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 used Jupyter Notebook as the Integrated Development Environment (IDE). The libraries required are; numpy, pandas, matplotlib, pickle or joblib and scikit-learn. These are pre-installed in the latest version of Anaconda. If you don't have any of these libraries you can pip install them or update conda. The dataset used for this model is the Pima Indians Diabetes dataset which consists of several medical predictor variables and one target variable, Outcome.
Data always plays a critical role in the ability to research, study, and combat public health emergencies, and nowhere is this more true than in the case of a global crisis. Access to data sets--and tools that can analyze that data at cloud scale--are increasingly essential to the research process, and are particularly necessary in the global response to the novel coronavirus (COVID-19). To aid researchers, data scientists, and analysts in the effort to combat COVID-19, we are making a hosted repository of public datasets, like Johns Hopkins Center for Systems Science and Engineering (JHU CSSE), the Global Health Data from the World Bank, and OpenStreetMap data, free to access and query through our COVID-19 Public Dataset Program. Researchers can also use BigQuery ML to train advanced machine learning models with this data right inside BigQuery at no additional cost. "Making COVID-19 data open and available in BigQuery will be a boon to researchers and analysis in the field," says Sam Skillman, Head of Engineering at Descartes Labs.
Speech-to-text (STT), also known as automated-speech-recognition (ASR), has a long history and has made amazing progress over the past decade. Currently, it is often believed that only large corporations like Google, Facebook, or Baidu (or local state-backed monopolies for the Russian language) can provide deployable "in-the-wild" solutions. Following the success and the democratization (the so-called "ImageNet moment", i.e. the reduction of hardware requirements, time-to-market and minimal dataset sizes to produce deployable products) of computer vision, it is logical to hope that other branches of Machine Learning (ML) will follow suit. The only questions are, when will it happen and what are the necessary conditions for it to happen? If the above conditions are satisfied, one can develop new useful applications with reasonable costs. Also democratization occurs - one no longer has to rely on giant companies such as Google as the only source of truth in the industry.
Artificial Intelligence (AI) brings massive opportunity for FinTech. Artificial intelligence with Machine Learning is aimed at high frequency challenges traditional methods of information gathering that are used by even the largest firms. It uses Deep Learning that is based on datasets. This dataset and model is continuously being updated and provides guidance, recommendations, and prediction. The technology enables machines to learn and improve themselves, and to make more efficient decisions.
A challenge on the data science community site Kaggle is asking great minds to apply machine learning to battle the COVID-19 coronavirus pandemic. As COVID-19 continues to spread uncontrolled around the world, shops and restaurants have closed their doors, information workers have moved home, other businesses have shut down entirely, and people are social distancing and self-isolating to "flatten the curve." It's only been a few weeks, but it feels like forever. If you listen to the scientists, we have a way to go still before we can consider reopening and reconnecting. The worst is yet to come for many areas.
Countries around the world – including the US, South Korea and Taiwan – are using artificial intelligence (AI) to help slow the spread of COVID-19. The technology is being used to speed up the development of testing kits and treatments, to track the spread of the virus, and to provide citizens with real-time information. In South Korea, the government mobilised the private sector to begin developing coronavirus testing kits soon after reports of a new virus began to emerge from China. As part of this drive, Seoul-based molecular biotech company Seegene used AI to speed up the development of testing kits, enabling it to submit its solution to the Korea Centers for Disease Control and Prevention (KCDC) three weeks after scientists began working on it. The company's founder and chief executive, Chun Jong-yoon, told CNN that had AI not been used, the process would have taken two to three months.
Not only does this provide useful information to users in the moment, but it has also helped raise awareness and increase the adoption of Lexikon. Since launching the Lexikon Slack Bot, we've seen a sustained 25% increase in the number of Lexikon links shared on Slack per week. You just listened to a track by a new artist on your Discover Weekly and you're hooked. You want to hear more and learn about the artist. So, you go to the artist page on Spotify where you can check out the most popular tracks across different albums, read an artist bio, check out playlists where people tend to discover the artist, and explore similar artists.
For building any machine learning model, it is important to have a sufficient amount of data to train the model. The data is often collected from various resources and might be available in different formats. Due to this reason, data cleaning and preprocessing become a crucial step in the machine learning project. Whenever new data points are added to the existing data, we need to perform the same preprocessing steps again before we can use the machine learning model to make predictions. This becomes a tedious and time-consuming process!
Ever felt bored when you are all alone? Had a thought of talking to someone who could give you witty replies? If that is the case why not train one to be? I mean a deep learning model. Yes, since the past half-decade deep learning has grown humongously powerful with evolution of state-of-the-art architectures and algorithms that were brought up into the limelight as part of tons of research that's happening around the world.