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) …
A paper coauthored by over 112 researchers across 160 data and social science teams found that AI and statistical models, when used to predict six life outcomes for children, parents, and households, weren't very accurate even when trained on 13,000 data points from over 4,000 families. They assert that the work is a cautionary tale on the use of predictive modeling, especially in the criminal justice system and social support programs. "Here's a setting where we have hundreds of participants and a rich data set, and even the best AI results are still not accurate," said study co-lead author Matt Salganik, a professor of sociology at Princeton and interim director of the Center for Information Technology Policy at the Woodrow Wilson School of Public and International Affairs. "These results show us that machine learning isn't magic; there are clearly other factors at play when it comes to predicting the life course." The study, which was published this week in the journal Proceedings of the National Academy of Sciences, is the fruit of the Fragile Families Challenge, a multi-year collaboration that sought to recruit researchers to complete a predictive task by predicting the same outcomes using the same data.
At its core, Artificial Intelligence and its partner Machine Learning (abbreviated as AI/ML) is math. Specifically, it's probability – the application of weighted probabilistic networks at a computational scale we've never been able to perform before, which allows the computed probabilities to become self-training. It's that characteristic more than any other that makes AI seem like wizardry. The little cylinder on the kitchen counter that suddenly lights up when you call it by name feels like something out of science fiction, but that entire process is the end product of the re-ingestion of new data to help fine-tune a highly complex probabilistic graph. The voice assistant recognizes its "name" not because it's self-aware but because it has been programmed to match an audio waveform to a database of known waveforms with certain characteristics.
Researchers at the University of California, San Francisco have recently created an AI system that can produce text by analyzing a person's brain activity, essentially translating their thoughts into text. The AI takes neural signals from a user and decodes them, and it can decipher up to 250 words in real-time based on a set of between 30 to 50 sentences. As reported by the Independent, the AI model was trained on neural signals collected from four women. The participants in the experiment had electrodes implanted in their brains to monitor for the occurrence of epileptic seizures. The participants were instructed to read sentences aloud, and their neural signals were fed to the AI model.
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When you hear the phrase "artificial intelligence," what's the first thing that comes to mind? Depending on how you've seen AI, you might see it as a beautiful or scary thing. As the CEO of a startup that's built an AI-based marketing software tool, I believe it's the former. In fact, according to a 2019 study, "40% of marketing and sales teams say data science encompassing AI and machine learning is critical to their success as a department." It's easy to see why.
Thanks to the advent of the latest innovations in Artificial Intelligence (AI) and machine learning (ML), smart cities -- with a specific focus on the utilities sector -- are undergoing unprecedented changes. The Capgemini Research Institute estimated that, together with the energy sector, the utility vertical can save between $237 billion to $813 billion USD from intelligent automation at scale. Utility companies have been experimenting with AI use cases such as predictive maintenance, yield optimization, and demand/load forecasting. In 2019, more than half of energy and utilities organizations have deployed at least one practical implementation of AI technology, reaping its consistent benefits. Even the public seems eager to enjoy the positive innovations brought forward by the AI transformation.
In a previous blog post, we explored the importance of machine learning (ML) and delved into the five most important things that business leaders need to know about ML. First, recall that supervised learning is concerned with the prediction and classification of data. Now it's time to dive deeper. We saw that accuracy (the percentage of your data that your model predicts/classifies correctly) is not always the best metric to measure the success of your model, such as when your classes are imbalanced (for example, when 99% of emails are spam and 1% non-spam). Another space where metrics such as accuracy may not be enough is when you need your model to be interpretable.
The saying goes: "If you're not on the edge, you're taking up too much space". And compute itself is now moving to the edge, forcing datacentre operators to wring the last drops of productivity from their infrastructure, ahead of a future supporting multi-sensor internet of things (IoT) devices over 5G for machine learning, and even artificial intelligence (AI). Jennifer Cooke, research director of cloud-to-edge datacentre trends at IDC, says datacentre operators need to start thinking about how many systems they will need to roll out, and the people they will need to support them. "Cost becomes the prohibitive factor," she says. Edge will take different forms.
Mixing quantum computing and Artificial Intelligence (AI) may sound like a new buzzword. However, since quantum computing advances are hinting at profound changes in the very notions of computation, it is natural to reexamine various branches of computer science in the light of these disruptions. As usual, before entering the quantum realm, it is important to get an overview of the classical world. Artificial Intelligence is difficult to define. Probably because intelligence, by itself, is difficult to define.