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) …
When Tokuso Hamai saw the colorized version of an old black-and-white photo of a picnic held under cherry tree blossoms sometime before World War II, forgotten memories of family members, most of whom died in the atomic bombing of Hiroshima in 1945, came pouring out. "In colorized photos, people come to life," said Hamai, now 86. "I often played near (the picnic site), and sometimes I would do some naughty things and get scolded by my father." The power of a colorized photo to reignite lost memories was eye-opening for Anju Niwata, the student who gave Hamai the colorized photo as a present three years ago. The 75th anniversary of the end of World War II is Saturday, and Niwata, now 18, said she hopes it will bring attention to her project with a Tokyo University professor to painstakingly colorize photos using artificial intelligence and their own research to spark lost memories for the rapidly aging generation who experienced the war.
Usually, we go for feature engineering or feature selection steps after EDA. But we have fewer features and emphasis on actually using the model. So we are moving forward towards the next steps. If you have reached this step then give yourself a pat on the back because we just finished the first major section of our project. Take a break for a while, do stretches, change the song to your playlist, and then start off into the next section of this article.
Classification algorithms learn how to assign class labels to examples, although their decisions can appear opaque. A popular diagnostic for understanding the decisions made by a classification algorithm is the decision surface. This is a plot that shows how a fit machine learning algorithm predicts a coarse grid across the input feature space. A decision surface plot is a powerful tool for understanding how a given model "sees" the prediction task and how it has decided to divide the input feature space by class label. In this tutorial, you will discover how to plot a decision surface for a classification machine learning algorithm.
In the not so distant future, robots may very well be a major component of modern human life. Whether we look at the driverless revolution to the use of drones, we can expect to see these machines to inhabit the space around us fairly soon. Multi-robot situations will become an increasingly common occurrence, and so engineers will need to ensure they can exist in the same space without hindering each other. In the scenario of drones, the machine needs to be capable of making instant decisions to adjust its trajectory to prevent an accident, whilst also making sure it doesn't lose the target destination. These sorts of changes need to be made in seconds whilst also recognising that the situation may change as a result of a movement from the other drone.
For 23 years, Larry Collins worked in a booth on the Carquinez Bridge in the San Francisco Bay Area, collecting tolls. The fare changed over time, from a few bucks to $6, but the basics of the job stayed the same: Collins would make change, answer questions, give directions and greet commuters. "Sometimes, you're the first person that people see in the morning," says Collins, "and that human interaction can spark a lot of conversation." But one day in mid-March, as confirmed cases of the coronavirus were skyrocketing, Collins' supervisor called and told him not to come into work the next day. The tollbooths were closing to protect the health of drivers and of toll collectors. Going forward, drivers would pay bridge tolls automatically via FasTrak tags mounted on their windshields or would receive bills sent to the address linked to their license plate. Collins' job was disappearing, as were the jobs of around 185 other toll collectors at bridges in Northern California, all to be replaced by technology.
Inspired by Google DeepMind's team, Shakir Mohamed, William Isaac, and Implikit's founder Marie-Therese Png article, Decolonial AI, my experience with Data Science and readings, I'll try to propose a production strategy that compensates the lack of scalable ethics in Data Science Systems and make it embedded since the beginning of the development, saving the cost of change later. The main problem that I'll approach might be kind of obvious. Data Science does not implement efficient and scalable Ethical guidelines. Data Science is not Customer Centric, yet. The reason, I'll detail along the article is: Implicitly, our work might be motivated by solely on optimizing revenue, costs, human and non-human operational resources under the facade of enriching Customer Experience when we are launching Data-based products. This is as complicated to say as is it to tackle.
Deep-learning approaches based on convolutional neural networks (CNNs) are gaining interest in the medical imaging field. We evaluated the diagnostic performance of a CNN to discriminate femoral neck fractures, trochanteric fractures, and non-fracture using antero-posterior (AP) and lateral hip radiographs. Patients and methods -- 1,703 plain hip AP radiographs and 1,220 plain hip lateral radiographs were included in the total dataset. The CNN made the diagnosis based on: (1) AP radiographs alone, (2) lateral radiographs alone, or (3) both AP and lateral radiographs combined. The diagnostic performance of the CNN was measured by the accuracy, recall, precision, and F1 score.
In the traditional hard-coded approach, we program a computer to perform a certain task. We tell it exactly what to do when it receives a certain input. In mathematical terms, this is like saying that we write the f(x) such that when users feed the input x into f(x), it gives the correct output y. In machine learning, however, we have a large set of inputs x and corresponding outputs y but not the function f(x). The goal here is to find the f(x) that transforms the input x into the output y.
Artificial Intelligence (AI) in Fintech Industry market report gives attention to market segmentation, market size, and forecast of 2019-2024 to help stakeholders in making a good decision for the future investments. The report on Artificial Intelligence (AI) in Fintech Industry market covers the key trends of the industry which impact its growth with reference to the competitive arena and key regions. The study highlights the challenges this industry vertical will face along with the growth opportunities which would support the business development in existing & untapped markets. Besides this, the report also includes few case studies including those which take into account the corona virus pandemic, with an intention to offer a clear picture of this business sphere to all stakeholders. Sachin is into market research and web marketing since the last 2 years and has worked on multiple projects across various industries.
Over the past decade, researchers have developed a growing number of deep neural networks that can be trained to complete a variety of tasks, including recognizing people or objects in images. While many of these computational techniques have achieved remarkable results, they can sometimes be fooled into misclassifying data. An adversarial attack is a type of cyberattack that specifically targets deep neural networks, tricking them into misclassifying data. It does this by creating adversarial data that closely resembles and yet differs from the data typically analyzed by a deep neural network, prompting the network to make incorrect predictions, failing to recognize the slight differences between real and adversarial data. In recent years, this type of attack has become increasingly common, highlighting the vulnerabilities and flaws of many deep neural networks.