Category: Machine Learning

 By Cristina Mircea

Gardening is a rewarding activity indeed, both for your mind, as well as for your body. Unfortunately though, most of us can’t find enough time to dedicate to it, as our hectic lifestyles get in the way. That’s where this smart garden robot comes in, to make sure you don’t have to sow yourself, but just reap the benefits.

Sybil is a small, but a very capable device with machine learning capabilities. It can autonomously plant, weed, and map your entire garden.

Continue reading… “Machine Learning Bot Can Replace Your Gardener, It Plants and Weeds on Its Own”

by Matthew Hart

The European Space Agency (ESA) is working on a “digital twin” of Earth in the hopes of better understanding our planet’s past, present, and future. The project, first announced in September of last year, will deploy AI, as well as quantum computing, to build Earth’s digital doppelgänger in virtual space. And the scientists hope this Digital Twin Earth will help them forecast extreme, climate change-induced weather events.

Popular Mechanics reported on the digital Earth, which ESA scientists discussed during the agency’s 2020 Φ-week event. The scientists say their digital model will help humanity to “monitor the health of the planet,” as well as simulate the effects of human behavior on the environment.

The scientists are going to evolve the digital twin over the next decade, constantly feeding real-world data into the model; data that will come from the EU’s Copernicus program, which captures atmospheric data, such as air quality changes. They’ll then use neural networks (computer algorithms) to identify patterns in Earth’s weather systems, and hopefully begin making accurate predictions.

“Machine learning and artificial intelligence could improve the realism and efficiency of the Digital Twin Earth—especially for extreme weather events and numerical forecast models,” European Center for Medium-Range Weather Forecasts (ECMWF) Director General, Florence Rabier, said at the event. Rabier and her colleagues also noted that the satellites collecting the data for the models are deploying AI programs.

Continue reading… “Scientists Are Building a ‘Digital Twin’ of Earth”

By Ron Schmelzer

The conversation around Artificial Intelligence usually revolves around technology-focused topics: machine learning, conversational interfaces, autonomous agents, and other aspects of data science, math, and implementation. However, the history and evolution of AI is more than  just a technology story. The story of AI is also inextricably linked with waves of innovation and research breakthroughs that run headfirst into economic and technology roadblocks. There seems to be a continuous pattern of discovery, innovation, interest, investment, cautious optimism, boundless enthusiasm, realization of limitations, technological roadblocks, withdrawal of interest, and retreat of AI research back to academic settings. These waves of advance and retreat seem to be as consistent as the back and forth of sea waves on the shore.

This pattern of interest, investment, hype, then decline, and rinse-and-repeat is particularly vexing to technologists and investors because it doesn’t follow the usual technology adoption lifecycle. Popularized by Geoffrey Moore in his book “Crossing the Chasm”,  technology adoption usually follows a well-defined path. Technology is developed and finds early interest by innovators, and then early adopters, and if the technology can make the leap across the “chasm”, it gets adopted by the early majority market and then it’s off to the races with demand by the late majority and finally technology laggards. If the technology can’t cross the chasm, then it ends up in the dustbin of history. However, what makes AI distinct is that it doesn’t fit the technology adoption lifecycle pattern.

But AI isn’t a discrete technology. Rather it’s a series of technologies, concepts, and approaches all aligning towards the quest for the intelligent machine. This quest inspires academicians and researchers to come up with theories of how the brain and intelligence works, and their concepts of how to mimic these aspects with technology. AI is a generator of technologies, which individually go through the technology lifecycle. Investors aren’t investing in “AI”, but rather they’re investing in the output of AI research and technologies that can help achieve the goals of AI. As researchers discover new insights that help them surmount previous challenges, or as technology infrastructure finally catches up with concepts that were previously infeasible, then new technology implementations are spawned and the cycle of investment renews.

Continue reading… “Going Beyond Machine Learning To Machine Reasoning”