MUNICH INNOVATION ECOSYSTEM’s Post

𝑻𝒉𝒆 𝑩𝒆𝒈𝒊𝒏𝒏𝒊𝒏𝒈 𝒐𝒇 𝑨𝑰: 𝑯𝒐𝒘 𝑰𝒕 𝑨𝒍𝒍 𝑩𝒆𝒈𝒂𝒏 𝒂𝒏𝒅 𝑾𝒉𝒆𝒓𝒆 𝑾𝒆'𝒓𝒆 𝑮𝒐𝒊𝒏𝒈 𝘖𝘳𝘪𝘨𝘪𝘯𝘴: 𝘊𝘰𝘯𝘤𝘦𝘪𝘷𝘪𝘯𝘨 𝘵𝘩𝘦 𝘍𝘶𝘵𝘶𝘳𝘦 - 𝐀 𝐒𝐩𝐚𝐫𝐤 𝐢𝐧 𝐭𝐡𝐞 1950𝐬: The saga of artificial intelligence (AI) began as a daring concept in the minds of forward-thinkers. It was in 1956, at the Dartmouth Conference, where the journey of AI truly began, coining the term that would define this field of endless possibilities. This was more than just an academic pursuit; it was the blueprint of the future. 𝘌𝘢𝘳𝘭𝘺 𝘔𝘪𝘭𝘦𝘴𝘵𝘰𝘯𝘦𝘴: 𝘓𝘢𝘺𝘪𝘯𝘨 𝘵𝘩𝘦 𝘎𝘳𝘰𝘶𝘯𝘥𝘸𝘰𝘳𝘬 - 𝐏𝐢𝐨𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦𝐬: The nascent stages of AI were marked by groundbreaking work in algorithms that could mimic aspects of human intelligence. These initial steps were modest, focusing on logical problem-solving and basic task automation, setting a foundation for what AI could eventually become. 𝘛𝘦𝘤𝘩𝘯𝘰𝘭𝘰𝘨𝘪𝘤𝘢𝘭 𝘌𝘷𝘰𝘭𝘶𝘵𝘪𝘰𝘯: 𝘌𝘹𝘱𝘢𝘯𝘥𝘪𝘯𝘨 𝘏𝘰𝘳𝘪𝘻𝘰𝘯𝘴 - 𝐄𝐫𝐚 𝐨𝐟 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: As technology advanced, so did AI, with the 1980s and 1990s heralding the rise of machine learning and neural networks. This period was characterized by significant improvements in AI's ability to learn from data, making strides in fields like pattern recognition, natural language processing, and decision-making. 𝘚𝘪𝘯𝘵𝘦𝘟 𝘈𝘐: 𝘚𝘩𝘢𝘱𝘪𝘯𝘨 𝘵𝘩𝘦 𝘗𝘳𝘦𝘴𝘦𝘯𝘵 𝘢𝘯𝘥 𝘍𝘶𝘵𝘶𝘳𝘦 - 𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐀𝐈 𝐚𝐧𝐝 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬: Our role at SinteX AI has been pivotal in translating AI's theoretical potential into tangible business solutions. Through White Label AI Development, we've enabled businesses to harness sophisticated AI tools, enhancing their competitiveness and innovation. - 𝐓𝐚𝐢𝐥𝐨𝐫𝐞𝐝 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐃𝐢𝐯𝐞𝐫𝐬𝐞 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: Recognizing the unique challenges faced by different industries, we've specialized in custom software development. Our approach ensures that AI is not just an add-on but a deeply integrated solution driving efficiency, growth, and transformation. 𝘛𝘩𝘦 𝘊𝘰𝘯𝘵𝘦𝘮𝘱𝘰𝘳𝘢𝘳𝘺 𝘈𝘐 𝘌𝘳𝘢: 𝘐𝘯𝘵𝘦𝘨𝘳𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘐𝘮𝘱𝘢𝘤𝘵 - 𝐀𝐈'𝐬 𝐔𝐛𝐢𝐪𝐮𝐢𝐭𝐨𝐮𝐬 𝐈𝐧𝐟𝐥𝐮𝐞𝐧𝐜𝐞: In the current landscape, AI's influence is all-encompassing. From enhancing personal devices to revolutionizing healthcare and streamlining business operations, AI's role is integral to modern advancements. Its capacity for data analysis and adaptive learning makes it an invaluable asset across sectors. - 𝐂𝐨𝐦𝐦𝐢𝐭𝐦𝐞𝐧𝐭 𝐭𝐨 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐀𝐈: As we navigate the future, our commitment at SinteX AI extends beyond innovation. We are dedicated to fostering ethical AI development, ensuring that our advancements contribute positively to society and the environment. #aihistory #deeplearning

"From Insights to Implementation: Navigating AI Horizons with Metaorange Digital Latest Blog!" From machine learning algorithms to natural language processing, we delve into the cutting-edge technologies driving global tech transformation. With expert analysis, real-world examples, and actionable insights, our blog post is your go-to resource for staying informed and inspired in the world of AI. Don't miss out – Read it now! 𝐑𝐄𝐀𝐃 𝐌𝐎𝐑𝐄 >> https://lnkd.in/gJK7X9C2 𝐅𝐎𝐑 𝐌𝐎𝐑𝐄:- 𝐂𝐎𝐍𝐍𝐄𝐂𝐓 𝐔𝐒 > https://lnkd.in/deujNXgA  𝐅𝐎𝐋𝐋𝐎𝐖 𝐔𝐒 > Metaorange Digital #ai #artificialintelligence #machinelearning #naturallanguageprocessing #techtransformation #tech #technology #digitaltransformation #datascience #innovation #businesstransformation #insights #implementaton #blogpost #metaorange #expertanalysis #realworldexamples #actionableinsights #stayinformed #stayinspired #metaorangedigital

Recognizing the potential of generative AI in the realm of academic research and libraries, EBSCO is making proactive strides to incorporate AI into the company’s products, undertaking AI pilot projects in specific environments. https://m.ebsco.is/uf01n Read the press release to learn more. #generativeAI #artificialintelligence #searchdiscovery

🌿 New research alert! 🌿 How can generative AI help in different parts of research? With Large Language Models (LLM) gaining in popularity and new alternative AI engines constantly emerging, questions remain about how these new data management tools can assist scholars in improving the research design and implementation. In an attempt to answer this question, a new research article by Oleg Dashkevych ✔ and Prof. Boris A. Portnov in the journal Technology and Society focuses on one particular research field – definition and identification of smart cities, – and compares the answers provided by three different AI engines (ChatGPT, InferKit, DeepAI) with the answers given in a sequence of research papers, prepared without the use of AI and recently published by the same authors. In particular, the following aspects of the original studies were re-analysed using the AI input: a) problem definition; b) summary of current knowledge; c) identification of unknowns; d) research strategy; and e) recommendations for research and practice. The study reveals that the recommendations of AI engines are, at times, inconsistent and data sources cited are often inaccurate. However, as such engines scan multiple open sources and retrieve relevant information, they can help to bridge gaps in the summary of background studies and streamline the research design, by supplementing missing or overlooked information. Read the full paper here: https://lnkd.in/daJNvcqk #LargeLanguageModels #LLM #AI #ChatGPT #SmartCities

🚀 New Blog Alert: "5 Ways Generative AI Can Supercharge Your Research Workflow" 🌐 Exploring the AI Revolution in Research: Dive into the world of generative AI with my latest blog post, inspired by Anton Korinek's seminal paper, “Generative AI for Economic Research: Use Cases and Implications for Economists.” 🔍 Korinek's study reveals how Large Language Models (LLMs) like ChatGPT are reshaping research. These AI tools transcend the role of mere assistants, emerging as co-creators and innovators in various research fields. 🔥 Broad Applications Beyond Economics: While focusing on economic research, the insights from Korinek's paper apply across diverse research areas, offering a blueprint for leveraging AI effectively. 🌟 Join me in this exploration as we break down how to use AI to enhance your research efficiency, regardless of your field. 🔗 Stay tuned for deep dives into each domain and practical tips on harnessing AI’s full potential in research. https://lnkd.in/gKxc-NH4

Navigating the AI Landscape in Research: Insights from a workshop

Top 10 Takeaways: 1. AI beats humans on some tasks, but not on all. AI has surpassed human performance on several benchmarks, including some in image classification, visual reasoning, and English understanding. Yet it trails behind on more complex tasks like competition-level mathematics, visual commonsense reasoning and planning. 2. Industry continues to dominate frontier AI research. In 2023, industry produced 51 notable machine learning models, while academia contributed only 15. There were also 21 notable models resulting from industry-academia collaborations in 2023, a new high. 3. Frontier models get way more expensive. According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels. For example, OpenAI’s GPT-4 used an estimated $78 million worth of compute to train, while Google’s Gemini Ultra cost $191 million for compute. 4. The United States leads China, the EU, and the U.K. as the leading source of top AI models. In 2023, 61 notable AI models originated from U.S.-based institutions, far outpacing the European Union’s 21 and China’s 15. 5. Robust and standardized evaluations for LLM responsibility are seriously lacking. New research from the AI Index reveals a significant lack of standardization in responsible AI reporting. Leading developers, including OpenAI, Google, and Anthropic, primarily test their models against different responsible AI benchmarks. This practice complicates efforts to systematically compare the risks and limitations of top AI models. 6. Generative AI investment skyrockets. 7. The data is in: AI makes workers more productive and leads to higher quality work. In 2023, several studies assessed AI’s impact on labor, suggesting that AI enables workers to complete tasks more quickly and to improve the quality of their output. These studies also demonstrated AI’s potential to bridge the skill gap between low- and high-skilled workers. Still, other studies caution that using AI without proper oversight can lead to diminished performance. 8. Scientific progress accelerates even further, thanks to AI. 9. The number of AI regulations in the United States sharply increases. 10. People across the globe are more cognizant of AI’s potential impact—and more nervous. A survey from Ipsos shows that, over the last year, the proportion of those who think AI will dramatically affect their lives in the next three to five years has increased from 60% to 66%. Moreover, 52% express nervousness toward AI products and services, marking a 13 percentage point rise from 2022. In America, Pew data suggests that 52% of Americans report feeling more concerned than excited about AI, rising from 37% in 2022. https://lnkd.in/dYrRbSgi

Generative AI (GenAI) is having a breakout year. Investment is surging, and experts are calling it a game-changer. This article from Tech Brew pulled out of few of the key points from the Stanford AI report: https://lnkd.in/eUSXav9g. A few takeaways: 💡 GenAI investment is surging, which highlights the field's growing importance and potential and suggests a demand for skilled professionals. 💡AI can't beat humans at everything: While AI is powerful, there are still human strengths. Training in GenAI helps bridge this gap and allows humans to work effectively alongside AI. Is your team or organization ready to harness its potential? Browse our GenAI courses for your team or organization: https://lnkd.in/dnAwce2M #GenerativeAI #FutureOfWork #AI

Steve Carnevale Shuo Chen Toby Ewing Erin S. Rym Ayadi Ian MacRae Michelle Tempest Nathan Cook Michael Platt Paweł Świeboda 'The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine the performance implications of AI on realistic, complex, and knowledge-intensive tasks. The pre-registered experiment involved 758 consultants comprising about 7% of the individual contributor-level consultants at the company. After establishing a performance baseline on a similar task, subjects were randomly assigned to one of three conditions: no AI access, GPT-4 AI access, or GPT-4 AI access with a prompt engineering overview. We suggest that the capabilities of AI create a “jagged technological frontier” where some tasks are easily done by AI, while others, though seemingly similar in difficulty level, are outside the current capability of AI. For each one of a set of 18 realistic consulting tasks within the frontier of AI capabilities, consultants using AI were significantly more productive (they completed 12.2% more tasks on average, and completed task 25.1% more quickly), and produced significantly higher quality results (more than 40% higher quality compared to a control group). Consultants across the skills distribution benefited significantly from having AI augmentation, with those below the average performance threshold increasing by 43% and those above increasing by 17% compared to their own scores. For a task selected to be outside the frontier, however, consultants using AI were 19 percentage points less likely to produce correct solutions compared to those without AI. Further, our analysis shows the emergence of two distinctive patterns of successful AI use by humans along a spectrum of human-AI integration. One set of consultants acted as “Centaurs,” like the mythical halfhorse/half-human creature, dividing and delegating their solution-creation activities to the AI or to themselves. Another set of consultants acted more like “Cyborgs,” completely integrating their task flow with the AI and continually interacting with the technology.' https://lnkd.in/g7CJGv_p

The AI Index 2024 annual report, a comprehensive look at global AI progress, finds that AI systems exceeded human performance on additional benchmarks in areas like image classification, visual reasoning, and English understanding. However, costs to train cutting-edge AI systems skyrocketed, with OpenAI’s GPT-4 language model using an estimated $78 million worth of computing power. Google’s even larger Gemini Ultra model cost a staggering $191 million to train, according to estimates in the report. “Frontier models get way more expensive,” the authors explain. “According to AI Index estimates, the training costs of state-of-the-art AI models have reached unprecedented levels.”... #artificialintelligence #machinelearning #ai #investments #trainingcosts