From Copilots to Operators: How Enterprise AI Is Becoming an Operational Control Layer

“Musk Will Get Richer, People Will Get Unemployed”: Hinton on AI & ChatGPT 5.2 jump

Google Nested Learning – AI memorizes like our brain

Speaker at the international conference “Innovation Roundtable”, 22 March 2022

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In the upcoming Innovation Roundtable conference, a presentation will focus on effective organization and management of AI and data-driven projects. Key strategies include the establishment of digital hubs tailored to an organization’s infrastructure and strategic goals, and the prioritization of digital transformation impacts. Emphasis will be placed on change management, highlighting the critical role of communication and team transformation into change champions. Additionally, the presentation will address challenges such as the scarcity of best practices for AI projects and strategies for mitigating risks associated with AI’s unpredictability. Real-world examples will illustrate successful project setups and organizational structures, offering insights into data governance alignment with business objectives and elucidating the process of demystifying AI model outcomes to showcase predictive value.

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Interleaving algorithm for optimization of neural networks with self-learning perceptrons

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Exploring the Efficiency of Interleaving Algorithms in Neural Network Optimization, this study introduces a novel application of team draft interleaving, diverging from traditional A/B testing methods. By simulating a sports team selection process, this approach enhances compound selection from a dataset. Highlighting its utility in artificial intelligence, particularly in self-learning perceptrons, the method enables perceptrons to adapt activation functions dynamically. This preemptive adjustment, facilitated by interleaving, marks a significant departure from conventional error backpropagation, demonstrating potential for more responsive learning mechanisms in neural networks.

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Unlocking the Power of Data Standardization: A Guide to CDISC and FAIRplus Cookbook for Streamlined Research. Transforming data management to foster adoption of FAIR principles

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In the realm of life sciences research, the FAIRplus Cookbook emerges as a pivotal guide for integrating the FAIR principles—Findability, Accessibility, Interoperability, and Reusability—into data management practices. This invaluable resource offers a series of practical recipes for the FAIRification of data, catering to a diverse audience, including researchers and data stewards. It underscores the significance of data standardization, as demonstrated through the application of CDISC-SDTM models for clinical trial data, to enhance the quality and utility of scientific data. By leveraging the FAIRplus Cookbook, the scientific community can navigate the complexities of data management, ensuring that research data are not only standardized but also aligned with ethical considerations and regulatory requirements. This guide thus stands as a testament to the collaborative effort to foster a culture of open, standardized, and ethically responsible data sharing in life sciences.

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Speaker at “8th Artificial Intelligence, Data Analytics and insights summit – DACH Region”, 11th-12th November 2021

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At the “8th Artificial Intelligence, Data Analytics and Insights Summit – DACH Region,” held on 11th-12th November 2021, pivotal discussions unfolded, featuring insights into Artificial Intelligence’s application in Manufacturing and the intricate challenges of data governance within the pharmaceutical sector. This summit gathered over 250 senior managers and international leaders, delving into groundbreaking research and fostering a platform for advanced discourse in AI, data analytics, and governance. Further details are available at the conference’s official site.

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Total Data Quality Management: A Comprehensive Approach to Data Quality

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Total Data Quality Management (TDQM) embodies a holistic approach to enhancing data integrity across all facets of an organization’s data lifecycle. This methodology prioritizes the accuracy, completeness, consistency, and relevance of data, ensuring its strategic alignment with business objectives. TDQM integrates practices such as data profiling, cleansing, governance, and quality monitoring to mitigate risks and elevate decision-making capabilities. Central to TDQM are the principles of data governance and management, which establish the framework for data quality standards, stakeholder roles, and the implementation of data strategies. Additionally, TDQM stresses the importance of data security and privacy, safeguarding the organization’s and stakeholders’ trust. Through comprehensive components including data analysis, integration, and continuous quality monitoring, TDQM ensures data serves as a robust, strategic asset, facilitating competitive advantage in a data-centric business landscape.

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Speaker & Chair at “Pharma Digital Transformation Conference”, 25th November 2021

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At the “Pharma Digital Transformation Conference” on 25th November 2021, digital leaders will delve into the pharma industry’s digital and data science challenges. The opening session, co-chaired with Novartis’ Director of Digital Integrated Solutions, promises to set the tone for a day of insightful discussions. Explore further details and access the conference video through the provided links.

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The (Un)reliability of Saliency methods – Google Research

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In the exploration of deep model interpretation, saliency methods emerge as a popular technique for evaluating feature importance. They assign importance scores to input features, indicating their utility in model performance. High scores suggest significant performance degradation in their absence. However, investigations, such as those by Google Research, reveal the inherent unreliability of these methods. The crux of the issue lies in their sensitivity to non-influential factors and failure to maintain input invariance, leading to potentially misleading attributions. This challenges the effectiveness of saliency methods in providing accurate explanations of deep learning behaviors.

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Speaker at “AI, Data Analytics & Insights Summit – DACH”, 11th – 12th November 2021

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At the upcoming “AI, Data Analytics & Insights Summit – DACH” on 11th – 12th November 2021, a session will be dedicated to exploring Artificial Intelligence applications within Research and Development. This interactive, senior-level online meeting will convene 250 experts from the DACH region, offering a unique platform for sharing insights and advancements in the field.

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What I learnt talking to people and speaking at Conferences

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In effective communication, the essence lies in simplicity and relevance. Overloading information overwhelms, whereas tailoring content to the audience’s interests and preferences enhances retention and engagement. This approach, termed listener-centered communication, pivots away from speaker-centric narratives, focusing instead on what resonates with the audience. By initiating conversations that address current challenges and priorities, one can craft messages that are both compelling and concise. Leading with benefits rather than personal burdens ensures the audience’s attention is captured, paving the way for productive discourse. This methodology advocates for a strategic, audience-aligned communication, emphasizing the power of delivering precisely what is necessary, no more.

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Leveraging NLP in Knowledge Management: a Case Study of Lab Document Management

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In a pioneering effort to streamline laboratory knowledge management, a sophisticated system leveraging Natural Language Processing (NLP) and machine learning models, including BERT and GPT, was developed to efficiently manage a massive repository of scanned documents. By applying advanced techniques such as topic modeling, document clustering, and semantic similarity analysis, this system significantly improved document accessibility, categorization, and retrieval. The creation of a detailed ontology, integrated with public data sources, further enhanced data interoperability and research collaboration, showcasing the transformative potential of NLP in handling complex data landscapes.

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How many fields in data science?

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Data Science is a research activity… mostly Data-driven scientific discovery is regarded as the fourth science paradigm   The twenty-first century has ushered in a new age that is coined as data science  and big data analytics. Data-driven scientific discovery is regarded as the fourth science paradigm. Data science has been a core driver of the new-generation science, technologies and economy, and is driving new researches, innovation, profession, applications and education across both disciplines and business domains.  There are many scientific and technical challenges associated with big data, ranging from data capture, creation, storage, search, sharing, modeling, representation, analysis, learning, visualization, explanation, and decision making. Among the many data characteristics and complexities to be addressed, I mention  the hybridization of heterogeneous, multisource, hierarchical, interactive, dynamic,…

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Speaker at FUTURE Labs 2021

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At FUTURE Labs 2021, the spotlight on Artificial Intelligence’s role in Research and Development underscores its pivotal contribution to shaping the laboratories of tomorrow. The conference, renowned for its diverse assembly from academia to industry giants across various sectors, including Biotech, Pharma, and more, serves as a crucible for innovation. It invites a confluence of ideas and visions, aiming to redefine laboratory operations and efficiency. With discussions spanning nine crucial themes, including AI & Machine Learning, Digital Transformation, and Data Management, the event promises a comprehensive exploration of the technological forefront, all delivered in English, facilitating a global discourse.

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