As the popularity of AI-powered generative chatbots increases, critics are calling out creators of tools like OpenAI to come forward and take necessary steps to ward off any harm. In particular, emphasis was put on the likes of enabling the creators of the technology to take necessary precautions. Students were seen copying the content off the net, thanks to ChatGPT, and submitting the reports as a part of their own. Similarly, content farms were using it to produce more spam and then we saw the likes of bad actors spreading misinformation through such means. OpenAI was under fire to do something quickly as there appeared to be way too much risk attached.

According to the Gartner hype cycle, the least amount of time a product takes to hit the'plateau' of expectations is two years. The hot chatbot has shattered all records of a product lifecycle, going through all stages of the cycle within 3 months. Launched in November-end last year, ChatGPT has already been through the innovation trigger, inflated expectations, disillusionment, enlightenment, and is now reaching a mature period of measured expectations, leading to industry adoption. A contributing factor to this might be the bot's meteoric growth, which scaled to 10 million users within 40 days. For contrast, Instagram took almost a year to reach the same milestone.

Let's get straight to it. I recently created a Java Career Roadmap 2023 using ChatGPT.

Don't miss out on ET Prime stories! Get your daily dose of business updates on WhatsApp. India is set to emerge as a major telecom technology exporter with the development of the 4G and 5G stack, in which several countries have shown interest, said Ashwini Vaishnaw, Union minister for communications, railways, electronics and information technology. India is building roads at a record rate and the country's national highway network will rise 37% in the next two years, said Nitin Gadkari, minister of road transport and highways. India will be among nations that shape the future of products, devices and technologies, said minister of state for electronics and information technology Rajeev Chandrasekhar.

Welcome to the latest issue of your guide to AI, an editorialized newsletter covering key developments in AI research, industry, geopolitics and startups during January 2023. This one is a monster so it might get clipped in your inbox (read the online version in case!). Nathan wrote an oped in The Times for why university spinouts are a critical engine for our technology industry and why spinout policy needs urgent reform. The Times Higher Education profiled our open source data term database, spinout.fyi. Nathan commented on The Financial Times' Big Read on The growing tensions around spinouts at British universities. The State of AI Report provided two key figures to The Economist's piece on The race of the AI labs heats up. Register for next year's RAAIS, a full-day event in London that explores research frontiers and real-world applications of AI-first technology at the world's best companies. As usual, we love hearing what you're up to and what's on your mind, just hit reply or forward to your friends:-) BioNTech acquired London and Tunis-based AI startup InstaDeep for $680M (cash stock) - this was a huge deal.

Real-world data usually exhibits a long-tailed distribution,with a few frequent labels and a lot of few-shot labels. The study of institution name normalization is a perfect application case showing this phenomenon. There are many institutions worldwide with enormous variations of their names in the publicly available literature. In this work, we first collect a large-scale institution name normalization dataset LoT-insts1, which contains over 25k classes that exhibit a naturally long-tailed distribution. In order to isolate the few-shot and zero-shot learning scenarios from the massive many-shot classes, we construct our test set from four different subsets: many-, medium-, and few-shot sets, as well as a zero-shot open set. We also replicate several important baseline methods on our data, covering a wide range from search-based methods to neural network methods that use the pretrained BERT model. Further, we propose our specially pretrained, BERT-based model that shows better out-of-distribution generalization on few-shot and zero-shot test sets. Compared to other datasets focusing on the long-tailed phenomenon, our dataset has one order of magnitude more training data than the largest existing long-tailed datasets and is naturally long-tailed rather than manually synthesized. We believe it provides an important and different scenario to study this problem. To our best knowledge, this is the first natural language dataset that focuses on long-tailed and open-set classification problems.

Object goal visual navigation is a challenging task that aims to guide a robot to find the target object based on its visual observation, and the target is limited to the classes pre-defined in the training stage. However, in real households, there may exist numerous target classes that the robot needs to deal with, and it is hard for all of these classes to be contained in the training stage. To address this challenge, we study the zero-shot object goal visual navigation task, which aims at guiding robots to find targets belonging to novel classes without any training samples. To this end, we also propose a novel zero-shot object navigation framework called semantic similarity network (SSNet). Our framework use the detection results and the cosine similarity between semantic word embeddings as input. Such type of input data has a weak correlation with classes and thus our framework has the ability to generalize the policy to novel classes. Extensive experiments on the AI2-THOR platform show that our model outperforms the baseline models in the zero-shot object navigation task, which proves the generalization ability of our model. Our code is available at: https://github.com/pioneer-innovation/Zero-Shot-Object-Navigation.

With the world going crazy over chatGPT, I wanted to see how I can use it more effectively to learn and understand. So, I posed the AI a simple question and it gave the response that I am including below. I have not made any attempts to modify the response but you will see the responses are pretty self-explanatory (and to be honest quite amazing!)

In addition to leveraging the official OpenAI content moderation API, Mismatch also wants to use OpenAI to assist in the moderation process. "We are working to create guardrails that actually leverage OpenAI to pass our content to them and ask a series of questions and prompts," Hartle said. Mismatch is "figuring out the right ways to have OpenAI and these large language models help moderate this process. These models are the best thing at parsing natural language right now, so it makes a lot of sense to also try to use them as a secondary system."

Is ChatGPT down, at capacity or is it something else? Let's try some easy fixes! The official website address for free and plus members of ChatGPT AI is https://chat.openai.com If the site appears to be non-operational, you can easily determine if ChatGPT actually is "down".