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) …
At the core of any AI project lies a great deal of annotated data for machine learning. Whether the end product is a customer service chatbot or a sentiment analysis engine, anybody building machine learning models eventually requires access to a vast amount of training data. Capturing enough accurate, quality data at scale is a common challenge for individuals and businesses alike. In this article, we outline four ways to source raw data for machine learning, and how to go about conducting data annotation. The internet contains thousands of publicly available datasets ready to be used, analyzed and enriched.
In a novel approach that could help reduce carbon emissions, a team of scientists led by Stony Brook's Anatoly Frenkel have described a way to use artificial intelligence (AI) to facilitate the conversion of carbon dioxide (CO2) into methane. By using this method to track the size, structure, and chemistry of catalytic particles under real reaction conditions, the scientists can identify which properties correspond to the best catalytic performance, and then use that information to guide the design of more efficient catalysts. "Improving our ability to convert CO2 to methane would'kill two birds with one stone' by making a sustainable non-fossil-fuel energy source that can be easily stored and transported while reducing carbon emissions," said Anatoly Frenkel, a chemist with a joint appointment at the U.S. Department of Energy's Brookhaven National Laboratory (BNL) and Stony Brook University. Frenkel is a professor of Materials Science in the College of Engineering and Applied Sciences. Frenkel's group has been developing a machine-learning approach to extract catalytic properties from x-ray signatures of catalysts collected as chemicals are transformed in reactions.
After watching dotNetConf videos over the last couple of weeks, I've been really excited to try out some of the new image classification techniques in Visual Studio. The dotNetConf keynote included a section from Bri Actman, who is a Program Manager on the .NET Team (the relevant section is on YouTube from 58m16 to 1hr06m35s). This section showed how developers can integrate various ML techniques and code into their projects using the ModelBuilder tool in Visual Studio – in her example, photographs of the outdoors were classified according to what kind of weather they showed. As well as the keynote, there's another relevant dotNetConf talk by Cesar de la Torre which is also available here on what's new in ML.NET And the way to integrate this into my project looks very straightforward – right click on the project - Add Machine Learning - and choose what type of scenario you want to use, as shown in the screenshot below. I've highlighted the feature that I'm really interested in – image classification.
Email hacking is a commonly used malicious tactic in our increasingly connected world. Cybercriminals compromise email accounts to enter the IT premises of an organization and carry out attacks ranging from fraud and spying to information and identity theft. Without effective security measures to stop email hacks, potential victims can suffer serious consequences. The cyberespionage group Fancy Bear, which specializes in politically motivated attacks, has reportedly targeted the reelection campaign of a U.S. senator earlier this year via credential phishing tactics. Fancy Bear has been garnering headlines since 2015 for targeting political organizations in the U.S., Ukraine, France, Germany, Montenegro, and Turkey.
Streamlit is an open-source Python library that makes it easy to build beautiful apps for machine learning. You can easily install it via pip in your terminal and then start writing your web app in Python. In this article, I'm going to show some interesting features about Streamlit, building an app with the purpose of inspecting data and build ML model on them. To do so, I will use the very basic Iris dataset and perform some classifications on it. However, if you are interested in more advanced potentialities of this tool, I suggest you read this tutorial.
The TAIAO project (Time-Evolving Data Science / Artificial Intelligence for Advanced Open Environmental Science) will advance the state-of-the-art in environmental data science by developing new machine learning methods for time series and data streams that are able to deal with large quantities of big data in real time, which are tailored to deal with data collected on the New Zealand environment. We will build a new open source framework to implement machine learning on time series data, provide an open available repository with datasets to improve reproducibility in environmental data science, and build capability in fundamental and applied data science, accessible to all New Zealanders. This programme is a new collaboration between the Universities of Waikato, Auckland and Canterbury, Beca and MetService and includes world-leading data scientists, data engineers, and environmental scientists. We will work with regional councils, iwi and co-governance entities to implement the methods we develop to support governance and management decisions with analyses based on large volumes of data that they cannot currently process. We will also make use of our existing strong international collaborations to grow our own data science capabilities and attract top international researchers to work with us on challenging data science problems.
The Center for Human Nature, Artificial Intelligence, and Neuroscience (CHAIN) is pleased to announce their 7th academic seminar as follows. Artificial Intelligence (AI) is nowadays capable of coming up with creative and inventive outputs that until not long ago just human beings were capable to produce. Music, literature and art are already being created by computers and machines. Can these outputs be protected by copyright and patent laws? If they are protectable, who should be deemed the owner of the resulting copyright and patent?
Intelligence in this "clean room" environment can be defined with respect to accumulated systematic methods of reasoning. So a logic proof system can be defined to be more efficient at solving a mathematical task than a comparable human. The more civilization transitions into a virtualized world, the more likely humans will find synthetic minds that are'more intelligent'. Classical definitions of computation tend to favor sequential processes. A consequence is that more natural parallel processes such as evolution tend to be ignored.
The Naval Academy and the Naval Institute will host a one-day conference titled "The Promise and Risk of the AI Revolution" Tuesday, Oct. 22, from 8:55 a.m.-3:10 p.m. in Alumni Hall. The conference is free and open to the public. Government, military, academic and industry leaders will gather to discuss the potential opportunities and dangers of advancing artificial intelligence (AI) in both military and civil capacities. Speakers will discuss the difference between the hype and reality of AI as well as ethical implications of its use. On-site registration is available upon arrival; breakfast and lunch will be provided to registered participants.