Arwayy

21/05/2022

Applied AI: you have a million interns
Frontier AI: you have one intern who’s as fast as a million.

20/03/2019

A new website has gone live to check if you can tell a real face from an AI-generated fake in this world of uncertainty.

The website, WhichFaceIsReal. com, is created by Jevin West of the Information School and Carl Bergstrom of the biology department at the University of Washington.

West and Bergstrom gained some degree of fame after presenting a class titled ‘Calling Bu****it in the Age of Big Data’ back in 2017.

Their website continues along these lines and tasks visitors with, as you probably guessed, picking the real face over the fake (I was quietly confident, but I’d put my success rate around 50 percent).

In a post explaining their website, West and Bergstrom wrote:

“While we’ve learned to distrust user names and text more generally, pictures are different. You can’t synthesize a picture out of nothing, we assume; a picture had to be of someone. Sure a scammer could appropriate someone else’s picture, but doing so is a risky strategy in a world with google reverse search and so forth. So we tend to trust pictures. A business profile with a picture obviously belongs to someone. A match on a dating site may turn out to be 10 pounds heavier or 10 years older than when a picture was taken, but if there’s a picture, the person obviously exists.

No longer. New adverserial machine learning algorithms allow people to rapidly generate synthetic ‘photographs’ of people who have never existed.”

The pair did not develop the technology behind it but wanted to bring attention to a serious problem in a fun way. “Our aim is to make you aware of the ease with which digital identities can be faked, and to help you spot these fakes at a single glance,” they claim.

Software engineers from NVIDIA developed the impressive algorithm for generating realistic faces. You may have already seen it at work on ThisPersonDoesNotExist. com.

The algorithm is trained on a ‘General Adversarial Network’ where two neural networks compete against each other; one creating fake images, the other attempting to spot the difference.

Currently, people are spotting the real person around 70 percent of the time. Some inconsistencies to look out for is the background of the photo and how things such as glasses and hair are rendered.

If attempting to determine whether a pic is real or not that you’ve come across, the duo advise looking for images of the same person from different angles. That, as of writing, is not possible for an AI to do.

02/03/2019

We are proud to announce our partnership with industry giants Dell and Cisco. Thank you for your support. Welcome abode

28/02/2019

We are now on twitter. Empower us by following on Twitter.

The latest Tweets from Arwayy (). Arwayy is the next generation IT company with potential understanding of Technology. Hyderabad, India

27/02/2019

We are happy to announce our Artificial intelligence(AI) development and Blockchain research commencement. To know more about AI please read the artical Below.

In the field of computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and other animals. Computer science defines AI research as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.More specifically, Kaplan and Haenlein define AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”. Colloquially, the term "artificial intelligence" is used to describe machines that mimic "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving".

As machines become increasingly capable, tasks considered to require "intelligence" are often removed from the definition of AI, a phenomenon known as the AI effect. A quip in Tesler's Theorem says "AI is whatever hasn't been done yet." For instance, optical character recognition is frequently excluded from things considered to be AI, having become a routine technology. Modern machine capabilities generally classified as AI include successfully understanding human speech, competing at the highest level in strategic game systems (such as chess and Go), autonomously operating cars, and intelligent routing in content delivery networks and military simulations.

Borrowing from the management literature, Kaplan and Haenlein classify artificial intelligence into three different types of AI systems: analytical, human-inspired, and humanized artificial intelligence. Analytical AI has only characteristics consistent with cognitive intelligence; generating a cognitive representation of the world and using learning based on past experience to inform future decisions. Human-inspired AI has elements from cognitive and emotional intelligence; understanding human emotions, in addition to cognitive elements, and considering them in their decision making. Humanized AI shows characteristics of all types of competencies (i.e., cognitive, emotional, and social intelligence), is able to be self-conscious and is self-aware in interactions with others.

Artificial intelligence was founded as an academic discipline in 1956, and in the years since has experienced several waves of optimism, followed by disappointment and the loss of funding (known as an "AI winter"), followed by new approaches, success and renewed funding. For most of its history, AI research has been divided into subfields that often fail to communicate with each other. These sub-fields are based on technical considerations, such as particular goals (e.g. "robotics" or "machine learning"), the use of particular tools ("logic" or artificial neural networks), or deep philosophical differences. Subfields have also been based on social factors (particular institutions or the work of particular researchers).

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. Approaches include statistical methods, computational intelligence, and traditional symbolic AI. Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics. The AI field draws upon computer science, information engineering, mathematics, psychology, linguistics, philosophy, and many other fields.

The field was founded on the claim that human intelligence "can be so precisely described that a machine can be made to simulate it". This raises philosophical arguments about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence which are issues that have been explored by myth, fiction and philosophy since antiquity. Some people also consider AI to be a danger to humanity if it progresses unabated. Others believe that AI, unlike previous technological revolutions, will create a risk of mass unemployment.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding; and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.