Author: admin

In the bustling heart of the financial world, a man named Dr Paul Dongha stood as a beacon of ethical wisdom and technological prowess. His journey began in the 1990s, a time when AI was a fledgling concept, and Paul was among the pioneers, diving deep into the realms of artificial intelligence with a passion that would define his life’s work. He spent years teaching and researching AI, motivated by the possibilities it held for the future.

Fast forward to the present, and Paul has become a guardian of integrity in the ever-evolving landscape of data and AI ethics. Currently, as Head of Responsible AI and AI Strategy at Natwest Banking Group, and before that as Group Head of Data & AI Ethics at Lloyds Banking group, his mission is clear: to ensure that the powerful tools of AI are used responsibly and ethically, safeguarding both the organizations,  customers and society.

Paul’s vision for an ethical AI framework is comprehensive and deeply human. He believes that technology alone cannot navigate the murky waters of ethical dilemmas; human judgment is essential. Key to achieving this is through the creation of an AI ethics board.Paul has co-chaired an  an organization-wide ethics board, a forum of senior leaders tasked with making critical decisions about the deployment of AI models. This board ensures that every model is scrutinized not just for its technical merits, but for its potential impact on society and individual lives.

Paul also understands that knowledge is power. He champions rigorous training, communication, and awareness programs across the organization, ensuring that everyone, from data scientists, risk management and compliance teams to executives, is are well-versed in the ethical risks and responsibilities associated with AI. His commitment to fostering a culture of ethical awareness is unwavering, recognizing that a well-informed team is the foundation of responsible AI deployment.

In his heart, Paul knows that AI holds immense potential for good. He envisions a world where AI can bring tailored learning to underserved communities, revolutionize healthcare with groundbreaking discoveries like Alphafold 3, and drive sustainability initiatives that protect our planet. His dedication to these ideals is a testament to his belief in AI’s power to transform society for the better.

Through his tireless efforts, Dr Paul Dongha has carved a path where technology and ethics walk hand in hand, ensuring that the future of AI is bright, responsible, and profoundly human.

Building a Responsible AI Assurance Framework

Paul argues for a generative AI assurance framework that covers people, process, and technology across an enterprise.

On the people side, an ethics board or committee should be established. This committee, composed of senior leaders, would be responsible for making decisions on model deployment considering ethical risks such as bias, discrimination, reputational harm, etc. Training programs on AI ethics and responsible AI should also be implemented for various personas across the organization, including data scientists, non-technical users, legal, risk management, data privacy teams, and executives.

On the process side, for organizations with complex business processes, such as banks, ensuring model validation and risk management teams have the right support, technology, and skills is crucial. Additionally, existing data science or DevOps lifecycles should be augmented to include ethical checks, focusing on explainability, bias removal, and fairness during model creation.

Finally, on the technology side, building guardrails into the models themselves is necessary. Techniques include debiasing training data and using libraries like Interpret ML, Fairlearn, and AIF360 to detect and address bias throughout the data science lifecycle.  Model cards documenting the model’s characteristics, assumptions, training data, testing results, and ethical impact assessments are also crucial for responsible AI practices. Lastly, maintaining a model inventory that tracks all models in production, including risk levels, deployment duration, and retraining needs, is essential for effective oversight.

Rise of AI and the Urgency of Ethics

Paul Dongha describes his personal journey in the field of AI.

After earning a PhD and working in AI research and education during the 1990s, the lack of career opportunities in AI led him to pursue a successful career in finance. Paul has lead manay enterprise-wide transformation programs, building and designing complex quantitative systems involving Big Data feeds.

However, around 2018, he recognized the significant advancements in machine learning and AI being utilized by major companies. This resurgence of AI, particularly the complex and probabilistic nature of generative models, sparked a renewed concern for the ethical risks involved.

By 2020, the potential for rapid AI growth solidified his belief in the critical need to address these complex ethical issues. This realization reignited his passion for AI, leading him back to the field in 2019.

Leading the Way in Ethical AI

Paul reports encountering a positive reception for his work on ethical AI. The companies he’s worked with have expressed genuine interest and curiosity in understanding this concept, despite its perceived mysteriousness. He attributes this openness to the growing awareness of ethical risks in AI and the potential for reputational damage if these risks are not addressed. Consequently, he’s been welcomed by development teams, data scientists, and machine learning engineers who are eager to collaborate and ensure responsible AI practices.

Urgency of Ethical AI Frameworks

Paul argues that ethical considerations surrounding AI are universal across industries.  While inherent risks like explain ability, bias, and robustness apply to all AI models, the severity of consequences varies by sector.

High-stakes sectors like healthcare and finance face particularly critical ramifications for model errors. Public services are also concerning due to the potential impact on people’s lives.  

In light of these risks, Paul emphasizes the importance of a robust assurance framework for all organizations using AI. This framework, driven by senior leadership, s should establish safeguards to mitigate ethical risks across various industries. He concludes that there’s no avoiding these challenges, and responsible AI development necessitates addressing these issues head-on.

Building a Responsible AI Future

Paul stresses the importance of robust program management when implementing a generative AI assurance framework.  Success hinges on several key principles. First, a clearly defined vision and measurable goals ensure everyone is working towards the same objective. Second, strong leadership commitment is crucial to drive collaboration between the various teams involved.  Third, AI ethics specialists play a critical role in bridging the gap between technical teams and fostering an understanding of the ethical considerations behind responsible AI.  

Furthermore, a successful program requires an organizational culture that acknowledges the importance of ethical AI and fosters a sense of shared responsibility. The evolving nature of the AI field necessitates an adaptable framework that can accommodate ongoing learning and experimentation.  This dynamic environment, though challenging, also presents exciting opportunities for innovation. Finally, Paul highlights that the problem-solving nature and novelty of building an AI assurance framework can be motivating for engineers, fostering a positive program environment.

Staying Informed and Proactive

Paul Dongha addresses the challenge of navigating the ever-evolving regulatory landscape surrounding AI. The global nature of AI development presents a complex situation with various organizations formulating regulations. These include the OECD’s principles, the EU’s legislation, and individual US states enacting their own laws. Similar trends are observed in Canada and other countries, with industry-specific regulations emerging as well.

To stay informed, Paul recommends establishing a dedicated horizon scanning team within an organization. This team, consisting of just a few people, would be responsible for monitoring publications from relevant organizations like the OECD and EU. Legal firms specializing in tracking these regulations and communication with industry regulators are also valuable resources.

Staying ahead of the curve, however, proves more challenging. Rather than attempting to predict future regulations, Paul suggests a proactive approach. Organizations should identify potential risks within their specific AI use cases and proactively mitigate them. This focus on responsible AI practices aligns with ethical obligations and positions them to contribute meaningfully to shaping future regulations based on real-world experience.

Measuring the Effectiveness of a Generative AI Assurance Framework

Paul emphasizes the importance of measurable metrics to assess the effectiveness of a generative AI assurance framework. He proposes a set of key metrics that track various stages of the model development process.

• Ethical Impact Assessment Completion: This metric monitors the number of models that have successfully completed a mandatory ethical impact assessment.
• Ethical Impact Assessment Success Rate:  This metric goes beyond completion rates and focuses on the percentage of models that pass the ethical impact assessment, indicating a successful mitigation of potential ethical risks.
• Ethics Board Review: these metric tracks the number of models presented to the ethics board for additional scrutiny and approval, ensuring a higher level of oversight for potentially sensitive models.
• Model Validation Success Rate: This metric focuses on the technical aspects, tracking the percentage of models that pass the model validation team’s assessments, ensuring the models function as intended without technical flaws.
• Model Inventory Completion:  This metric tracks the number of models that have successfully reached the final stage by being added to the organization’s risk model inventory. A complete inventory provides a clear picture of all deployed models for ongoing monitoring and risk management.

By monitoring these metrics, organizations can gain valuable insights into the effectiveness of their AI assurance framework in identifying and mitigating risks throughout the entire AI development lifecycle.  These metrics not only assess the health of the framework but also demonstrate a commitment to responsible AI practices.

Mitigating the Unforeseen

Paul recognizes the inherent difficulty of anticipating unforeseen consequences with AI.  However, he proposes two key strategies to mitigate these risks.

Firstly, Paul emphasizes the importance of fostering diversity within data science teams.  This includes diversity in terms of gender, background, culture, and age.  A broader range of perspectives during model development can help identify potential biases or unintended consequences that a homogenous team might overlook.

Secondly,  Paul describes a structured process called “consequence scanning.”  This proactive approach involves a series of steps for teams to identify potential negative behaviors a model might exhibit.  By considering “what-if” scenarios and corresponding mitigation strategies, teams can address potential issues before they arise.  For example, consequence scanning might involve examining how a model could unintentionally discriminate against a certain group, even if such discrimination wasn’t the intended purpose.  Additionally, this process includes analyzing the training data for potential biases or a lack of diversity that could lead to skewed outcomes when deployed in different regions.

By implementing these two strategies, organizations can take a proactive approach to mitigating unforeseen consequences and fostering more responsible AI development.

Beyond Code

Paul dispels the myth that data and AI ethics specialists require extensive technical expertise. While a foundational understanding of probabilistic models, common algorithms like gradient descent and XGBoost, and transformer models is beneficial, the core competency lies in ethical considerations.

A strong understanding of how and where bias can creep into AI models is crucial. This includes recognizing bias in raw data, training data labeling, discrepancies between training and testing data sets, and potential mismatches between training data origin and deployment location.

Beyond technical knowledge, Paul emphasizes the importance of empathy for the impact of AI models on users.  

Most importantly, Paul highlights passion as the most critical quality. A genuine enthusiasm for the field and a desire to leverage AI for positive outcomes are essential for success in this role.

AI for Social Good

Paul expresses optimism about the positive societal impacts of AI. He highlights several examples:

• Personalized Customer Interactions: AI enables highly personalized interactions with customers across various sectors, including banking, eLearning, and more. This allows for tailored experiences and targeted information delivery, exceeding previous capabilities.
• AI on Mobile Devices:  AI systems can be delivered on mobile devices, making them accessible even in regions with limited traditional IT infrastructure. This opens doors to applications like personalized learning opportunities in regions with underdeveloped educational systems.
• Medical Advancements: AI is playing a significant role in medical breakthroughs. For instance, DeepMind’s AlphaFold 3 has the potential to revolutionize disease identification, treatment, and vaccine development, leading to a major boost in drug discovery. Personalized medicine also holds immense potential.
• Environmental Sustainability: AI can be a powerful tool in achieving the UN’s Sustainable Development Goals.  It can be used to predict pathways to net-zero emissions and optimize energy efficiency.  For example, AI models are being used to optimize workload distribution in data centers, leading to significant efficiency improvements.

Paul emphasizes that AI ethics plays a crucial role in maximizing these benefits by mitigating potential harms and limitations associated with AI development. He expresses his enthusiasm for the future of AI, particularly its potential to solve problems and contribute meaningfully to society.

In the rapidly evolving pharmaceutical and biotech industries, efficient and effective clinical data management (CDM) is paramount. As companies strive to bring new therapies to market faster, the demand for high-quality, accurate, and complete data from clinical trials has never been greater. CDM not only ensures compliance with stringent regulatory standards but also significantly reduces the time from drug development to marketing by streamlining data collection, cleaning, and management processes. EDETEK, with its innovative CONFORM™ Informatics platform, stands at the forefront of this transformation, providing cutting-edge solutions that meet the diverse and complex needs of modern clinical trials.

Peter Smilansky, the Senior Vice President, Product Strategy has over 25 years of information technology experience in the life sciences industry and leads the product teams to ensure the EDETEK conform platform vision and philosophy is applied throughout. 

Let us dive into this insightful and enriching interview to know more about EDETEK and Peter’s vision for the future of EDETEK:

Can you provide our readers with an overview of the current data management needs of pharmaceutical and biotech companies?

Clinical Data Management (CDM) is a vital element to ensure the delivery of high-quality, accurate, complete, and statistically sound data from clinical trials and studies.  CDM reduces the time from drug development to marketing by efficiently collecting, cleaning, and managing subject data in accordance to stringent regulatory standards. This process combines meticulous data collection and electronic data capture with the involvement of team members across all stages of clinical trial design and execution.

In the evolving landscape of clinical development, clinical data management continues to play a critical role, leveraging analytical tools and software technologies to streamline management of study data according to established standards and government regulations while ensuring integrity, confidentiality and regular quality assessment of the data. 

How has this changed in recent years?

The pandemic served as a critical inflection point, driving the industry towards adopting data management technologies that enable faster study configuration and real-time data access and analytics. This shift has not only improved our responsiveness to emergencies but has also underscored the importance of robust, adaptable software platforms capable of continuous study quality monitoring and data management. The pandemic catapulted the industry towards adopting new and more nimble processes. This change in thinking led to the implementation of real-time data processing frameworks capable of managing studies of greater volume and complexity.

Cloud-based solutions driven by scalability, security, flexibility, and cost-effectiveness have become an increasingly popular choice in the life sciences industry.  EDETEK’s CONFORM™ Informatics platform helps clinical trial sponsors and their data management and biometrics teams to ingest, monitor, clean, transform, analyze and submit the data swiftly.

Our CONFORM platform’s capabilities of reusable and pluggable data ingestion adapters, zero-code business rule designer, real-time data aggregation, validation, scientific alerting and visualization of operational and patient data has set new standards for effective data management.  EDETEK pioneered these before the COVID pandemic — but we observed more adoption by pharma clients in the last 3 years.

Modern algorithms as well as AI/ML technologies are revolutionizing the landscape of clinical research by enabling the analysis of vast datasets that include patient demographics, medical histories, laboratory results, safety reports and treatment outcomes. These emerging technologies allow researchers to envision patterns that traditional methods could miss. For instance, predictive modeling and tools provide early quality assessment of studies and sites enabling data managers to identify and resolve issues earlier and less expensively than was historically possible.

The Analytics and Data Review component of EDETEK’s CONFORM IQ features numerous configurable interfaces for data managers, medical monitors, data scientists, biostatisticians, and third parties to collaborate effectively in a unified environment for comprehensive data reviews.  One of many analytical features includes proactive subject level safety reviews and early signal detection leading to improved medical and quality assessments from start to finish. CONFORM IQ delivers risk assessment and improved quality throughout the clinical trial conduct.

AArtificial Intelligence (AI) and Machine Learning (ML) are radically transforming clinical data management from a traditionally passive to a dynamically interactive process. AI algorithms can monitor clinical and operational data in real-time, efficiently showing inconsistencies that could compromise data integrity or violate regulatory standards. Our CONFORM IQ platform is being enhanced with an AI-supported ecosystem with tools like Chat.IQ, which fosters a more natural, seamless interaction with data. This tool allows scientists and analysts to post complex questions and receive immediate results. 

With generative AI, CONFORM IQ will respond to queries with instant data listings and visualizations, allowing data managers to review detailed inquiries, such as identifying incomplete visits, missing procedures, adverse events and concomitant medications with non-matching dates and many other quality issues without re-running and waiting for a typical batch of validation rules. We are also working on AI-based algorithms to evaluate the audit trail enabling the system to suggest better CDM study designs and effectiveness of rule-based data validation. One of the upcoming AI deliverables in CONFORM is going to be machine automated metadata and business rule configuration of a clinical trial to reduce CDM study design work.

What are the biggest data management challenges encountered by life science organizations?

One of the main challenges in clinical data management is ensuring data quality and integrity. Data discrepancies and errors can have a significant impact on the reliability and validity of the study results. Therefore, implementing robust quality control measures early in trial conduct is essential to address this challenge.

Integrating IoT and other data providers into clinical trials results in complexities, particularly around the volume of data, untraditional data formats, the diversity of data sources and ensuring their seamless synchronization. The CONFORM platform addresses these challenges by offering clients access to over 120 configurations from a validated global library of data adapters. These adapters support a wide range of medical systems and devices including IoTs. 

With IoTs the technical challenges include the reliable ingestion and storing of massive amounts of data that are often coming in real time while performing data aggregation applicable to a particular provider and clinical trial. We have resolved these challenges with our scalable data processing and storage technologies, and it makes IoT data actionable rapidly.  At the same time, EDETEK’s medical alerting application evaluates these data streams in near real time, and when necessary, our platform immediately informs medical reviewers and sites of the points or patterns of interest. This capability provides business and patient care benefits in cardiovascular and other therapeutic areas.

Another challenge in clinical data management is data integration and standardization. Clinical trials often involve multiple sites, each using different systems and formats for data collection. This can lead to difficulties in integrating and harmonizing the data from various sources. Standardizing data collection processes and implementing data management systems that support interoperability can help overcome this challenge. Additionally, establishing clear data standards and guidelines can ensure consistency and uniformity in data collection across studies.

Data security and privacy also pose significant challenges in clinical data management. Clinical trial data contains sensitive information about patients, and it is crucial to protect this information from unauthorized access or breaches. We are also working on automatic creation of study metadata based on corporate and therapeutic standards as well as previous history of study configurations assisting data managers in this complex and time-consuming task. Implementing robust security measures, including encryption, access controls, and automatic search and removal of PHI data, can help safeguard patient privacy and ensure compliance with legal and regulatory requirements.

How can software address these challenges?

Ensuring continuous quality of data is crucial to obtaining reliable and valid study results. This can be achieved through robust data validation processes, including checks for missing or inconsistent data, outliers, and adherence to predefined data standards. Additionally, implementing data cleaning and transformation techniques can help address issues such as data duplication, formatting errors, and incomplete records.

Modern data management is a combination of business processes and corresponding software tools. First, the traditional method of running simple edit checks in study-based data capture systems (i.e. EDC) is always there. Second, more complex validation rules apply to aggregated data that come from a multitude of clinical trial data sources. To achieve this data managers run sophisticated software for data transformation so disparate data sources can be brought into a normalized form called a quality review model. 

Third, both raw and review enabled clinical datasets as well as operational study data are being analyzed by analytical software and AI to find “deep” issues and predict potential future issues. Fourth, new generation of WEB-based intuitive data review systems allow data managers and other business functions to slice and dice the data, visualize information graphically to find potential data and medical problems manually. Conversational AI is a great tool to make this process even more efficient.

What platform does EDETEK provide to overcome these challenges?

EDETEK’s CONFORM™ platform has emerged as a transformative business and technical solution for increasingly complex clinical trials. It creates an end-to-end digital interoperable ecosystem to rapidly orchestrate the movement, quality evaluation and remediation, and analysis and submission of clinical research data through a seamless and transparent experience for all business stakeholders.

CONFORM™ is a comprehensive, fully integrated end to end platform for the digital trials of the future. The CONFORM helps manage the myriad of clinical data management challenge with the four C’s:

1. CONNECT – to an infinite number of external data sources seamlessly
2. COLLECT – data faster with automated acquisition and management tasks
3. CONFORM – your data to any protocol, industry and corporate standards and regulations
4. CONSUME – your data using comprehensive clinical data review analysis and reporting systems

What other services does EDETEK offer to help life science companies bring products to patients faster?

In addition to clinical data management, EDETEK provides comprehensive metadata and business events-driven clinical software solutions to fulfill clinical trial data engineering and business analytics needs. We achieved this through excellence in technology and its biometrics applications, enabled by our deep knowledge in pharmaceutical clinical research and development. In addition to the Informatics Platform, EDETEK offers a complete eClinical Platform that includes EDC, CTMS, ePRO, eCOA, IWRS and other applications. EDETEK also provides business services for customers that use our eClinical and Informatics platforms. EDETEK’s SaaS platforms and solutions are based on industry standards and best practices. EDETEK delivers precisely targeted solutions designed to achieve critical data management and analysis objectives thereby helping assure quality, speed, and cost efficiencies.

Check – Digital Magazine

Heping Liu, the Senior Machine Learning Principal at Workday, whose journey from academia to the pinnacle of technological innovation is a testament to resilience, strategic foresight, and an undying passion for artificial intelligence. In the world of enterprise software, where innovation drives success and adaptability defines longevity, Workday stands out as a beacon of excellence. With a mission to empower organizations to adapt to a changing world, Workday leverages innovative technology to deliver cloud applications for human resources, finance, planning, spend management, and analytics. 

Journey to Workday

Heping Liu’s journey as a technology leader is rooted in a rich tapestry of academic and entrepreneurial experiences. Beginning with a master’s degree thesis, Dr. Liu explored the potential of neural networks to forecast financial indices and used the Markowitz model for portfolio optimization. This early exposure to the power of computational intelligence laid the groundwork for his pursuit of a Ph.D. in 2004, where his focus was on advancing the field of AI. Transitioning from academia to industry, Dr. Liu embarked on a challenging yet rewarding path through several startup companies, driven by the dream of establishing his own venture and eventually taking it public.

The entrepreneurial landscape offered Dr. Liu invaluable insights into the nuances of building and leading a company. In Beijing, he founded Unigroup AI Technologies Inc., where he not only managed the company but also spearheaded the development of advanced AI technologies and products. This experience underscored the critical importance of assembling a cohesive team, navigating the complexities inherent to startups, securing funding, and remaining at the forefront of technological trends. These lessons became integral to Dr. Liu’s leadership style, characterized by strategic planning and a keen ability to surmount challenges.

Joining Workday Inc. marked a new chapter in Dr. Liu’s career, presenting the formidable task of integrating cutting-edge AI solutions within an established enterprise framework. He successfully proposed, initiated, and led several pivotal projects that were showcased at the Annual Workday Product & Technology Conferences, known as the Annual Spelunking Conference. These initiatives, such as the Workday Resource Forecasting/Optimization Platform, Forecasting-as-a-Service and a conversational front end for the Workday system using large language models (LLMs), exemplify Dr. Liu’s visionary leadership and his capacity to overcome technical and strategic hurdles.

Workday’s Mission and Vision

Workday, Inc. is a leading provider of enterprise cloud applications for finance and human resources. Its mission is to deliver innovative cloud-based solutions that empower businesses to thrive in the digital age. By providing user-friendly applications that streamline operations, Workday enables organizations to reach their full potential. Leveraging AI and machine learning, the company seeks to revolutionize how businesses manage human resources, finance, and other critical functions.

Workday envisions a future where every organization has access to the tools and resources needed to succeed in a digital-first world. The company aspires to be at the forefront of digital transformation in the enterprise software industry, offering advanced technology that enhances efficiency, collaboration, and growth. Workday’s vision is to empower businesses to make data-driven decisions and seamlessly integrate technology into all aspects of their operations, fostering a more connected and productive workforce.

Building and Managing Effective Teams

A key component of Dr. Liu’s leadership philosophy is building and managing motivated and effective teams. He places significant emphasis on fostering an environment where team members are self-driven, disciplined, and aligned with both the technological and cultural aspects of the organization. Clear communication is paramount, ensuring that each team member understands their role and the broader organizational objectives. Dr. Liu believes in granting autonomy to his team, allowing them to explore creative solutions while offering guidance and support as needed. This approach cultivates a culture of collaboration, respect, and continuous learning, where team members are encouraged to take ownership of their projects and grow their skill sets.

Hiring the right talent is critical to Dr. Liu’s management strategy. Beyond technical skills, he prioritizes cultural fit. Mentoring and professional development are integral to Dr. Liu’s approach, enabling team members to thrive and innovate. Recognizing and celebrating achievements further enhances morale and motivation, creating an environment where individuals feel valued and driven to excel.

Long-Term Vision and Goals

Dr. Liu’s long-term vision involves continuing to drive innovation in areas such as forecasting, optimization, machine learning, generative and multimodal AI, and Artificial General Intelligence (AGI). His focus is on creating impactful solutions that have the potential to transform industries. A key aspect of this vision is advocating for and building a data environment that is friendly to generative and conversational AI. Utilizing distributed system design, Dr. Liu aims to develop cutting-edge products and services based on generative and multimodal AI models, while exploring the application of time series foundation models to various types of time series data and optimization models. 

Within Workday, Dr. Liu’s future goals are centered around intelligent solutions by advancing time series forecasting and optimization models, enhancing distributed systems, and exploring and developing generative and conversational AI applications. By integrating advanced AI and machine learning techniques, he seeks to make Workday’s HR and financial data enterprise solutions more intelligent and adaptive. Leveraging generative and conversational AI, Dr. Liu aims to create innovative tools that enhance user generative and conversational experience and provide deeper data insights. He is committed to advocating for a domain-friendly data environment that supports efficient use of generative and conversational AI, ensuring scalability, reliability, security, and performance of AI-driven products and services in the big data environment.

Workday Culture and Values

Workday’s culture is centered around the belief that happy employees lead to happy customers. The company fosters a collaborative, inclusive, and innovative work environment. Workday prioritizes employee well-being, diversity, and inclusion, and emphasizes a culture of continuous learning, open communication, and mutual respect. 

Workday’s values encompass several key aspects: employees, customer service, innovation, integrity, fun, and profitability. The company is committed to fostering a supportive environment where employees are encouraged to excel and grow, with a strong emphasis on respect and continuous improvement. Dedicated to delivering exceptional value, Workday prioritizes customer satisfaction and success. The company embraces innovation, learn from outcomes to remain competitive, and deliver long-term value. Honesty and accountability are central. Workday also promotes a fun and enjoyable work atmosphere. While profitability is important, the company prioritizes living its values and making sustainable financial decisions.

The Future of AI

As the AI landscape continues to evolve, generative AI and large language models are poised to revolutionize various industries by transforming how data is interacted with, processes are automated, and insights are generated. The potential emergence of Artificial General Intelligence (AGI) represents a significant milestone, with AGI systems anticipated to possess broad cognitive abilities across diverse tasks and domains. According to OpenAI, AGI refers to highly autonomous systems that can outperform humans in practically all economically valuable work, encompassing levels of interaction, problem-solving, and innovation that surpass current AI capabilities.

At Workday, AI is deeply integrated into the company’s platform, driving intelligent predictions and automation. The company is actively preparing for the opportunities presented by advancements in AI, committed to incorporating the latest technologies to enhance its services and maintain a leading edge in innovation. By embracing the potential of generative AI, Workday aims to continue delivering cutting-edge solutions that empower its users and transform industries.

Transforming the Tech Industry

Given the opportunity, Dr. Liu would focus on the early development of generative AI and the integration of function calling with large language models (LLMs). The potential of generative AI spans various applications, enhancing AI’s ability to generate high-quality content, design innovative solutions, and assist in creative processes across multiple domains. Integrating function calling with LLMs significantly enhances their usability and effectiveness, allowing LLMs to execute specific tasks, access external databases, and interact with various software systems seamlessly. This integration leads to more intelligent and context-aware AI agents, improving user experience and productivity.

Impact on AI

Dr. Liu has significantly impacted the field of artificial intelligence through his contributions to advancing AI technologies, developing products across various domains, and educating young professionals about AI. His research has resulted in the publication of approximately 20 papers in academic journals, presenting innovative AI, computational intelligence, and deep learning algorithms and models with applications in investment and pricing, revenue management, energy, healthcare, and nano-manufacturing.

In the industry, Dr. Liu has led the development of AI-driven platforms, products, and services across multiple companies. His initiatives include creating time series price forecasting and supply chain optimization models for the food industry, developing a next-generation analytics engine for online advertising, and pioneering big data analytics and clustering modeling for IP intelligence and reputation. These contributions have been utilized in advertisement targeting, transaction fraud prevention, and marketing industries, showcasing Dr. Liu’s ability to drive innovation and deliver tangible business value.

In 2018, Dr. Liu founded an AI technology company, leading teams to build a conversational product for the finance and investment industry using Natural Language Understanding (NLU) technologies. The product’s major components include a conversational platform based on NLU technologies, a knowledge graph platform utilizing Neo4J and APOC and graph algorithms, a search platform based on ElasticSearch, a big data ETL platform using Spark, Kafka, and Redis, and a web crawling platform to collect real-time finance text and quantitative data.

At Workday, Dr. Liu has proposed, initiated, and led several key projects, presenting them at the Annual Workday Product & Technology Conferences, known as the Annual Spelunking Conference. These projects and proposals include the Workday Resource Forecasting/Optimization Platform, Forecasting-as-a-Service, a conversational front end for the Workday system using large language models (LLMs), and transforming the Object Management Service (OMS) into an AI-Agent friendly data environment. Dr. Liu’s contributions to AI continue to drive innovation and enhance Workday’s ability to deliver intelligent, adaptive enterprise solutions.

In addition to his professional work, Dr. Heping Liu dedicates time to mentoring and teaching young professionals, helping them learn the latest advancements in AI. His commitment to education and mentorship underscores his dedication to fostering the next generation of AI leaders, equipping them with the skills and knowledge necessary to navigate and contribute to the dynamic field of artificial intelligence.

The Future of Forecasting and Optimization

Over the next five years, machine learning is poised to play an increasingly central role in forecasting and optimization platforms. Advances in generative models will enable more generalized predictions and optimized decision-making processes, driving efficiency and innovation across industries. A significant trend in forecasting has emerged, with companies leveraging transformer models to build generalized large time series foundation models. Examples include Amazon’s Chronos, Salesforce’s Moirai, Google’s TimesFM, and Datadog’s Toto.

These large time series pre trained foundation models represent a paradigm shift in forecasting. Traditionally, developing an effective forecasting model required a forecasting expert to tailor a model to a specific dataset. However, with these generalized models, this expertise is no longer a necessity. The models can generalize across various datasets, democratizing prediction modeling by reducing the reliance on specialized forecasting expertise and the need for specific data and computational resources. 

AI/ML and Big Data Analytics: The Next Big Trend

The next big trend in AI/ML and big data analytics is expected to be in generative and multimodal AI and AGI. These technologies are set to continue evolving, with some models becoming much larger and others, oriented to specific industries or applications, becoming smaller. Equipped with either large or small language models, the application of AI agents is anticipated to become widespread.

Industry leaders, such as ChatGPT 3.5 and subsequent versions, serve as language foundational models, and companies are exploring ways to apply these foundation models to their businesses. AI agents based on these foundation models have demonstrated the potential to connect LLMs with company domain knowledge, providing automated services. AI agents act as cognition amplifiers, anticipating user needs and helping them accomplish tasks. They offer predictive, conversational and generative capabilities along with advanced analytics, providing users with intelligent and context-aware interactions.

The internet may evolve into a network of AI agents, with humans focusing more on reviewing and approving the work of AI agents. With the introduction of LLMs, user interaction with data is shifting from customized user web interfaces to conversational approaches. This revolutionizes how users interact with proprietary data, enabling dialogues, report generation, dataset comparison and analysis, model building, and actionable insights. The interaction between users and their data becomes more intelligent, leading to a trend where user needs shift towards business intelligence as users have more freedom to ask complicated analytical questions.

Both AI agents and conversational interaction require a friendly data environment and smart function calling, which are challenging to build. Continuous improvement of model accuracy is necessary, but understanding domain-specific data remains a significant challenge. To assist AI agents and conversational interaction, labeling and tagging data will become critical to create a more friendly data environment.

Workday recognizes these industry trends and opportunities. The company has partnered with Salesforce to develop a new AI employee service agent that will automate time-consuming tasks, provide personalized support, and surface data-driven insights to help employees work smarter and faster. The combination of Salesforce’s new Agentforce Platform and Einstein AI with the Workday platform and Workday AI will enable organizations to create and manage agents for various employee service use cases. This AI agent will work with and elevate humans to drive employee and customer success across the business. At Workday, AI is at the core of the platform, powering intelligent predictions and automation like no one else can.

Advice to Aspiring Entrepreneurs

To budding entrepreneurs aspiring to venture into the AI sector, Dr. Heping Liu offers valuable advice: stay curious, be resilient, and prioritize ethical considerations in your work. Understanding your target market, staying attuned to industry trends, and being prepared to pivot when necessary are critical components of success in the dynamic and rapidly evolving field of AI. Continuous learning and adaptation are essential, as the AI landscape is constantly changing and presenting new opportunities and challenges.

Furthermore, Dr. Heping Liu emphasizes the importance of ethical considerations in AI development. Strive to develop AI solutions that are fair, transparent, and beneficial to society. By prioritizing ethical considerations, entrepreneurs can ensure that their innovations contribute positively to the world and build trust with users and stakeholders. This approach not only enhances the impact of AI solutions but also aligns with the broader societal goal of harnessing technology for the greater good.