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 asymmetry in the flow of events that is expressed by the phrase ‘time’s arrow’ traces back to the second law of thermodynamics. In the microscopic regime, fluctuations prevent us from discerning the direction of time’s arrow with certainty. Here, we find that a machine learning algorithm that is trained to infer the direction of time’s arrow identifies entropy production as the relevant physical quantity in its decision-making process. Effectively, the algorithm rediscovers the fluctuation theorem as the underlying thermodynamic principle. Our results indicate that machine learning techniques can be used to study systems that are out of equilibrium, and ultimately to answer open questions and uncover physical principles in thermodynamics. The phrase ‘arrow of time’ refers to the asymmetry in the flow of events. A machine learning algorithm trained to infer its direction identifies entropy production as the relevant underlying physical principle in the decision-making process.
After they published the article, many responses came from different media houses and notable people trying to shed some light on what exactly happened. First, let's look at the legitimacy of the article. Surely it was the generated one but with no human intervention? So, it is just another media overhype? I mean cherry-picking the best and presenting it to you in a way that sells.
According to a recent McKinsey survey, just under a third of organizations use artificial intelligence for multiple business functions. Scaling AI transformations appears to be the most important challenge in this arena, and based on my experience, the key limiting factor here is that AI projects are typically human-workload intensive, resulting in lengthy and expensive projects. But what if AI could help organizations implementing AI? New technologies and concepts have recently come to the market to help accelerate and improve the AI implementation process. While most of these technologies are still maturing, they have already delivered significant benefits to the organizations that have adopted them. For each of these three steps, I will describe the new concepts available and their impacts.
Machine vision has long found a place in food safety, working 24/7 without fatigue. But as data access increases and processing power improves, machine vision is finding even more opportunities through the added capabilities of artificial intelligence (AI). To take one example, traditional machine vision tends to struggle to inspect for contamination in sun-dried tomatoes. But it's an application that's well suited to AI. "Similar to a human, AI is very good at dealing with a lot of variations in whatever's being looked at," says Quinn Killough, senior business development manager for Landing AI, a company that provides end-to-end AI platforms for manufacturing. "That type of application, because there's so much variability in what a tomato could look like or what kind of contamination could be on it, it was a pretty tough machine vision problem in general. A human can do it easily. And it turns out AI can do it fairly easily as well. Being able to deal with all that variation in what you're looking at, it makes it very well suited for AI."
Since its release, GPT-3, OpenAI's massive language model, has been the topic of much discussion among developers, researchers, entrepreneurs, and journalists. Most of those discussions have been focused on the capabilities of the AI-powered text generator. But much about GPT-3 remains obscure. The company has opted to commercialize the deep learning model instead of making it freely available to the public. And though the AI has shown to be capable of many interesting feats, it's not yet clear if GPT-3 will become a real product or will join the endless array of abandoned projects that never found a viable business model. Earlier this month, as reported by users who have access to the beta version of the language model, OpenAI declared the initial pricing plan of GPT-3.
The time you spend writing emails could be cut drastically. A college student has launched a Long Island artificial intelligence startup that writes emails automatically from a few fragmentary notes. Matt Shumer, 20, who launched Melville-based OthersideAI Inc. in July, is beta-testing the software as a Chrome browser extension for users of the Google Gmail service. Versions for Microsoft Corp.'s Outlook and other email clients are planned, said Shumer, who is the company's chief executive. OthersideAI's Quick Response software can reduce time spent on emails by 75% by learning the way "a user thinks and responds," the company said.
The General Services Administration is modernizing how agencies review regulations using machine learning (ML), in a procurement through its Centers of Excellence (CoE) initiative. GSA awarded a $9.9 million contract to Deloitte and to Esper, Inc. for ML support for agencies. ML can review rules and regulations to identify trends in the data, which can help eliminate redundancies and streamline the process of writing new ones. Both the CoEs within GSA's Technology Transformation Services and the Federal Systems Integration and Management Center (FEDSIM) have used ML, a subset of artificial intelligence, to conduct regulatory reviews. The contract extends their work to CoE partner agencies.
This course material is aimed at people who are already familiar with ... What you'll learn This course is about the fundamental concepts of machine learning, facusing on neural networks. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very good guess about stock prices movement in the market.
Facial-recognition tech can see around hoodies or big shades, so pair them with a face covering. Plus, you'll get protection against coronavirus particles and tear gas. There are makeup tutorials online for edgy face paint intended to trick face-recognizing algorithms, but these designs are unproven. Also, it's probably easier for humans to track you if you look like a member of Insane Clown Posse. Make yourself less memorable to both humans and machines by wearing clothing as dark and pattern-free as your commitment to privacy.
Please don't complain to me about literally anything if you've touched human flesh since March. Being very single, I have not, and my Grubhub guy doesn't want a hug. So I am doomed, instead, to online dating in the context of a pandemic. Let me walk you through the torture. It starts typically enough, with endless scrolling through profiles of now-offensively-irrelevant travel photos.