This sensor reads a person’s blood sugar while they sleep and alerts first responders in the event that they slip into a coma.
The stakeholders are diabetics and their loved ones. Also Physicians caring for diabetics.
I would market this directly to physicians, but I would also target health insurance companies to get market saturation.
Comment: My comment is directed at the smart water sensor article where a sensor can alert a homeowner or manager to the presence of a water leak in the structure. I think it would be extra helpful if this technology could be connected to a water valve that could shut the water off to the apartment or building in the event of a leak. Great article!
Solar panels are generally fixed and their optimal collection is limited due to collection angle.
Cloudy days traditional solar panels are inefficient in collecting sunlight
Solar panels take up a large amount of space.
Most solar panels are fixed direction, the beta.ray can rotate according to the sun direction, maintaining optimal collection angle – the small sphere, 75% smaller than a panel that collects the same amount of energy.
Cloudy days the beta.ray can improve efficiency by 50% due to concentration of sunlight.
The beta.ray is small and can be placed on any flat surface.
The spherical shape of the solar collector, together with an integrated solar tracking system, cover far smaller surface area than solar panels of equal efficiency, and allow a collection of energy, even if the light is very low
Machine learning has a big potential in the supply chain and distribution of food products in developing countries. Beyond just creating healither foods, the technology can be merged with weather data to improve distribution of non-perishable foods.
Possible commercial customers (shopping malls etc)
Residential buildings – 10 MW of energy is needed to heat 20,000 modern residential apartments, whereas an average Facebook data centre uses 120 MW.
Companies with data centers
City governments and local utility providers
List and contact stakeholders (listed above)
Model partnerships between heat producers and consumers which are geographically efficient.
Value the cost incentive – will it save money overall with the new infrastructure investment?
Comment on CyberRain:
This technology could have the potential to be expanded to cities that make use of rainwater for other uses, or monitor city-wide water management during unpredictable rainfall seasons and drought conditions. It could also be incorporated into water payment systems to create incentives for water savings on irrigation.
1.It checks your location’s weather conditions, regularly and wirelessly sending updated sprinkling times to the customer’s CyberRain controller.
2.Works with all standard automatic sprinkler systems, connects to existing valve wires.
3.Allows the customer to set up their own water-wise sprinkling schedule based on their landscape.
Stakeholders: Home owners
Deployment: Raise awareness on the importance of using water wisely, demonstrate water and money that can be saved by adopting this technology, integrate into households by creating incentives through a partnership with water utility companies.
This technology could revolutionize the way we consume and feed ourselves. Currently though, the only proved advantage is related to animal welfare. Although there are some studies showing a lower associated GHG footprint than with traditional meat, more research needs to be done. Finally, product cost remains astronomical and scaling strategy unknown.
Comment on post ‘Not a Plastic Bag’: This is a very promising technology. I would say that part of the current problem associated with alternative plastic bags is the lack of clarity to the consumer. Are they actually more sustainable? If so, which ones are best amongst the many options? This is a case where consumers need to be better informed in order to actually put pressure on retailers to adopt such technologies and create systemic change.
CO2 gas in the atmosphere is a major contributor to global warming. While many governments,organizations, scientists, individuals, etc. are working on ways to reduce emissions from our everyday actives to prevent more CO2 from entering our atmosphere , some companies are not looking into ways to reduce the amount of CO2 already in the atmosphere through carbon capture.
Each Climeworks system is a 7-foot tall machine resembling a large fan that sucks up 50 tons of CO2 annually out of the atmosphere by using a chemical process to absorb the gas and bind it to filter materials in the system
The materials can then be stored or used for another purpose, such as fertilizer, which is used by a greenhouse near the company’s first plant
Climeworks is different from other carbon storage systems because its plants have a much smaller footprint and use less water than competitors
Competitors (other carbon capture companies)
Organizations looking to offset their emissions
Increase pilot tests to determine feasibility of plants in different environments: “According to a Climeworks spokesperson, the main goal of the pilot is to gauge how the technology performs in the harsh winter conditions of northeast Iceland and to understand how the systems handle other air impurities, such as sulphur compounds.”
Decrease costs in order to scale up and take advantage of their first mover advantage – they are the first carbon capture company to reach the commercially viable stage
Partner with corporations to create new uses for the concentrated CO2 byproducts
Is this product fully bio-degradable in landfills? As the article mentions there are issues with bio-based plastics being fully bio-degradable and non-toxic. While this is a big step in the right direction it might not fully solve the problem.
Meat and animal products are an important source of protein with a relevant nutritional value. Nonetheless, the environmental impact in terms of land use, water consumption and CO2 emissions have become of significant importance, also due to the continuous increasing consumption. By 2050 world meat production is projected to double, most of which is expected in developing countries. 
According to the Food and Agriculture Organization, the livestock sector generates more greenhouse gas emissions [Co2e] than transport, by 18%. 
It is also a major source of land and water degradation. Livestock’s requires vast tracts of land and a significant demand for feed crops, both contribute to biodiversity loss. Moreover, it’s among the most damaging sectors water resources, not only because of its water consumption, but also contributing to water pollution, eutrophication and the degeneration of coral reefs, due to the manure of livestock.
Machine learning is a subset of AI, that generates algorithms that can learn from data and make predictions on it. In other words make machines learn from experience, experience coming in form of data and the more the data, the more it learns. Machine learning can be useful for making data-driven predictions or decisions.
To create healthier food, companies like Hamptons Creek are automating the extraction and analysis of plant proteins. This includes examining their molecular features and functional performance such as gelling, foaming, and emulsifying properties. Ultimately, the goal is to feed this research to an AI an through machine-learning algorithms identify the most-promising proteins for use in the creation of vegan food that tastes similar to animal products (mayonnaise, muffins, spreads, and other foods). Finally, we are applying generative design to food production. 
According to Lee Chae, Hampton Creek’s head of research and development, Hampton Creek applies deep machine learning to plant biological data to meet its objective of creating healthier food. 
This is not really a new technology rather than a new technology application. To deploy this application, more enterprises in the food industry should be making research in this field, or applying the outcomes. More importantly, I personally don’t think that every enterprise should be conducting the same research rather than to have an open database with the results, would be a great way to expand the adoption of this knowledge. Furthermore, this new application raises the question of what other industries could be impacted by machine learning, through generative design? Let’s take for example the polymer industry, imagine to feed the AI data about the plant properties to replace plastics.
Since deep learning and machine learning can be applied to several databases, to make predictions as well as generative design, many industries could benefit from this technology. In my opinion is a private sector driven technology, especially due to its large upfront capital investment. The first step to deploy it is to have a reliable database, this can be either from a primary or a secondary source, depending on the application.
Although it does require a group of people specialized in artificial intelligence, the results could be applied to the industry with no further disruption, this reduces the barriers to technology adoption.