For over a decade, the tech industry has been chasing unicorns--those elusive startups valued at over 1 billion. The obsession began in 2013, when Aileen Lee--a Palo Alto–based VC--coined the term that captured the imaginations first of founders and investors, and then prime ministers and presidents. But these mythical beasts are also rare: only 1 percent of VC-backed startups ever reach this status. As society enters the age of AI, and financial markets put renewed value on business fundamentals, our understanding of what makes a successful tech company is evolving. Promise alone doesn't make a national, regional or global champion.

Identifying and then implementing an effective response to disruptive new AI technologies is enormously challenging for any business looking to integrate AI into their operations, as well as regulators looking to leverage AI-related innovation as a mechanism for achieving regional economic growth. These business and regulatory challenges are particularly significant given the broad reach of AI, as well as the multiple uncertainties surrounding such technologies and their future development and effects. This article identifies two promising strategies for meeting the AI challenge, focusing on the example of Fintech. First, dynamic regulation, in the form of regulatory sandboxes and other regulatory approaches that aim to provide a space for responsible AI-related innovation. An empirical study provides preliminary evidence to suggest that jurisdictions that adopt a more proactive approach to Fintech regulation can attract greater investment. The second strategy relates to so-called innovation ecosystems. It is argued that such ecosystems are most effective when they afford opportunities for creative partnerships between well-established corporations and AI-focused startups and that this aspect of a successful innovation ecosystem is often overlooked in the existing discussion. The article suggests that these two strategies are interconnected, in that greater investment is an important element in both fostering and signaling a well-functioning innovation ecosystem and that a well-functioning ecosystem will, in turn, attract more funding. The resulting synergies between these strategies can, therefore, provide a jurisdiction with a competitive edge in becoming a regional hub for AI-related activity.

T-Hub, which leads India's innovation ecosystem, announced that it has co-invested in Bluecopa through T-Fund along with Blume Ventures and other notable investors like Titan Capital, Speciale Invest, Bharat Founders Fund, T2D3, Amplify, and Force Ventures. Finance operations automation platform, Bluecopa raised USD 2.3 million in this seed funding round. Also joining the funding round are seasoned entrepreneurs Krish Subramanian, Rajaraman Santhanam, Founders of Chargebee, Rohit Chennamaneni, Founder of Darwinbox, Asad Khan, and Jay Singh, Founders of LambdaTest. T-Fund is primarily a Co-Investment Fund, which invests alongside established Angels, Angel Networks, and venture capitalists. T-Hub is the Investment Manager and will formulate investment and business development strategies for this fund. It will source startups from various sectors, including but not restricted to Information Technology, Cleantech, Agritech, Fintech, Healthtech, Logistics, Artificial Intelligence (AI), Machine Learning (ML), Internet of Things, Augmented Reality (AR), Virtual Reality (VR), Hardware, Blockchain, and Consumer tech.

Mphasis accelerates the world-leading Quantum Computing Ecosystem in partnership with the University of Calgary and the Government of Alberta. The Quantum Lab is set to accelerate the development of quantum skills in the city to enable job creation. CALGARY, AB, June 9, 2022 – Mphasis, (BSE: 526299; NSE: MPHASIS), an Information Technology (IT) solutions provider specializing in cloud and cognitive services, today joined the Government of Alberta and the University of Calgary to announce the launch of the world-leading Quantum City – Canada. Quantum city will further establish Alberta as a leading technology hub and will accelerate the development of the quantum ecosystem in Calgary. The partnership aims to utilize the synergy between academia, industry, and government to put the process of ideation to market at the forefront.

In the race to harness the power of cloud computing, and further develop artificial intelligence, academics have a new concern: falling behind a fast-moving tech industry. In the US, 22 higher education institutions, including Stanford and Carnegie Mellon, have signed up to a National Research Cloud initiative seeking access to the computational power they need to keep up. It is one of several cloud projects being called for by academics globally, and is being explored by the US Congress, given the potential of the technology to deliver breakthroughs in healthcare and climate change. Under the US proposal, authored by Fei-Fei Li and John Etchemendy from the Stanford Institute for Human-Centered Artificial Intelligence, a national cloud platform would enable more academic and industry researchers to work at the leading edge of AI, and help train a new generation of experts. Li and Etchemendy's NRC proposal cautions about declining government funding for basic and foundational research and highlights the US's history of federally funding research into innovations -- from gene sequencing to the internet itself.

Whether they be for generating artificial intelligence used in data analysis, simulation, modelling, and the data sciences, advanced AI algorithms are the foundations of the Next Digital Wave. Democratizing access to AI resources and nurturing the next generation of AI talent is crucial to Canada's success in riding that wave. There was a time, not so long ago, when AI was for the elite: those in ivory towers and silicon campuses around the world. The democratization of AI starts with access: access to advanced computing platforms, job training, talent development, incubators, accelerators, and funding opportunities. If Canada's innovators and entrepreneurs are going to move quickly and effectively in the AI space, we need to see widespread uptake of the latest tools and methods.

In FX trading, artificial intelligence (AI) is the most potentially disruptive technology for predictive analysis. However, when creating an AI application, there is always the problem of gaps in the data and technology. How has a joint venture between Refinitiv and the Bank of China addressed these challenges, and moved an AI innovation from idea to reality in a short space of time? Using AI innovation in FX trading has been on people's minds for quite some time. However, it has now become a more practical proposition because of advances in big data and machine learning (ML).

According to Sanjeev Malhotra, CEO of NASSCOM- CoE For IoT & AI, he is currently building an innovation ecosystem for IoT & AI startups in India, working with large enterprises, SMEs, government and VCs. For the weekly interview column, Analytics India Magazine connected with Sanjeev Malhotra, CEO at the Centre of Excellence For IoT & AI at NASSCOM. Having spent many years leading product strategy, development and global operations in large and mid-size companies, Sanjeev's domain has spanned many different areas of mobile internet software, e-commerce, SaaS, server technologies, IoT and connected devices. According to Sanjeev Malhotra, he is currently building an innovation ecosystem and driving co-innovation with larger enterprises, SMEs and startups for the largest technology industry body in India. Working closely with the Indian government, Nasscom's CoE- IoT & AI focuses on emerging technologies like artificial intelligence, IoT, robotics etc. and their application in auto, manufacturing, healthcare, agriculture, retail, fintech, etc. AIM: What is your role here at NASSCOM in terms of fostering technologies like IoT and AI? Sanjeev Malhotra: Emerging technologies are growing and developing at an incredible pace, and there are so many things that are important to harness completely.

A successful digital economy requires cybersecurity to be a vital enabler, protecting the interests of individuals and businesses and enabling the resilience of businesses and services. Since 2013, Singapore's medium- to long-term directions for cybersecurity is to develop R&D expertise and capabilities to improve the trustworthiness of cyber infrastructures and systems with an emphasis on security, reliability, resilience, and usability among government agencies, academia, and industry. Various initiatives to support research, innovation, and enterprise have been implemented under the Whole-of-Government National Cybersecurity R&D (NCR) Programme.8 The program supports a synergistic range of initiatives to advance technological state-of-the-art in thematic National Satellites of Excellence in universities, grants for local research projects, international research collaborations, and joint technology developments with industry. Innovation is fostered through cross-sector R&D discussions and partnerships and fast-tracked by national testbeds for safe and repeatable cybersecurity experiments.

Innovation ecosystems can be naturally described as a collection of networked entities, such as experts, institutions, projects, technologies and products. Representing in a machine-readable form these entities and their relations is not entirely attainable, due to the existence of abstract concepts such as knowledge and due to the confidential, non-public nature of this information, but even its partial depiction is of strong interest. The representation of innovation ecosystems incarnated as knowledge graphs would enable the generation of reports with new insights, the execution of advanced data analysis tasks. An ontology to capture the essential entities and relations is presented, as well as the description of data sources, which can be used to populate innovation knowledge graphs. Finally, the application case of the Universidad Politecnica de Madrid is presented, as well as an insight of future applications.