Boston Consulting Group (BCG)’s Post

Coincidentally, I recently used ChatGPT to create Python code, despite having no prior experience in coding—and the results were astonishing. This experience highlighted for me what this article explores in depth: the power of Generative AI to not just increase productivity but to fundamentally expand our capabilities. The era of the Gen-AI augmented worker isn’t just coming; it’s here. This technology enables us to tackle challenges beyond our existing skill set, transforming the way we approach complex tasks. If you haven’t yet explored how GenAI can elevate your work, now is the time to dive in.

Can employees use GenAI to complete tasks beyond their current capabilities? My colleagues from BCG Henderson Institute and BCG X conducted an experiment to find out

Boston Consulting Group (BCG) The three universally basic IP based elements that make AI work are: 1. patented devices with high computational capability (fast GPUs, quantum, computers, etc.), 2. patented energy technologies (battery/electricity power supply), and 3. copyrighted metadata. The importance of computational capability has been taken for granted. Without electricity, the machine won't work. Also, battery accounts for 40% of the total cost of an electric machine, e.g., electric car. For in-house AI applications, the electricity bills of a C-level friend of ours has doubled because of using AI.💰🤑💸 Metadata is an enabler, a digital content, NOT a technology. It's like a treasure map, curated by human for treasure hunting. Without metadata, NO data can be found/retrieved, even by the most advanced technologies, like AI, quantum computers, etc. https://lnkd.in/g-aJFnXR We are using our expertise/IP, a copyrighted multilingual metadata, to do what AI can't do in data analytics NOW. Do you or any contacts of yours need our expertise/IP? Thanks. 關於 AI 人工智能的一些事實 (Chinese version): https://lnkd.in/eeVbdcza Some fact findings about AI (English version): https://lnkd.in/gwcPNUPP

An inspiring reflection on what data is, its objective nature (perhaps wrongly so), with notions of decontextualization, classification, transparency, and the use of metrics. This paper forces us to take a step back at a time when data is taking on an increasingly central role in science, with the open data policy, the appearance of publications centered on the description of data, called data papers, and of course the new approaches to data science, AI, with increasingly automatic processing of data (e.g. unsupervised learning, ...). Of course, this for me doesn't mean going back to the last century, but just taking a step aside, taking a reflexive, epistemological eye on these new practices, which are rapidly and profoundly changing the way we do science. Well, that's my very personal reading of this article by C. Thi nguyen, from University of Utah College of Humanities. To be more widely discussed!

Want to see the real-life results of how Marvin uses Marvin? Right now we're collecting primary & secondary research about the state of research repositories in 2024. Of course we had to use Marvin to get a head start! We started with this hypothesis: "Research repositories are a necessity for customer-centric companies in 2024." But we wanted to validate it before getting too deep in the research. So we ran our hypothesis through Auto Analysis, and Marvin found supporting evidence, counterpoints, and even suggested a revised hypothesis for our report. It's not a perfect analysis, but it saved our marketing team weeks of manual analysis. Even better, it revealed surprising patterns we didn't uncover on our own! Check out the initial Auto Analysis report (yes, it's entirely generated by AI) 👇 p.s. We know this is a brave new way of thinking about user research and data analysis, so we'd love your feedback. What do you think of this Auto Analysis report?

🌟 New Paper! 🌟 "Unleashing collective intelligence for public decision-making: the Data for Policy community" 👉 We are thrilled to share the release of our inaugural report by Data for Policy CIC, now published in Data & Policy! 📊📘 ✍️Together with my brilliant colleagues Zeynep Engin, PhD Emily Gardner, Andrew Hyde, and Jon Crowcroft, we've not only chronicled the journey of our community over the past nine years but also mapped how the ecosystem of actors and topics related to data and policy have evolved. ✅ The report highlights the importance of harnessing collective intelligence in studying and informing the future of data and AI governance. 🧠💡 ✅ It introduces a six-area framework designed to map the vast landscape of data-driven policy research. 🌍🔍 ✅ The framework provides researchers and policymakers a guide to examine ongoing and future data and policy developments 🤝📘 🔗 Open Access: Explore the full report here: (Data & Policy Journal, Cambridge University Press & Assessment) https://lnkd.in/eKFdBpRj We welcome your thoughts and feedback as we collectively seek to study and inform #data for #policy. 🌐📝 #DataForPolicy #CollectiveIntelligence #PublicPolicy #DataDriven #Innovation #AI #Technology #DataGovernance #artificialintelligence

OpenAI's Sam Altman in a Bloomberg interview at Davos today gave some insights into their attitude to publishers and the future of media: "....There is this belief held by some people that “man you need all of my training data and my training data is so valuable” and actually that is generally not the case. We do not want to train on the New York Times data for example.  " … "A lot of our research has been how can we learn more from smaller amounts of very high quality data.  " … "What we want to do with publishers, if they want, is when one of our users says: “What happened at Davos today?” be able to say: Here's an article from Bloomberg, here's an article from New York Times and here’s like a little snippet — or probably not a snippet, there's probably some cooler thing that we can do with the technology." … "I think there's going to be great new ways to consume and monetize news and other published content." I hope there is a cooler thing we can do with the technology. Sounds a lot like a faster horse. Full video interview: https://lnkd.in/g3VBAqXV

Interesting paper on the rate of change in AI. The key point is "If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027, and 50% by 2047. The latter estimate is 13 years earlier than that reached in a similar survey we conducted only one year earlier" Please read that carefully. Then reflect on what it means. It is significant. Machines are already outperforming humans in some areas. That will only grow... at pace, in the next couple of years. Jobs and careers will evolve. I've already heard AI music that can pass off as an original by the artist. The Heygen AI video is just incredible. I actually think the paper is already out of date! In project delivery, will we move from traditional 'roles' to 'tasks and skills'. I sense that the entire employment market will get disrupted and 99% of the people I speak to aren't aware of what is coming. What are you seeing? Do you agree with the 2778 researchers? I am delivering a data analyst bootcamp to a major consultancy tomorrow. I am having to update my presentation for every cohort. The technology is advancing at break neck speed. On another topic... GPT store is now live. Have a look and please share any cool stuff. It will be interesting to see how employers react to 'side hustles' that overlap with employee core roles. Or non-compete clauses and lockout periods. Any thoughts? Fascinating times. https://lnkd.in/eeVHjpV3

Just few days ago Altair acquired Cambridge Semantics Inc. If you are an aerospace engineer like me you may ask “so what?!?”. Well… translating from the AI/IT lingo… a graph database is really a cool piece of technology if you want to extract data from differet sources across your company in crazy different formats and make use of it. And this is the central and fundamental piece for use cases like digital twin, predictive maintenance, smart factory, supply chain analytics, smart testing (all that flight test data!)…