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) …
In 2005, Ray Kurzweil said, "the singularity is near." Now, AI can code in any language, and we're moving to way better AI. GPT-3 got "mindboggling" results by training on a ton of data: Basically the whole Internet. It doesn't need to train on your specific use-case (zero-shot learning). It can fool 88% of people, and we're still in the baby stage.
This article builds upon my previous two articles where I share some tips on how to get started with data analysis in Python (or R) and explain some basic concepts on text analysis in Python. In this article, I want to go a step further and talk about how to get started with text classification with the help of machine learning. The motivation behind writing this article is the same as the previous ones: because there are enough people out there who are stuck with tools that are not optimal for the given task, e.g. using MS Excel for text analysis. I want to encourage people to use Python, not be scared of programming, and automate as much of their work as possible. Speaking of automation, in my last article I presented some methods on how to extract information out of textual data, using railroad incident reports as an example.
You might be wondering why I started first with the need for data augmentation rather than its meaning, but that's the best way to learn anything quickly. So, let dive into Data Augmentation. Training DATA is the backbone of an entire Deep Learning project, more the data, more the features that can be extracted, and thus better the accuracy of the model. Deep Learning models are directly dependent on the amount of data, but it's not always that we have sufficient data to train our images. This problem is best solved by data augmentation.
I work predominantly in NLP for the last three months at work. It's been a long time I work on the image data. Hence, I decided to build a unique image classifier model as part of my personal project and learning. One thing I am really missing in the current pandemic is traveling. These days I used to see a lot of travel vlogs and travel pictures on Instagram, wondering when we will go back to the normal world. This strikes me to create an image classifier model with five classes like Mountain, Beach, Desert, Lake, and Museum.
I propose to consider the question, 'Can machines think?' This should begin with definitions of the meaning of the terms'machine' and'think'. The definitions might be framed so as to reflect so far as possible the normal use of the words, but this attitude is dangerous. If the meaning of the words'machine' and'think' are to be found by examining how they are commonly used it is difficult to escape the conclusion that the meaning and the answer to the question, 'Can machines think?' is to be sought in a statistical survey such as a Gallup poll. Instead of attempting such a definition I shall replace the question by another, which is closely related to it and is expressed in relatively unambiguous words. The new form of the problem can be described in terms of a game which we call the'imitation game'. It is played with three people, a man (A), a woman (B), and an interrogator (C) who may be of either sex. The interrogator stays in a room apart from the other two. The object of the ...
Null safety is the largest change we've made to Dart since we replaced the original unsound optional type system with a sound static type system in Dart 2.0. When Dart first launched, compile-time null safety was a rare feature needing a long introduction. Today, Kotlin, Swift, Rust, and other languages all have their own answers to what has become a very familiar problem. If you run this Dart program without null safety, it throws a NoSuchMethodError exception on the call to .length. The null value is an instance of the Null class, and Null has no "length" getter. This is especially true in a language like Dart that is designed to run on an end-user's device. If a server application fails, you can often restart it before anyone notices. But when a Flutter app crashes on a user's phone, they are not happy. Developers like statically-typed languages like Dart because they enable the type checker to find mistakes in code at compile time, usually right in the IDE. The sooner you find a bug, the sooner you can fix it. When language designers talk about "fixing null reference errors", they mean enriching the static type checker so that the language can detect mistakes like the above attempt to call .length on a value that might be null. There is no one true solution to this problem. Rust and Kotlin both have their own approach that makes sense in the context of those languages. This doc walks through all the details of our answer for Dart. It includes changes to the static type system and a suite of other modifications and new language features to let you not only write null-safe code but hopefully to enjoy doing so. If you want something shorter that covers just what you need to know to get up and running, start with the overview. When you are ready for a deeper understanding and have the time, come back here so you can understand how the language handles null, why we designed it that way, and how to write idiomatic, modern, null-safe Dart. The various ways a language can tackle null reference errors each have their pros and cons. Code should be safe by default. If you write new Dart code and don't use any explicitly unsafe features, it never throws a null reference error at runtime.
President Trump joins Fox News medical contributor Dr. Marc Siegel for an exclusive interview on'Tucker Carlson Tonight.' Joe Biden should take the same cognitive test that President Trump recently took, the president said Wednesday during an interview with Fox News medical contributor Dr. Marc Siegel. "In a way he has an obligation to," Trump said of the presumptive Democratic presidential nominee, adding that the presidency requires "stamina" and "mental health." Trump said he took the test to prove to the media that he was fit to serve in the presidency after reports supposedly questioned his cognitive ability. Trump has used the argument that Biden -- at age 77, three years older than Trump -- is too old to run for president . The argument is a cornerstone strategy of Trump's reelection campaign against the former vice president.
Or, if you prefer, you can state it as weak versus strong AI (it's okay to list them in either order). If you've been reading about AI in the popular press, the odds are that you've seen references to so-called strong AI and so-called weak AI, and yet the odds further are that both of those phrases have been used wrongly and offer misleading and confounding impressions. Time to set the record straight. First, let's consider what is being incorrectly stated. Some speak of weak AI as though it is AI that is wimpy and not up to the same capabilities as strong AI, including that weak AI is decidedly slower, or much less optimized, or otherwise inevitably and unarguably feebler in its AI capacities.
"Brings us to aws flicks took the at the time unconventional decision to go all in on aws many years ago at this point, and that's treated. The the whole idea around blessed programming languages where you make a strong decision within an organization to restrict the number of programming languages with an organization and it it that constraint ends up helping the organization make decisions more quickly and allow for engineering mobility and so on. This has been the case with aws when when Netflix? Strongly moved onto aws and continue to do that. That extends to medfly show. A better flow is an open source framework, but it has a tight coupling with aws. So why is the tight coupling to aws useful for machine learning framework? Sue I won't say that. We are tightly coupled to eight of us. So when leave it open sourcing MEDOFF. No at that point in time, because we had a good amount of operational expertise with aws, we chose indicating the details are ready for this cloud integration, ...
In their book The End of Capitalism (As We Knew It), J.K. Gibson-Graham, a two-person writing team, examine a conundrum: after innumerable examinations of capitalism's inherent contradictions, and despite decades of projects devoted specifically to accelerating its demise, capitalism seems as vibrant as ever. Gibson-Graham ask, "In the face of these efforts, how has capitalism maintained such a strong grip on political economy?" The answer they offer is oblique but striking: perhaps it hasn't. More precisely, they suggest that the conventional wisdom that economic life is dominated by capitalist relations is not, in fact, true. They point to the wide range of forms of economic engagement that fall outside the limits of traditional political economy -- domestic activity, relations of care, mutual support, self-sustenance, and more -- to argue that capitalism is only one amongst a range of concurrent forms of economic life -- and perhaps not even the most common.