AI gets smarter, safer and more visual with GPT-4 release, says OpenAI

AI gets smarter, safer and more visual with GPT-4 release, says OpenAI

AI gets smarter, safer and more visual with GPT-4 release, says OpenAI

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The hottest AI technology foundation, OpenAI’s GPT, got a big update on Tuesday which is now available in the premium version of the Chatbot ChatGPT.

The new GPT-4 can generate much longer text strings and respond when people give it images, and it’s designed to do a better job of avoiding the AI ​​pitfalls seen in the old GPT-3.5 , OpenAI said on Tuesday. For example, on the bar exams that lawyers must pass to practice law, GPT-4 ranks in the top 10% of scores, compared to the lowest 10% for GPT-3.5, the court said. artificial intelligence research company.

GPT stands for Generative Pretrained Transformer, a reference to the fact that it can generate text on its own and uses an artificial intelligence technology called transformers that Google pioneered. This is a type of AI called a large model language that is trained on large swathes of data harvested from the internet, mathematically learning to spot patterns and replicate styles.

OpenAI has made its GPT technology available to developers for years, but ChatGPT, which debuted in November, offered a simple interface that sparked an explosion of interest, experimentation, and concern about the downsides of technology. ChatGPT is free, but it falters when demand is high. In January, OpenAI started offering ChatGPT Plus for $20 per month with guaranteed availability and now GPT-4 foundation.

GPT-4 progress

“In casual conversation, the distinction between GPT-3.5 and GPT-4 can be subtle. The difference appears when the complexity of the task reaches a sufficient threshold,” OpenAI said. “GPT-4 is more reliable, creative, and able to handle much more nuanced instructions than GPT-3.5.”

Another major advancement of GPT-4 is the ability to accept input data that includes text and photos. The OpenAI example asks the chatbot to explain a joke showing a clunky decades-old computer cable plugged into the small Lightning port of a modern iPhone.

Another is better performance avoiding AI problems like hallucinations – poorly crafted answers, often offered with as much seeming authority as the answers the AI ​​gets right. GPT-4 is also more effective at thwarting attempts to get it to say the wrong thing: “GPT-4 scores 40% higher than our last GPT-3.5 in our internal contradictory assessments of the facts,” a declared OpenAI.

GPT-4 also adds new “dirigibility” options. Today, users of large language models (LLMs) often have to engage in elaborate “prompt engineering”, learning to build specific cues into their prompts to get the right kind of responses. GPT-4 adds a system command option that allows users to set a specific tone or style, such as programming code or a Socratic tutor: “You are a tutor who always responds in the Socratic style. You never give the answer to the student, but always try to ask the right question to help them learn to think for themselves.”

Stochastic parrots and other problems

OpenAI acknowledges the significant shortcomings that persist with GPT-4, though it also touts progress in avoiding them.

“It can sometimes make simple errors of reasoning… or be overly gullible in accepting obvious misrepresentations from a user. And sometimes it can fail difficult problems in the same way as humans, such as the introduction security flaws in the code it produces,” OpenAI said. Additionally, “GPT-4 can also be confidently wrong in its predictions, not caring to double check the work when it is likely to be wrong.”

Large language models can deliver impressive results, appearing to understand huge amounts of topics and converse in human-sounding if somewhat stilted language. Fundamentally, however, IA LLMs really don’t know anything. They are simply able to string words together in a statistically very refined way.

This statistical but fundamentally somewhat hollow approach to knowledge has led researchers, including former Google AI researchers Emily Bender and Timnit Gebru, to warn of the “dangers of stochastic parrots” that come with large language models. Language model AIs tend to encode biases, stereotypes, and negative sentiments present in training data, and researchers and others using these models tend to “confuse… performance gains with actual understanding natural language”.

OpenAI, Microsoft and Nvidia partnership

OpenAI received a big boost when Microsoft said in February that it was using GPT technology in its Bing search engine, including chat features similar to ChatGPT. On Tuesday, Microsoft announced that it is using GPT-4 for Bing work. Together, OpenAI and Microsoft pose a major search threat to Googlebut Google also has its own extended language model technology, including a chatbot called Bard that Google is testing privately.

Microsoft uses GPT technology both to evaluate the searches people type into Bing and, in some cases, to offer more elaborate conversational responses. THE the results can be much more informative than those of earlier search engines, but the more conversational interface that can be optionally invoked has had issues that make it seem unbalanced.

To form GPT, OpenAI used Microsoft’s Azure cloud computing service, including thousands of Nvidia’s A100 graphics processing units, or GPUs, paired together. Azure can now use Nvidia’s new H100 processors, which include specific circuitry to speed up AI Transformer calculations.


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