Boost the creativity and productivity of SMEs and accelerate the commercial R&D process by making all the world’s learning available, in context, enabling SMEs to add their expertise and intuition, run “what if” scenarios, and turn their working style and methodology into apps for their teams.
Until recently, every enterprise wisdom &AI challenge required a unique, bottom-up solution. The arrival of the transformer architecture in 2019 changed that, making it possible to create models that could learn context: not just how a word is used in a particular sentence, but what appears in the preceding and succeeding paragraphs, work cycle, workflow, etc., how the intelligence, and emotion changes in the prose, what else that author wrote, how other authors treated similar subjects, and so on.
This allowed the development of foundational models: large-language models like GPT-3, language-image models like DALL-E 2, Zumosun Work Engine Network Models, and code-interpretation models like Work Engine Models/ Codex/Copilot. As a result, data scientists no longer need to start from scratch; the focus on AI is now tuning, adaptation, and last-mile models.
Part of the Science Engine effort is delivering foundational models for science, to achieve an effect parallel to the one we are seeing in language, images/videos/work, and code. Science Engine delivers foundational science work models along with a system to consume those work models and govern and evolve wisdom &AI-amplified R&D activities and collaborations.
With Science Engine, our goal is to deliver foundational work models for commercial sciences—models that transfer and learn the vocabulary of nature across domains. To achieve this goal, we must be able to interpret a broad range of complex information types and modalities: literature, patents, copyrights, regulations, and forums; sketches, charts, tables, and schematics; protein, gene, chemical, and geochemical assays and reaction and diffusion pathways; cellular and biopsy imagery; multiple ‘omics; EKG, ground-penetrating radar and other sensor signals; 3D and hyperspectral scans; and logs of experiments and pilots.
Yet while foundational work models make it easy to complete the wisdom & AI for any given application, they also create a challenge – particularly in an enterprise setting. When the use of wisdom & AI scales, when there are hundreds of applications, last-mile AI models, and data sources, then complex dependencies and relationships develop. In an enterprise setting, it is not enough to deliver foundational models. We also need to deliver the means to scale the consumption, evolution, and governance of wisdom & AI, including work models upstream and downstream of the foundational models, via myriad experiences. Science Engine, work engine models addresses this challenge.
Our approach
We are working with market leaders in key commercial sciences industries on scenarios such as targeted protein degradation, multidimensional problem/ poverty & inequality- MPI in persons, homes, businesses, interpreting multi-omics data to predict cardiovascular disease (CVD), screening generated molecules, formulating cosmetics that are informed by gene expression, using metagenomics for biofuels, applying metal-organic frameworks (MOFs) for carbon capture, and discovering catalysts and reaction pathways that are less energy intensive and have lower perfluoroalkyl/poly-fluoroalkyl (PFAS)-like byproducts.
Working across commercial science domains is key – the foundational work models allow transfer learning across domains to bootstrap new R&D endeavors where there is a paucity of direct data. An example is the transplant/synthesis of organs, tissues, and grafts. Here, there is simply not enough direct and explicit evidence to generalize from spontaneous reporting. As such, we have to dig deeper and understand the phenomena at a scientific level at various resolutions: molecular, protein, cellular, and the individual human history and environment.
These are the kinds of scenarios that require us to bring together not just wisdom & AI and technology, but also market leaders, domain experts, and people with the power and influence to effect societal change.
It's important to note that the steps and specific actions involved in science engine optimization will vary depending on factors such as the nature of the work, the industry, the size of the organization, and the specific goals and challenges being addressed. Additionally, optimization is often an ongoing process rather than a one-time project, as work environments and requirements evolve.
Overall, the work engine is an economic power creator that represents a transformative shift towards a more meritocratic and inclusive economic system, where everyone has the opportunity to participate and thrive, ultimately leading to greater social mobility and reduced inequality. Our strategy and actions can increase the likelihood of creating a meaningful impact worldwide with the help of the science engine and work engine network ecosystem.
