If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
I'm sure you've seen the headlines: The artificial intelligence (AI) market is due to balloon in the coming years, and AI will change the world and solve problems we've never had answers to. While I believe that AI can certainly be useful and has some real-world applications already, I think a reality check of what AI does (and doesn't do) well is in order. The truth of AI is that production deployment lags behind claims from today's headlines. According to research conducted by MIT, for example, AI "Pioneers," which are defined as "organizations that both understand and have adopted AI," make up just 20% of all organizations. What is creating the disparity between theory and production?
Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utilities additional stress, especially given the likely impacts of future global climatic and socioeconomic changes. Here, we propose a modeling framework based in statistical learning theory for predicting the climate-sensitive portion of the coupled water-electricity demand nexus. The predictive models were built and tested on six Midwestern cities. The results showed that water use was better predicted than electricity use, indicating that water use is slightly more sensitive to climate than electricity use. Additionally, the results demonstrated the importance of the variability in the El Nino/Southern Oscillation index, which explained the majority of the covariance in the water-electricity nexus. Our modeling results suggest that stronger El Ninos lead to an overall increase in water and electricity use in these cities. The integrated modeling framework presented here can be used to characterize the climate-related sensitivity of the water-electricity demand nexus, accounting for the coupled water and electricity use rather than modeling them separately, as independent variables.
We've all heard data is the new oil a thousand times by now. Arguably though, we can all live without data, or even oil, but there's one thing we can't do without: water. Preserving water and catering to water quality is a necessity, and data can help do that. On the occasion of World Water Day, ZDNet discussed the use of data to preserve water with Gary Wong. Wong is the Global Water Industry Principal for OSIsoft, and was recently named one of the world's 50 most impactful leaders in water & water management.
Data is shaping almost every area of our lives. There are now hundreds of companies offering everything from farm management and precision tools to bots and drones. Some tractors have computing power that would have turned Nasa's moon-landing mission green with envy. What started in farm equipment is moving into the field – at least in the developed world. More and more data is available as farmers use sensors for soil sampling and mobile apps, cameras and drones to monitor pests and diseases.
To get information about water use, Watson uses "visual recognition" to scan images of land parcels for valuable information, according to Pesenti. But unlike less-powerful image detection software, Watson doesn't just identify a specific object -- say, a crop field -- in an image. Instead, it combs through lots and lots of information about the image -- like the objects it contains and the colors of those objects -- and uses that information to "understand" the image as a whole. In the case of OmniEarth, researchers can use Watson not just to determine if a given parcel of land contains a crop field, but also to calculate the exact amount of water used by that parcel based on all of the information contained in the photo. What's more, Watson doesn't need to know much about water consumption to tell OmniEarth if people are using too much water.
IBM's Watson is pitching in to tackle California's drought. The supercomputer, which may be best known for destroying human opponents in games like Jeopardy and Go, has been enlisted by environmental consulting firm OmniEarth to track water use across California. OmniEarth announced the partnership on Friday. But for over a month, the company has been tapping into Watson's computing power to scan satellite and aerial images of California's lush valleys and barren deserts to figure out how Californians are using their dwindling water reserves. Even without OmniEarth or Watson's help, Californians are working to track and cut down their water consumption.