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) …
More than 100 sessions dedicated to PyData (artificial intelligence, machine learning, ethics...) and Python topics (programming, DevOps, Web, Django...). Sprints are an informal part of the conference, where all are welcome to exchange ideas, hack on exciting projects, and create lasting connections.
AI jobs are on the upswing, as are the capabilities of AI systems. The speed of deployments has also increased exponentially. It's now possible to train an image-processing algorithm in about a minute -- something that took hours just a couple of years ago. These are among the key metrics of AI tracked in the latest release of the AI Index, an annual data update from Stanford University's Human-Centered Artificial Intelligence Institute published in partnership with McKinsey Global Institute. The index tracks AI growth across a range of metrics, from papers published to patents granted to employment numbers.
Artificial Intelligence and Decision-Making Defence Policy, Technology AI will change decision-making in defence in multiple ways. Keith Dear argues that AI will change decision-making in the defence and security arena in four principal ways. First, by enabling'cognitive manoeuvre' the use of predictive analytics to enable much earlier intervention. Second, by forcing humans to take themselves'out of the loop' for decision-making by out-performing them in an increasing number of domains. Third, by providing advice that is correct, but difficult to explain.
Researchers from all over the world contribute to this repository as a prelude to the peer review process for publication in traditional journals. We hope to save you some time by picking out articles that represent the most promise for the typical data scientist. The articles listed below represent a fraction of all articles appearing on the preprint server. They are listed in no particular order with a link to each paper along with a brief overview. Especially relevant articles are marked with a "thumbs up" icon.
Welcome to the October edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted articles about AI that can solve physics problems, paint portraits, judge criminals, play video games and even recognize smells! Let's start, as usual, with the comic of the month: The DeepMind's bot AlphaStar managed to enter the Grandmaster league in Starcraft II. This league is the highest of the seven ranked leagues of the game.
The MobileNet model was used by applying transfer learning on the 7 skin diseases to create a skin disease classification system on Android application. The proponents gathered a total of 3,406 images and it is considered as imbalanced dataset because of the unequal number of images on its classes. Using different sampling method and preprocessing of input data was explored to further improved the accuracy of the MobileNet. Using under-sampling method and the default preprocessing of input data achieved an 84.28% accuracy. While, using imbalanced dataset and default preprocessing of input data achieved a 93.6% accuracy. Then, researchers explored oversampling the dataset and the model attained a 91.8% accuracy. Lastly, by using oversampling technique and data augmentation on preprocessing the input data provide a 94.4% accuracy and this model was deployed on the developed Android application.
Neuromorphic computing is seen as potentially fertile territory in which to mimic the workings of the brain. It has taken a significant step forwards with the development of the SpiNNaker supercomputer at the University of Manchester. There is still a way to go. SpiNNaker can simulate in the thousands of neurons. The brain runs to billions of neurons.
The graph represents a network of 2,908 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 02 November 2019 at 04:50 UTC. The requested start date was Friday, 01 November 2019 at 00:01 UTC and the maximum number of tweets (going backward in time) was 5,000. The tweets in the network were tweeted over the 3-day, 3-hour, 57-minute period from Monday, 28 October 2019 at 20:02 UTC to Thursday, 31 October 2019 at 23:59 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.
Artificial Intelligence has already become a widespread technology, solving a large number of tedious tasks of enterprises. Looking at its capabilities, this year marked the biggest year in funding for AI ventures yet. The Q2 of 2019 saw a total investment of US$7.4 billion in AI startups, largely going to transportation and healthcare companies. Let's have a look at the top 10 AI investments that took place in October 2019. Brightfield, a New York City-based AI and Big Data analytics company, optimizes contract labor spend and program performance for employers, MSPs, and staffing firms earlier this month received US$53 million in early-stage funding round.