Skip to main content

OpenAI and future of AI

With the advent of AI in almost every industry, right from self driving cars to robot nurses, there is a general concern as to how AI might impact humanity. Although AI offers a lot including medical industry and space exploration, it is slowly making a pathway into every technology. The presence of AI can be felt on every device including mobile phones. There is a general belief that AI is a threat to humanity. The reach of AI in every aspect of our life is inevitable. So how do we make sure that it benefits humanity as a whole?

Elon Musk along with other visionaries have come together to take up the baton to help the AI community to work towards a common goal – to make AI benefit humanity. OpenAI is planning to establish itself as a leading non-profit research institution. To make its research accessible to all, OpenAI will collaborate with other institutions and researchers to make their research open source.

To know more about OpenAI follow their official website which as of now only has one blog post - https://www.openai.com/blog/introducing-openai/

We at Cerelabs are optimistic about the role of OpenAI, and will closely follow it to understand how we can contribute towards the vision of making AI benefit humanity.
Labels:

Comments

Post a Comment

Popular posts from this blog

How is AI Saving the Future

Meanwhile the talk of AI being the number one risk of human extinction is going on, there are lot many ways it is helping humanity. Recent developments in Machine Learning are helping scientists to solve difficult problems ranging from climate change to finding the cure for cancer. It will be a daunting task for humans to understand enormous amount of data that is generated all over the world. Machine Learning is helping scientists to use algorithms that learn from data and find patterns. Below is a list of few of the problems AI is working on to help find solutions which otherwise would not have been possible: Cancer Diagnostics : Recently, scientists at University of California (UCLA) applied Deep Learning to extract features for achieving high accuracy in label-free cell classification. This technique will help in faster cancer diagnostics, and thus will save a lot of lives. Low Cost Renewable Energy : Artificial-intelligence is helping wind power forecasts of u...
6 comments
Read more

GPU - The brain of Artificial Intelligence

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast. Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data. Compared to CPUs, GPUs take far less time to train models that perform classification and prediction. Characteristics of GPUs that make them ideal for machine learning Handle large datasets Needs far less data centre infrastructure Can be specialized for specific machine learning needs Perform vector computations faster than any known processor Designed to perform data parallel computation NVIDIA CUDA GPUs today are used to build deep learning image processing tools for  Adobe Creative Cloud. According to NVIDIA blog future Adobe appli...
7 comments
Read more

In the World of Document Similarity

How does a human infer whether two documents are similar? This question has dazzled cognitive scientists, and is one area under which a lot of research is taking place. As of  now there is no product that is able to match or surpass human capability in finding the similarity in documents. But things are improving in this domain, and companies such as IBM and Microsoft are investing a lot in this area. We at Cere Labs, an Artificial Intelligence startup based in Mumbai, also are working in this area, and have applied LDA and Word2Vec techniques, both giving us promising results: Latent Dirichlet Allocation (LDA) : LDA is a technique used mainly for topic modeling. You c an leverage on this topic modeling to find the similarity between documents. It is assumed that more the topics two documents overlap, more are the chances that those documents carry semantic similarity. You can study LDA in the following paper: https://www.cs.princeton.edu/~blei/papers/BleiNgJordan20...
Post a Comment
Read more