Advancements in generative AI significantly reshaped the way businesses operate in 2023. Many HR teams and departments began experimenting with these tools to streamline tasks and improve efficiencies. As we look ahead to 2024, what can we expect from generative AI when it comes to the future of work? We asked six technology experts to share their predictions.

Effective AI Governance Will Be Integral

AI is already embedded into our daily lives in many ways, and we should expect this trend to not only continue, but flourish, in 2024.

According to Gartner research, more than 80% of businesses are set to use Generative AI by 2026.

However, Ed Challis, Head of AI Strategy and General Manager for Communications Mining, UiPath argues that “the ability to deploy the technology responsibly is still largely immature, with concerns from executives around risk and governance.

“2024 will be the year this perception changes and organizations see AI progress from aspiration to implementation,” said Challis. “Effective AI governance is critical for driving strong AI results. If implemented correctly, it can go further than positively affecting productivity and efficiency – it can also enhance an organization’s risk and governance posture.”

AI’s Increasing Importance In Recruitment & Training

When looking at recruitment and training, Merijn te Booij, GM of Workforce Engagement at Genesys believes that AI will be used by companies to transform how they approach training and employee attrition.

“I predict next year we will see generative AI transform employee training and curriculum building,” said te Booij. “Currently, companies deliver standardized training across their workforce or employee segments in like-roles because it’s not scalable to tailor it to individuals. Often, we see the same training applies to senior, long-tenured people-leaders as it does junior-level staffers who are still early in their careers. Generative AI capabilities will vastly reshape how organizations build training personalized to each employee. It will also help organizations automate coaching, making it more efficient to deliver individualized support based on the precise needs of each employee.

In the future, te Booij believes that “organizations will also be able to tap into generative AI to help them predict attrition, retention, and career possibilities. Through more personalized coaching and training, it can provide insights into understanding which employees may leave or who may have higher potential. This will be discerned from signals within conversations, displayed behaviors, and performance, helping organizations understand where their employees fall on the spectrum so they can personalize plans to re-engage them, assign training, and help them advance their careers.”

Pamela Maynard, CEO, Avanade agrees, highlighting the countless benefits of adopting AI within business to streamline recruitment:

“AI allows recruiters to use the power of data to make better decisions, which can include analyzing CVs and job applications as well as assisting with the sourcing and screening of candidates. As AI begins to develop and has more data behind it, the technology can carry out tasks that usually require human intelligence, such as predicting a candidate’s success ahead of hiring and even how they may fit culturally, in an organization.

“With the help of AI in their role, recruiters can be more creative, and innovative and bring bold ideas to their work. With AI on hand to help with administrative tasks, workers can gain around 20 additional hours per week. The recruiting process becomes more time-efficient for recruiters as time-consuming and repetitive tasks such as screening, candidate sourcing, and initial communications can become automated.”

Humanity Is At The Core Of Our AI Innovation Journey

However, with AI permeating into so many of our lives, businesses will find themselves in need of a balance between new technologies and the essential human element. Companies will ponder how best to upskill the workforce alongside advancing technology, for organizational growth.

In line with maintaining the human element, Aaron Skonnard, CEO and Co-Founder of Pluralsight argues that “if you want to get the most out of AI, and any other technology, you need people with the skills to leverage that technology and who have skills across other domains important to your business.

AI can only take an organization so far. It’s the humans powering the technology that will truly drive innovation.

Skonnard also believes that “the next wave of tech learning requires leaders to bring learning directly to their teams, within the flow of work. Learning should become more of a conversation today, where content is the answer to specific questions that arise in the flow of work. Tech learning solutions need to embrace that conversational modality and experience. By making learning a natural part of technologists’ workflow, organizations will naturally begin to see their skills gaps close.”

The Centerpiece Of Leadership Transformation: Putting Employees At The Core

With employee expectations on the rise, we anticipate seeing a paradigm shift towards employee-centric leadership that fosters transparency, work/life balance, and a transformation in organizational strategies to meet these evolving workforce expectations.

Richa Gupta, CHRO, Globalization Partners (G-P) highlights that the future of work is here, but notes it is time for the leaders of the future to stand up and prioritize promoting a flexible workforce to maintain the UK’s position as a leading global economy for the new year.

“Employees expect more from leadership than ever before – more transparency, more commitment to work/life balance, and more willingness to incorporate their sentiments and preferences into the workplace. The mindset has shifted from ‘my paycheque to my purpose’; ‘my boss to my coach’; ‘my annual performance review to ongoing development conversations’. It is not an employer’s market anymore; it’s an employee’s market and workers aren’t afraid to search elsewhere to find a workplace, or leader, that fits their needs.

Gupta believes that in response “there will continue to be not only a transformation of leadership styles but in the overall organization of leadership strategy. Successful leaders will need to prioritize qualities and strategies that promote a flexible and open-minded workforce where credibility, reliability, and trust are paramount.”

Collaborative Teamwork Set To Shift Towards Greater Intentionality

With the evolving challenges that ensue from responsibly navigating the adoption of AI within business, Bryan Stallings, Chief Evangelist, Lucid Software predicts that “2024 will see teams becoming more confident and intentional about increasing the frequency with which they collaborate asynchronously.

“This shift is being driven by firms realizing that endless follow-up and planning meetings are hindering impactful collaboration and productivity. Effective working will continue to be defined by positive outcomes and the journey of how teams achieve those outcomes will be equally important. Additional gains will be realized as teams continue to embrace agile practices, and leverage techniques that help them to align more quickly on a shared vision and collaborate effectively to deliver great results.

Stallings concludes that as this future of work continues to take shape over the next twelve months, we will soon be saying, “How did we ever work any other way?”

A Glimpse Into 2024

As seen in 2023, it’s clear AI is reshaping the workforce of the future. 2024 will likely continue to bring surprising developments in AI applications, presenting new opportunities for companies to enhance employee support, whilst also streamlining processes.

However, the impact of AI on the world of work in 2024 and beyond is still unfolding. Business leaders must seize the moment here to implement upskilling initiatives, so employees have access to effective and continuous tech training regimes that will boost their familiarity with the integral tools moulding the future of work.

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

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While there’s no shortage of uncertainty as we countdown to 2024, the crystal ball seems to have a few things in focus for the next trip around the sun. From the long-anticipated shift from third-party cookies to first-party data to the harnessing of AI and the evolution of e-commerce, here are five key tech trends set to shape how brands connect with customers.

The Race For First-party Data

2024 is set to be the year of first-party data with Chrome deprecating third-party tracking cookies over 2024, following the lead of other browsers having already implemented similar changes. Brands will be focused on strategies to build their valuable first-party data, built up from purchasing signals tracked via loyalty programs, registered users, CRM, and so on.

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Brands will be turning to publishers to harness their vast contextual and enriched datasets from either registered users or gleaned from the type of content being consumed in real time. Combinations of both publisher and advertiser data via data cleanrooms have been a topic of interest and will be interesting to see how this is picked up over the year.

The shift promises contextual interest targeting, enabling a more precisely tailored match for brands between creative and audiences. The focus on sharper targeting translates into reaching the right audience with specific intent, ultimately, leading to increased conversion rates and effectiveness.

Focus On Innovative Advertising

The ongoing adoption of more premium and effective ad solutions is a growing trend, which is only set to continue from an effectiveness and user experience perspective. Clients are reporting that campaign budgets can be extended up to seven times more just by using more effective formats.

Brands are demanding more premium sponsorship opportunities and publishers are accommodating via a range of high-impact and bespoke formats. This means more curated and higher quality ad experiences on-site, resulting in a longer-lasting impact from awareness to engagement through to conversion. We’re seeing further development toward solutions overlaying first-party data or contextual insights with premium ad formats. This delivers more personalized and relevant experiences, which are particularly effective for eCommerce.

And, as smartphone users worldwide reach an estimated 4.6 billion, with expectations of surpassing 5.1 billion by 2028, mobile internet traffic already claims nearly 60 percent of total web traffic. In response to this unprecedented growth, there’s the need for a more expansive, premium mobile ad format that would not only extend the in-ad experience via a seamless, scrolling user experience; it also drive brand awareness and communicate additional product information in an unobtrusive, impactful way.

Innovative new ad formats like BrandStory outshine competitors with triple the ad space and 2.8 times greater time in view than single-scroll ad formats. This addresses the surging demand from brands worldwide for more real estate to drive real results by seamlessly intertwining awareness, exploration, and action within one comprehensive solution.

E-Commerce To Continue Upward Trajectory

We’re witnessing the takeoff of eCommerce across the board, supercharged from the shift during the lockdown as businesses of all sizes realized the value of having a direct relationship with their consumers. On top of the sales, eCommerce is allowing advertisers to own the data relating to the customer and the sale, which is a huge factor in the boom.

eCommerce will see sustained growth as brands demonstrate a willingness to invest in channels that streamline the conversion process and build that direct line to their customers. We’re seeing take-up of in-banner transactions, shoppable video, contextual targeting, and dynamic eCommerce ads already playing a pivotal role in the transformation.

Harnessing AI

AI is quickly moving from a novelty to being embedded within a multitude of platforms to increase effectiveness and revolutionize the landscape, both in the overall marketing function and the specific ways we engage with technology. This includes from a generative perspective of creating content and messaging to getting a better handle on insights and planning, particularly with the abundance of first-party data. The wealth of information from this data will serve as a fertile ground for extensive learning and the development of models tailored to audience insights.

Surge In Digital Outdoor And Connected TV Channels

In the coming year, brace for a significant expansion in alternative advertising channels, particularly digital outdoor and connected TV. We anticipate substantial growth and innovative strategies as these channels evolve to become pivotal players in the advertising landscape. We have even been seeing clients connecting their digital out-of-home and digital display campaigns with live data, meaning interactions with the digital campaign can be relayed to the digital out-of-home screens – another space to watch.

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The Future Of Consumer-driven Digital Experiences

As we step into 2024, the world of advertising is gearing up for some exciting opportunities for publishers, advertisers, and consumers alike. The strong focus on first-party data, the use of AI, the evolution of online shopping, the rise of different advertising channels, and the march toward new ad formats are all painting a picture of innovation, integration, and adaptability.

It’s a call for advertisers and publishers to work together, align their strategies with what consumers are looking for, and create a landscape where creativity, data insights, and modern technology come together for a more engaging advertising experience.

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

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The global population continues to expand, and artificial intelligence (AI) is the emerging tool to make growing urban areas more progressive, productive, and environmentally friendly. In terms of sustainable urban planning, AI’s complex computer algorithms can learn and make predictions to facilitate better traffic flow, energy management, waste management, environmental monitoring, public safety, and more.

Thanks to AI-driven solutions and growing amounts of data from sensors, cameras, and sources in every part of the cityscape, our urban areas have the potential to become far more sustainable and vibrant.

How AI is gaining a presence in urban planning and development overall

AI is everywhere we look — in cars that can park and drive autonomously, in homes as smart speakers, and even in climate control systems. As such, the push to integrate AI into city planning should come as no surprise.

Pre-existing digital infrastructure is making it possible for AI to take root in everyday places around us, as most modern cities already have some level of technological development.

Today, urban developers are integrating autonomous and self-learning systems into that digital foundation. This gateway is wide open and allows city planners to introduce AI into virtually every aspect of the urban landscape.

How AI can enhance sustainability in cities

Smarter traffic management, less congestion, and better public transportation are all possible thanks to AI. Artificial intelligence platforms can assess traffic patterns in real-time by utilizing data from numerous sources — including traffic cameras, GPS devices, and IoT sensors — to predict and manage traffic flow through traffic lights, vehicle rerouting, and real-time traffic updates. With infrastructure like cameras and sensors to record real-time traffic conditions, high-speed networks to transmit the data, and AI analytics platforms, public transportation systems will soon streamline a city’s operations, anticipate repair requirements, and provide more personalized service.

By optimizing energy usage and promoting renewable energy, AI is creating more energy-efficient cities by streamlining the monitoring of smart grids, tracking power consumption, and anticipating problems before they happen. It can examine energy consumption patterns in individual homes and businesses, and find ways to cut back.

Still, large-scale implementation of this technology will require several updated developments in urban and city infrastructure. These include smart meters that track a building’s energy consumption, AI-driven renewable energy sources, and a modern electrical grid equipped with sensors, communication networks, and control systems.

By automating recycling and waste management, AI has the potential to make cities more eco-friendly and sustainable. AI-optimized waste collection routes, for example, can help reduce greenhouse emissions and overall fuel consumption. With AI-powered robots automating recycling processes, cities can dramatically decrease the amount of trash in their landfills. To facilitate automated recycling and waste disposal, however, cities will need to incorporate garbage trucks and trash containers with sensors to track garbage accumulation, schedule optimized collection routes, and create automated sorting facilities.

How AI can help make cities and residents healthier and safer

AI also has the potential to help cities take preventative steps in safeguarding the health of their local environments and residents.

For example, AI systems can keep tabs on air pollution levels, empowering city planners to take preventative measures and inform citizens of health hazards. These systems can also be used to help city and urban planners track water purity, forecast demand, and enhance water distribution systems for consistent supply.

Yet, cities will need to implement critical infrastructure before they can enjoy AI-driven environmental monitoring. These include sensor networks measuring air and water quality, sensors monitoring real-time weather conditions, cloud-based platforms processing the data, and AI-powered warning systems that allow communities to take proactive steps in protecting the environment and public health.

AI can also improve public safety and security in cities with systems that evaluate data from diverse sources, discover patterns in criminal activity, and deliver actionable insights to law enforcement services. These can then be used to evaluate data from weather sensors, predict natural disasters, and reduce casualties.

For this to happen, cities must integrate several technological updates, such as high-resolution cameras across the city, as well as emergency communication networks connecting first responders, AI systems, and the public. On top of this, computing systems must be incorporated to best analyze safety data and provide actionable intelligence to help implement improvements.

AI can even participate in urban development by simulating how different scenarios will impact a city’s growth, infrastructure, and environment with systems that can accurately predict when cities require infrastructure maintenance. As a result, these systems can help city and urban planners maximize land usage by simulating proposed urban development initiatives.

The necessary information for AI-driven city planning and infrastructure maintenance comes from high-resolution satellite images, LiDAR, and other geospatial data sources. This data is uploaded to digital platforms that create digital twins of a city, and then run it through a series of “what-if” simulations.

AI will play a pivotal role in making the cities of tomorrow more sustainable — perhaps even a leading role.

Most of these technological advancements are already possible and underway in cities around the world. Thanks to AI’s rapid pace of advancement, upcoming years hold unprecedented potential for positive shifts in eco-friendly urban centers.

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

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Generative AI has captured the imagination of business leaders around the world. However, given that the technology may soon be performing tasks or heavily supporting human decision-making in operational areas like supply chain and procurement, there are significant risks to consider as we navigate into these new waters.

The truth is that without some degree of human intervention, AI could wreak havoc on supply chains. For example, if AI is able to detect or preempt a run on a specific commodity, food or medicine, then it could also trigger an autonomous buying cycle ahead of that run, thereby exacerbating it. In the worst-case scenario, this could drive shortages in life-saving supplies like cancer medicines which have proven to be vulnerable to supply chain fragilities.

Of course, AI can also be a force for good in supply chain and procurement functions, but there are several dangers that could arise if it’s left unchecked. For example:

Purchase Order Fraud and Supplier Selection Fraud –

Procurement and Accounts Payable teams have gotten used to a type of fraud committed by hackers where they impersonate a procurement organization and ask suppliers for sensitive financial information by sending them a fake Purchase Order. AI can be used to generate fake purchase orders that look authentic much more quickly, thus dramatically multiplying the costs of this kind of attack.

Additionally, suppliers might have to worry about the accuracy or fairness of an AI selecting the winning supplier as a result of a sourcing process. For example, the customer or maybe hackers could potentially manipulate the AI system to favor certain suppliers over others, meaning a potential loss of a lucrative contract.

Release of Sensitive Intellectual Property or Private Information via AI – 

Unless handled properly, data entered into generative AI tools can be viewed by others using the service. While the spread of this information may happen without malicious intent, it is still very alarming that sensitive IP and trade secrets could potentially be stolen by AI. That’s why Samsung and other companies have recently banned or paused use of generative AI technology by their employees while investigating potential risks.

Algorithmic Bias, Lack of Transparency in Supplier Recommendations –

Many new AI systems provide algorithmic recommendations for suppliers. These recommendations incorporate training data that may show bias toward historically marginalized suppliers and run against the stated intentions of Supplier Diversity programs operated by many procurement and supply chain organizations. If biases present in the data are not properly addressed, the AI could perpetuate or even exacerbate these biases, leading to unfair outcomes and legal liability. It may also work against the efforts to develop new suppliers or suppliers from strategically relevant and historically underrepresented communities.

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For example, one researcher on algorithmic bias found that algorithms used in making hiring decisions would down-rank candidates whose application contained the word “woman”.

Based on training data, the two features that most predicted the candidate would be hired were if their name was “Jared” and if they had played lacrosse. These algorithmic recommendations that can now appear inside of systems like SAP and Oracle, as well as powering rankings for all sorts of corporate functions, are also potential legal liabilities. In fact, New York has just adopted a law that will hold companies legally responsible for algorithmic bias in automated hiring tools that are deemed to run afoul of labor laws or create the basis for an Equal Opportunity Employment Office complaint.

Given the similarities of automated tools that are used in selecting the right person for a job and selecting the right supplier for a contract or purchase order, it is troubling to imagine how these bias scenarios could play out against the interests of procurement and supply chain groups. Making the AI’s decisions as transparent as possible will be critical in order to avoid these pitfalls.

Disruption in Critical Supplies Caused by AI-induced “Panic Buying” Demand Surges –

The classic model of a “run on the bank” is a panic induced by the fear caused by the need to access a needed resource. Earlier this year, we saw this dynamic play out dramatically in the Silicon Valley Bank collapse, but the fundamental structure of the disruption scenario is no different than many other common shortages.

It’s easy to imagine that as AI becomes more fused with autonomous systems that monitor markets and conduct buy cycles for repetitive transactions – similar to algorithmic trading in financial markets – that the risk of causing a shortage by trying to “beat the market” ahead of a price spike could unintentionally cause a disruption. For categories such as food, energy and medicine, the consequences of such disruptions could prove immensely painful to procurement and supply management teams, not to mention communities and citizens.

Understanding the consequences of these autonomous buying and selling agents in a supply chain will be challenging because of self-fulfilling prophecies.

For example, let’s imagine that a company that is worried about hurricanes sets up an AI agent to monitor satellite imagery of weather patterns in order to purchase bananas and other perishable fresh fruit before a hurricane makes land. Now let’s imagine that several companies have configured the exact same agent.

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Due to a satellite error, an alert is mistakenly triggered saying: “Hurricane detected! Lead times will soon increase. Recommendation: Increase the size of your banana order.” It is not hard to imagine such a trigger itself causing a temporary banana shortage by suddenly and unexpectedly increasing the aggregate demand for bananas, triggering what supply chain managers refer to as a “bullwhip” effect. This dynamic can be understood as Level 2 Chaos, which Yuval Noah Hari defines as chaos that “…reacts to predictions about it.”

There is certainly a great deal of AI hype floating around, and for many of us in procurement and supply chain, that is nothing new. In fact, there have previously been periods of AI hype and so-called “AI winters” when the rising expectations of total automation met the disappointing reality of the many limitations or drawbacks of current models. And yet, these models are only getting better, and the speed of improvement has never been faster.

We must have internal discussions and form clear boundaries and specific intentions ahead of this technology’s rapid acceleration. Commonsense guidelines and guardrails may help to ensure that AI is used responsibly and effectively in our procurement and supply chains. This approach will also help enhance the benefits that generative AI will provide to organizations, while minimizing potential risks and challenges.

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

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Reports of unprecedented heat records globally have dominated the headlines of this year. As on 8 August, the United States had witnessed 15 weather and climate events causing over $1 billion in damages each. This surge in natural disasters, well above the 1980-2022 annual average, underscores the urgency for advanced risk management solutions. Artificial Intelligence (AI) emerges as a powerful tool to navigate escalating climate risks, enabling businesses to secure their future proactively.

Enhancing Precision in Climate Risk Management

The intensifying unpredictability of climate patterns has complicated risk management for businesses. Though historically sound, traditional models often need to anticipate the intricate impacts of climate change. These models, rooted in historical data, need help to account for the rapid and dynamic shifts occurring in today’s climate landscape. This inherent limitation has led to instances where businesses have been caught off guard by unprecedented and unexpected weather events, resulting in financial losses and operational disruptions.

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AI offers a revolutionary approach to climate risk management.

AI excels at deciphering complex data sets, revealing intricate patterns and correlations. By analyzing diverse data points from past and present climate trends, AI crafts a comprehensive narrative, providing businesses with a clearer vision of potential future risks. This refined forecasting equips businesses to take proactive measures against emerging climate risks.

Imagine a company operating in a region with historically low flood risk. With AI’s predictive capabilities, this company can now detect rising flood risks due to shifting weather patterns, enabling them to implement preventive measures or secure suitable insurance coverage beforehand.

Climate Risks: A Nascent Investment Avenue

AI’s lasting value in forecasting extends beyond business considerations, stretching into the financial landscape. By enhancing risk assessment tools for physical, transition, and economic climate risks, AI empowers investors to venture into climate-related opportunities. This injection of capital bolsters industries vulnerable to climate change and nurtures the growth of resilient enterprises. For instance, armed with AI-derived insights, an entrepreneur could direct investments toward infrastructure projects in regions susceptible to rising sea levels.

In our pursuit of tackling escalating climate challenges, AI emerges as a pivotal solution within a toolkit of essential technologies and strategies to enhance climate resilience. AI surpasses mere adaptation to ignite proactive transformation by refining precision in risk management and unveiling novel investment pathways. As businesses and investors uncover AI’s potential, they protect their interests and contribute to a future marked by resilience and sustainability.

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

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Manual, human-powered processes have stunted the development of resilient and innovative cities. Traditional infrastructure development and maintenance brings burdensome costs and sometimes decades-long timelines. But AI and associated technologies promise seismic transformation.

The world is on the cusp of a new era where sustainable and resilient infrastructure is the norm, and cost-effective, speedy development is possible. Nowhere is this transformation more promising than in water resource management and building and infrastructure construction.

Developing Resilient Water Infrastructure

Droughts and floods and, in turn, harmful water pollution events will only increase in the coming decades as the planet warms. Existing AI tools can help monitor, conserve and optimize water resources.

Managing and Monitoring Resources:

In times of extreme drought, AI can help leaders make tough decisions about resource allocation and quality management. For example, the use of long short-term memory (LSTM) and convolutional neural networks (CNN) in South Korea’s Nakdong River Basin to simulate water quality and water levels. The models accurately predicted water quality parameters, helping water resource managers better understand and govern supply.

Optimizing Agriculture:

Water scarcity also poses a significant threat to agriculture—which accounts for over 70% of global water use.

Optimizing irrigation practices for sustainable water resource management is possible with precision agriculture powered by AI tools. These tools can analyze data from soil moisture sensors, weather forecasts, satellite or drone imagery and plant characteristics to determine precise irrigation requirements.

Designing for the Future:

The U.S. built much of its drinking water infrastructure before 1950. These outdated utilities are completely unprepared for modern-day population fluctuations and the threat of rising sea levels and flood risk. One AI-powered solution to this problem is generative design, which harnesses computational algorithms and artificial intelligence to rapidly generate optimized drinking water infrastructure that consider a range of future variables, such as population change and climate risk. For the development of new and upgrade of existing infrastructure, generative design is a cost-effective and proactive solution.

Designing Cities of the Future 

AI is also revolutionizing building construction and urban development, from initial design to ongoing maintenance. Using algorithms, cities can speed the pace of innovation while enhancing building and infrastructure quality and resilience

Streamline Design and Planning: AI is redefining the design and planning stages of construction. Advanced software platforms like Naska.AI leverage on-site data to automate critical processes like quality control and progress tracking. This not only reduces project risks and uncertainties but also expedites decision-making, enabling more efficient resource allocation and timely adjustments to project plans.

Boost Efficiency: AI’s transformative potential extends to construction projects’ execution. Technologies like Built Robotics transform heavy machinery like excavators into autonomous construction robots. This not only reduces the risk of worker injuries but enhances productivity by automating precise and repetitive tasks.

Post-Construction Monitoring: Once buildings and other infrastructure come to life, AI-enabled sensors can serve as guardians of structural health and sustainability through the IoT. These sensors can track structural integrity, energy consumption patterns, and occupancy levels in real time. By routinely gathering and analyzing data, AI systems enable proactive maintenance and resource optimization, ensuring that buildings and infrastructure remain stable and sustainable for generations to come.

Forging the Cities of Tomorrow

As we navigate the challenges of changing infrastructure needs and extreme weather, AI will guide us toward a more resilient urban environment. Developing adaptable and energy-efficient water management systems, buildings and other large-scale infrastructure is critical. AI’s role is to serve as an innovation catalyst and a force multiplier, enhancing human design and engineering capabilities to meet the needs of the future.

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

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The proliferation of AI in the telehealth technology (healthtech) industry unfolds a different perspective. In recent years, AI-powered bots are providing personalized online consultation to a majority of patients who own a smartphone. The quality of healthcare has improved significantly with the emergence of AI-driven medical support. From being a virtual reality in the pore-COVID years, AI-based telehealth is considered as the future of medicine. In fact, AI-based frameworks have strongly facilitated the growth of telehealth services across the world. But, what has the application of AI done in the broader sense to the healthcare industry? 

AI in Telehealth Technology in 2023

The stage is set for the rapid adoption of AI-based technologies in healthcare. AI in telehealth is one of the most prolific areas of innovation, investment, and growth. For doctors as well as patients, AI is a boon. And, this is just the start. 79% of healthcare organizations are expected to increase their investments in AI-led telehealth technologies and administration. Nearly 40% of organizations are already using AI capabilities such as deep learning, NLP, and automation for patient health record documentation and text analytics, personalized recommendations, mobile wellness, medical imaging, patient flow optimization, and diagnostics and report generation.

In a true sense, AI has gradually emerged as a strong enabler in the telehealth segment. Transforming the entire business landscape related to healthcare operations, telehealth can be a proven option to improve patient convenience right from the doorstep. 

IMPACT OF AI ON TELEHEALTH

• Is AI driving the accuracy of diagnosis?

Telemedicine is a well-designed aspect dominating patient diagnosis on a large scale. Online monitoring of diabetic retinopathy in the US, has proved to successfully reduce the physical footfalls of such patients. In fact, statistics available from the L.A. County Department of Health Services proved that telemedicine has reduced risks of travel for patients affected with retinopathy, thereby, reducing risks of operational disabilities. AI has been the core framework in designing a technology-based interactive interface to facilitate the system of diagnosing serious illnesses while continuing treatment processes.

But, can AI really help with the correct diagnosis of complicated diseases?

Imagery of data besides, predictive data modeling can largely help as explained by the experts. Reducing the scope of physical visits to doctors, AI has made healthcare available to the customer’s doorstep.

But, has AI brought in a cost-effective approach precisely?

Reducing physical visits of the patients to the POS (Hospitals/ Clinics etc.); AI technology has focussed more on patient convenience. 

• Is AI helping doctors? 

Reducing footfalls within hospitals through an AI-based telehealth service framework can actually help the doctors more than the patients. Helping doctors with effective time management, AI interventions can get things automated for mutual convenience. On the other hand, queuing up patients within a specified time limit enables doctors with the availability of valid data to plan in accordance with a patient’s requirements and the availability of desired services. AI, therefore, enables complete virtual assistance, and doctors can work in harmony without any major human support system.

• Is AI helping elderly patients?

Telehealth is often linked with telemedicine, assisting users to enhance the quality of engagement. Robo advisers could be deployed to help elderly patients in order to reach out to necessary support systems. In fact, Japan has been identified as a key market, investing resources, to develop robots to provide end-to-end assistance to the patients concerned.

• How is AI bringing in better convenience?

AI has opened up a world of possibilities to monitor the health of aged patients through a remote platform. Enabling data analysis and collaboration, patient monitoring has reached a new level, while AI-assisted augmented reality framework has defined end-to-end patient convenience.

Does AI simplify clinical processes?

The strategy to unleash predictive potential has helped in creating and executing a direct approach for successful treatment. In-person consultations are being reduced to a large extent, and AI in telehealth has changed the entire outlook of healthcare services across markets. 

• Has AI made hospital visits easier?

AI has played a significant role in improving patient care services. Patient wait times have reduced significantly, ensuring less frustration and anxiety when visiting the clinic or hospital for various ailments or follow-up checkups. AI has improved service patterns, in entirety, be it ambulance services or blood collection requirements through an augmented reality-based support system.

CONCLUSION

The significance of AI in telehealth is still in a nascent stage. AI could be used to reduce operational expenses related to healthcare services while differentiating a healthcare brand in terms of cost leadership and effectiveness of operations. Decreasing patient wait times, while recommending the available options for optimal treatment has validated such technology interventions in the first place. Going forward it is explained that AI has validated a complete accessibility of service from the customer’s doorstep in continuity. Reducing the risk of painful and invasive treatments during the initial phase, AI enablers in healthcare define convenience on a larger scale. 

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

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AI is becoming more prevalent in our day-to-day lives, from self-parking cars to personalized advertising. With the evolution of AI tools taking great strides forward and seemingly accelerating at the moment, experts in an open letter have recently called for a slow-down or temporary hold on research and development. The letter was signed by Elon Musk, Steve Wozniak, and Yuval Noah Harari, among others, who want to press pause until the potential societal impacts of the tech can be properly assessed and regulatory guidelines and safeguards can be put in place. 

Those who have seen the Pixar film Wall-E, you’ll know that the depiction of our future is one where humans have become totally complacent and reliant on technology. It’s scary to think of how AI, if not regulated, could affect consumerism, well-being, and health. But, while many of those fears are unfounded, there should be no doubt that the unintended consequences and side-effects of applying AI in a decision-making role (such as bias and disinformation) are real and can be very serious in practice.

The race is on for AI regulation

It’s truly remarkable how far generative AI has come over the past couple of years, no doubt spurred on by the intense race between Google and Microsoft. As a society, it’s right that we should be concerned that these technology giants and others develop AI responsibility. However, it’s silly to expect them to suddenly now hit the brakes, especially when they have so much to gain. Market players have no incentive to slow down and are all competing for their share. It seems like new tools from the big players are being announced weekly, if not daily, most recently with Meta testing generative AI ad tools. However, this is exactly why regulators should be thinking about the impact of AI and acting right now. 

Those working with AI should welcome external regulation. As the legitimate concerns around AI’s potential impacts on society increase, governments around the world are looking at how they can best regulate AI without stifling its incredible potential.

The problem with privacy laws

While there are varying opinions across the world around how AI should be regulated, the most effective approach will be for governments to create new AI-specific laws that help to not only govern it but apply it to our advantage to benefit all parts of society. However, as we have seen with the piecemeal development of data privacy law, if individual countries take their own approach to the regulation of AI, it could cause more harm than good (eg. friction, added costs from a heavy compliance burden, protection gaps, misinformation, and fear). Taking a local approach will lead to AI learning in different ways – instead, we need a globally unified approach.

A United Nations of the Internet

For AI development to benefit all of society, the key pieces that regulation must get right are building global standards around AI system transparency, data privacy, and ethics. We need to create a ‘United Nations of the Internet’ for unified regulation across the world. This means there will be a consistent understanding of its potential and limitations, no one country will have advantages over another, and businesses in multiple markets where they can apply AI seamlessly. It will also be important to unite on regulation for AI to build trust and demonstrate its potential, that way we can help society embrace it, rather than fear it. 

ChatGPT is a prime example and a reason that AI regulation is in the spotlight. It has sparked fear that it will take jobs, cause unemployment, and damage society. In actual fact, if regulated and applied correctly, AI will enhance society by opening up more meaningful jobs and personal time rather than repetitive tasks.

Unlocking AI’s Potential

We are standing on the brink of a new wave of human potential, fuelled by the power of AI. To truly unlock its potential for good, we need to learn the lessons of our experience with data privacy laws and find a way to create and agree upon the global standards around the ethical and positive use of AI tools. 

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

The post AI Needs Regulation – But Let’s Not Repeat the Mistakes Made With Privacy Laws appeared first on AiThority.

Everywhere you turn these days, there is news about the power of artificial intelligence (AI) to transform our lives – from diagnosing cancers to writing movie scripts. Now, AI practitioners are turning their attention to electric vehicle (EV) batteries. 

Data-driven, machine-learning-based approaches have received much attention from both academia and industry over the past decade. Research into the use of AI has shown promise for accurately predicting the dynamics of nonlinear multi-scale and multi-physics electro-chemical systems. Everything from battery state of health (SOH) estimation, safety and risk prediction, to cycle life prediction and Battery Lifetime Prognostics, closed-loop optimization of fast-charging protocols to identifying degradation patterns of lithium-ion batteries from impedance spectroscopy using machine learning. The predictive ability of AI is however challenged when it comes to EV lithium-ion battery research and development , where extensive datasets do not yet exist. This is particularly true when predicting the cycle life of the battery and the cells making up the pack. Small changes to the chemical and/or physical properties of the anode, cathode, electrolyte, and even the separator can have a significant impact on the battery’s performance.

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The enormity of the task is better understood when the time required to predict a cell’s life through experimentation alone is considered. 

Due to the long service life of batteries, simulating the cycle life of the cell during operation in an EV would require a test regime of at least 500 cycles. Following the extreme fast charge, discharge, and rest cycle, would normally limit the number of cycles to about 17 per day. This means that for every iteration of even the smallest of changes it would take more than a full month of uninterrupted testing to predict the cycle life.

However, in practice it is not uncommon to conduct up to 200 experiments in parallel – often comprising groups of  8 cells or more.

Running these test procedures while recording all the measurements every second from every one of the thousands of independent battery test channels, creates an enormous amount of data. Even with the solution being programmed, automated, and cloud-based, allowing all these experiments to be conducted every week, the batteries would still need many months of testing to simulate the cycle life.

This is a time-consuming and expensive bottleneck in battery research.

When conducting their cutting-edge research into Extreme Fast Charging (XFC) battery technologies, researchers at our company found they needed a better way to predict each battery’s end of life without losing valuable data insights and transparency.

By choosing to use Artificial Intelligence to do the ‘heavy-lifting’, our platform became one of the first battery-tech companies to implement AI in the R&D phase of EV battery development.

Deploying AI in battery R&D saves time and money 

Analyzing the results obtained from the complex set of experiments carried out on diverse battery-cell chemistries and designs is very similar to clinical trials typically conducted on sample sets of patients. This lead the research team to investigate the possibility of merging the chemical battery’s world with the concept and methodology applied to these patient clinical trials.

Using Evolution Intelligence’s AI-for-AI platform, we built and optimized their models, which indicated that the Kaplan-Meier (KM) graphs and methodologies would  be ideally suited to investigating, learning, and predicting the battery lifetime. This would speed up the battery R&D cycle – in particular predicting the battery lifetime and, equally important – learning from the explainable algorithm what were its significant markers and metrics, a very significant and valuable lesson in understanding our unique chemistry and its boundaries.

The genetic KM algorithm takes into consideration the hundreds of “genes” originating from chemistry formulations, cell design decisions, and production process measurements. Coupled with aggregated and augmented test measurements, a forest of decision trees is created dynamically to explain the results.

This ad-hoc clustering method calculates the survivability of each battery  in the resulting tree leaf and uses that information to maximize the pre-determined parameters. In essence, as the machine learns and improves the tree leaves – making the batteries more similar to each other – the better the lifetime predictions become.

The resulting nodes in the trees are the algorithm’s insights about how to improve battery life and can be used to establish cell design parameters, manufacturing parameters, or specific timed measurements.

Thus, the lifecycle can be predicted with a 15 percent accuracy after having completed only 125 cycles, instead of 500-1500 – thereby freeing up resources and slashing the time needed for evaluation and decision-making. What is more, the algorithm allows researchers to cut any experiment short that does not meet the set objectives, whilst adding the successful candidate’s results to the database for inclusion in future experiments.

Moving to the next generation of predictors

As the project moves from R&D onto real life pilot lines, the importance of the transparency of the models becomes less important, while accuracy and early prediction points become crucial for the manufacturing and operability phases of the battery.

At this stage the KM graphs were replaced by tighter clustering algorithms that learn the highly-augmented time-series measurements of the battery’s life as it progresses.

With thousands of calculated trajectories of key performance indicators over the current lifetime of the battery, the algorithm optimizes for statistical significance and chooses the most important X such vectors that hold most of the signal to accurately predict the remaining useful life of the pack.

Here, Evolution’s AI-for-AI platform was used to optimize and select thousands of “genetic algorithm” generations from millions of potential combinations.

The results allow researchers to accurately predict multiple future targets at earlier prediction points (for instance: cycles 32, 62, and 122 – equal to 2, 4 and 8 testing days, respectfully) with appropriately high accuracies.

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In the Predicted vs Actual “Cycle @ retention 85%” graph below, the training set is shown in blue, while the newer, never-seen-by-the-algorithm, test set is in orange, demonstrating a high prediction accuracy.

Clearly, the benefit of AI and machine learning during the R&D process is proving invaluable in enabling researchers to evaluate the impact of changing more than one variable at a time, thereby speeding up the process to accurately determine a battery’s lifecycle.

However AI and machine learning’s ability to forecast battery life has other benefits to high-tech research companies – it provides management, and investors, with a valuable tool to predict the viability of untried nascent technologies. 

AI used in EV battery R&D delivers other important hidden benefits

In the rapidly developing EV battery sector, with new breakthroughs being announced and delivered almost daily, it may be difficult to identify which technologies to pursue or invest time and money in. Even more so if you have a small team of researchers competing with large well-funded corporations.

Prediction and forecasting of key battery performance metrics in the early stages of a project can give  decision makers a more tangible indication of the likelihood of the success of a given idea, technology, or even business model. 

In a landscape filled with exciting technologies and noble ideas, not all will eventually converge into a successful product or technology. Predictive results obtained through AI improve confidence in the chances of a successful convergence.

At the same time, the deployment of AI and machine-learning can often supplement hard-to-find skills and talent in battery R&D. This is particularly helpful in smaller organizations that are competing in the same space as the highly funded and well-established industry stalwarts. Effective implementation of AI gives these smaller operations a better ‘David and Goliath’ shot at leveling the playing field.

Conclusion

With the rapid advances being made in AI, the technology when applied to the EV battery industry is set to assume a pivotal role in reducing costs and improving performance. Our R&D success in developing a cutting-edge extreme fast charging battery technology is proof of the effectiveness of AI in saving time and money previously spent on processes such as repetitive life cycle testing.  

However, even though AI is ideally positioned to revolutionize EV battery R&D, it has many other, equally profound applications in the all-electric vehicle industry.

Everything from managing sensitive global supply chains, to aggregating and evaluating data gathered from fleets through cloud computing to improve battery management. By creating smarter batteries with embedded sensing capabilities, and self-healing functionality, connected EVs’ battery management systems can continuously monitor the ‘state of health’, and even rejuvenate selected battery cells or modules if required. 

What is more, when enabled through the IoT and over-the-air updates, EV performance and safety can be enhanced to operate closer to the extremities of the envelope. Whether this comes in the form of optimizing the range or reducing the time to charge – one thing is for sure: AI will play a key role in reducing “range anxiety” and speed up the adoption of BEVs.

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

The post The Role of AI in Improving Battery Cell R&D Productivity and Speeding up the Adoption of BEVs appeared first on AiThority.

Most scientific advances arrive at a safe distance. When NASA released the first jaw-dropping photos from the James Webb Space Telescope last Summer, stargazers around the world marveled at distant supernovae without any fear of getting caught up in the cataclysm. Science was on the move, but it wasn’t at our doorstep.

Not so, with the latest generation of artificial intelligence (AI), surnamed “generative” for its uncanny ability to translate instructions into compelling text, images and more. When ChatGPT rocketed to more than 100 million users in two months – a dozen times faster than game changers like the iPhone or the internet – it was clear that some new future was already here.

The sudden arrival raises concerns for many.  But this specific moment in AI discourse is also a powerful opportunity to shape AI toward the public good by investing in public resources to make it easier to build safe and inclusive technologies.

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There are good reasons to fear the emergence of capable AI. As with previous industrial revolutions, AI has the potential to concentrate wealth and power, to upset the balance between nations or between law and disorder, to undermine human potential, to cement ongoing injustice or to force rapid change on a beleaguered working class, (including *gulp* each one of us.)  But it is also worth reserving a moment for wonder and possibility.

Like NASA’s Telescope, the latest generation of AI represents a human achievement, expanding on what we knew to be possible. It is the product of many hands, working together in broad daylight through academic conferences and publicly available tools. Private businesses like Meta and Alphabet have (uncharacteristically) given away free AI software that required millions in electricity and hundreds of millions in research salaries to develop; a credit to America’s culture of competitive, public innovation.

New technologies typically launch into the hands of a few. The average tailor in the 1840s could scarcely afford a power loom. Human computers doing math by hand in the 1960s would wait decades more to own the digital machines that were displacing their occupation. But the floodgates to utilize ChatGPT, Bing, Bard and newer offerings are wide open. Anyone with an internet connection can experiment with the most advanced technology on Earth and consider ways future versions might impact their lives..

When Facebook passed 100M monthly users in 2009, there was no one yet qualified to study its impact, real or potential, on society. AI has arrived escorted by chaperones: academic communities and industrial teams doing hard work on health and safety, attribution, and alignment to human goals and values. The speed of debate about AI’s risks is evidence of the guardrails being laid in front of us.

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AI is experiencing a fragile Spring, where its rapid growth is happening in public view instead of being driven underground by fear and greed. How can we extend that Spring into a Summer where AI advances benefit all, and reflect the values of our society?

One clue lies in the fact that the latest AI advances are themselves the result of public resources. ImageNet, a public databank of images organized in 2009 by the prescient Dr. Fei Fei Li (now of Stanford), is widely recognized to have been an essential catalyst for the Deep Learning revolution that gave way to today’s groundbreaking LLMs. Today, an AI startup called HuggingFace (after a welcoming emoji) plays an outsized role in making data and code associated with AI research available to a broad community.  But without a public mandate, these community investments are fragmented and vulnerable.

During the genomics revolution of the 1990s, the US National Laboratory system played a key role in administering GenBank, a public database of genetic information accessible to all researchers. Sharing data publicly became a de facto requirement for publication – along with consideration of Ethical, Legal and Social Implications (ELSI) of each piece of research. Countless medical advances, and a foundation for ethical genomic research, can be traced back to the coordinated research culture that this created.

Today, AI is at a similar crossroads.  We have a window where investments in public data, software, and educational resources can have an outsized impact on keeping AI research transparent, inclusive, and ethical. If we want AI that is reflective of our values, it’s incumbent upon elected officials, government, academia and the private sector to come together to invest in public research that helps to reflect those values. Opening the AI Summer to as many people as possible is the best way of making sure that our children grow up looking at AI as a wondrous tool for their benefit, and not their competition.

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

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