Here at Newsbridge, Multimodal Artificial Intelligence (AI) is the underlying technology that fuels our smart story production solutions.
As so, it serves as a central point of importance that requires continuous effort and resources dedicated to research and development.
With the European Commission’s recent investment of €20 billion in AI innovation research, Newsbridge projects both gradual and sweeping AI technological advancements over the next several years. As these changes could impact platform evolution, we have placed an enormous weight on continuous Deep Tech research and development.
As a case in point, Newsbridge has invested in and is currently sponsoring cutting-edge AI laboratory initiatives at various higher education institutions with the goal of making moonshot technologies business realities.
This link between top-tier educational research and industry application is vital to Newsbridge’s technological success, as we strongly believe in and have seen firsthand the power of early stage research, which serves as a more cooperative and long-term value model.
It is one that also promotes co-learning and shared value. It's an exchange that offers a mentor and mentee experience for PhD students in liaison with Newsbridge’s tech department, composed of AI Engineers and Computer Scientists with extensive experience and backgrounds in various corporate sectors.
By partnering with university research labs, Newsbridge ensures new-age and professional collaboration. We are then able to offer customized and continuous AI developments as applied to the platform.
So why is the Newsbridge team so passionate about this type of partnership? As it turns out, the most commercialised and successful AI research and associated milestones have long been linked to university initiatives.
In fact, if we go back to the first real introduction to AI some experts say it all began at a university conference.
Hosted by John McCarthy and Marvin Minsky in 1956, The Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) was the first of its kind to bring together top researchers and influencers in various fields to discuss the potential of Artificial Intelligence. Although there was no true logical framework, there was the agreement that AI was possible and not just something seen in Sci-Fi movies.
After a series of research funding projects (many from top tier universities) the 1980s and 1990s experienced a renewal of interest in AI. Consequently, many “landmark” goals were achieved.
Some examples include: IBM’s Deep Blue chess computer program that could make human decisions and beat even world chess champions, the creation of Dragon Systems Speech Recognition Software implemented on Windows, and the development of Kismet, a robot that could detect and display human emotions, just to name a few.
💡 So the main idea here is that it took extensive time and research between conceptualization and implementation of basic AI technology.
With this logic, we could say that with each decade there will be revolutionary advances in AI technology. This makes sense, as between 2010 and 2015 we saw major breakthroughs in AI such as Jeopardy’s IBM Watson -winning machine and ImageNet Challenge, which demonstrated that machines could detect images better than humans.
More recently, the emergence of GAN (generative adversarial network) is yet another major advancement in the field of AI. Facebook’s Chief AI Scientist Yan Lecun describes it as: “the most interesting idea in the last 10 years in Machine Learning”.
With research groups, universities and other innovative think tanks playing such a large role in the history of industry-adopted AI advancements, how have international governments responded in terms of investment?
Over the past 5 years, AI research has grown 13% worldwide. Of all of the AI sectors in existence, research in Machine Learning, Probabilistic Reasoning, Neural Networks and Computer Vision represent the largest areas of growth and research output.
For starters, the Chinese government’s latest venture capital fund is estimated to invest $30 billion in AI technologies via state firms while the U.S. just recently announced (2020) its aim to double the national AI research budget to $2 billion.
With its strong stem background and research infrastructure, Europe is one of the leading hubs for AI research with the highest level of international collaboration within the field. In fact, as of 2017, Europe hosts the largest share of the top 100 AI research institutions wold-wide.
This is no surprise, as there have been numerous initiatives across Europe. Most notably, the European AI Strategy was spearheaded by the European Commission aimed at boosting Europe’s “digital, industrial socio-economic capacities and while ensuring strategic autonomy.” As a tangible indicator of its commitment, the Commission also increased its annual AI investments +70%.
Just as the roots of AI were planted in university research, the same remains true for large-scale advancements as we move forward in this historical period of AI development.
Traditionally, there is a divide (internationally) when it comes to educational research and industry application. Companies are many times interested in “quick wins” and one-off research projects that may not be sustainable, only temporarily profitable.
That said, the more durable co-op model is preferable, one that allows companies to remain connected to institutions for a longer period of time, nurturing and continuously improving their tech research. In this way projects of interest are spearheaded as they emerge... a classic example being the sponsorship of university research labs.
From an ROI perspective, the benefits of this R&D partnership are three-fold.
First, employee engagement should never be undervalued. The motivation and internal hype generated by setting research goals allows companies to be in an ever-evolving state, constantly challenging vision and growth. For example, here at Newsbridge the entire team knows where the company is heading over the next 3 years in terms of R&D. This is something that gives the team something to look forward to, work hard for and be proud of.
Second, when working with research projects, the company has the ability, at any given time, to release an early beta version or milestone. At Newsbridge, when these beta features are deployed earlier, the team has a better perspective on early client feedback. This way we can understand if the technology is in demand and thus possessing a higher link to future profitability.
Third, it is important to look into energy consumption costs. When taking a research project into account- there is an initial fund available that guarantees project replication in a commercialized manner- something that is profitable yet that clients can still afford. By managing costs in a controlled environment- the company is able to have an overarching idea of the eventual ROI and plan accordingly.
“AI research in a lab, or a PhD lasting several years- it’s not an immediate return on investment. It takes time, but we’re focused on delivering the highest quality AI while promoting shared value and working on tailored and complex projects.”
- Frederic Petitpont, CTO Newsbridge
As Harvard Business Review state’s in its article titled “Why Companies and Universities Should Forge Long-Term Bonds”, whether government funding is available or not, companies should be looking toward contributing toward local economies and early stage research- which lies in a university context.
When companies fund or co-fund PhD candidates or postdoctoral researchers, there is a shared co-mentoring environment in which both sides benefit immensely. From here, if something promising emerges, additional funding is provided. Understanding that this new model is needed to help break the ‘quick win’ paradigm, resulting in higher quality AI breakthroughs, it is a top priority for best-in-class AI powered solutions who also promote shared value.
Newsbridge is a cloud media hub platform for live & archived content.
Powered by Multimodal Indexing AI and a data driven indexing approach, Newsbridge provides unprecedented access to content by automatically detecting faces, objects, logos, written texts, audio transcripts and semantic context.
Whether it be for managing and accessing live recordings, clipping highlights, future friendly archiving, content retrieval or content showcasing and monetization - the solution allows for smart & efficient media asset management.
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