Frankfurter Buchmesse Digitales Manifest: Auswirkungen auf die Gesellschaft werden im Buch Decisively Digital besprochen.
Zu Beginn der 75. Frankfurter Buchmesse, einem weltweit bedeutenden Literatur- und Kulturereignis, bei dem ich selbst zu Gast bin, möchte ich die Gelegenheit nutzen, einige meiner Gedanken zur Digitalisierung und ihren Auswirkungen auf unsere Gesellschaft mit Ihnen zu teilen.
In diesem Manifest formuliere ich Überlegungen, die weit über technologische Aspekte hinausgehen und unsere kollektive Verantwortung für die Gestaltung einer digitalen Zukunft betonen, in deren Mittelpunkt der Mensch steht. In einer Zeit, in der Bücher und Gedanken mehr denn je eine Brücke zwischen der analogen und der digitalen Welt schlagen können, scheint mir dieser Moment besonders geeignet, einen Dialog anzustoßen, der weit über die technologischen Aspekte der Digitalisierung hinausgeht.
Manifest für eine digital-bewusste Gesellschaft
Die Diskussion um die Digitalisierung verengt sich allzu oft auf technische Aspekte und die Wettbewerbsfähigkeit der Wirtschaft. Diese Reduktion ist nicht nur verkürzt, sondern gefährlich. Ich argumentiere, dass die Digitalisierung eine umfassende gesellschaftliche Herausforderung darstellt, die weit mehr als nur technologische Überlegungen erfordert. Sie stellt uns vor komplexe moralische und ethische Fragestellungen, die dringend politisch angegangen werden müssen.
Wenn die politischen Entscheidungsträger die vielschichtigen Dimensionen der Digitalisierung weiterhin ignorieren, droht uns eine dystopische Zukunft. Ich sehe die Gefahr einer Gesellschaft, die in einem Strudel von Effizienzsteigerung und Selbstoptimierung gefangen ist, während der individuelle und kollektive Sinn des Lebens verloren geht.
Die digitale Revolution hat eine subtile Form des Kapitalismus geschaffen, die weitreichende psychologische Auswirkungen hat. Diese neuen Mechanismen beeinflussen nicht nur unser Konsumverhalten, sondern durchdringen alle sozialen Räume unseres Lebens: unsere Autos, unsere Häuser und sogar unsere zwischenmenschlichen Beziehungen.
Die digitale Technologie hat zweifellos ihre Verdienste. Sie hat eine globale Zivilisation ermöglicht, die nationale und kulturelle Grenzen überwindet. Aber diese Universalität hat auch ihren Preis: Wir dürfen nicht zulassen, dass unsere kulturellen Traditionen und individuellen Weisheiten zu musealen Objekten einer vergangenen Epoche werden.
Als Wirtschaftsinformatiker plädiere ich für einen menschenzentrierten Zugang zur Technologie. Es ist an der Zeit, eine neue Gesellschaftsstruktur zu entwerfen, die die vielfältigen Möglichkeiten der Digitalisierung in den Dienst des Menschen stellt. Die Schlüsselkompetenzen dieses neuen Zeitalters sind Selbstorganisation, Selbstverantwortung und Selbstermächtigung. Diese Fähigkeiten sind nicht mehr nur wünschenswerte Ideale, sondern absolute Notwendigkeiten für die Zukunft.
Die Delegation kognitiver Funktionen an Maschinen birgt die Gefahr, dass wir unsere Fähigkeit zu kreativem und kritischem Denken verlieren. Das menschliche Gedächtnis ist nicht nur ein Speicherort, sondern auch ein Generator für freie, kreative Gedanken.
In meinem Buch Decisively Digital stelle ich die weit verbreitete Annahme in Frage, dass eine zukünftige Superintelligenz in der Lage sein wird, den menschlichen Intellekt in allen Bereichen zu übertreffen. Die eigentliche Gefahr liegt nicht in der KI selbst, sondern in ihrem möglichen Missbrauch. Ethik lässt sich nicht einfach in Algorithmen übersetzen, weil menschliche Moral zu komplex und kontextabhängig ist.
Wenn wir neuronale Netze trainieren, projizieren wir Aspekte unserer Menschlichkeit in diese Systeme. Sie werden somit zu einem Spiegel unserer Hoffnungen, Träume und Ängste. Diese Erkenntnis sollte uns bewusst sein, wenn wir über die Anwendung und Regulierung künstlicher Intelligenz nachdenken.
Die Zeit ist reif für tiefgreifende Veränderungen. Wir müssen einen konstruktiven Dialog über die gesellschaftlichen und politischen Auswirkungen der Digitalisierung führen. Wir müssen uns selbst und die nächste Generation aufklären und stärken. Denn letztlich ist die digitale Welt ein Abbild unserer Welt – machen wir sie lebenswert.
Ich freue mich auf einen inspirierenden Austausch in den kommenden Tagen auf der Frankfurter Buchmesse – und darüber hinaus auch in den sozialen Medien (Twitter, LinkedIn, Instagram).
Following the talk, I was inspired by a conversation to leverage the power of GPT-4 and create an automatically generated summary of the Microsoft Teams transcript. This approach not only streamlines information sharing but also showcases the practical applications of advanced AI technology.
Below, I will share the key insights generated by GPT-4 and also include some captivating images from the event:
Decisively Digital: AI’s Impact on Society
In my talk, I drew inspiration from my book Decisively Digital, which discusses the impact of AI on society. I shared about the innovative projects underway at Microsoft’s AI for Good Lab. In light of GPT-4’s recent launch, I also highlighted our mission to leverage technology to benefit humanity.
AI’s impact on society is discussed in Decisively Digital: From Creating a Culture to Designing Strategy. John Wiley & Sons. (photo by Gerhard Müller)
By harnessing Generative AI, we can stimulate the creation of innovative ideas and accelerate the pace of advancement. This cutting-edge technology is already transforming industries by streamlining drug development, expediting material design, and inspiring novel hypotheses. AI’s ability to identify patterns in vast datasets empowers humans to uncover insights that might have gone unnoticed.
Generative AI can Augment our Thinking
For instance, researchers have employed machine learning to predict chemical combinations with the potential to improve car batteries, ultimately identifying promising candidates for real-world testing. AI can efficiently sift through and analyze extensive information from diverse sources, filtering, grouping, and prioritizing relevant data. It can also generate knowledge graphs that reveal associations between seemingly unrelated data points, which can be invaluable for drug research, discovering novel therapies, and minimizing side effects.
„Now is the time to explore how Generative AI can augment our thinking and facilitate more meaningful interactions with others.“
Alexander Loth
At the AI for Good Lab, we are currently employing satellite imagery and generative AI models for damage assessment in Ukraine, with similar initiatives taking place in Turkey and Syria for earthquake relief. In the United States, our focus is on healthcare, specifically addressing discrepancies and imbalances through AI-driven analysis.
Our commitment to diversity and inclusion centers on fostering digital equality by expanding broadband access, facilitating high-speed internet availability, and promoting digital skills development. Additionally, we are dedicated to reducing carbon footprints and preserving biodiversity. For example, we collaborate with the NOAH organization to identify whales using AI technology and have developed an election propaganda index to expose the influence of fake news. Promising initial experiments using GPT-4 showcase its potential for fake news detection.
„We live in a rapidly changing world, facing big challenges.“ (photo by Gerhard Müller)
ChatGPT will be Empowered to Perform Real-time Website Crawling
While ChatGPT currently cannot crawl websites directly, it is built upon a training set of crawled data up to September 2021. In the near future, the integration of plugins will empower ChatGPT to perform real-time website crawling, enhancing its ability to deliver relevant, up-to-date information, and sophisticated mathematics. This same training set serves as the foundation for the GPT-4 model.
GPT-4 demonstrates remarkable reasoning capabilities, while Bing Chat offers valuable references for verifying news stories. AI encompasses various machine learning algorithms, including computer vision, statistical classifications, and even software that can generate source code. A notable example is the Codex model, a derivative of GPT-3, which excels at efficiently generating source code.
Microsoft has a long-standing interest in AI and is dedicated to making it accessible to a wider audience. The company’s partnership with OpenAI primarily focuses on the democratization of AI models, such as GPT and DALL-E. We have already integrated GPT-3 into Power BI and are actively developing integrations for Copilot across various products, such as Outlook, PowerPoint, Excel, Word, and Teams. Microsoft Graph is a versatile tool for accessing XML-based objects in documents and generating results using GPT algorithms.
Hardware, particularly GPUs, has played a pivotal role in the development of GPT-3. For those interested in experimenting with Generative AI on a very technical level, I recommend Stable Diffusion, which is developed by LMU Munich. GPT-3’s emergence created a buzz, quickly amassing a vast user base and surpassing the growth of services like Uber and TikTok. Sustainability remains a crucial concern, and Microsoft is striving to achieve a CO2-positive status.
Generative AI Models have garnered Criticism due to their Dual-use Nature
Despite its potential, Generative AI models such as GPT-3 have also garnered criticism due to their dual-use nature and potential negative societal repercussions. Some concerns include the possibility of automated hacking, photo manipulation and the spread of fake news (➡️ deepfake disussion on LinkedIn). To ensure responsible AI development, numerous efforts are being undertaken to minimize reported biases in the GPT models. By actively working on refining algorithms and incorporating feedback from users and experts, developers can mitigate potential risks and promote a more ethical and inclusive AI ecosystem.
Moving forward, it is essential to maintain open dialogue and collaboration between AI developers, researchers, policymakers, and users. This collaborative approach will enable us to strike a balance between harnessing the immense potential of AI technologies like GPT and ensuring the protection of society from unintended negative consequences.
Microsoft‘s responsible AI principles: fairness, reliability & safety, privacy & security, and inclusiveness, underpinned by transparency and accountability. (photo by Gerhard Müller)
GPT-3.5 closely mimics human cognition. However, GPT-4 transcends its forerunner with its remarkable reasoning capabilities and contextual understanding. GPT models leverage tokens to establish and maintain the context of the text, ensuring coherent and relevant output. The GPT-4-32K model boasts an impressive capacity to handle 32,000 tokens, allowing it to process extensive amounts of text efficiently. To preserve the context and ensure the continuity of the generated text, GPT-4 employs various strategies that adapt to different tasks and content types.
GPT-4 Features a Robust Foundation in Common Sense Reasoning
One of GPT-4’s defining features is its robust foundation in common sense reasoning. This attribute significantly contributes to its heightened intelligence, enabling the AI model to generate output that is not only coherent but also demonstrates a deep understanding of the subject matter. As GPT-4 continues to evolve and refine its capabilities, it promises to revolutionize the field of artificial intelligence, expanding the horizons of what AI models can achieve and paving the way for future breakthroughs in the realm of generative AI.
Results of the live Twitter survey: 47.8% predict the emergence of AGI is possible within the next 5 years (photo by Bernd Vellguth)
In the near future, advanced tools like ChatGPT will elucidate intricate relationships without requiring us to sift through countless websites and articles, further amplifying the transformative impact of Generative AI.
I appreciate the opportunity to share my insights at the German Chapter of the ACM.
Did you enjoy this GPT-generated Summary of my Talk?
Leveraging GPT-4 to generate a summary of my talk was an exciting experiment, and I have to admit, the results are impressive. GPT was able to provide a brief overview of the key takeaways from my talk.
Now, I would love to hear about your experiences with GPT. What are your experiences with GPT so far? Feel free to share your thoughts in the comments section of this Twitter thread or this LinkedIn post:
OpenAI GPT-4 launch illustrated with Stable Diffusion (CC BY-SA 4.0)
With today’s launch of OpenAI’s GPT-4, the next generation of its Large Language Model (LLM), generative AI has entered a new era. This latest model is more advanced and multimodal, meaning GPT-4 can understand and generate responses based on image input as well as traditional text input (see GPT-4 launch livestream).
Generative AI has rapidly gained popularity and awareness in the last few months, making it crucial for businesses to evaluate and implement strategies across a wide range of industries, including e-commerce and healthcare. By automating tasks and creating personalized experiences for users, companies can increase efficiency and productivity in various areas of value creation. Despite being in development for decades, it’s high time for businesses to apply generative AI to their workflows and reap its benefits.
Before you dive into OpenAI GPT-4, let’s take a quick look back at the evolution of generative AI…
The history of generative AI begins in the late 1970s and early 1980s when researchers began developing neural networks that mimicked the structure of the human brain. The idea behind this technology was to assemble a set of neurons that could pass information from one to another with some basic logic, and together the network of neurons could perform complicated tasks. While minimal advances were made in the field, it remained largely dormant until 2010, when Google pioneered deep neural networks that added more data, hardware, and computing resources.
In 2011, Apple launched Siri, the first mass-market speech recognition application. In 2012, Google used the technology to identify cats in YouTube videos, finally reviving the field of neural networks and AI. Both Google and NVIDIA invested heavily in specialized hardware to support neural networks. In 2014, Google acquired DeepMind, which built neural networks for gaming. DeepMind built AlphaGo, which went on to defeat all the top Go players, a pivotal moment because it was one of the first industrial applications of generative AI, which uses computers to generate human-like candidate moves.
OpenAI was founded to democratize AI as a non-profit organization
In 2015, OpenAI was founded to democratize AI and was established as a non-profit organization. In 2019, OpenAI released GPT-2, a large-scale language model capable of producing human-like text. However, GPT-2 sparked controversy because it could produce fake news and disinformation, raising concerns about the ethics of generative AI.
In 2021, OpenAI launched DALL-E, a neural network that can create original, realistic images and art from textual description. It can combine concepts, attributes, and styles in novel ways. A year later, Midjourney was launched by the independent research lab Midjourney. Also in 2022, Stable Diffusion, an open-source machine learning model developed by LMU Munich, was released that can generate images from text, modify images based on text, or fill in details in low-resolution or low-detail images.
OpenAI launched ChatGPT in November 2022 as a fine-tuned version of the GPT-3.5 model. It was developed with a focus on enhancing the model’s ability to process natural language queries and generate relevant responses. The result is an AI-powered chatbot that can engage in meaningful conversations with users, providing information and assistance in real-time. One of the key advantages of ChatGPT is its ability to handle complex queries and provide accurate responses. The model has been trained on a vast corpus of data, allowing it to understand the nuances of natural language and provide contextually relevant responses.
Today’s launch of OpenAI GPT-4 marks a significant milestone in the evolution of generative AI!
This latest model, GPT-4, is capable of answering user queries via text and image input. The multimodal model demonstrates remarkable human-level performance on various professional and academic benchmarks, indicating the potential for widespread adoption and use. One of the most significant features of OpenAI GPT-4 is its ability to understand and process image inputs, providing users with a more interactive and engaging experience.
Users can now receive responses in the form of text output based on image inputs, which is a massive step forward in the evolution of AI. Depending on the model used, a request can use up to 32,768 tokens shared between prompt and completion, which is the equivalent of about 49 pages. If your prompt is 30,000 tokens, your completion can be a maximum of 2,768 tokens.
Token limitations of GPT models with real-world scenarios
Bing has already integrated GPT-4 and offers both, chat and compose modes for users to interact with the model. With the integration of GPT-4, Bing has significantly enhanced its capabilities to provide users with more accurate and personalized search results, making it easier for them to find what they are looking for.
The disruptive potential of generative AI is enormous, particularly in the retail industry. The technology can create personalized product recommendations and content, and even generate leads, saving sales teams time and increasing productivity. However, the ethical implications of generative AI cannot be ignored, particularly in the creation of disinformation and fake news.
To sum up, generative AI is here to stay, and companies must evaluate and implement strategies swiftly. As generative AI technology advances, so do the ethical concerns surrounding its use. Therefore, it is critical for companies to proceed with caution and consider the potential consequences of implementing generative AI into their operations.
Are you already using generative AI for a more productive workflow?
What improvement do you expect from OpenAI GPT-4 in this regard? I look forward to reading your ideas in the comments to this LinkedIn post:
Machine Learning kompakt und Blick in das Kapitel „Neuronale Netze und Deep Learning“
Nachdem ich bereits Erfahrung als Buchautor (hier und hier) gesammelt habe, hatte ich kürzlich die Gelegenheit als Technical Reviewer ein sehr spannendes Buchprojekt zu unterstützen. Das Buch Machine Learning kompakt: Alles, was Sie wissen müssen, geschrieben von Andriy Burkov, fand ich dabei dermaßen interessant, dass ich es gerne im Folgenden kurz vorstellen werde:
Machine Learning kompakt von Andriy Burkov ist ein hervorragend geschriebenes Buch und ein Muss für jeden, der sich für Machine Learning interessiert.
Andriy Burkov gelang ein ausgewogenes Verhältnis zwischen der Mathematik, intuitiven Darstellungen und verständlichen Erklärungen zu finden. Dieses Buch wird Neulingen auf dem Gebiet als gründliche Einführung zu Machine Learning zugutekommen. Darüber hinaus dient das Buch Entwicklern als perfekte Ergänzung zu Code-intensiver Literatur, da hier die zugrunde liegenden Konzepte beleuchtet werden.
Microsoft Azure Machine Learning Studio
Machine Learning kompakt eignet sich außerdem als Lehrbuch für einen allgemeinen Kurs zu Machine Learning. Ich wünschte, ein solches Buch gäbe es, als ich studiert habe!
Protip: viele der im Buch vorgestellten Machine-Learning-Algorithmen können Sie einfach und bequem in Microsoft Azure Machine Learning Studio selbst ausprobieren: https://aka.ms/mlst
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