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) …
The digitization of historical handwritten document images is important for the preservation of cultural heritage. Moreover, the transcription of text images obtained from digitization is necessary to provide efficient information access to the content of these documents. Handwritten Text Recognition (HTR) has become an important research topic in the areas of image and computational language ... [Show full abstract] processing that allows us to obtain transcriptions from text images. State-of-the-art HTR systems are, however, far from perfect. One difficulty is that they have to cope with image noise and handwriting variability.
There are many ways to implement a supervised learning system. The most popular of those is the neural network-- a brilliant algorithm that was loosely inspired by the connections of neurons in our own brains. Part II of this book is dedicated to neural networks. We'll grow the program from Part I into a full-fledged neural network. We'll have to overcome a few challenges along the way, but the payoff will be worth it: the final neural network will be way more powerful than the fledgling program we'll start off with.
Some blasts from the past surfaced this week, including revelations that a Russia-linked hacking group has repeatedly targeted the US electrical grid, along with oil and gas utilities and other industrial firms. Notably, the group has ties to the notorious industrial-control GRU hacking group Sandworm. Meanwhile, researchers revealed evidence this week that an elite NSA hacking tool for Microsoft Windows, known as EpMe, fell into the hands of Chinese hackers in 2014, years before that same tool then leaked in the notorious Shadow Brokers dump of NSA tools. WIRED got an inside look at how the video game hacker Empress has become so powerful and skilled at cracking the digital rights management software that lets video game makers, ebook publishers, and others control the content you buy from them. And the increasingly popular, but still invite-only, audio-based social media platform Clubhouse continues to struggle with security and privacy missteps. If you want something relaxing to take your mind off all of this complicated and concerning news, though, check out the new generation of Opte, an art piece that depicts the evolution and growth of the internet from 1997 to today.
The insurance industry is a competitive sector representing an estimated $507 billion or 2.7 percent of the US Gross Domestic Product. As customers become increasingly selective about tailoring their insurance purchases to their unique needs, leading insurers are exploring how machine learning (ML) can improve business operations and customer satisfaction. The greatest opportunities seem to lie, perhaps unsurprisingly, in claims and underwriting. No other sources have taken a comprehensive look at the impact of AI among the leading insurance companies in the U.S. We researched this sector in depth to help answer questions business leaders are asking today: This article aims to present a comprehensive look at the four leading insurance companies and their use of AI. Our "top 4" rankings are based on the National Association of Insurance Commissioners' 2016 ranking of the top 25 insurance companies.
The modern business landscape has transformed entirely with the likes of emerging technologies such as artificial intelligence, machine learning and automation. Such technologies have also transfigured modern application architectures as well as IT operations. Integrating AI into IT operations enables IT teams to perform more complex tasks and automate problem resolution in complex IT environments. This integration of AI into IT operations has led to the emergence of the term, AIOps that leverages big data, analytics and machine learning capabilities to simplify IT ops management. According to Gartner, 50% of organizations will use AIOps with application performance monitoring to provide insight into mission-critical applications and IT operations.
Google Creative Lab this week revealed a project called Alto. Alto by Google Creative Lab is a "teachable object using the Coral USB Accelerator." It's made to allow users to get a very basic handle on machine learning in a way that's adaptable to hardware projects of all sorts. The code and template for this project are all free for access. ALTO stands for A Little Teachable Object.
Artificial intelligence has maintained a steady graph of growth in the past few years. But the pandemic led to a rapid digital transformation, which further prompted rapid innovation in the realm. As per McKinsey's State of AI survey published in November 2020, half of the survey respondents had stated their companies had adopted AI in at least one function. Experts well versed in the AI domain predict that it'll continue to experience vast expansion and development in meaningful ways in 2021 and beyond. Let's mull over some of the developments in the domain of AI that you can expect.
As an IBM master inventor, professor at UC Irvine, and author of "Own the A.I. Revolution: Unlock Your Artificial Intelligence Strategy to Disrupt Your Competition," Sahota is also a lead artificial intelligence adviser to the United Nations and is helping find ways for AI to provide solutions and prevent future pandemics. Even now, AI is being used to create systems that can impact how treatments for COVID-19 are used. One such AI tool was developed at UC Irvine last year to help predict the probability of patients needing ICU care. This involved collecting the data of patients to get common symptoms of the coronavirus as well as how to accelerate treatment and care options. Other examples include AI-powered walking sticks for the blind, tools to help those who can't speak, and health care apps that use a cell phone to detect diabetes, tuberculosis and skin diseases through the camera and microphone.
After reading the first article, we saw that we had only 1 phase of execution there. In that phase, we find the updated weight values and rerun the code to achieve minimum error. However, things are a little spicy here. The execution in a multilayer neural network takes place in two-phase. Phase-1 is similar to that of a neural network without any hidden layers.
As technology improves, AI and ML applications are becoming increasingly pivotal for businesses to stay ahead of their competition. The time will soon come when a business that doesn't leverage AI in its decision making processes will find itself out in the cold. While AI holds a lot of potential, the technology is still nascent and prone to error. A big reason for this is the so-called "cold start" problem. ML algorithms rely on historical data being fed to them, so they can learn and get better and better at predicting future data patterns.