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What is Clubhouse? Everything to know about the invite-only app

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What is Clubhouse? Everything to know about the invite-only app

It’s part Facebook, part podcast and super exclusive.

The audio-based social network app Clubhouse has been making headlines since it launched in March, but this weekend, it was propelled to a new tier of mainstream awareness when Elon Musk used it. Currently the world’s richest person, Musk appeared on the iPhone-only app Sunday night to talk with Vlad Tenev, CEO of Robinhood, the stock-trading app for amateur brokers.

Musk’s conversation created so much hype that stock for a similarly named company that is completely unrelated to the social app — Clubhouse Media Group — jumped 117%, Reuters reported. 

Here’s everything to know about Clubhouse’s innovative format and functionality, its short life so far and how to get an invite. 

How does it work?

Software analyst Jordan Minor described the network as “if Twitter was a podcast you lived inside of.” 

“Clubhouse is a space for casual, drop-in audio conversations — with friends and other interesting people around the world,” offers Clubhouse’s brief self-description. “Go online anytime to chat with the people you follow, or hop in as a listener and hear what others are talking about.” 

The audio-based social network features no pictures or videos, just public and private conversation rooms. Users have only profile pictures and followers. Chats are not recorded; users get around this by livestreaming conversations on other platforms, including YouTube. 

When users want to talk, they virtually raise their hand. Room creators and moderators determine whom to grant talking permission to. 

Musk significantly surpassed the maximum amount of people allowed per room — only 5,000 users are technically supposed to be in each conversation. “General rudeness” is allowed, but hate speech, sexism and abuse are not, and a spokeswoman for the app told Reuters they’ve already had to ban some users. 

How did it come to be?

Clubhouse’s launch coincided with the beginning of the coronavirus pandemic, in March 2020. The San Francisco-based app is run by the company Alpha Exploration Co., which got a $12 million investment from venture capital behemoth Andreessen Horowitz after Clubhouse had been live for just two months, PCMag reported. The app has about 10 staffers, a spokeswoman told Reuters.

How exclusive is it?

Clubhouse is invite-only, with each new user getting two invites to use as they please, accruing more over time as they use the app, according to PCMag. 

Invites have become such a hot commodity, they’re being sold on Alibaba’s second-hand marketplace in China (where the app isn’t even available in Apple’s App Store) and in the US on Reddit, eBay and Craigslist, Reuters reported. 

Celebrities including Kevin Hart, Drake and Tiffany Haddish are on the app, as are many black creators.

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Understanding the differences between biological and computer vision

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Understanding the differences between biological and computer vision

Join Transform 2021 this July 12-16. Register for the AI event of the year.


Since the early years of artificial intelligence, scientists have dreamed of creating computers that can “see” the world. As vision plays a key role in many things we do every day, cracking the code of computer vision seemed to be one of the major steps toward developing artificial general intelligence.

But like many other goals in AI, computer vision has proven to be easier said than done. In 1966, scientists at MIT launched “The Summer Vision Project,” a two-month effort to create a computer system that could identify objects and background areas in images. But it took much more than a summer break to achieve those goals. In fact, it wasn’t until the early 2010s that image classifiers and object detectors were flexible and reliable enough to be used in mainstream applications.

In the past decades, advances in machine learning and neuroscience have helped make great strides in computer vision. But we still have a long way to go before we can build AI systems that see the world as we do.

Biological and Computer Vision, a book by Harvard Medical University Professor Gabriel Kreiman, provides an accessible account of how humans and animals process visual data and how far we’ve come toward replicating these functions in computers.

Kreiman’s book helps understand the differences between biological and computer vision. The book details how billions of years of evolution have equipped us with a complicated visual processing system, and how studying it has helped inspire better computer vision algorithms. Kreiman also discusses what separates contemporary computer vision systems from their biological counterpart.

While I would recommend a full read of Biological and Computer Vision to anyone who is interested in the field, I’ve tried here (with some help from Gabriel himself) to lay out some of my key takeaways from the book.

Hardware differences

In the introduction to Biological and Computer Vision, Kreiman writes, “I am particularly excited about connecting biological and computational circuits. Biological vision is the product of millions of years of evolution. There is no reason to reinvent the wheel when developing computational models. We can learn from how biology solves vision problems and use the solutions as inspiration to build better algorithms.”

And indeed, the study of the visual cortex has been a great source of inspiration for computer vision and AI. But before being able to digitize vision, scientists had to overcome the huge hardware gap between biological and computer vision. Biological vision runs on an interconnected network of cortical cells and organic neurons. Computer vision, on the other hand, runs on electronic chips composed of transistors.

Therefore, a theory of vision must be defined at a level that can be implemented in computers in a way that is comparable to living beings. Kreiman calls this the “Goldilocks resolution,” a level of abstraction that is neither too detailed nor too simplified.

For instance, early efforts in computer vision tried to tackle computer vision at a very abstract level, in a way that ignored how human and animal brains recognize visual patterns. Those approaches have proven to be very brittle and inefficient. On the other hand, studying and simulating brains at the molecular level would prove to be computationally inefficient.

“I am not a big fan of what I call ‘copying biology,’” Kreiman told TechTalks. “There are many aspects of biology that can and should be abstracted away. We probably do not need units with 20,000 proteins and a cytoplasm and complex dendritic geometries. That would be too much biological detail. On the other hand, we cannot merely study behavior—that is not enough detail.”

In Biological and Computer Vision, Kreiman defines the Goldilocks scale of neocortical circuits as neuronal activities per millisecond. Advances in neuroscience and medical technology have made it possible to study the activities of individual neurons at millisecond time granularity.

And the results of those studies have helped develop different types of artificial neural networks, AI algorithms that loosely simulate the workings of cortical areas of the mammal brain. In recent years, neural networks have proven to be the most efficient algorithm for pattern recognition in visual data and have become the key component of many computer vision applications.

Architecture differences

Above: Biological and Computer Vision, by Gabriel Kreiman.

The recent decades have seen a slew of innovative work in the field of deep learning, which has helped computers mimic some of the functions of biological vision. Convolutional layers, inspired by studies made on the animal visual cortex, are very efficient at finding patterns in visual data. Pooling layers help generalize the output of a convolutional layer and make it less sensitive to the displacement of visual patterns. Stacked on top of each other, blocks of convolutional and pooling layers can go from finding small patterns (corners, edges, etc.) to complex objects (faces, chairs, cars, etc.).

But there’s still a mismatch between the high-level architecture of artificial neural networks and what we know about the mammal visual cortex.

“The word ‘layers’ is, unfortunately, a bit ambiguous,” Kreiman said. “In computer science, people use layers to connote the different processing stages (and a layer is mostly analogous to a brain area). In biology, each brain region contains six cortical layers (and subdivisions). My hunch is that six-layer structure (the connectivity of which is sometimes referred to as a canonical microcircuit) is quite crucial. It remains unclear what aspects of this circuitry should we include in neural networks. Some may argue that aspects of the six-layer motif are already incorporated (e.g. normalization operations). But there is probably enormous richness missing.”

Also, as Kreiman highlights in Biological and Computer Vision, information in the brain moves in several directions. Light signals move from the retina to the inferior temporal cortex to the V1, V2, and other layers of the visual cortex. But each layer also provides feedback to its predecessors. And within each layer, neurons interact and pass information between each other. All these interactions and interconnections help the brain fill in the gaps in visual input and make inferences when it has incomplete information.

In contrast, in artificial neural networks, data usually moves in a single direction. Convolutional neural networks are “feedforward networks,” which means information only goes from the input layer to the higher and output layers.

There’s a feedback mechanism called “backpropagation,” which helps correct mistakes and tune the parameters of neural networks. But backpropagation is computationally expensive and only used during the training of neural networks. And it’s not clear if backpropagation directly corresponds to the feedback mechanisms of cortical layers.

On the other hand, recurrent neural networks, which combine the output of higher layers into the input of their previous layers, still have limited use in computer vision.

visual cortex vs neural networks

Above: In the visual cortex (right), information moves in several directions. In neural networks (left), information moves in one direction.

In our conversation, Kreiman suggested that lateral and top-down flow of information can be crucial to bringing artificial neural networks to their biological counterparts.

“Horizontal connections (i.e., connections for units within a layer) may be critical for certain computations such as pattern completion,” he said. “Top-down connections (i.e., connections from units in a layer to units in a layer below) are probably essential to make predictions, for attention, to incorporate contextual information, etc.”

He also said out that neurons have “complex temporal integrative properties that are missing in current networks.”

Goal differences

Evolution has managed to develop a neural architecture that can accomplish many tasks. Several studies have shown that our visual system can dynamically tune its sensitivities to the common. Creating computer vision systems that have this kind of flexibility remains a major challenge, however.

Current computer vision systems are designed to accomplish a single task. We have neural networks that can classify objects, localize objects, segment images into different objects, describe images, generate images, and more. But each neural network can accomplish a single task alone.

Gabriel Kreiman

Above: Harvard Medical University professor Gabriel Kreiman. Author of “Biological and Computer Vision.”

“A central issue is to understand ‘visual routines,’ a term coined by Shimon Ullman; how can we flexibly route visual information in a task-dependent manner?” Kreiman said. “You can essentially answer an infinite number of questions on an image. You don’t just label objects, you can count objects, you can describe their colors, their interactions, their sizes, etc. We can build networks to do each of these things, but we do not have networks that can do all of these things simultaneously. There are interesting approaches to this via question/answering systems, but these algorithms, exciting as they are, remain rather primitive, especially in comparison with human performance.”

Integration differences

In humans and animals, vision is closely related to smell, touch, and hearing senses. The visual, auditory, somatosensory, and olfactory cortices interact and pick up cues from each other to adjust their inferences of the world. In AI systems, on the other hand, each of these things exists separately.

Do we need this kind of integration to make better computer vision systems?

“As scientists, we often like to divide problems to conquer them,” Kreiman said. “I personally think that this is a reasonable way to start. We can see very well without smell or hearing. Consider a Chaplin movie (and remove all the minimal music and text). You can understand a lot. If a person is born deaf, they can still see very well. Sure, there are lots of examples of interesting interactions across modalities, but mostly I think that we will make lots of progress with this simplification.”

However, a more complicated matter is the integration of vision with more complex areas of the brain. In humans, vision is deeply integrated with other brain functions such as logic, reasoning, language, and common sense knowledge.

“Some (most?) visual problems may ‘cost’ more time and require integrating visual inputs with existing knowledge about the world,” Kreiman said.

He pointed to following picture of former U.S. president Barack Obama as an example.

ObamaPicture

Above: Understanding what is going on it this picture requires world knowledge, social knowledge, and common sense.

To understand what is going on in this picture, an AI agent would need to know what the person on the scale is doing, what Obama is doing, who is laughing and why they are laughing, etc. Answering these questions requires a wealth of information, including world knowledge (scales measure weight), physics knowledge (a foot on a scale exerts a force), psychological knowledge (many people are self-conscious about their weight and would be surprised if their weight is well above the usual), social understanding (some people are in on the joke, some are not).

“No current architecture can do this. All of this will require dynamics (we do not appreciate all of this immediately and usually use many fixations to understand the image) and integration of top-down signals,” Kreiman said.

Areas such as language and common sense are themselves great challenges for the AI community. But it remains to be seen whether they can be solved separately and integrated together along with vision, or integration itself is the key to solving all of them.

“At some point we need to get into all of these other aspects of cognition, and it is hard to imagine how to integrate cognition without any reference to language and logic,” Kreiman said. “I expect that there will be major exciting efforts in the years to come incorporating more of language and logic in vision models (and conversely incorporating vision into language models as well).”

Ben Dickson is a software engineer and the founder of TechTalks. He writes about technology, business, and politics.

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How Legacy Games still has a good business selling CD games at Walmart

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How Legacy Games still has a good business selling CD games at Walmart

Join Transform 2021 this July 12-16. Register for the AI event of the year.


Legacy Games has been publishing and distributing casual PC games at retail since 1998. And believe it or not, it’s still in business and its founder Ariella Lehrer is back in charge of the company that targets women who are 40 years old or older.

Lehrer started the Los Angeles company 23 years ago to make games for women at retail. She left in 2017 to move on to augmented reality game maker Hitpoint. Legacy Games stayed small with just a handful of people, but it kept its relationships with key retailers such as Walmart. And it still has Walmart as a client. Meanwhile, most of its competitors have moved on to more attractive markets. So after three years at Hitpoint, Lehrer returned as CEO of Legacy Games in October and she has started a new indie publishing program.

Legacy has helped game developers find new casual game customers through Legacy’s unique distribution channels, such as Walmart. Now the company is diversifying its game portfolio by working with indie game developers. Lehrer said in an interview with GamesBeat that she is signing up a variety of indie developers who are making PC and mobile games that target casual gamers. Roughly 70% of the customers are older women, and about 30% are men.

“We are signing up cool indie game developers, and that’s overdue,” Lehrer said. “I came back and found it was still kicking, and maybe I can push it toward digital. I’m really focused on bringing Legacy Games into the digital age.”

Going digital and physical

Above: Legacy Games targets its games at women over 40.

Image Credit: Legacy Games

Since coming back, Lehrer launched a digital store and she expects the company triple its digital sales in 2021.

She is signing up developers that have highly rated casual games on Steam, but have otherwise had limited distribution. Many developers have had a hard time in the pandemic. A survey by the Game Developers Conference found that 34% of game developers saw their business decline, and a report from Video Game Insights found more than 50% of indies never make more than $4,000.

“We found there are all these wonderful indie games on Steam, but our customers don’t go on Steam,” she said.

Lehrer distributes the games on the company’s web site. And if any do particularly well on the digital storefront, then the company will see if they will sell at Walmart, where the company sells around 3,000 units a week. Legacy can package the games together in a bundle on DVD discs. Successful digital bundles will then be sold at retail.

“It’s a lovely little business,” she said. “We have been profitable every year except for the Great Recession” in 2008.

legacy 3

Above: Legacy Games was started in 1998.

Image Credit: Legacy Games

It got started with a hit game called Emergency Room, originally created for IBM. Lehrer got the rights back and then sold it at retail at Walmart, and the title sold more than a million units. At its height, Legacy Games had about $5 million in revenues. That was never that exciting to investors. But the company has stayed steady and it did raise money once a while ago from Targus. The company made 20 different games based on television licenses like Law & Order, Criminal Minds, Murder She Wrote, Tarzan, and others. Lehrer kept it going but stayed on

Legacy has 18 of 24 spots on the shelf for casual games at Walmart stores. All of the competitors have loved on to other markets. Lehrer said she values the relationship with Walmart, which is the last national retail company standing when it comes to selling casual game DVD bundles, Lehrer said. Legacy Games also sells its games on retailers’ online websites, such as Walmart.com, Amazon.com, Staples.com, and through the following online distributors: Arvato, Avanquest, and Synnex. Additionally, Legacy Games sells its games through other traditional outlets like Steam, Microsoft Windows, and wherever casual games can be sold profitably.

“Others have said it’s a shrinking market at retail and they are going somewhere else exciting,” said Lehrer. “I think there is an opportunity here. There’s still an opportunity to sell these kinds of games at retail. I had a feeling these women were underserved. They buy their products at Walmart. They love casual games like hidden object games, or match-3, or time management, and they want to play on the PC.”

While Lehrer was gone, three part-time employees ran the company. Then she came back and she has added three more full-time employees. And now the company’s revenues are close to $1 million.

New developers

Lehrer has signed up 15 new game studios this year. These include JumpGate (Project Blue Book), Thomas Bowker (Lyne), Joel McDonald (Prune), Flippfly (Evergarden) and Walkabout (Wanderlust: Travel Stories), Joybits (Doodle God), and BufoProject (Classic Card Games 3D), among others.

“We’re going to try out different genres, different ways of packaging, different pricing and we will see what resonates,” Lehrer said.

Legacy Games has a long history of working with established casual game developers such as Artifex Mundi, Brave Giant, Alawar, Microids, Jet Dogs, Crisp App Studios, and many more. Rivals include Big Fish Games. The company has publishing contracts with more than 50 game developers, and it sells more than 500 individual games. One of the regular hits is the Amazing Games bundle at Walmart, with titles including Supernatural Stories, Fantastic Fables, True Crime, Murder Mystery, Greatest Hits, and Magical Matches.

“There are many fewer retail and digital sites to purchase casual PC games than there were a few years ago,” Lehrer said. “Many of our competitors have switched their focus to mobile. Our customers find Steam overwhelming. I believe there is a significant revenue opportunity for indie developers to reach new customers and generate incremental revenue by partnering with Legacy.”

One of the developers using Legacy’s publishing services is Aaron San Filippo, co-owner of Flippfly, a three-person studio near Madison, Wisconsin. In an interview, he said Legacy reached out to him a couple of months ago to get his game Evergarden, which is a mysterious puzzle gardening title, onto its platform. It will be launching soon in the digital store and it has a chance for physical distribution, San Filippo said.

San Filippo said he launched the game on Steam a few years ago and it didn’t connect well with that audience. Steam was more about hardcore gamers, and so the casual gaming audience of Legacy seemed a lot more appealing. The game also debuted on Linux and iOS, and it did best on iOS.

“It goes to the target market for our games,” San Filippo said. “We’re always looking for more opportunities. This is all about diversifying our income streams. Additional revenue streams are worthwhile, even if it’s small. I’m hopeful this will do well.”

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GPT-3’s free alternative GPT-Neo is something to be excited about

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GPT-3's free alternative GPT-Neo is something to be excited about

Join Transform 2021 this July 12-16. Register for the AI event of the year.


The advent of Transformers in 2017 completely changed the world of neural networks. Ever since, the core concept of Transformers has been remixed, repackaged, and rebundled in several models. The results have surpassed the state of the art in several machine learning benchmarks. In fact, currently all top benchmarks in the field of natural language processing are dominated by Transformer-based models. Some of the Transformer-family models are BERT, ALBERT, and the GPT series of models.

In any machine learning model, the most important components of the training process are:

  1. The code of the model — the components of the model and its configuration
  2. The data to be used for training
  3. The available compute power

With the Transformer family of models, researchers finally arrived at a way to increase the performance of a model infinitely: You just increase the amount of training data and compute power.

This is exactly what OpenAI did, first with GPT-2 and then with GPT-3. Being a well funded ($1 billion+) company, it could afford to train some of the biggest models in the world. A private corpus of 500 billion tokens was used for training the model, and approximately $50 million was spent in compute costs.

While the code for most of the GPT language models is open source, the model is impossible to replicate without the massive amounts of data and compute power. And OpenAI has chosen to withhold public access to its trained models, making them available via API to only a select few companies and individuals. Further, its access policy is undocumented, arbitrary, and opaque.

Genesis of GPT-Neo

Stella Biderman, Leo Gao, Sid Black, and others formed EleutherAI with the idea of making AI technology that would be open source to the world. One of the first problems the team chose to tackle was making a GPT-like language model that would be accessible to all.

As mentioned before, most of the code for such a model was already available, so the core challenges were to find the data and the compute power. The Eleuther team set out to generate an open source data set of a scale comparable to what OpenAI used for its GPT language models. This led to the creation of The Pile. The Pile, released in July 2020, is a 825GB data set specifically designed to train language models. It contains data from 22 diverse sources, including academic sources (Arxiv, PubMed, FreeLaw etc.), Internet webpages (StackExchange, Wikipedia etc.), dialogs from subtitles, Github, etc.

Source: The Pile paper, Arxiv.

For compute, EleutherAI was able to use idle compute from TPU Research Cloud (TRC). TRC is a Google Cloud initiative that supports research projects with the expectation that the results of the research will be shared with the world via open source code, models, etc.

On March 22, 2021, after months of painstaking research and training, the EleutherAI team released two trained GPT-style language models, GPT-Neo 1.3B and GPT-Neo 2.7B. The code and the trained models are open sourced under the MIT license. And the models can be used for free using HuggingFace’s Transformers platform.

Comparing GPT-Neo and GPT-3

Let’s compare GPT-Neo and GPT-3 with respect to the model size and performance benchmarks and finally look at some examples.

Model size. In terms of model size and compute, the largest GPT-Neo model consists of 2.7 billion parameters. In comparison, the GPT-3 API offers 4 models, ranging from 2.7 billion parameters to 175 billion parameters.
model size
Caption: GPT-3 parameter sizes as estimated here, and GPT-Neo as reported by EleutherAI.

As you can see, GPT-Neo is bigger than GPT-2 and comparable to the smallest GPT-3 model.

Performance benchmark metrics. EleutherAI reports that GPT-Neo outperformed the closest comparable GPT-3 model (GPT-3 Ada) on all NLP reasoning benchmarks.

GPT-Neo outperformed GPT-3 Ada on Hellaswag and Piqa. Hellaswag is an intelligent multi-choice sentence completion benchmark that has a context paragraph and four endings. Piqa measures common sense reasoning where the machine has to pick one out of two sentences that make the most sense. GPT-Neo also outperformed GPT-3 Ada on Winogrande, a benchmark that uses common sense to resolve ambiguous pronouns in a sentence.

However GPT-3 Davinci, the largest version of GPT-3, with about 65 times as many parameters, comfortably beats GPT-Neo in all the benchmarks, as you would expect.

benchmarks

Caption: Model metrics as reported by EleutherAI, except GPT-3 175B, which is from Open AI’s GPT-3 paper.

Examples. Let’s look at a few side-by-side examples of generated text from the largest GPT-3 model (from various GPT-3 Davinci examples found online) and GPT-Neo (that I generated using HuggingFace’s GPT-Neo 2.7B Transformers implementation).

The first example we will look at is completion of ELI-5 format sentences, where the text in italics was the prompt given to the model.

eli5

I would say both GPT-Neo and GPT-3 worked equally well in this example.

The next experiment is to check if GPT-Neo can correct grammatically incorrect sentences like GPT-3 can. Again the italicized parts are the prompt provided to the model.

grammar correction

Again, GPT-Neo worked great in correcting “eat” to “ate,” especially considering the model was not specifically trained to do this.

Finally, the famous “discovery of English-speaking unicorns” piece (GPT-2 version here and GPT-3 version here) reimagined and written by GPT-Neo. The prompt given to the model is in italics and bold.

In a shocking finding, scientists discovered a herd of unicorns living in a remote, previously unexplored valley, in the Andes Mountains. Even more surprising to the researchers was the fact that the unicorns spoke perfect English.

The only evidence was a single word: “hello.”

“The unicorns are very good listeners, so they learned to speak English from watching humans,” said Dr. David Orme, chief executive of the Zoological Society of London, in a press release. “In fact, they also learned to speak Spanish from watching humans. They said hello to us in both languages.”

While the discovery is stunning in and of itself, the news gets even more mind-blowing when you consider that the unicorns were there for thousands of years. They were “living fossils,” Orme said, which is a fancy way of saying that their origins have yet to be determined.

“It is extremely exciting that we now have evidence of a population of unicorns, that we know to be around and that is living,” Dr. John Fitzpatrick, a conservation and anthropology professor at the University of California, said in a press release. “This shows that they are not just a myth and that humans are not the only species on Earth to have developed language.”

The findings have been published in a new study in the journal Proceedings of the Royal Society B: Biological Sciences.

A ‘unicorn’ from the Andean mountains in Colombia. (Image: The Royal Society)

The discovery was made this summer in a remote but stunning valley in the Andean Mountains in Colombia called Bureta. It’s believed the unicorns were in their 20s. “It’s a very unusual place to find these animals and at the moment there is no evidence that humans have been there before,” Orme said.

The scientists said the unicorns had been living in that valley as long as their species has, which is estimated at at least 200,000 years.

This means the area’s rich history of megafauna, including dinosaurs, pterosaurs and saber-toothed cats, is still far from over.

“If it is true in a relatively isolated valley near Bureta Colombia that is more than 200,000 years old and now also having a population of these animals, then Bureta is truly a unique and special place,” Fitzpatrick said.

Once again, GPT-Neo was able to generate a coherent, almost-believable article without missing out on the central themes — unicorn discovery, the English-speaking aspect, the Andes, etc.

All in all, the performance metrics of GPT-Neo 2.7B in NLP benchmarks is better than GPT-3 2.7B (Ada), but much worse than the GPT-3 175B (Davinci). But qualitatively, GPT-Neo 2.7B’s completions and writing were as good as even GPT-3 175B (Davinci), the largest GPT-3 model.

The bottom line here is: GPT-Neo is a great open source alternative to GPT-3, especially given OpenAI’s closed access policy.

Abhishek Iyer is the founder of FreeText AI, a company specializing in text mining and review analysis.

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