eo’s Post

Fantastic initiative, and a real pleasure to be part of it. Hearing about the experiences of other centres across the globe makes clear how important but also how incredibly fragile the whole field is: most centres lack robust infrastructure support and investment, making it difficult to run and maintain activities and, especially, to build and secure the next generation(s) of scholars in the area. For example, for any PhD student we would like to support at the Centre for the Study of Philanthropy & Public Good, we need to find and secure a total funding of around £75,000-£120,000. Thereafter, to fund a single postdoctoral position one is looking at a minimum of £50,000 per annum (including National Insurance, pension, in costs, etc.) that need to be raised. Then, in the absence of any permanent positions in the area over here, all that investment, time, support, and the expertise that has been built over that period, currently often do not even benefit the Scottish (and at times European) philanthropy research field as early career colleagues are faced with an infinitesimally small number of national and international opportunities that might arise but, more likely than not, do not, leading to exits and brain drain from the field in general. So, assuming that one considers the field and its contributions to be important, there is some real work to be done about making it more sustainable in general, but also more attractive and accessible for younger researchers... Just some of the many issues that will need to be explored as part of our forthcoming workshop series “Together for Good: crafting Scotland’s philanthropic journey” - https://lnkd.in/e5tXgmZR

this is why doing your research is so important. If you understand the process you understand the importance. this goes into everything that you do. For instance, when I do a marketing plan I want to know everything I can about the company or product. This way I know how to break it down and build it back up into what they're trying to get out to the consumer. The reason I say this, is because a lot of times the person who owns the business has no idea of what the consumer is looking for but they know about their product or company. #business #sales #marketing

Come say hello to our team and find out how AgiLab can help you streamline your processes!

Are you looking for resources to help make your team-based collaborative science more efficient and effective? It all starts with understanding what we mean when we talk about ‘Team Science.’ Read more on Substack at https://lnkd.in/gkFnhDwM or visit The Team Science Lab at https://lnkd.in/gbNRnswq!

I’m excited to share three new pieces of HAVIC Lab research now out as pre-prints; the first two championed by PhD student, Friederike Charlotte Hechler! Thanks also to wonderful collaborators across Australia and the world, including Outi Tuomainen, Emily S. Cross, Emmanuele Tidoni, Patrick Nalepka, Aitor Miguel Blanco and others not on LinkedIn. See a brief summary of each below, with full-texts available at the following links: 1. "What does 'often' even mean?" Revising and Validating the Comprehensive Autistic Trait Inventory in Partnership with Autistic People. 🌈 🧠 🤝 https://lnkd.in/edUaM_gd 2. The Role of Intentional Stance Beliefs and Agent Appearance on Gaze Use during Joint Attention. 🤖 👀 👈 https://lnkd.in/eth_SERP 3. The Temporal Context of Eye Contact Influences Perceptions of Communicative Intent. ⏰ 👀 💬 https://lnkd.in/e6yPGc67 🌐 You can learn more about our lab here: https://lnkd.in/ewcX_HXe

Information is healthy.

Greetings, everyone! I'm happy to share some exciting news with you all. As many of you may already know, I've had the privilege of working at Mignon Technologies, a company dedicated to pushing the boundaries of AI technology. Today, I'm delighted to reveal that Mignon has transformed into Literal Labs, marking a significant milestone in our journey of innovation and exploration! Let me highlight some key aspects of our cutting-edge technology: 🔋 Ultra Low Power: Our technology achieves unparalleled energy efficiency by sidestepping the heavy computational demands typically associated with traditional neural networks. 🧠 On-chip Training: We possess a unique capability to efficiently train models directly on hardware, streamlining the learning process. ⚡ High Throughput: We've significantly boosted processing speeds, ensuring rapid and efficient decision-making capabilities. 🔍 Explainable AI: Transparency is paramount. Our technology leverages propositional logic and event-driven computing to provide clear insights into decision-making processes. I'm excited about the potential of Literal Labs and the opportunities it opens up for us to drive meaningful impact across various industries. Our team is unwaveringly committed to pushing the boundaries of possibility, and I feel privileged to be part of this extraordinary journey. Keep an eye out for further updates as we continue to innovate and propel technological advancements at Literal Labs! #rebrand #AI #confidential_AI #Green_AI #Literal_Labs

#SynBYSS seminar with Prof. Jacquin Niles at Massachusetts Institute of Technology and Dr. Yu (Jeremy) Zhao at New York University The video link is below

🤝 The BIRDIE project is a meeting between technical innovation and groundbreaking biological science. We spoke to Niek Welkenhuysen, technical development lead at Fluicell, about the importance of letting the needs of researchers drive technology development. Niek keeps himself busy by always having a new project at hand. If not at work, then by creating new space for animals in his house or by striving towards his next goal as an endurance athlete. 🏃♂️ 🚴♀️ 🏊♂️ Read more about Nieks role in BIRDIE and his approach to technology development. 👉 https://lnkd.in/eznftutF

Transformative technologies such as AI make difficult tasks more accessible and convenient. Since 2018, the use of AI in research has increased drastically, with annual publication rates of 3–5 times higher than pre-2017. Currently, >100,000 manuscripts using AI are published annually within science and engineering, and >20,000 of these belong to the agricultural and environmental fields. Given the magnitude of use, clear communication on how AI is used and how it helps advance scientific knowledge is essential. Clear communication is perhaps more necessary with AI than previous technologies due to its broad and flexible spectrum of uses, the “black-box” nature of deep-learning algorithms, and ongoing debates regarding AI's predictive power versus knowledge of first-principles mechanistic and process-based theories and models. In this commentary, we provide guidelines and discussion points to the scientific community to ensure transparent and effective communication of AI research in agricultural and environmental research publications.