Investors are continuously seeking profitable investment opportunities in startups and, hence, for effective decision-making, need to predict a startup's probability of success. Nowadays, investors can use not only various fundamental information about a startup (e.g., the age of the startup, the number of founders, and the business sector) but also textual description of a startup's innovation and business model, which is widely available through online venture capital (VC) platforms such as Crunchbase. To support the decision-making of investors, we develop a machine learning approach with the aim of locating successful startups on VC platforms. Specifically, we develop, train, and evaluate a tailored, fused large language model to predict startup success. Thereby, we assess to what extent self-descriptions on VC platforms are predictive of startup success. Using 20,172 online profiles from Crunchbase, we find that our fused large language model can predict startup success, with textual self-descriptions being responsible for a significant part of the predictive power. Our work provides a decision support tool for investors to find profitable investment opportunities.

The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and jobs creation. Data Science can support SMEs to optimise production processes, anticipate customers' needs, predict machinery failures and deliver efficient smart services. Businesses can also harness the power of Artificial Intelligence (AI) and Big Data and the smart use of digital technologies to enhance productivity and performance, paving the way for innovation. However, integrating data science decisions into an SME requires both skills and IT investments. In most cases, such expenses are beyond the means of SMEs due to limited resources and restricted access to financing. This paper presents trends and challenges towards an effective data-driven decision making for organisations based on a case study of 85 SMEs, mostly from the West Midlands region of England. The work is supported as part of a 3 years ERDF (European Regional Development Funded project) in the areas of big data management, analytics and business intelligence. We present two case studies that demonstrates the potential of Digitisation, AI and Machine Learning and use these as examples to unveil challenges and showcase the wealth of current available opportunities for SMEs.

The evolution in technology and artificial intelligence is gaining more and more relevance, bringing into play the staff recruitment process in companies. With the process of evolution in the spheres of consumption of the human being, it is more and more common to find ourselves with the constant development in the technological field, achieving advances that a few decades ago seemed impossible, or at least worthy of a good science fiction movie. These advances have considerably boosted countless industries (regardless of their field), exponentially benefiting the economy of millions of individuals, brands, institutions and even agencies. An example of this is found with the dating marketingwhere thanks to the registration of each movement made by web users, companies or agencies can allocate different advertising campaigns directly to their target, thus saving considerable monetary figures in sending unsuccessful messages, thanks to the specific knowledge of users, which would not be possible without a little technological help. This is due in part to the high adoption of the internet worldwide, since according to the Digital 2022 study carried out in a study by We Are Social and Hootsuite, as of January there were approximately 4,950 million Internet users connected around the world (approximately 62.5 percent of the world's population), with the consequence that today there are more than 4,620 million users browsing social networks.

Machine learning is a data analysis approach that automates the development of analytical models. It's an Artificial Intelligence discipline predicated on the idea that machines can learn from data, recognize patterns, and make choices without or with little human interaction. Machine learning systems change independently, learning from new data and past processes since data is continually generated. Most big data businesses recognize the importance of machine learning, such as industrial learning, which gathers data from various sources such as the Internet of Things, sensors, etc. Now is the moment to implement ML strategy in your organization if you want to get the most out of your business data and automate activities in ways you never imagined.

Retailers, banks, and many other companies collect and analyze information, realizing that data runs the Business. For business development, it is necessary to test hundreds of hypotheses through various methods, and here comes Data Science. Data science applies various big data tools and machine learning (ML), including algorithms and methods of artificial intelligence (AI). The task of ML is to "teach" a program to take appropriate actions based on the huge amount of processed data. Big data is the way of collecting, storing, processing and analyzing information.

Artificial intelligence (AI) for translation is something Google and other companies have provided for individuals. It can be accessed on your phone. However, translation is still a much larger and complex issue than many people realize. The business community has many complex and unique needs that add to the challenge of accurate and reliable translation, and AI is showing increasing capability. One of the keys to business translation is the simple reality that each business sector has its own terms, phrases, and even idioms.

Artificial Intelligence (AI) is clearly a growing force in the business sector. Recently, AI has broken the wrong perspective of many executives and business owners that it is applicable just to big companies like Facebook, Apple, Amazon, Netflix and Google. It is serving every corner possible when it comes to showing its potential. AI is taking centre stage across a wide variety of industries, including retail and manufacturing. New products are being embedded with virtual assistants, while chatbots are answering customer questions on everything from online office supplier's site to web hosting service provider's support page.

Video has become a perfect communication, remote consulting, collaboration, entertainment, and even monitoring and control tool for companies from different business sectors as well as public institutions. Companies have even started to use video as a data collection tool within robotic process automation (RPA), which has made this type of content a valuable asset. Let me emphasize this idea: Every business, whether it's a factory, a marketing agency or a restaurant, generates an enormous amount of video content today. Using video analytics, businesses can extract the most valuable data out of enormous amounts of video and preserve this information instead of terabytes of video. It is possible to track any spatial/temporal parameters and get instant notifications about discrepancies and violations.

Imagine a world without the internet, a Google search engine, magnetic-resonance imaging, or the Human Genome Project—a sampling of American innovations that, like many scientific tools and research efforts, evolved from U.S. R&D investments. Until Congress recently boosted federal R&D funding as a share of the U.S. economy, such investments had been on the decline—sliding from a high just shy of 1.9% of the gross domestic product in 1964 to 0.62% of the nation's gross domestic product (GDP) in 2018—impeding scientific advances, slowing innovation, and clipping the nation's share of global R&D funding at a time when the country faces challenges, economic and human, triggered by coronavirus disease 2019 (COVID-19). The American Association for the Advancement of Science presented recommendations on how to advance scientific discovery, expand innovation, drive economic advancement, and ensure the scientific community supports opportunities for all at the “Fueling American Innovation and Recovery” hearing held by the U.S. House of Representatives Budget Committee on 8 July. Sudip Parikh, AAAS CEO and executive publisher of the Science family of journals, was among the expert witnesses at the hearing that examined ways to reinvigorate U.S. economic competitiveness, renew federal investment in scientific R&D, and address the global pandemic. Since 1995, the global ranking of U.S. R&D investment as a percentage of its GDP slipped from 4th to 10th place, said House Budget Committee Chairman John Yarmuth during the hearing. The United States now lags behind competitors such as South Korea, Taiwan, Japan, and Germany in R&D investments, said Deborah L. Wince-Smith, president and CEO of the Council on Competitiveness, during her testimony at the House Budget Committee hearing. Yarmuth warned that delays in scientific research projects as well as other economic challenges triggered by COVID-19 could diminish the U.S. position as a leader of global R&D funding. Updating a long-standing framework that guides federal investment in scientific research, ensuring effective coordination of federal responses to the crises facing the nation, and committing to provide scientific evidence to propel racial equity in science and national policy-making formed the core of Parikh's recommendations to the panel. Parikh pointed to research that found U.S. R&D as a share of GDP is well below its historic peak and below the current investment levels of nine other countries. U.S. funded R&D projects drive innovation forward. The crises at hand require a federal investment of 1.9% of GDP, a level that represents an annual funding increase of 11% in scientific R&D, said Parikh. Support for the full spectrum of innovation is needed, including “fundamental science, mission-driven technology, useful knowledge programs to meet local, national and international needs with the federal government as a key partner,” said Parikh. New approaches to R&D funding models and networks should be explored, including “project grants, people-centered grants, teams and hubs, and prizes.” “Broadly, federal research is effective in producing discoveries that lead to high-impact, novel inventions, often in technology areas that have not yet received much industry attention,” said Parikh. AAAS tracks administration and congressional appropriations through the R&D Budget and Policy program, an outreach effort that keeps scientists and policy-makers informed through regular analyses, reports and media outreach. AAAS also engages in advocacy efforts to address executive branch and congressional actions that pose negative consequences for U.S. scientific research activities. AAAS has issued, for instance, statements on the U.S. withdrawal from the World Health Organization, which collaborates across borders to support human health, and an administration proposal to limit the participation of international students at U.S. academic institutions despite a long history of important scientific contributions that foreign national students have made to the U.S. scientific enterprise. “From the beginning, the Trump administration has taken a hard-fiscal line on most research and development programs, favoring Department of Defense technology development and acquisition at the expense of basic and applied research, even Defense research activities,” noted Matthew Hourihan, director of the AAAS R&D Budget and Policy Program. In fiscal year 2017, Congress rejected steep spending cuts outlined in the administration's budget proposal and instead adopted significant spending increases, particularly for the Department of Energy's Office of Science for basic science and R&D programs. By fiscal year 2020, congressional R&D increases stood as a rebuttal to the president's consistent budget reduction proposals that sought more than $12 billion in spending to be shed from federal basic and applied research programs, according to a 2019 analysis by the AAAS R&D Budget and Policy Program. Federal R&D investment supports government laboratories, research activities at federal agencies, academic institutions, and private-sector facilities to drive U.S. scientific advances. The 2020 fiscal year spending package Congress approved dedicated the largest funding increases to the life sciences, particularly for the National Institutes of Health's basic research on human health and related topics, and made low-carbon energy and space exploration programs the second-largest funding recipients, as documented by the AAAS R&D Budget and Policy dashboard that tracks congressional appropriations trends across the scientific enterprise. Despite the rise in the levels of federal R&D funding from 2000 to 2017, “the share of total U.S. R&D funded by the federal government declined from 25% to 22%,” according to “The State of U.S. Science and Engineering,” which highlights the 2020 “Science and Engineering Indicators,” a suite of reports that provide findings on thematic scientific topics. The National Science Board is required to deliver the findings to Congress and the president every 2 years. The business sector and U.S. academic institutions of higher education have stepped up financial support for R&D programs and activities, aware that R&D investments often spark novel scientific knowledge that, in turn, opens new research avenues, contributes to the training of young scientists, and helps fuel the U.S. economy. The business sector plays the most prominent role, having outpaced federal R&D funding to become primarily responsible for the rise in R&D support since 2000. Universities and colleges are the second-largest contributors to R&D and play an important role in the progress of the nation's overall R&D activities by funding more than half of both U.S. basic research and the training of incoming scientists and engineers, according to the 2020 “Science and Engineering Indicators.” Yet, the combination of an overall decline in state support for public universities and colleges, and a leveling off of federal R&D funding for academic institutions at an annual $30 billion, risks a weakening of the United States' standing in innovation, reports suggest. More encouraging, federal science and engineering support for historically black colleges and universities delivered HBCUs a 5.4% R&D funding increase for research and experimental development, according to the National Center for Science and Engineering Statistics. Joining Parikh and Wince-Smith at the House hearing were two other expert witnesses, including Paul Romer, a Nobel-Prize winning economist and professor at New York University. Romer called on U.S. universities to give talented science and technology students a larger voice and leeway to pursue their own innovative ideas, not just those of professors. He said science and engineering degree fellowships should be granted to highly talented undergraduates to help expand U.S. innovation. Economist Willy Shih, a professor at Harvard Business School, said the COVID-19 pandemic has exposed U.S. reliance on other nations for equipment, devices, and pharmaceuticals, a reality that requires expanded federal investment in basic scientific research and direct stimulus spending for technology investments. “If the United States does not make needed investments in its future, increase its scope and rate of innovation, its fundamental capacity to grow its economy, create jobs, maintain national security, solve societal challenges and provide a social safety net, it will continue to erode—and its geopolitical leadership will be at increasing risk,” said Wince-Smith.

Invasion of technology in the business sector is a developing aspect. Business agencies are looking for ways to broad base their financial stability through improving technology. When the business sector is looking for quick solutions and progress, they are ready to put forward the idea of making data assets to transformational technologies like Artificial Intelligence (AI) and machine learning, and automation. The data assets when computerised by the automation process are considered as a safe haven to business. They nullify the risk of leaving behind or the slow process of data. Emerged over the last few years, analytics stood as a core capability for business with a data-driven decision making culture.