One goal of AI work in natural language is to enable communication between people and computers without resorting to memorization of complex commands and procedures. Automatic translation – enabling scientists, business people and just plain folks to interact easily with people around the world – is another goal. Both are just part of the broad field of AI and natural language, along with the cognitive science aspect of using computers to study how humans understand language.
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It's Monday morning of some week in 2050 and you're shuffling into your kitchen, drawn by the smell of fresh coffee C-3PO has brewed while he unloaded the dishwasher. "Here you go, Han Solo, I used the new flavor you bought yesterday," C-3PO tells you as he hands you the cup. C-3PO arrived barely a month ago and already has developed a wonderful sense of humor and even some snark. He isn't the real C-3PO, of course--you just named him that because you are a vintage movie buff--but he's the latest NeuroCyber model that comes closest to how people think, talk, and acquire knowledge. He's no match to the original C-3PO's fluency in 6 million forms of communication, but he's got full linguistic mastery and can learn from humans like humans do--from observation and imitation, whether it's using sarcasm or sticking dishes into slots. Unlike the early models of such assistants like Siri or Alexa who could recognize commands and act upon them, NeuroCybers can evolve into intuitive assistants and companions.
If you thought judgment, ethics, and even creativity were the unique purview of humans, think again. The latest industry analyst predictions about artificial intelligence (AI) are out, and they're certain to oust a ton of assumptions we've made to date. Read on to find out just how smart, creative, and sincere AI will become during the next few years. Organizations are just starting to tap the incredible computational powers of AI for creativity, human productivity, and business results. Noting that South Africa granted the first patent to a creative AI system in 2021, Forrester researchers predicted creative AI systems will win dozens of patents in 2022.
Language generation is the hottest thing in AI right now, with a class of systems known as "large language models" (or LLMs) being used for everything from improving Google's search engine to creating text-based fantasy games. But these programs also have serious problems, including regurgitating sexist and racist language and failing tests of logical reasoning. One big question is: can these weaknesses be improved by simply adding more data and computing power, or are we reaching the limits of this technological paradigm? This is one of the topics that Alphabet's AI lab DeepMind is tackling in a trio of research papers published today. The company's conclusion is that scaling up these systems further should deliver plenty of improvements.
DeepMind, the AI lab backed by Google parent company Alphabet, has long invested in game-playing AI systems. It's the lab's philosophy that games, while lacking an obvious commercial application, are uniquely relevant challenges of cognitive and reasoning capabilities. This makes them useful benchmarks of AI progress. In recent decades, games have given rise to the kind of self-learning AI that powers computer vision, self-driving cars, and natural language processing. In a continuation of its work, DeepMind has created a system called Player of Games, which the company first revealed in a research paper published on the preprint server Arxiv.org this week.
Over the past decades, Artificial Intelligence (AI), has played a robust and growing role in the world. What many people don't realize is that AI presents itself in several different forms that impact everyday life. Logging into your email, social media, car ride services, and online shopping platforms all involve AI algorithms to ensure a better user experience. The medical field is one key area where AI is experiencing rapid growth; specifically, in managing treatment and diagnostics. There is significant research undertaken into how AI can help aid in clinical decisions, increase the efficiency of treatment, and support human judgment.
Called RETRO (for "Retrieval-Enhanced Transformer"), the AI matches the performance of neural networks 25 times its size, cutting the time and cost needed to train very large models. The researchers also claim that the database makes it easier to analyze what the AI has learned, which could help with filtering out bias and toxic language. "Being able to look things up on the fly instead of having to memorize everything can often be useful, in the same way as it is for humans," says Jack Rae at DeepMind, who leads the firm's research in large language models. Language models generate text by predicting what words come next in a sentence or conversation. The larger a model, the more information about the world it can learn during training, which makes its predictions better.
Since the start of 2021, The Trevor Project, the largest suicide prevention organization for LGBTQ young people, has used an AI technology called the Crisis Contact Simulator to train its counselors on how to talk to in-crisis youth. The tool essentially simulates what a conversation like that may look like with the help of AI chatbots. At launch, the CCS came with access to one such "persona." Today, The Trevor Project is adding a second one called Drew. The new chatbot represents a fictional youth in their early 20s who lives in California and faces bullying and harassment. Since implementing its first persona, the aptly named Trevor, in February, the organization says the technology has helped train more than 1,000 counselors.
DeepMind says that teaching machines to realistically mimic human language is more complex than simply throwing increasing amounts of computing power at the problem, despite that being the predominant strategy in the field. In recent years, most progress in building artificial intelligences (AIs) has come from increasing their size and training them with ever more data on the biggest computer available. But this makes the AIs expensive, unwieldy and hungry for resources. A recent system created by Microsoft and Nvidia required more than a month of supercomputer access and almost 4500 high-power graphics cards to train, at a cost of millions of dollars. In a bid to find alternatives, AI firm DeepMind has created a model that can look up information in a vast database, in a similar way that a human would use a search engine.