Brinc, a global leader in corporate innovation and venture acceleration, has acquired Callmi, a MENA-centric mentorship platform that offers real-time, one-on-one video consultations with top-tier experts, including C-suite executives and industry thought leaders including Jad Halaoui, Co-founder and COO at Washmen, Nelio Leone, founding team of Careem (MENA’s first unicorn), and Siddiq Faird, Smart Crowd Founder and Director.
The platform will be integrated into Brinc’s founder pathways to offer specialized mentorship to Brinc Academy alumni and participants in Brinc’s sector-specific accelerator programs, further enhancing support for entrepreneurs within its ecosystem.
As a firm, Callmi is deeply committed to their philosophy of giving back. Embedded in its mentorship model, an impressive 40 percent of Callmi mentors commit to donating proceeds they earn on Callmi to charities of their choice, a practice that Brinc fully supports and is dedicated to upholding.
Brinc and Callmi will donate 100 percent of its fees earned from the first 100 calls in May to the Red Crescent, Dubai Center for Special Needs, and Abu Dhabi Mangroves charities. This approach aids various philanthropic causes while allowing both mentors and mentees to make a positive societal impact from their very first interaction.
Through this acquisition, Brinc is set to further establish its support for the immediate needs of entrepreneurs in the rapidly growing MENA region, while also connecting innovators across the globe. Brinc will continue to invest into the Callmi product development to include calendar syncing, multi-user sessions, and multi-language support to better serve a diverse global audience. Brinc intends to maintain rigorous standards for Callmi, ensuring high-quality consultations through strict vetting of mentors, ongoing monitoring of consultation quality, and user feedback mechanisms.
Manav Gupta, CEO & founder of Brinc, stated, “This acquisition is not just a strategic step towards enhancing our global footprint, but also a reaffirmation of our commitment to the MENA region. Integrating Callmi into our suite of services enriches our offerings with its innovative solutions and deep local insights. We’re excited about how Callmi will help us empower entrepreneurs in the MENA region, driving the development of industry leaders who are well-versed in both local and global markets.”
Looking ahead, Brinc envisions significant growth and an extended reach, leveraging Callmi’s features and the planned expansions to enhance accessibility and scalability. Democratizing access to expert knowledge, this marks a crucial milestone for Brinc in its mission to empower entrepreneurs and professionals within the MENA region. This year, the Callmi team plans to expand their operations to Brinc’s existing markets followed by introducing new ways for founders to engage with experts online (group mentoring, interactive workshops, etc.) as well as offline (mentor meetups, in-person events, etc).
Brinc highly encourages founders to take advantage of an exclusive sign-up offer currently available. For those interested in mentoring, now is the perfect time to join Brinc’s growing community of industry leaders and experts.
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Scientists from MIT and the University of Basel in Switzerland have introduced a novel machine-learning framework that employs generative artificial intelligence (AI) models to automatically map out phase diagrams for novel physical systems. This groundbreaking approach addresses the challenge of quantifying phase changes in complex systems with limited data.
Phase transitions, such as the freezing of water, are commonplace, but detecting phase changes in novel materials or intricate physical systems presents unique challenges. Traditional manual techniques rely heavily on theoretical expertise and can be time-consuming. To overcome these limitations, the researchers turned to generative AI models to develop a more efficient and data-driven approach.
Their framework, detailed in a paper published in Physical Review Letters, leverages generative models to recognize phases and detect transitions in physical systems. Unlike conventional machine-learning techniques that require extensive labeled datasets, this approach utilizes physics-informed machine learning and does not depend on large training datasets.
The researchers demonstrated the effectiveness of their method in detecting phase transitions by identifying order parameters that signify changes in the system. By incorporating knowledge about the physical system directly into the machine-learning scheme, the framework outperforms traditional techniques and enhances computational efficiency.
Moreover, this approach opens up possibilities for various binary classification tasks in physical systems, such as detecting entanglement in quantum systems or selecting the most suitable theoretical model for a given problem. It could also contribute to improving large language models like ChatGPT by optimizing parameters for better performance.
Looking ahead, the researchers aim to explore theoretical guarantees regarding the number of measurements required to detect phase transitions effectively and estimate the computational resources needed for implementation.
Funding for this research was provided by the Swiss National Science Foundation, the MIT-Switzerland Lockheed Martin Seed Fund, and MIT International Science and Technology Initiatives.
Source: news.mit.edu

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The Advanced Research Projects Agency – Infrastructure (ARPA-I) of the United States Department of Transportation (USDOT) is inviting input from interested parties regarding the potential utilization of artificial intelligence (AI) within transportation. They are also seeking insights into the emerging challenges and opportunities associated with the development and implementation of AI technologies across all modes of transportation.
The objective of this Request for Information (RFI) is to gather feedback from a diverse range of stakeholders regarding AI opportunities, challenges, and associated matters in transportation, in accordance with Executive Order (EO) 14110 of October 30, 2023, titled “Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence.”
Interested parties are encouraged to submit written comments electronically to Docket Number DOT–OST–2024–0049 via the Federal eRulemaking Portal. Comments must be received by July 2, 2024. Submissions, excluding personal information, will be made available to the public on regulations.gov, as per DOT’s Privacy Act Statement.
For inquiries regarding this RFI, individuals may contact [email protected]. Additionally, Mr. Timothy A. Klein, Director of Technology Policy and Outreach at the Office of the Assistant Secretary for Research and Technology, can be reached at 202-366-0075 or via email at [email protected].
Source: traffictechnologytoday.com

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