In a world overshadowed by the rapid deterioration of its climate, AI engineers are beginning to apply machine learning technology to issues of environmental justice and crisis. In California, where wildfires have ravaged over 570,000 acres of land and forced 41,000 evacuations already this summer, AI is being used to prevent and monitor forest fires throughout the state.
There is strong demand from governments, environmentalists, and citizens alike to keep our oceans clean and preserve the biodiversity and critical services they provide to life on Earth. Many emerging companies and organizations are changing the landscape of ocean conservation by developing innovative AI monitoring techniques and robotic technologies to identify pollution in our oceans and waterways and provide an expedited approach to cleaning it up.
Illegal logging is an environmental and economic loss for tropical regions like the Amazon. Illegal logging is associated with billions of dollars of lost revenue each year in addition to depressed timber prices, yet effective strategies to curb illegal deforestation are hard to find. Harnessing AI serves as a promising innovation for enhancing the accuracy of forest monitoring, with the potential to be adapted to the Amazon basin and other tropical regions.
In recent years, we’ve witnessed the shocking imagery of Australia, the western United States, Europe, and the Amazon rainforest engulfed in flames, the unprecedented intensity, duration, and extent of the fires inflicting irreversible damage on countries, economies, ecosystems, and the atmosphere. Managing and conserving forest resources remains a top priority, but mapping and monitoring global forests are crucial to understanding how to properly manage and protect them, especially in monitoring wildfires and illegal deforestation, as well as knowing which forests to prioritize preserving. Identifying and monitoring forests with strong conservation value and high carbon stock is also a huge leverage point for targeted intervention of Artificial Intelligence.
In this article, we explore some practical uses of AI driven automated text generation. We demonstrate how technologies like GPT-3 can be used to better your business applications by automatically generating training data which can be used to bootstrap your machine learning models. We also illustrate some example uses of language transformations like transforming english into legalese or spoken text into written.
In a world overshadowed by the rapid deterioration of its climate, AI engineers are beginning to apply machine learning technology to issues of environmental justice and crisis. In California, where wildfires have ravaged over 570,000 acres of land and forced 41,000 evacuations already this summer, AI is being used to prevent and monitor forest fires throughout the state.
The application of AI in creative spaces like music and art is not new, but recent years have seen a dramatic expanse in machine learning capabilities in music composition. AI is being used by researchers and startups to compose soundtracks and soundscapes, and to create original songs within the style of specific genres and artists. Just as work by writers regularly appears alongside robot authors in sports, financial, and other news, Human musical artists are increasingly sharing the stage with artificial intelligence.
Within this last year alone, there has been a paradigm shift in model development as research groups are ingesting (nearly) the entire world's worth of information on the internet to train massive deep learning models capable of performing fantastic or frightening feats, depending on your perspective. In this article, we explore an AI compositional technology, known as generative modeling, and demonstrate its ability to simulate human-realistic text.
As an AI Consultant, I get a front row seat to AI and innovation; I repeatedly see when it succeeds, and when it fails. I’ve been building AI and machine learning based systems for over 20 years, in many different companies, cultures, and problem domains. During this time, I’ve noticed a number of patterns that cause the deployment of AI systems and AI powered features to fail.
Imagine a world where at home AI healthcare tools get smarter and more able to heal you every day. These tools are incredibly data driven — where they are continuously collecting data off your body, about your environment, your nutrition and activity — and then these algorithms are continuously learning from this data not just from you, but from millions of other patients and doctors who know how to make sense of this information.
Explainable AI refers to the ability to interpret model outcomes in a way that is easily understood by human beings. We explore why this matters, and discuss in detail tools that help shine light inside the AI "black box" -- we wish to not just understand feature importance at the population level, but to actually quantify feature importance on a per-outcome basis.
The national weather service operates a massive sensor array including sensors located on weather balloons, in airplanes, on land, in sea buoys, and from satellites. Just like a confluence of variables can indicate a hurricane is on the way, we believe an infectious disease forecasting service powered by AI can predict the likelihood of a new wave of C19 and other infectious diseases.
Professional athletes have long had the advantage of analysis from expensive body worn sensors. AI models, when applied to readily available video, however, offer much promise to more cost conscious amateur athletes. We ask the question, can we better an individual goalie’s chances of stopping a penalty kick with advanced AI?
Can amateur athletes improve their performance using artificial intelligence and nothing more than a smart phone? As an AI practitioner and a dedicated runner, I decided to find out leveraging body pose estimation, an advanced AI technology that automatically generates a skeleton for people in each frame of a video.
