Insilico Medicine has achieved a world first by successfully designing, synthesizing, and validating a new drug from the ground up and taking just 46 days to do so. It achieved this impressive feat using AI. This is the first time that AI has been used to successfully create a new drug, and it took record time compared to traditional methods. The company used Generative Adversarial Networks (GANs) back in 2016 to design new kinds of molecules and have further developed the system, combining it with reinforcement learning (RL) in order to develop new drugs and biomarkers. The new drug works by blocking the activity of the DDR1 kinase, which is implicated in fibrosis.

What if the solution to solving crime, lowering murder rates and fighting the opioid crisis could be found through a marriage of computer science and entrepreneurship? That's exactly the goal of Crimer, a crime-prediction software that began as a project last year in an LSU Computer Science class. The students who created the software also created a company, named Crimer as well, made up of 12 employees--11 of whom are current or former LSU computer science students. "We collect crime data from the Internet and use it to build a national crime prediction map over the United States," said Alexander "Lex" Adams, a May 2019 LSU Computer Science graduate and founder and chief executive officer of Crimer. "A variety of machine-learning algorithms are responsible for the extraction, transformation, loading and predicting of crime reports. We complement our crime data with a variety of auxiliary data--weather, terrain, population and more."

You probably didn't know this but today, April 10th, 2019 banking execs are being grilled by Congress. As capitalism is blooming in 2019, where FANG stocks alone have gained $600 Billion since December, 2018 (5 months). The Banks are doing well too. It's timely then to mention that a report by IHS Markit predicts the global business value of artificial intelligence in finance will be $300 billion by 2030. Whether it's detecting fraud or helping automate processes, there's no denying AI has become more commercially viable in banking.

There is a growing demand for data-labelling services that are "localised"- both linguistically and culturally relevant to India From an opportunity point of view, there are about a lakh jobs posted on various portals currently There is a growing demand for data-labelling services that are "localised"- both linguistically and culturally relevant to India NEW DELHI: Five years ago, Hyderabad resident Tulasi Mathi was forced to quit her job as a maths teacher due to health issues and the birth of her two children. But today, the 29-year-old does data labelling and makes up to Rs 15,000 a month. The money isn't much but it's more than she made as a teacher, and enough to pay her kids' school fees and her own expenses. Today, she scans videos and marks and labels objects encountered by self-driving cars. Her output is used to train artificial intelligence algorithms powering such cars. All Mathi knows is that it makes her life easier.

New York City Mayor Bill de Blasio joined Fox News' Tucker Carlson for a discussion on automation in the workforce and his new "robot tax." Far-left New York City Mayor Bill de Blasio, whose campaign for the Democratic presidential nomination is getting less than 1 percent support in polls, wants to create a "robot tax" and a massive new government bureaucracy to slow the progress and innovation that have made America the world's economic powerhouse. The unpopular mayor is terrified of robots, computers with artificial intelligence and other advanced machines that will eventually be able to do things only people can do today, eliminating millions of jobs. He neglects to mention the obvious fact that technological advances also create new jobs – like auto workers replacing blacksmiths, airline pilots replacing stagecoach drivers, and photographers replacing portrait painters. Under de Blasio's proposed "robot tax," companies that replace jobs with automation would have to pay the equivalent of five years of payroll taxes for each employee whose job is lost, making cost-saving innovations far less attractive.

Machine learning enables us to build systems that can predict the world around us: like what movies we'd like to watch, how much traffic we'll experience on our morning commute, or what words we'll type next in our emails. There are many types of models and tasks. Face detection models transform raw image pixels into high level signals (like the presence and position of eyes, noses, and ears) and then use those signals to locate all faces in an image. Time series models can use sensor measurements to extract long-term trends and seasonal patterns in order to predict future observations. Text prediction models extract information about the meaning of past sentences, grammaticality, and emotions in the text in order to predict the next word or phrase that you'll type.

Japan will allow dozens of autonomous vehicles to freely roam around streets near competition areas prior to the 2020 Tokyo Olympics. Bloomberg reports that the local government is working alongside car manufacturers such as Toyota and Nissan to roll out self-driving cars on its streets for a week before the games commence next July. Japan is reportedly keen to show of its autonomous vehicle prowess on the world stage. Certain details about the program remain under wraps, but it is reported that up to 100 self-driving cars will be offering rides around the Olympics venues. Once the Games finish, on-road testing of autonomous cars will continue throughout Japan until 2022, as the country wants to introduce self-driving cars to its streets by 2025.

During the Parkland, Florida, school shooting in 2018, the shooter was caught on a security camera pulling his rifle out of a duffle bag in the staircase 15 seconds before discharging the first round. However, the School Resource Officer didn't enter the building because he wasn't confident about the situation, and the Coral Springs Police Department had no idea what the shooter even looked like until 7 minutes and 30 seconds after the first round was fired. If the video system had included technology to recognize the gun threat in real time, alerts could have been sent to the security team. An announcement could have been made right away for all students and faculty in Building 12 to barricade their doors, and law enforcement could have responded a lot faster to a real-time feed of timely and accurate information. Aegis AI offers such a technology, which the company says enables existing security cameras to automatically recognize gun threats and notify security in real-time.

Artificial Intelligence (AI) will create as many jobs in the UK as it will displace over the next 20 years, a report has said. The analysis, by accountancy giant PwC, found AI would boost economic growth, creating new roles as others fell away. But it warned there would be "winners and losers" by industry sector, with many jobs likely to change. Opinion is split over AI's potential impact, with some warning it could leave many out of work in future. The pessimists argue AI is different to previous forms of technological change, because robots and algorithms will be able to do intellectual as well as routine physical tasks.

Forecasting methods usually fall into three categories: statistical models, machine learning models and expert forecasts, with the first two being automated and the latter being manual. Statistical methods, including time series models and regression analysis, are considered traditional, while machine learning methods, such as neural network, random forest and the gradient-boosting model, are more modern. Yet when selecting a forecasting method, the "modern vs. traditional" or "automated vs. manual" comparisons can mislead. Preferences will depend on the modeler's training: Those with data science training will prefer machine learning models, while modelers with business backgrounds have more trust in expert forecasts. In fact, each of the three methods has different strengths and can play important roles in forecasting.