Daily Archives: September 20, 2021

Even advocates of artificial intelligence (AI) will acknowledge that the concepthas endured some false starts over the years. However, the past decade hasbrought a transformation in how AI is perceived in sport -- with the clubs, leagues,organizations and businesses that underpin the industry discovering the innovationsthat can emerge through the simulation of human intelligence in machines.

Members of the public rely on this technology every day, and we take it forgranted, says Dr. Patrick Lucey, chief scientist at sports data and analyticsprovider Stats Perform.

The availability of data these days is one big difference that has driven AIadoption. There is also a greater appreciation of AI, and the return on investmentis there to see via objective measures. It also helps that AI can be applied acrossall business segments.

Barriers to Adoption

AI is fueled by crunching swathes of data via iterative processing and algorithmsthat allow software platforms to identify patterns and predict future outcomes. So,it follows that increasing volumes of data being collected and analyzed in thesports industry in recent years have refined such processes, generating moreaccurate results and bottom-line benefits.

However, given its definition, it is hardly surprising that AI has also been adifficult notion to grasp for many, especially given how it is often usedinterchangeably with machine learning a strand of AI that focuses on howcomputers can imitate the way that humans learn.

This barrier to adoption, though, has slowly evaporated as clubs and franchiseshave gradually learned to gauge the real-life results from an idea that manyinitially considered to be abstract.

When terms like AI and data science were first being bandied around, I was oneof those who didnt understand the value of it, says Ben Mackriell, VP data, AIand pro products at Stats Perform.

But now there is a greater level of understanding in the market in general thatAI is simply a mechanism that enables better experiences, with the coreingredient being data. The challenge is to make AI consumable and break downsome of the myths. The process is complex, but the output doesnt have to becomplex.

Journey of Understanding

Sports clubs have been on this journey of understanding how deploying AI canultimately improve results and there is certainly no turning back now.From a performance perspective, more than 350 clubs across various sports relyon Stats Performs data and technology services, of which AI is a centralcomponent.

Stats Perform was the first company to offer player tracking technology inbasketball more than a decade ago. It is now unthinkable for a team in the NBA as well as any other leading league not to have analysts on the payroll.It is an area that has grown exponentially over the past 10 years, Mackrielladds. Most Premier League clubs had one or two analysts a decade ago. Now, itis common for them to have more than 10 people working across multipleaspects data analytics.

Clubs are hiring data engineers now and you would not have seen that evenjust three years ago.

Vivid Illustration

During this summers delayed UEFA Euro 2020 soccer tournament, Stats Performpresented a vivid illustration of how consumable its AI capabilities can be forfans across Europe and beyond with its Euros Prediction model.Through Stats Performs public-facing digital platform, The Analyst, themodel estimated the probability of each match outcome by using a series of inputs that ranged from historical team and player performances to betting marketodds and team rankings.

Hundreds of thousands of scenarios were being crunched every time a goalwent in, Mackriell says.For clubs though, AI-driven predictive modelling can provide insights that delveeven deeper.Stats Performs Playing Styles framework, for example, takes into considerationnumerous events and factors to determine a teams tendencies. Eight playingstyles are put under the microscope, from build-up play to counter attacks.

Such data-based insights can then be used to identify the roles of individualplayers within each style and also analyze crucially, in an age of sky-high salariesand transfer fees how a possible new signing would slot into the existing teamsystem.Every action and phase on the field is broken down, Mackriell adds. Everyaction on the pitch can be quantified in terms of how likely it is to lead to a goal,and you can see how individuals contribute towards a goal-scoring opportunity.

This supports decision-making and assists in terms of scouting and investing inthe team.One of the most common questions we are asked by a club is: How will thisplayers skills translate into our team and league. That is where teams areseeing a return on investment with AI.

Moneyball

For sports clubs and franchises, AI is Moneyball 2.0 using data to introducelayers of predictive insights that can help them make sound businessdecisions.Most importantly, it is about focusing on solving a problem at the outset.We spend time with clubs across multiple sports to identify the problems theyare trying to solve, Mackriell says. This problem-solving approach is how wedeploy AI as a company, rather than just trying to bring together AI tech anddata.

Given increasing levels of data coverage, the results for clubs and franchisesworldwide will become increasingly sophisticated, according to Lucey.Sport has been a slow adopter as clubs are understandably private about howthey operate, he says. Like anything in sport, though, once there is success,there is a snowball effect.

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AI in Pro Sport: Laying the Groundwork - SportTechie

The team included Simmons & Simmons and Jacob Turner of Fountain Court Chambers to bring a 360 degree legal, regulatory, technical and commercial AI perspective

LONDON, Sept. 20, 2021 /PRNewswire/ -- One of the world's leading AI smart symptom checkers has taken the groundbreaking decision to publish a statement explaining how it works.

Healthily, supported by Best Practice AI together with Simmons & Simmons and Jacob Turner of Fountain Court Chambers today publish the first AI Explainability Statement to have been reviewed by the UK Information Commissioner's Office (ICO).

The Healthily AI Explainability Statement explains how Healthily uses AI in its app including why AI is being used, how the AI system was designed and how it operates.

The statement, which can be viewed here, provides a non-technical explanation of the Healthily AI to its customers, regulators and the wider public.

Around the world, there is a growing regulatory focus and consensus around the need for transparent and understandable AI. AI Explainability Statements are public-facing documents intended to provide transparency, particularly so as to comply with global best practices and AI ethical principles, as well as binding legislation. AI Explainability Statements such as this are intended to facilitate compliance with Articles 13, 14, 15 and 22 of the GDPR for organisations using AI to process personal data. The lack of such transparency has been at the heart of recent EU court cases and regulatory decisions, involving Uber and Ola in the Netherlands and Foodinho in Italy.

Healthily, a leading consumer digital healthcare company, worked with a team from the AI advisory firm, Best Practice AI, the international law firm Simmons & Simmons, and Jacob Turner from Fountain Court Chambers to create the first AI Explainability Statement in the sector.

They also engaged with the ICO. A spokesperson for the ICO confirmed:

"In preparing its Explainability Statement, Healthily received feedback from the UK's data protection regulator, the Information Commissioner's Office (ICO) and the published Statement reflects that input.

Story continues

It is the first AI Explainability Statement which has had consideration from a regulator.

The ICO has welcomed the Healthily publication of its Explainability Statement as an example of how organisations can practically apply the guidance on Explaining Decisions Made With AI".

Matteo Berlucchi, CEO of Healthily said:

"We are proud to continue our effort to be at the forefront of transparency and ethical AI use for our global consumer base. It was great to work with Best Practice AI on this valuable exercise."

Simon Greenman, Partner at Best Practice AI, said:

"Businesses need to understand that AI Explainability Statements will be a critical part of rolling out AI systems that retain the necessary levels of public trust. We are proud to have worked with Healthily and the ICO to have started this journey."

To learn more about how Best Practice AI, Simmons & Simmons LLP, and Jacob Turner from Fountain Court Chambers built the AI Explainability Statement, please contact us below.

Notes for Editors

About Healthily

Healthily is the first AI healthcare platform to put self-care at the heart of healthcare, with a mix of user-friendly health tools, an award winning app and a Smart Symptom Checker, one of the most accurate and advanced symptoms checkers in the world coupled with medical-grade information all approved by the Healthily Clinical Advisory Board. The first self-care platform registered as a Class 1 Medical Device, Healthily helps anyone, anywhere decide when to see a doctor and how to manage wellbeing safely at home. The Healthily AI platform is also licensed to telemedicine companies, health insurers, national health services and big pharma to help them scale their services more cost effectively. All part of the Healthily mission to help one billion people find their health through informed self-care. For more information visit https://www.livehealthily.com

About Best Practice AI Ltd

Best Practice AI is a London based AI management consultancy that advises corporates, start-ups and investors on AI strategy, implementation, risk and governance. The firm is a member of the World Economic Forum's Centre for the Fourth Industrial Revolution and work on the WEF's Empowering AI Leadership Board Toolkit and AI Governance Frameworks. They are on the WEF's Global AI Council and the UK All Party Parliamentary Group on AI's Enterprise Adoption Task Force. The firm publishes the world's large library of AI case studies and use cases at https://www.bestpractice.ai/

About Simmons & Simmons

Simmons & Simmons is an international law firm with a dedicated AI Group and extensive data protection compliance experience. The firm has around 280 partners and 1300 staff working in Asia, Europe and the Middle East across 21 offices in 19 countries.They work across Asset Management & Investment Funds, Financial Institutions, Healthcare & Life Sciences and Telecoms, Media & Technology (TMT). For more information visit https://www.simmons-simmons.com

About Jacob Turner and Fountain Court Chambers

Jacob Turner is a barrister at Fountain Court Chambers with AI and data protection experience. He is the author of Robot Rules: Regulating Artificial Intelligence. He advises governments, regulators and businesses on AI regulation.

Fountain Court Chambers is a leading commercial chambers with expertise across financial and commercial disputes, regulatory proceedings and commercial crime.

ContactsTim Gordon press@bestpractice.ai

press@livehealthily.com or Matteo Berlucchi, CEO, matteo@livehealthily.com

Carl Philip Brandgard CarlPhilip.Brandgard@simmons-simmons.com

Helen Griffiths Helen@fountaincourt.co.uk

ICO Information

For more information on ICO guidelines for explaining decisions made with AI visit

https://ico.org.uk/for-organisations/guide-to-data-protection/key-dp-themes/explaining-decisions-made-with-artificial-intelligence/

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Healthily and Best Practice AI publish world's first AI Explainability Statement reviewed by the ICO - Yahoo Finance

Artificial intelligence is only a tool, but what a tool it is. It may be elevating our world into an era of enlightenment and productivity, or plunging us into a dark pit. To help achieve the former, and not the latter, it must be handled with a great deal of care and forethought. This is where technology leaders and practitioners need to step up and help pave the way, encouraging the use of AI to augment and amplify human capabilities.

Those are some of the observations drawn from Stanford University's recently released report, the next installment out of itsOne-Hundred-Year Study on Artificial Intelligence, an extremely long-term effort to track and monitor AI as it progresses over the coming century. The report, first launched in 2016, was prepared by a standing committee that includes a panel of 17 experts, and urges that AI be employed as a tool to augment and amplify human skills. "All stakeholders need to be involved in the design of AI assistants to produce a human-AI team that outperforms either alone. Human users must understand the AI system and its limitations to trust and use it appropriately, and AI system designers must understand the context in which the system will be used."

AI has the greatest potential when it augments human capabilities, and this is where it can be most productive, the report's authors argue. "Whether it's finding patterns in chemical interactions that lead to a new drug discovery or helping public defenders identify the most appropriate strategies to pursue, there are many ways in which AI can augment the capabilities of people. An AI system might be better at synthesizing available data and making decisions in well-characterized parts of a problem, while a human may be better at understanding the implications of the data -- say if missing data fields are actually a signal for important, unmeasured information for some subgroup represented in the data -- working with difficult-to-fully quantify objectives, and identifying creative actions beyond what the AI may be programmed to consider."

Complete autonomy "is not the eventual goal for AI systems," the co-authors state. There needs to be "clear lines of communication between human and automated decision makers. At the end of the day, the success of the field will be measured by how it has empowered all people, not by how efficiently machines devalue the very people we are trying to help."

The report examines key areas where AI is developing and making a difference in work and lives:

Discovery:"New developments in interpretable AI and visualization of AI are making it much easier for humans to inspect AI programs more deeply and use them to explicitly organize information in a way that facilitates a human expert putting the pieces together and drawing insights," the report notes.

Decision-making:AI helps summarize data too complex for a person to easily absorb. "Summarization is now being used or actively considered in fields where large amounts of text must be read and analyzed -- whether it is following news media, doing financial research, conducting search engine optimization, or analyzing contracts, patents, or legal documents. Nascent progress in highly realistic (but currently not reliable or accurate) text generation, such as GPT-3, may also make these interactions more natural."

AI as assistant:"We are already starting to see AI programs that can process and translate text from a photograph, allowing travelers to read signage and menus. Improved translation tools will facilitate human interactions across cultures. Projects that once required a person to have highly specialized knowledge or copious amounts of time may become accessible to more people by allowing them to search for task and context-specific expertise."

Language processing:Language processing technology advances have been supported by neural network language models, including ELMo, GPT, mT5, and BERT, that "learn about how words are used in context -- including elements of grammar, meaning, and basic facts about the world -- from sifting through the patterns in naturally occurring text. These models' facility with language is already supporting applications such as machine translation, text classification, speech recognition, writing aids, and chatbots. Future applications could include improving human-AI interactions across diverse languages and situations."

Computer vision and image processing:"Many image-processing approaches use deep learning for recognition, classification, conversion, and other tasks. Training time for image processing has been substantially reduced. Programs running on ImageNet, a massive standardized collection of over 14 million photographs used to train and test visual identification programs, complete their work 100 times faster than just three years ago." The report's authors caution, however, that such technology could be subject to abuse.

Robotics: "The last five years have seen consistent progress in intelligent robotics driven by machine learning, powerful computing and communication capabilities, and increased availability of sophisticated sensor systems. Although these systems are not fully able to take advantage of all the advances in AI, primarily due to the physical constraints of the environments, highly agile and dynamic robotics systems are now available for home and industrial use."

Mobility: "The optimistic predictions from five years ago of rapid progress in fully autonomous driving have failed to materialize. The reasons may be complicated, but the need for exceptional levels of safety in complex physical environments makes the problem more challenging, and more expensive, to solve than had been anticipated. The design of self-driving cars requires integration of a range of technologies including sensor fusion, AI planning and decision-making, vehicle dynamics prediction, on-the-fly rerouting, inter-vehicle communication, and more."

Recommender systems:The AI technologies powering recommender systems have changed considerably in the past five years, the report states. "One shift is the near-universal incorporation of deep neural networks to better predict user responses to recommendations. There has also been increased usage of sophisticated machine-learning techniques for analyzing the content of recommended items, rather than using only metadata and user click or consumption behavior."

The report's authors caution that "the use of ever-more-sophisticated machine-learned models for recommending products, services, and content has raised significant concerns about the issues of fairness, diversity, polarization, and the emergence of filter bubbles, where the recommender system suggests. While these problems require more than just technical solutions, increasing attention is paid to technologies that can at least partly address such issues."

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Artificial intelligence success is tied to ability to augment, not just automate - ZDNet

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.

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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