人工智能被认为是一种拯救世界、终结世界的技术。毋庸置疑，人工智能时代就要来临了，科幻电影中的场景将成为现实，未来已来！

目前，人工智能各个流派发展现状如何？

为什么lisp开始是为了人工智能创造的，可是现在人工智能火了，lisp却被冷落了？

前端人工智能框架有哪些？

Singer’s demo caused quite the stir and for good reason: tools such as this will continue to improve, and we all know it. It signals a future where many of the tasks we perform now will be automated or at least aided by AI. Technology that has the potential to eliminate tasks, and by extension the potential to eliminate jobs, will invariably elicit some fear. This is especially true in an industry that already has a firm grasp on how technology can disrupt entire industries. While AI will no doubt eliminate the need to manually perform many tasks, its benefits will also open up opportunities and help to automate menial tasks which will free us up to focus on the more meaningful work that provides additional value. The question becomes: how might we leverage AI and the benefits it brings to help in the design process? Additionally, how might things go wrong when leveraging AI in the design process?

Artificial intelligence has already changed the world in some pretty dramatic ways and will certainly do even more so in the future. But it’s a component technology. The transistor has changed the world. But saying, “How are transistors going to change the world?” is almost the wrong layer of abstraction—it’s like trying to understand a river by talking about H20. So artificial intelligence will participate meaningfully in causing technologies to become more intelligent and will shift how we try to deliver value to people. Less and less will technologies need to do what we want them to do through straightforward mechanical and structural solutions and more and more they’ll solve the problems through the increment of intelligence.

We’ve described a third view, in which AIs actually change humanity, helping us invent new cognitive technologies, which expand the range of human thought. Perhaps one day those cognitive technologies will, in turn, speed up the development of AI, in a virtuous feedback cycle:

Examples such as those described in this essay suggest that AI systems can enable the creation of new cognitive technologies. Things like the font tool aren’t just oracles to be consulted when you want a new font. Rather, they can be used to explore and discover, to provide new representations and operations, which can be internalized as part of the user’s own thinking. And while these examples are in their early stages, they suggest AI is not just about cognitive outsourcing. A different view of AI is possible, one where it helps us invent new cognitive technologies which transform the way we think.

This essay describes a new field, emerging today out of a synthesis of AI and IA. For this field, we suggest the name artificial intelligence augmentation (AIA): the use of AI systems to help develop new methods for intelligence augmentation. This new field introduces important new fundamental questions, questions not associated to either parent field. We believe the principles and systems of AIA will be radically different to most existing systems.

Our process starts with using one’s own intuition to define a step-by-step plan thought to potentially solve a complex problem. The algorithm then looks at each individual step and gives feedback about which steps are possible, which are impossible and ways the plan could be improved. The human then refines the initial plan using the advice from the AI, and the process repeats until the problem is solved. The hope is that the person and the AI will eventually converge to a kind of mutual understanding.

Much like a brain deciphering pieces of information, the artificial neural network looks at the information it hasbeen given and generates the next word, based on its neighbouring words. Over time, it “learns” which words to focus on, and where to make the best contextual connections based on previous examples. This process is a form of “deep learning” and allows translation systems to continuously learn and improve as time goes on. In NMT, deciphering context is called “alignment” and happens in the attention mechanism, which is situated between the encoder and decoder in the machine’s system.

Keep in mind: There is no such thing as "an artificial intelligence". AI is a collection of methods and ideas for building software that can do some of the things that humans can do with their brains. Researchers and developers develop new AI methods (and use existing AI methods) to build software (and sometimes also hardware) that can do something impressive, such as playing a game or drawing pictures of cats. However, you can safely assume that the same system cannot both play games and draw pictures of cats. In fact, no AI-based system that I've ever heard of can do more than a few different tasks. Even when the same researchers develop systems for different tasks based on the same idea, they will build different software systems. When journalists write that "Company X's AI could already drive a car, but it can now also write a poem", they obscure the fact that these are different systems and make it seem like there are machines with general intelligence out there. There are not.

Some advice for journalists writing about artificial intelligence

AGI, like an animal in the wild, is supposed to be able to deal, at runtime, with unforeseen circumstances. An ability to adapt quickly and reliably will not only push forward the next generation of robotic explorers and personal assistants, but can also be seen as a key aspect of intelligence. Intelligence is a term with many meanings.

However, learning at runtime is an ability which gives intelligent animals a key survival advantage. What has made machine learning so successful is a narrower idea of learning.

OpenAI and other researchers have released a few tools capable of identifying AI-generated text. These use similar AI algorithms to spot telltale signs in the text. It’s not clear if anyone is using these to protect online commenting platforms. Facebook declined to say if it is using such tools; Google and Twitter did not respond to requests for comment.

OpenAI released a more capable version of its text-generation program, called GPT-3, last June. So far, it has only been made available to a few AI researchers and companies, with some people building useful applications such as programs that generate email messages from bullet points. When GPT-3 was released, OpenAI said in a research paper that it had not seen signs of GPT-2 being used maliciously, even though it had been aware of Weiss’s research.

Weiss discovered GPT-2, a program released earlier that year by OpenAI, an AI company in San Francisco, and realized he could generate fake comments to simulate a groundswell of public opinion. “I was also shocked at how easy it was to fine tune GPT-2 to actually spit out the comments,” Weiss says. “It's relatively concerning on a number of fronts.”Besides the comment-generating tool, Weiss built software for automatically submitting comments. He also conducted an experiment in which volunteers were asked to distinguish between the AI-generated comments and ones written by humans. The volunteers did no better than random guessing.

I keep in touch with the PullString crew afterward as they move on to creating other characters (for instance, a Call of Duty bot that, on its first day in the wild, has 6 million conversations). At one point the company’s CEO, Oren Jacob, a former chief technology officer at Pixar, tells me that PullString’s ambitions are not limited to entertainment. “I want to create technology that allows people to have conversations with characters who don’t exist in the physical world—because they’re fictional, like Buzz Lightyear,” he says, “or because they’re dead, like Martin Luther King.”