Hi, I am TuhinBanik; born in North East India and raised in Kolkata. Throughout my childhood days, I had a keen interest in technology and it used to drive me crazy whilst thinking on how techgiants come up with new inventions and technologies. At a very young age, I was curious about the things which might change civilization in the near future. Well, I came out with four main pillars which will make a change in the universe in future. With the above points on my mind, I have come up with a concept under the brand name of ThatWare, where my main vision is to enhance digital marketing with full automation using artificial intelligence and machine learning.

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The U.K. government is planning to fund thousands of postgraduate students that want to study a Masters or a PhD in artificial intelligence as it looks to keep pace with the U.S. and China. AI is poised to become the most significant technology for a generation but there are only so many people that know how to develop the technology, which could have a huge impact on industries such as healthcare, energy, and automotive. Business Secretary Greg Clark and Digital Secretary Jeremy Wright announced on Thursday that the government will commit up to £115 million towards training the next generation of AI talent. In a press release, the government said 1,000 students will receive funding to enable them to complete PhDs at 16 U.K. Research and Innovation AI Centres for Doctoral Training (CDTs), located across the country. The full list of centres can be found at the end of this article.

In a study that used artificial intelligence to analyze design elements, such as artwork and wall colors, in pictures of living rooms posted to Airbnb, a popular home rental website, the researchers found that people tended to follow cultural trends when they decorated their interiors. In the United States, where the researchers had economic data from the U.S. Census, they also found that people across socioeconomic lines put similar efforts into interior decoration. "We were interested in seeing how other cultures decorated," said Clio Andris, assistant professor of geography, Penn State and an Institute for CyberScience associate. "We see maps of the world and wonder, 'What's it like living there,' but we don't really know what it's like to be in people's living rooms and in their houses. This was like people around the world inviting us into their homes."

Our model, called GPT-2 (a successor to GPT), was trained simply to predict the next word in 40GB of Internet text. Due to our concerns about malicious applications of the technology, we are not releasing the trained model. As an experiment in responsible disclosure, we are instead releasing a much smaller model for researchers to experiment with, as well as a technical paper. GPT-2 is a large transformer-based language model with 1.5 billion parameters, trained on a dataset[1] of 8 million web pages. GPT-2 is trained with a simple objective: predict the next word, given all of the previous words within some text. The diversity of the dataset causes this simple goal to contain naturally occurring demonstrations of many tasks across diverse domains. GPT-2 is a direct scale-up of GPT, with more than 10X the parameters and trained on more than 10X the amount of data. GPT-2 displays a broad set of capabilities, including the ability to generate conditional synthetic text samples of unprecedented quality, where we prime the model with an input and have it generate a lengthy continuation. In addition, GPT-2 outperforms other language models trained on specific domains (like Wikipedia, news, or books) without needing to use these domain-specific training datasets. On language tasks like question answering, reading comprehension, summarization, and translation, GPT-2 begins to learn these tasks from the raw text, using no task-specific training data.

The robots are coming, and they can read. Artificial intelligence programs built by Alibaba (BABA) and Microsoft (MSFT) have beaten humans on a Stanford University reading comprehension test. "This is the first time that a machine has outperformed humans on such a test," Alibaba said in a statement Monday. The test was devised by artificial intelligence experts at Stanford to measure computers' growing reading abilities. Alibaba's software was the first to beat the human score.

Grab's ride-hailing business in its simplest form is about matchmaking Passengers looking for a comfortable mode of transport and Drivers looking for a flexible earning opportunity. Over the last 6 years, Grab has repeatedly fine-tuned its machine learning algorithms with the goal of ensuring that passengers get a ride when they want it, and that they are matched to the drivers that are closest to them. But drivers are constantly on the move, and at any one point there could be hundreds of passengers requesting a ride within the same area. This means that sometimes, the closest available drivers might still be too far away. The Analytics team at Grab attempts to analyze these instances at scale via clearly-defined metrics.

Embarking on the much-coveted road towards automation, India has launched its first humanoid robot cop in Kerala. Visitors to the Kerala Police Headquarters in the state capital will be greeted by the Robocop and will even direct you to where you need to go. The administration has decided to deploy the KP-Bot at the front office of the police headquarters. Inaugurating the humanoid robot cop named KP-bot on Tuesday, Chief Minister Pinarayi Vijayan made it the first ever police department in India to have a robot for police work. Chief Minister Vijayan inducted robot into the service with an honorary salute and the'RoboCop' responded with a perfect salute; the Robocop has been given the rank of Sub Inspector (SI).

The science of service centers has advanced with hold-time estimates, call-back options and voice-recognition technologies. Yet once the customer reaches an agent, odds are high that the agent will not be able to solve the problem in one go. Unsolved problems lead to more complaints, greater customer churn and wasted time of employees trying to calm upset customers. Artificial intelligence (AI) promises to substantially improve the experience. Early efforts are helping companies improve the overall customer experience, while reducing costs--in staff time, service escalations such as field technician visits, and defecting customers--in the bargain.

Traders follow a simple motto: buy low and sell high. But when the opposite happens, the stock market goes berserk. On a fine morning in 2012, the NYSE had to step in and cancel numerous trades due to erroneous trading by Knight Capital which saw the biggest drop ever since it went public. Instead of at least attempting to provide liquidity via limit trades, Knight's algorithm acted as a market order. Naturally, when the entire logic of trading was perverted courtesy of Knight's busted algorithm, everything went chaos, and stocks went higher because they went higher, and the higher they went, the greater the incentive for the algorithm to keep pushing the stock higher.