Cross-field basic research and R&D collaborations needed.
Knowledge economy  emerged from late 1900s — 2000s; prior to that, sources of the economy have experienced revolutions since the pre-industrial age. Labor intensity was replaced by systems of mass production as a result of the industrial revolution, now research-intensive and high-technology industries are dominating the future economy. In this generation, human intelligence is an asset. While business leaders often emphasize values of products in the markets, which is not necessarily relevant to research or knowledge, it is not deniable research or what’s more antecedent, academic research has profound effects in the products we have today. Some academic research ideas are exceptionally creative and think-outside-the-box that lead to revolutionizing changes in product creation. For example, current technologies we have, including computer vision and deep learning algorithms were inspired by the mechanism of the brain. The development of these sorts of technologies was built upon mature understanding in how neurons process information, which progress was contributed by psychologists with long history of engineering thinking and practices of mathematical and statistical approaches. Moreover, some art principles we know, including visual effects used by designers and architects were arisen by understanding of principles in perception psychology. By the understanding of how depth-by-shading is induced by certain visual stimuli, artists and designers are able to use these principles to make visual effects of interest.
Even though academic scientists are assets, unfortunately, today we often see services for helping academic PhDs to find industrial jobs but neglect enormous values that academic scientists can bring over. Business leaders have limitations to accommodate underlying assets that do not immediately promise profits. Nevertheless, the supply and demand relationship between academia and industry is not one-directional. Basic research is the precursor to applied research and many forms of real-world applications. The insights carried out from academic sciences are possible to result in profound impacts on technologies and shaping our day-to-day habits. Academic contributions are part of the economy and human behaviors. The values are bi-directional.
Not to mention, academic scientists also enjoy the feedback from the results of science applications. For example, breakthrough in deep learning technologies is feeding back to brain scientists to understand the brain. Groups of contemporary neuroscientists consented on what it needs for deep learning development is not like before when the understanding of brain mechanisms have first led to the launch of CNN in the 1980s; now there are a number of parameters sufficient to tweak with for optimization. Even though, deep learning still plays a big role in Neuroscience today for the purposes of brain modelling and neuronal manipulations so as to understand the brain . Who can bet there wouldn’t be another wave of brain-inspired technologies launching?
Even today, demands of basic science in applied research are not diminished. Quantum information science is needed for quantum computing . Sciences and engineering go hands in hands for today’s technology. Knowledge economy  is taking place in Silicon Valley and industrial fields including electronics and digital media in Seoul; petrochemical and energy industry in Brazil and aerospace and automotive engineering in Munich. Research project ideas and knowledge that leads to research breakthroughs are demanded in the leading research and entrepreneurial teams.
Academia and industry mean to work together, however, there are threats hindering the bond of collaborations. A serious one is the trust and resentment between academia and industry. For example, neuroscientists, unfortunately, become the target of blame for their low productivity in research breakthroughs in the understanding of the brain, which is claimed to be the detrimental reason for the impedance in development of brain-relevant technologies. On the other hand, critiques from Neuroscientists to the shallowness of business leaders can be easily found on Twitter and blogs. Scientists have little faith in business practices, and some, choose to stay away from businesses as they could possibly affect academic reputations.
Another issue standing in the way of cross collaborations is limitations in cross-field expertise; 12.5 percent of the American population holds a double degree in 2015 . When it comes to problem-solving that involves multi-level of knowledge, the number of degrees may not even be representative in terms of how effective in solving problems. Accumulation of expertise and intelligence plus open discussions are usually a way for problem-solving in research teams. We need people from different backgrounds, study fields and mindsets. A group of all engineers and research scientists from the same team does not yield the best combinations of ideas exchange.
Development of future technologies requires incredible amounts of communications, openness between parties and combinations of knowledge across study fields. This platform serves as a platform to connect dots between academic, industrial scientists/engineers and other professions to spur paces in basic research and research development. Possible future services include trending articles from academic scientists, case-by-case problem solving and Q&A from academic scientists.
-  https://en.wikipedia.org/wiki/Knowledge_economy
-  Richards, B. A. et al. A deep learning framework for neuroscience. Nat. Neurosci. 22, 1761–1770 (2019).
-  Author, N. G. Basic Research Needs for Microelectronics. 1545772 http://www.osti.gov/servlets/purl/1545772/ (2018) doi:10.2172/1545772.
-  American Community Survey (ACS) Census data.