Open AI: All you need to know

OpenAI is an AI research and deployment company founded by Elon Musk and Sam Altman in 2015 with headquarters in San Francisco, California. The founders started the company with a $1 billion endowment. Elon Musk left the company in February 2018 after he got busy with his work at Tesla.

OpenAI endorsed Artificial General Intelligence (AGI) for the advantage of the whole of humanity by building highly autonomous systems that outperform humans at most economically profitable work.

Through building safe and useful AGI, OpenAI believes their work guides others in achieving various outcomes. Various AI researches are conducted by OpenAI to promote and develop friendly AI. They collaborated with other research organizations and individuals to work towards safe artificial intelligence for benefiting the world. They have kept the research and patents of the company open to the public except for those which they anticipate can have a negative impact regarding safety. 

However, OpenAI was developed in parts as the founders were apprehensive about the probable catastrophe that could happen as the aftermath of careless handling of general-purpose AI. The company’s center of focus was the advances in AI and its potentialities. 

OpenAI system: What is inside?

The core of the AI system has two different neural networks- a vision network and an imitation network. These networks have striking capabilities to imitate human actions. These two networks are the stepping stone towards making true AI systems a possibility.

Have you wondered how a robotic arm picks up blocks and stack them in a particular manner? Well, this is made possible after the machine witnesses and comprehends a simulated demonstration performed by a human using VR technology.

Let us now dive into the details of these two networks. 

The vision network is trained with hundreds of thousands of simulated images with different lighting, textures and objects. The vision network ingests an image from the robot’s camera and provides results that show the positions of different objects.

The invitation network views a demo, processes the demo, and understands the connotation of the task. It then achieves the connotation from another starting configuration. Therefore, the imitation network should generalize the demo to a different setting. 

How does the imitation network generalize? It is done through training examples. There will be dozens of different tasks and thousands of demos for each task. Each training example includes a pair of demos that perform the same task. The network is first provided with absoluteness in the first demo and single observance in the second demo. Then the appropriate learning is provided to denote what actions the demonstrator took at that observance. The robot must learn the relevant portions from the first demo to predict the action effectively.

Now that we have comprehended the inside happenings of OpenAI, let us now discuss the next important aspect of OpenAI- Generative Pre-trained Transformer 3 (GPT-3). 

What is GPT-3? GPT-3 is the third generation language prediction model in the GPT-n series preceded by GPT-2 and is created by OpenAI. It is an autoregressive language that uses deep learning to produce human-like text.

How is GPT-3 powering the next-gen apps?

Nine months since the launch of OpenAI’s first product, more than 300 applications have been built using GPT-3. These applications range across different categories and industries, from productivity and education to creativity and games. With the help of more than thousands of developers across the globe, OpenAI currently generates an average of 4.5 billion words a day! The production traffic continues to scale daily.

If you give a prompt like a phrase or a sentence, GPT-3 completes the text using its NLP capabilities. Developers only need to show a few examples or ‘prompts’ to program GPT-3. APIs are made very simple for anyone to use and are adaptable enough to make their ML capabilities more efficient.

The apps developed using GPT-3 utilizes a suite of its diverse capabilities like:

Viable: To understand customers using useful insights from customer feedbacks in easily comprehensible summaries.

Fable Studio: To create a new genre of interactive stories and utilizing GPT-3 to power their story-driven ‘virtual substances’.

Algolia: To answer products to deliver pertinent and quick semantic search for their customers.

What are the use cases of GPT-3 OpenAI?

If you are on the lookout for AI-based project ideas or ways to boost user experience, here are some of the use cases of GPT-3 OpenAI:

• Text writing and storytelling 
• Translation 
• Code writing
• Answering questions 
• Music production
• App designing
• Ideas generation
• MVP development 

In terms of sectors or industries that this technology suits the most, you can try projects on entertainment, education, e-commerce, search engines, among others.

What is new with OpenAI?

OpenAI’s GPT-3 based pair programming model named Codex has been made open for private beta testers through an API as of August 2021. Codex is the back engine of GitHub Copilot, a pair programming software launched by GitHub a few months ago.

In this latest and improved version of Codex, the system was capable of generating python codes for tasks like printing, formation, and giving instructions in the English language.

Developers have to be well-versed in Python to check whether Codex is right. OpenAI Codex has the utmost capabilities in Python language. It is also proficient in other languages like JavaScript, Go, Perl, PHP, Ruby, Swift, Shell and TypeScript.

OpenAI: Looking Forward

AI systems have had an impressive journey so far. It keeps on whittling away from its inabilities and constraints through new and improved versions. Each version takes a step ahead and is reaching the human performance virtually on different tasks under different perspectives. If properly manipulated, the benefits AI-infused projects can offer the human race can be unfathomable.

AI adoption is progressing apace. Companies that were experimenting with AI are now using it in product deployments. Companies are putting a lot more AI efforts for substantial reasons.AI adapters should cut out common risk factors like bias in model development, or poorly serviced data to ascertain reliable AI production lines