more efficiently monitored to ensure greater control and optimization throughout the supply chain.

AI-based Video Solutions for Workplace Compliance 

AI-based cameras can also help keep workers safe. During 2020, supply chains had to quickly adapt to myriads of changes caused by the COVID-19 pandemic, further emphasizing the need for safe workplace practices. AI models embedded in CCTV cameras can monitor social distancing practices and provide mass fever detection in warehouses and other workplaces. Real-time fever detection can reduce work stoppages and delays and improve employee safety and trust—creating several competitive advantages. AI can also detect and prevent other workplace hazards and take real-time action, like instant corrective warnings to significantly improve safety and reduce accidents. 

Inventory Management with AI-based Video Solutions

AI-embedded closed-circuit television (CCTV) cameras offer increased visibility and control for a fully connected supply chain. These enhanced cameras automatically gather data in real-time through live video feeds. The AI-based systems then add decision-making capabilities to these devices, which are then able to analyze data, recognize critical and/or abnormal events, and send automated alerts. These crucial insights minimize risk and help to avoid undesirable scenarios, leading to better outcomes and more efficient management of supply chains.

AI-enabled cameras and video analytics are driving efficiencies across inventory management, as CCTV cameras backed with AI video analytics can instantly track and detect any kind of suspicious activity and send alerts in real-time. 

Looking to the Future 

Evolving technologies aim to constantly improve and streamline supply chain management,allowing organizations to increase efficiency without the need for additional resources. Accelerated by the pandemic, AI is being infused into many aspects of the supply chain to meet the increased demand of e-commerce and last-mile deliveries for customers world-wide. From autonomous forecasting and planning to customer communication management, AI will be capable of optimizing the data each organization collects to effectively improve the supply chain from end-to-end. 
[To share your insights with us, please write to sghosh@martechseries.com]

The post How AI-based Video Solutions are Creating Smarter Supply Chains appeared first on AiThority.

As sure as the sun will set, at least one hurricane will blow through the Gulf Coast region each year. The National Oceanic and Atmospheric Administration (NOAA) predicts that the 2021 Atlantic hurricane season will be an above-normal season, meaning the potential for floods and wind damage is all but inevitable. Though storms aren’t controllable, the human response to them is.

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Over the past year, LSU Civil and Environmental Engineering Assistant Professor Navid Jafari has worked alongside the U.S. Coast Guard in New Orleans to leverage unmanned aerial vehicles (UAVs), or drones, to facilitate its search and rescue operations immediately after a hurricane.

In early 2020, Jafari received a $180,000 National Science Foundation grant to work alongside Texas A&M University researchers Anand Puppala and Surya Congress and LSU Department of Environmental Sciences Professor Nina Lam and students Volodymyr Mihunov and Kejin Wang to find a way to quickly gather infrastructure data after a natural disaster strikes and help emergency operations centers (EOCs) make data-driven decisions that could save lives.

“Within the first 24 hours after a hurricane, the U.S. Coast Guard is the first search and rescue group getting calls from people in distress,” Jafari said. “The difficulty is that they are trying to navigate through an urban environment without the benefit of a bird’s eye view of where there are blockages, or the area is completely devastated and unrecognizable, and what the most efficient route will be to the target home. Using a drone could save time and lives.”

In April, Jafari was invited to U.S. Coast Guard training, where he worked with Lt. Jacob Bailey to set up exercises with and without drones. The takeaway was that drones helped lead boat operators to specific locations for rescue, but further research is needed to make improvements.

“There are still some missing pieces, but this is where the next step of our research comes in,” Jafari said. “A drone is small and hard to see the further away it gets, so how can we make it more visible to boat operators so they can follow it? Also, radio communication gets lost the further out the boats go, so we need to find a way for the drone to directly communicate with the boat so there’s no miscommunication.”

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Jafari said it could be as simple as putting colors on the drone to signal down to the boat operator. For instance, red could mean stop, yellow could mean proceed with caution, and green could mean go ahead. Jafari would also like to develop a platform to integrate drone and computer technology with the current built environment, which could include research on how to collect data from the drone’s camera sensors, such as water levels and where people are located.

“If you have a thermal infrared camera, you can locate bodies,” he said. “You could feed that information to the boat operator so they know exactly where to go and know what the hazards are in that area. At first light, the Coast Guard will take calls and get out there to rescue people. We would be out there with our drone and infrastructure knowledge helping them. Our hope is to have the federal government invest more money in technologies to collect better data and process it faster, which will save lives.”

Jafari, who is also a key player in NSF’s NEER (Nearshore Extreme Events Reconnaissance) program, led by Britt Raubenheimer at Woods Hole Oceanographic Institute, will conduct hurricane reconnaissance if and when a hurricane hits Louisiana or East Texas.

Within LSU and the state of Louisiana, Jafari is one of the few researchers who does pre-deployment of sensors to collect water data and conducts drone surveys of flood protection infrastructure before a storm hits. He teamed with Qin Chen at Northeastern University during the 2020 hurricane season to specifically deploy sensors before Hurricanes Laura and Delta to understand how wetlands can serve as natural flood protection for coastal communities. His hope is to continue this valuable reconnaissance research so that in case there is another Hurricane Harvey or Laura predicted to hit the Gulf Coast, LSU will be ready to act fast.

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The Governance Lab (The GovLab), NYU Tandon School of Engineering, Global AI Ethics Consortium (GAIEC), Center for Responsible AI @ NYU (R/AI), and Technical University of Munich (TUM) Institute for Ethics in Artificial Intelligence (IEAI) will jointly launch a free, online course, AI Ethics: Global Perspectives, on February 1, 2021. Designed for a global audience, it conveys the breadth and depth of the ongoing interdisciplinary conversation on AI ethics and seeks to bring together diverse perspectives from the field of ethical AI, to raise awareness and help institutions work towards more responsible use.

“The use of data and AI is steadily growing around the world – there should be simultaneous efforts to increase literacy, awareness, and education around the ethical implications of these technologies,” said Stefaan Verhulst, Co-Founder and Chief Research and Development Officer of The GovLab. “The course will allow experts to jointly develop a global understanding of AI.”

“AI is a global challenge, and so is AI ethics,” said Christoph Lütge, the director of the TUM IEAI. “Τhe ethical challenges related to the various uses of AI require multidisciplinary and multi-stakeholder engagement, as well as collaboration across cultures, organizations, academic institutions, etc. This online course is GAIEC’s attempt to approach and apply AI ethics effectively in practice.”

The course modules comprise pre-recorded lectures on either AI Applications, Data and AI, and Governance Frameworks, along with supplemental readings. New course lectures will be released the first week of every month.

“The goal of this course is to create a nuanced understanding of the role of technology in society so that we, the people, have tools to make ethical AI work for the benefit of society,” said Julia Stoyanvoich, a Tandon Assistant Professor of Computer Science and Engineering, Director of the Center for Responsible AI at NYU Tandon, and an Assistant Professor at the NYU Center for Data Science. “It is up to us – current and future data scientists, business leaders, policy makers, and members of the public – to make AI what we want it to be.”

The collaboration will release four new modules in February. These include lectures from:

• Idoia Salazar, President and Co-Founder of OdiselA, who presents “Alexa vs Alice: Cultural Perspectives on the Impact of AI.” Salazar explores why it is important to take into account the cultural, geographical, and temporal aspects of AI, as well as their precise identification, in order to achieve the correct development and implementation of AI systems;
• Jerry John Kponyo, Associate Professor of Telecommunication Engineering at KNUST, who sheds light on the fundamentals of Artificial Intelligence in Transportation System (AITS) and safety, and looks at the technologies at play in its implementation;
• Danya Glabau, Director of Science and Technology studies at the NYU Tandon School of Engineering, asks and answers the question, “Who is artificial intelligence for?” and presents evidence that AI systems do not always help their intended users and constituencies;
• Mark Findlay, Director of the Centre for AI and Data Governance at SMU, reviews the ethical challenges — discrimination, lack of transparency, neglect of individual rights, and more — which have arisen from COVID-19 technologies and their resultant mass data accumulation.

About TUM Institute for Ethics in Artificial Intelligence

TUM has long been a driving force in researching the mutual interactions of science, technology and society. Founded in 2019, the Institute for Ethics in Artificial Intelligence (IEAI) follows TUM’s strategy for “Human-Centered Engineering” and the university’s bold creation of the Munich Center for Technology in Society (MCTS) in 2012, whose mission is to better understand and reflexively shape the multiple interactions between science, technology and society.

The IEAI conducts inter-, multi-, and transdisciplinary research that promotes active collaboration between the technical, engineering and social sciences, while also actively courting interaction with a wide group of international stakeholders from academia, industry and civil society. This exhaustive approach enables the IEAI to truly and comprehensively address a growing group of ethical challenges arising at the interface of technology and human values. It also aids in the development of thoroughly operational ethical frameworks in the field of AI.

About The Global AI Ethics Consortium

The Global AI Ethics Consortium (GAIEC) joins forces with academic institutions, research centers and distinguished members of academia worldwide in order to foster trust in data and technology, maximize the potential of AI while limiting its harms, help all the involved parties navigate current uncertainty, and create ethical frameworks.

About the Center for Responsible AI at NYU (R/AI)The NYU Center for Responsible AI (R/AI) Established in Fall 2019 and directed by Julia Stoyanovich and Steven Kuyan, the Center is a comprehensive laboratory that is building a future in which responsible AI will be the only kind accepted by society. R/AI builds on the interdisciplinary research expertise that NYU has developed across the fields of AI, data science, engineering, social science, and law to create the world’s most comprehensive and sophisticated laboratory for understanding and accelerating responsible AI practices. R/AI operates a set of synergistic activities that include applied research, startup acceleration, and a range of education and public engagement efforts. The Applied Research Lab conducts cutting-edge interdisciplinary research to develop, implement, and disseminate best-in-class open source tools and frameworks for operationalizing responsibility at all stages of the AI lifecycle. The AI for Good startup incubator applies AI to societal problems that are otherwise overlooked in pursuit of broad capital market opportunities. The Talent and Education Program produces educational materials for data science practitioners, public sector decision makers, and members of the public.

About the New York University Tandon School of Engineering

The NYU Tandon School of Engineering dates to 1854, the founding date for both the New York University School of Civil Engineering and Architecture and the Brooklyn Collegiate and Polytechnic Institute. A January 2014 merger created a comprehensive school of education and research in engineering and applied sciences as part of a global university, with close connections to engineering programs at NYU Abu Dhabi and NYU Shanghai. NYU Tandon is rooted in a vibrant tradition of entrepreneurship, intellectual curiosity, and innovative solutions to humanity’s most pressing global challenges. Research at Tandon focuses on vital intersections between communications/IT, cybersecurity, and data science/AI/robotics systems and tools and critical areas of society that they influence, including emerging media, health, sustainability, and urban living. We believe diversity is integral to excellence, and are creating a vibrant, inclusive, and equitable environment for all of our students, faculty and staff.

The post Free, Online Course Brings Together 20 Global Experts in the Field of Ethical AI appeared first on AiThority.

Our world seems to be changing faster than ever – technologically, environmentally, socially – and in so many other ways. It is hard for any of us to keep up with the astonishing pace and scale of developments, and their impact for better or for worse on our own lives and the ways in which they affect the future of our planet. Yet too often it seems that those with the greatest stake in the future, are least empowered to shape it: the YOUTH. This is something the Commonwealth has for more than 50 years been working hard to change, and never more so than today.

Population growth means that there are now more young people in the Commonwealth than ever before, and this offers choices and challenges for all involved in planning and making policy, and for young people themselves. The combined population of the Commonwealth is now 2.4 billion, of which more than 60 percent are aged 29 or under, and one in three between the ages of 15 and 29.

Through social media, the youth is more connected, informed, engaged and globally-aware than ever before. Even so, their potential to drive progress and innovation is often overlooked or remains untapped, despite pioneering Commonwealth leadership over the decades on inclusiveness and intergenerational connection.

Since the 1970s, Commonwealth cooperation has supported member states with the provision of education and training for youth workers, who have a central role to play in encouraging, enabling, and empowering young people. Practitioners may be of any age and operate in many settings: youth clubs, parks, schools, prisons, hospitals, on the streets and in rural areas.

Commonwealth approaches and engagement recognize the dynamic role youth workers can play in addressing young people’s welfare and rights, and in connecting and involving them in the decision-making process at all levels. In some Commonwealth countries, youth work is a distinct profession, acknowledged in policy and legislation to deliver and certify the quality of practice, including through education and training. In others, it is institutionalized less formally through custom and practice. In some countries, there is little or no youth work activity – formal or informal.

To advance the cause of young people, and their direct participation in nation-building and the issues affecting them, the Commonwealth Secretariat supports the governments of member countries with technical assistance relating to policy and legislation in professionalizing youth work. A pioneering Commonwealth contribution is the Commonwealth Diploma in Youth Development, which has been delivered in almost 30 Commonwealth member states.

The new Commonwealth Degree and Diploma in Youth Work provide countries with a resource for developing human capital using a consortium business model that makes the training resources accessible at low cost for persons in low-income contexts.

The Commonwealth also supports the global collectivization of youth work professionals through the emerging Commonwealth Alliance of Youth Workers’ Associations (CAYWA), an international association of professional associations dedicated to advancing youth work across the Commonwealth. CAYWA facilitates the cross-pollination of ideas and collegial support among youth work practitioners and is developing into a unified global influence providing support to governments and all stakeholders in the youth work profession.

Expertise is offered by the Commonwealth Secretariat with the design of short courses and outcomes frameworks that support just-in-time and refresher training to augment diploma and degree qualifications. Guidance is also offered on establishing youth worker associations that can help towards building and sustaining professional standards, thereby safeguarding the quality of services offered to young people.

In 2019 a conference in Malta bringing together youth workers from throughout the Commonwealth continued to build recognition and professional standards of youth work in member countries. Among outcomes was the establishment of a week-long celebration of the extraordinary services of full-time practitioners and volunteers – recognized as youth workers – who support the personal development and empowerment of young people.

Youth Work Week, with the theme ‘Youth Work in Action’, will be observed 4 -10 November 2019 in the 53 member states of the Commonwealth.

Looking forward to the 2020 Commonwealth Heads of Government Meeting (CHOGM) in Rwanda next June, Youth Work Week will bring into sharper focus the challenges young people in our member countries face, and the opportunities they are offered – including through Commonwealth connection.

By recruiting and placing appropriately trained and properly supported youth workers, communities in Commonwealth countries can help young people channel their energies and talent in positive directions, especially during the transition from education into work.

Supported by positive role models and with mentors to whom they can relate, young people can be guided towards healthy and productive lives. When equipped to develop as well-rounded individuals and to contribute to the societies in which they live, young people can make immense contributions towards transforming our communities and our Commonwealth and – above all – to their own future.

Read more: OPINION: Access to Justice for All Is Not yet a Reality – But, There Are Ways Forward

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Latest Clutch Survey Finds More Than 60% of Job Seekers Unsure if Companies Use Artificial Intelligence (AI) to Review Applications During Recruitment 

To what extent would you be comfortable if someone told you that AI is screening your job applications? Well, don’t cringe yourself for not wanting AI to check your resume. You are not alone. In fact, more than 60% of the job seekers are actually unaware of how AI is actually helping companies find the best talent in the industry. A new survey from Clutch captures how AI, texting, social media, and other communication channels impact hiring practices, essential information for companies as they explore new ways to use technology in recruitment. Interesting, isn’t it?

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As AI becomes a mainstream technology to review applications during the recruitment process, job seekers could further question businesses’ use of AI (AI) to evaluate resumes and cover letters, according to a new survey from Clutch.

The survey found that 32% of job seekers doubt companies used AI to review their resumes or cover letters during their most recent job search, compared to 32% who believe companies used AI but remain unsure.

This uncertainty translates to job seekers’ mixed feelings about AI, the survey finds. More than half of recent job seekers (51%) believe AI currently isn’t advanced enough to assist with recruitment – including nearly 20% who doubt AI will ever be useful.

Does AI Improve Recruitment? 

AI’s level of reliability and use depends on how companies choose to apply it.

Only 11% of job seekers believe AI improves recruitment. Experts warn that AI can reinforce existing biases when it’s used to evaluate resumes and cover letters. For example, AI can replicate a company’s tendency to hire candidates of a certain gender or educational background.

Experts, however, believe AI can be beneficial if companies use AI for skill-based testing instead of screening candidates’ race, gender, or backgrounds.

“As long as your test isn’t easier for someone who went to MIT than it is for someone who went to community college, AI can be tremendously helpful,” said Harj Haggar, CEO and co-founder of Triplebyte, a company that uses background-blind testing to match candidates with tech companies.

AI can also supplement communication between companies and candidates. Currently, only 3% of applicants communicate with companies via AI-supported chatbots, but experts see the potential for growth.

Nearly One-Quarter of Job Seekers Text Companies During Recruitment

Nearly 25% of job seekers have texted a company representative during the interview process.

Courteney Kovacs, a senior professional in HR at Hudson Insurance Group, views texting as a valuable tool for communicating with potential employees, especially in a competitive hiring landscape.

Kovacs said, “Everybody is texting. If you can find a way to incorporate it into recruitment, why not?”

Candidates who text potential employers should use the same etiquette as writing a professional email, experts advise.

Nearly 20% of Job Seekers Use Social Media to Contact Companies

Nearly 1 in 5 job seekers (16%) connect with companies on social media.

Emily Rowe is the owner and CEO of Social Sensei, a Florida-based creative agency. She initially connected with three members of her core staff on Instagram before hiring them.

“If a business is on social media, they should expect to use it as a mode of communication,” Rowe said.

Traditional Communication Methods Remain Popular

Phone calls and email still remain essential to hiring.

Nearly three-quarters of recent hires connected with companies via phone calls (74%) and email (73%) during recruitment.

Clutch’s 2018 Recruiting Survey included 507 full-time employees who were hired in the past 6 months.

Currently, Clutch connects small and medium businesses with the best-fit agencies, software, or consultants they need to tackle business challenges together and with confidence. Clutch’s methodology compares business service providers and software in a specific market based on verified client reviews, services offered, work quality, and market presence.

Must Read: Google Taps CMU Dean Dr. Andrew Moore to Lead Cloud AI Research

The post AI in Recruitment: Welcome to the Realm of Uncertainty appeared first on AiThority.

The companies investing most of the money in AI are not actually AI businesses per se. The guys building the tools are not running the businesses.

Know My Company

Tell us about your journey into Artificial Intelligence and machine learning? Why did you launch Vainu Labs?

At Vainu, our goal is to understand everything ever written by every company on the Internet. Because of that, the amount of data we need to be able to process is insane. All the data is originally meant for humans to read. We are talking about unstructured textual content. Machine learning — teaching the computer by example — is the only solution at this point.

What is Vainu Labs and how does it leverage AI-ML technologies to work with data?

Our goal is to extract as much information as possible about every company online. We take solid facts that we can reflect on a single company. We leverage Machine Learning technology to understand and group unorganized textual content.

As a tech leader, how do you prepare for an AI-driven world?

We are constantly figuring out how to create pipelines to enable real human beings to provide us with answers we want from the data.

We can glean data from human brains. This is transferable to AI algorithms. By leveraging machine learning to complete human processes, we are preparing for the AI-driven world. In other words, we try to download the understanding of human brains and let humans instead focus on tasks where computers will fail.

We make sure we automate what we can automate today. Individual parts of tasks will be automated before the computer overloads.

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What message/advice do you have for young professionals looking to build a career in AI/ML?

Start by doing. All the libraries and technologies we are leveraging are relatively new. For example, Google’s TensorFlow (it’s open-source library for dataflow programming) is just a few years in the making. If you are a fast learner and eager to work, you can advance rapidly.

What is the state of ‘AI for Businesses’ in 2018-2020? How can business development teams better benefit from leveraging Vainu’s AI-powered platform?

The companies investing most of the money in AI are not actually AI businesses per se. The guys building the tools are not running the businesses. The major companies like Amazon, for example, are leveraging it and bringing on development teams who understand it. Companies with the most data and the most need for data processing are the organizations putting the biggest stake in AI-powered platforms. They have the data to make machine learning vital for them.

Vainu’s platform automates huge parts of the sales prospecting equation. Instead of performing manual searches in our platform, we are collecting examples automatically and explaining to our users why we are offering them new kind of prospects.

90% of Vainu’s business development is now driven by AI. AI makes the decision to whom we reach out to and when.

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Tell us more about your AI research programs and the most outstanding digital campaign at Vainu Labs?

Vainu Labs’ experimental AI is currently driving huge sales teams in the telecommunications space. With A/B testing, we can prove significant results in predicting how likely the prospects are to convert to customers. This has been proven by real sales work.

Also, we have been able to predict reliably how many employees the company will have. We can predict the new type of technology they will buy.

What are the major challenges for AI technology companies in making it more accessible to local communities? How do you overcome these challenges?

One of the challenges is how much time is consumed to generate proper data to make predictions. The availability of data to predict is not there for AI. So, the lack of data is an issue. Cleaning up the data is also an issue.

Vainu’s mission since we started four years ago has been to collect data on real-world events. We’ve accumulated a ton of data because of that and stand on the greatest data set in the B2B space.

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Tell us “The Good, The Bad, and The Ugly” of AI/ML technologies.

99% of AI is being taught by data sets provided by humans. It is implemented in such a manner that it can never learn to become better.

In that sense, machine learning is still tied to limitations of human intelligence.

However, the beauty of this is you can combine hundreds of thousands of people’s insights into AI.

In terms of the ugly, AI has this kind of sci-fi, futuristic vibe for a lot of people. Many of the c***** people make about AI are misleading or simply not true. Machine learning does a better job of explaining what tech leaders are practically doing to advance companies – teaching machines the tasks to perform.

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Would you agree that ‘Weaponization of AI” is a real danger? How steps do you take as a tech provider in preventing the misuse?

It isn’t a greater threat than weaponizing any technology. But weaponizing any technology is a serious threat to mankind. So much technology has already been wildly weaponized. The solution isn’t legislation. It is creating solutions that combat weaponized AI — it is about being a step ahead of weaponized AI and taking it head-on.

The Crystal Gaze

What AI start-ups and labs are you keenly following?

We are extremely interested in any company who can take complete human work to be fully automated with AI. For example, a company that can create a trading robot for a stock exchange. If you could take a complete job of a human being and have it driven by AI, that’s a real breakthrough.

What technologies within AI and computing are you interested in?

I’m interested in companies that can fully automate human tasks, freeing up the time for human beings to focus on things that computers are not capable of doing.

As an AI leader, what industries you think would be fastest to adopting AI/ML with smooth efficiency? What are the new emerging markets for AI technology markets?

Financial trading, stock markets come to mind.  I’m thinking about industries where the level of repetition is extremely high. For the field of sales prospecting, this could lead to companies with a high count of customers. For example, insurance companies, telecommunications, and banks.

What’s your smartest work-related shortcut or productivity hack?

Copy and paste from Stack Overflow is a gem for developers.

Tag the one person in the industry whose answers to these questions you would love to read:

Alan Turing (if I could go back in time and bring him into our era). He’s the famous mathematician who essentially laid the foundations for the concept of the modern computer.

Thank you, Toumas! That was fun and hope to see you back on AiThority soon.

• About Tuomas
• About Vainu Labs

About Tuomas

Tuomas Rasila founded Norfello in 2006, prior to which he worked at Finnish Defense Forces for R&D of C&C systems. Between 2006-2009, Norfello expanded from a one-man shop to a highly profitable small company with 14 employees. Norfello was the company behind the DocScanner, a pioneering scanning app that allowed users to create professional looking PDF documents through pictures from their smartphones. In addition to serving as CTO and co-founder of Vainu, Rasila also owns and operates Haave Inc., a tech company with two subsidiaries including Sensofusion, a drone defense technology company and RND Works, a software consulting service.

Rasila is an inventor at heart. He specializes in developing, designing and launching new software products and, as CTO of Vainu, oversees the product development team. As an entrepreneur and business owner, he also provides guidance and serves as a kind of bridge between product development and business development. While Rasila is a serial entrepreneur and business launcher, he also happens to be longtime friends of co-founder Honkanen; the two grew up with each other in Helsinki.

Rasila is based in Helsinki. Aside from his business ventures, he maintains membership with the Finnish Defense Forces as a reserve officer.

About Vainu Labs

A subset of Vainu.io, VainuLabs provides commercial data teams with the building blocks for machine learning projects, from making accurate predictions on company trajectories to scoring and segmenting companies based on desired outcomes. The company’s goal is to make B2B collaboration as advanced as its B2C counterparts, using the data that is readily available for smarter business interactions. As a privately owned, founder-led company, we’re able to work fast and respond to our customers’ needs without anything getting in our way.

The post AiThority Interview Series With Tuomas Rasila, CTO & Co-Founder at Vainu appeared first on AiThority.

In an Agile, Constantly-Transforming Future, the Most Important Skills for an Individual to Be Equipped with Is Learning to Learn

A joint report by the Secretariat of the All Party Parliamentary Group on AI, Big Innovation Centre, and professional services firm KPMG was launched today – identifying the key skills individuals and organizations will need if they are to survive and thrive in the unfolding future.

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The report finds that jobs in the labor market of the future will look very different from today and the transformation is likely to be dramatic. It also calls for companies and governments to equip citizens and employees for that future, to help them learn the new skills needed to be relevant in a world of constant transformation.

The report identifies three key skills categories people will need in the future:

• skills to build/manage AI
• skills to work with AI
• skills to live in an AI-driven society

There will be an increased demand for “broad” skills and the most valued quality in people will be adaptability and the ability to learn.

At the time of this announcement, Niki Iliadis, Innovation and Policy Foresight Manager at Big Innovation Centre, said, “It’s now time for good policy makers and good companies to make sure people are ready for a future filled with AI.”

“In an agile, constantly-transforming future, the most important skills for an individual to be equipped with is learning to learn – the ability to process new information into knowledge. The borders between hard skills such as computer science and digital, and soft skills like creativity and problem solving, are merging together.”

AI will be key to this. Not only will it find personal solutions to help individuals develop their talents, but it will also be able to match these to a specific task.

Many aspects of our society will be revolutionized by AI. The pessimistic view is that many jobs will be automated, causing the much feared ‘AI replacing humans’ scenario. The optimistic view is that it represents an opportunity for unprecedented progress and an exciting opportunity to explore new jobs and frontiers.

Iliadis added, “Simply put – whether you are a doctor, a teacher, or an accountant, you will deliver the same outcomes, but with an all-new job description. It’s the way we work hand-in-hand with AI that will make the difference.”

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Robert Bolton, Head of KPMG’s People and Change Centre of Excellence, said, “The trick is to create organizations and societies that grasp the reinvention of workplaces to make the most of what humans can do, working with AI. This is not simply a challenge of coding, but of leadership, design, imagination, and creativity.”

International companies are already developing programmes such as KPMG’s Workforce Shaping, in which clients learn to identify possible future scenarios and to use multiple mindsets.

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The report calls for policymakers, organizations, and the wider society to work together to incentivize, encourage, and reward skills across the spectrum – helping build individuals fit for the agility of the future.

The report comes following an APPG AI evidence session held on 26 February in Westminster, focusing on the impact of AI on Skills. The All-Party Parliamentary Group on Artificial Intelligence (APPG AI) was set up in January 2017 with the aim to explore the impact and implications of Artificial Intelligence.

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The New Product Allows Customers to Route Their Queries to Crowd Service Ambassadors and Improve Customer Service

Limitless, the UK-based inventor and provider of a SaaS platform offering crowdsourced customer service, has announced its latest product – LimitlessLiveMessenger™. LimitlessLiveMessenger is an asynchronous messaging product that can be easily deployed on any website and provides businesses with direct access to Limitless’ on-demand Crowd Service® platform.

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LimitlessLiveMessenger Could Cut the Typical Customer Service Costs

LimitlessLiveMessenger, the new product, allows customers to route their queries to Crowd Service Ambassadors – loyal customers or employees who provide customer support and helpful responses within a matter of minutes. Conversations resolved by the crowd are between 50-75% cheaper than typical customer service costs.

Currently, Limitless provides a fresh approach to customer service through intelligent routing, AI, reward and reputation mechanisms that allow brands to route inquiries to a pool of their own loyal customers and employees.

Limitless’ blended AI solution uses machine learning to help businesses achieve their contact diversion targets. The AI suggested responses are based on historic conversations which include sophisticated quality checks that constantly adapt to enhance performance. A live feedback loop aggregates reputation scores and peer-to-peer reviews which are all fed back into the machine learning algorithm to continuously improve and maintain the accuracy of the AI answers and the crowd’s performance. Where a human is more suited to the engagement, customer questions are routed to the on-demand Crowd Service Ambassadors.

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Brands Can Deploy LimitlessLiveMessenger Within Hours to Unlock Resources and Attract New Pools of Skilled Talent

LimitlessLiveMessenger™ can be easily deployed onto any website creating an instant way to deflect customer inquiries to trained Ambassadors. It integrates with all major customer relationship management systems (CRM), including Salesforce, Zendesk, Genesys, HelpShift and Oracle Service Cloud, so all customer interactions are returned to the CRM, ensuring a full contact history and a single view of the customer is maintained.

Roger Beadle, co-founder and CEO of Limitless, says, “LimitlessLiveMessenger™ allows brands to deploy a simple but effective digital channel; a seamless and cost-effective way to blend AI and human interactions to provide world-class customer service. Our existing clients have already seen a dramatic increase in customer satisfaction with queries being answered in less than five minutes and customer satisfaction above 90%.

Roger added, “With the rise in demand for asynchronous messaging, we realize there is a hunger among companies for greater agility and flexibility to manage contact volumes, 24/7. But we also understand that this creates huge resourcing challenges. LimitlessLiveMessenger™ provides a plug-and-play solution, which brands can deploy within hours to unlock resources and attract new pools of skilled talent within their own customer base. We are excited to provide our clients with the unique opportunity to create an on-demand support team which better reflects their customer base and reward their own customers at the same time.” 

LimitlessLive Messenger® Is a Great Step Towards Building Customer Centricity

Nigel Watson, CIO at Northumbrian Water, one of the first businesses to adopt the solution, said, “Being a customer-focused business is of utmost priority to us and is one of our key corporate values. Leveraging our loyal customer base using Limitless Live Messenger® is a great step towards maintaining our core focus on customer centricity.”

Household brands such as the Daily Mail Group and Northumbrian Water have implemented LimitlessLiveMessenger™ on their websites, while several more including National Express will soon have this new feature deployed as a key contact channel on their websites.

With LimitlessLiveMessenger™, Limitless will continue to raise the bar for customer support, using asynchronous messaging to ensure customer interactions are seamless from the customer’s point of view, making for highly meaningful and rich conversations.

The Limitless Crowd Service® platform trains their AI algorithms to handle increasing numbers of lower value, simple inquiries autonomously. Leaving the Ambassador crowds to handle higher value and more complex inquiries. Limitless was co-founded by Roger Beadle and Megan Neale and is supported by investment from Unilever Ventures, Downing Ventures, and various angel investors.

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