In 2005, Ray Kurzweil said, "the singularity is near." Now, AI can code in any language, and we're moving to way better AI. GPT-3 got "mindboggling" results by training on a ton of data: Basically the whole Internet. It doesn't need to train on your specific use-case (zero-shot learning). It can fool 88% of people, and we're still in the baby stage.

Bosses don't often play down their products. Sam Altman, the CEO of artificial intelligence company OpenAI, did just that when people went gaga over his company's latest software: the Generative Pretrained Transformer 3 (GPT-3). For some, GPT-3 represented a moment in which one scientific era ends and another is born. Mr Altman rightly lowered expectations. "The GPT-3 hype is way too much," he tweeted last month.

Offline learning is a key part of making reinforcement learning (RL) useable in real systems. Offline RL looks at scenarios where there is data from a system's operation, but no direct access to the system when learning a policy. Recent work on training RL policies from offline data has shown results both with model-free policies learned directly from the data, or with planning on top of learnt models of the data. Model-free policies tend to be more performant, but are more opaque, harder to command externally, and less easy to integrate into larger systems. We propose an offline learner that generates a model that can be used to control the system directly through planning. This allows us to have easily controllable policies directly from data, without ever interacting with the system. We show the performance of our algorithm, Model-Based Offline Planning (MBOP) on a series of robotics-inspired tasks, and demonstrate its ability leverage planning to respect environmental constraints. We are able to find near-optimal polices for certain simulated systems from as little as 50 seconds of real-time system interaction, and create zero-shot goal-conditioned policies on a series of environments.

A new language model, OpenAI's GPT-3, is making waves for its ability to mimic writing, but it falls short on common sense. Some experts think an emerging technique called neuro-symbolic AI is the answer.

"GPT-3 is not a mind, but it is also not entirely a machine. It's something else: a statistically abstracted representation of the contents of millions of minds, as expressed in their writing." In recent years, the AI circus really has come to town and we've been treated to a veritable parade of technical aberrations seeking to dazzle us with their human-like intelligence. Many of these sideshows have been "embodied" AI, where the physical form usually functions as a cunning disguise for a clunky, pre-programmed bot. Like the world's first "AI anchor", launched by a Chinese TV network and -- how could we ever forget -- Sophia, Saudi Arabia's first robotic citizen.

With the rise of computing power and advancements in algorithms, there's something amusing and interesting happening in the field of Artificial Intelligence and Machine Learning every day that it becomes difficult to keep up with the pace. Natural Language Processing is gaining new momentum as transformer models like BERT, GPT, RoBERT, XLNET and others are making it possible to create more advanced chatbots which can closely replicate a human and are easy to deploy and maintain along with huge cost savings. To be updated with the recent inventions and advancements, here's a list of top AI blogs I personally follow: Google has few of the best talents on the planet working for its Google research, Google Brain and the overall Google team. They have been successful in finding solutions to some of the most challenging computer science problems and are also the developers of ML platforms like Tensorflow. The Google research blog has all the articles, papers and other relevant content explaining what Google has been able to achieve.

Using Transformer models has never been simpler! Yes that's what Simple Transformers author Thilina Rajapakse says and I agree with him so should you. You might have seen lengthy code with hundreds of lines to implement transformers models such as BERT, RoBERTa, etc. Once you understand how to use Simple Transformers you will know how easy and simple it is to use transformer models. TheSimple Transformers library is built on top of Hugging Face Transformers library. Hugging Face Transformers provides state-of-the-art general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet, T5, etc.) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) and provides more than thousand pre-trained models and covers around 100 languages.

When the chief executive of a San Francisco artificial intelligence company tries to damp down the hype surrounding his own technology then you know that some people have become rather excitable. But that is exactly what Sam Altman attempted to do last month in response to the ecstatic reaction to OpenAI's latest GPT-3 program. "The GPT-3 hype is way too much," Mr Altman tweeted. "It's impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes." GPT-3, which stands for generative pre-trained transformer version three, is, in essence, a super-sophisticated auto-complete function, which sounds less than exciting.

What's in this issue: Here, I try to deconstruct the buzz about GPT-3, and in trying to do that, I dig deeper into what hype means in the context of emergent technologies and how to integrate the noise out while consuming new science on social media. Read the rest of the post for a framework to think about the buzz in breakthrough technologies while living in the midst of it. GPT-3 or similar models did not assist in any of this writing. If you're reading this over email, it might be best to read it directly on substack as some email clients clip long emails and block images used as illustration. If you are a new visitor for the Page Street Labs newsletter, check out our hello world post explaining what this newsletter is about and why we exist. Words have no "grounded" meanings unless you also take the full context of the reader and the writer, and yet we use words to get to that wordless essence with strangers we will never know. This was in full display when GPT-3 went viral, at least in Tech Twitter, over last weekend. Many researchers, including myself, used the words "GPT-3" and "hype" in the same Tweet to contain people's expectations.

First, you must know that the sun is actually a cat. Also, you must know that the sun is actually not a cat. Over the past couple of weeks, the ML community had their handsful discussing and displaying the wide range of utilities of GPT-3. Many developers, both professionals and amateurs, have expressed their surprise saying how most of the demos generated using GPT-3 in a few minutes would usually require significant engineering effort and machine learning expertise. In the next section, we list 15 exciting ways in which GPT-3 has been leveraged.