It was reported that Venture Capital investments into AI related startups made a significant increase in 2018, jumping by 72% compared to 2017, with 466 startups funded from 533 in 2017. PWC moneytree report stated that that seed-stage deal activity in the US among AI-related companies rose to 28% in the fourth-quarter of 2018, compared to 24% in the three months prior, while expansion-stage deal activity jumped to 32%, from 23%. There will be an increasing international rivalry over the global leadership of AI. President Putin of Russia was quoted as saying that "the nation that leads in AI will be the ruler of the world". Billionaire Mark Cuban was reported in CNBC as stating that "the world's first trillionaire would be an AI entrepreneur".

The sudoku game is something almost everyone plays either on a daily basis or at least once in a while. The game consists of a 9 9 board with numbers and blanks on it. The goal is to fill the blank spaces with suitable numbers. These numbers can be filled keeping in mind some rules. The rule for filling these empty spaces is that the number should not appear in the same row, same column or in the same 3 3 grid.

For several years, there has been a lot of discussion around AI's capabilities. Many believe that AI will outperform humans in solving certain areas. As the technology is in its infancy, researchers are expecting human-like autonomous systems in the next coming years. OpenAI has a leading stance in the artificial intelligence research space. Founded in December 2015, the company's goal is to advance digital intelligence in a way that can benefit humanity as a whole.

Digital Art created via Convolutional Neural Networks and Deep Learning.

The deep learning component of AI can be a high-performance computing problem as it requires a large amount of computation and a data motion (IO and network). Deep learning needs computationally-intensive training and lots of computational power help to enable speeding up the training cycles. High-performance computing (HPC) allows businesses to scale computationally to build deep learning algorithms that can take advantage of high volumes of data. With more data comes the need for larger amounts of computing needs with great performance specs. This is leading to HPC and AI converging, unleashing a new era.

Because this year's UseR 2020 in Munich couldn't happen as an in-person event, I will be giving my workshop on Deep Learning with Keras and TensorFlow as an online event on You can register for FREE via Eventbrite. Deep learning is an artificial intelligence that mimics the workings of a human brain in processing different data, creating patterns and interpreting information that is used for decision making. It is a subfield of machine learning in artificial intelligence and Its networks has the capability to learn, supervised or unsupervised, from data that is either structured or labelled. It is one of the hottest trends in machine learning at the moment and there are many problems where deep learning shines, such as Self Driving Cars, Natural Language Processing, Machine Translations, image recognition and Artificial Intelligence (AI) and so on.

You are getting an AI to generate text on different topics. This is an experiment in what one might call "prompt engineering", which is a way to utilize GPT-3, a neural network trained and hosted by OpenAI. GPT-3 is a language model. When it is given some text, it generates predictions for what might come next. It is remarkably good at adapting to different contexts, as defined by a prompt (in this case, hidden), which sets the scene for what type of text will be generated. Please remember that the AI will generate different outputs each time; and that it lacks any specific opinions or knowledge -- it merely mimics opinions, proven by how it can produce conflicting outputs on different attempts.

An ideal cloud platform offers robust and tuned AI services or solutions for many applications, including language translation, speech to text, text to speech, forecasting, and recommendations to build an effective machine learning and deep learning model.

However, our bot didn't "know" anything about "Chitra" or Tagore. It didn't generate fundamentally new ideas or sentences. It simply cobbled together parts of existing sentences from existing articles to make new ones. OpenAI, a for-profit company under a nonprofit parent company, has built a language generation program dubbed GPT-3, an acronym for "Generative Pre-trained Transformer 3." Its ability to learn, summarize, and compose text has stunned computer scientists like me. "I have created a voice for the unknown human who hides within the binary," GPT-3 wrote in response to one prompt. "I have created a writer, a sculptor, an artist.

Nearly every new day brings exciting news in the field of artificial intelligence. But what larger directional trends do these news items drive? Beyond the announcements and the hype, is AI really evolving? In this article, I'd like to focus not on far-off, vague hopes and wishes about AI, but instead on a few concrete developments that lie in the not-so-distant future. The trends outlined below are already beginning to materialize in the form of real-world research and applications.