Categorical data are commonplace in many Data Science and Machine Learning problems but are usually more challenging to deal with than numerical data. In particular, many machine learning algorithms require that their input is numerical and therefore categorical features must be transformed into numerical features before we can use any of these algorithms. One of the most common ways to make this transformation is to one-hot encode the categorical features, especially when there does not exist a natural ordering between the categories (e.g. a feature'City' with names of cities such as'London', 'Lisbon', 'Berlin', etc.). For each unique value of a feature (say, 'London') one column is created (say, 'City_London') where the value is 1 if for that instance the original feature takes that value and 0 otherwise. Even though this type of encoding is used very frequently, it can be frustrating to try to implement it using scikit-learn in Python, as there isn't currently a simple transformer to apply, especially if you want to use it as a step of your machine learning pipeline.

Since its invention by a Hungarian architect in 1974, the Rubik's Cube has furrowed the brows of many who have tried to solve it, but the 3-D logic puzzle is no match for an artificial intelligence system created by researchers at the University of California, Irvine. DeepCubeA, a deep reinforcement learning algorithm programmed by UCI computer scientists and mathematicians, can find the solution in a fraction of a second, without any specific domain knowledge or in-game coaching from humans. This is no simple task considering that the cube has completion paths numbering in the billions but only one goal state--each of six sides displaying a solid color--which apparently can't be found through random moves. For a study published today in Nature Machine Intelligence, the researchers demonstrated that DeepCubeA solved 100 percent of all test configurations, finding the shortest path to the goal state about 60 percent of the time. The algorithm also works on other combinatorial games such as the sliding tile puzzle, Lights Out and Sokoban.

CHICAGO -- Grant Thornton LLP is collaborating with Microsoft and Hitachi Solutions to turn information into foresight. The collaboration uses artificial intelligence (AI) and machine learning (ML) to help Grant Thornton identify its clients' nascent business needs. Grant Thornton can then design solutions to address its clients' challenges before they balloon. As one of the nation's largest accounting, tax and consulting firms, Grant Thornton works with clients to overcome all manner of hurdles, from financial and operational to technological and risk-related. "We focus on staying ahead of our clients' needs," explains Nichole Jordan, Grant Thornton's national managing partner of Markets, Clients and Industry.

The human record for solving a Rubik's Cube has been smashed by an artificial intelligence. The bot, called DeepCubeA, completed the popular puzzle in a fraction of a second - much faster than the quickest humans. While algorithms have previously been developed specifically to solve the Rubik's Cube, this is the first time it has done without any specific domain knowledge or in-game coaching from humans. It brings researchers a step closer to creating an advanced AI system that can think like a human. "The solution to the Rubik's Cube involves more symbolic, mathematical and abstract thinking," said senior author Professor Pierre Baldi, a computer scientist at the University of California, Irvine.

No longer does artificial intelligence only exist in sci-fi movies and books about dystopian futures. It's in the here and now, continuously transforming the way in which we live and work. Many of us interact with AI on a daily basis - we call on Siri to give us directions to nearby coffee shops or ask Alexa to order us goods on Amazon. AI is also seamlessly supplementing and enhancing operations across a variety of industries and increasingly disrupting internal company functions. However, at the same time, it's also becoming more and more apparent where AI still has limitations that prevent it from fully replicating human behavior.

In this special guest feature, Solomon Thimothy, CEO of DMA Digital Marketing Agency, believes that digital marketing advancements in 2018 have set a high bar for customer expectations. Customers now expect, deserve and demand that personalized, seamless transactions will only increase in 2019. By focusing on data-based AI solutions, organizations can ensure the customer journey will be more personalized and more profitable in the year to come. Solomon focuses his expertise and passion in helping businesses invest in long-term digital marketing for financial growth. His education from Northeastern Illinois University and North Park University provide him with the tools needed to lives up to its digital marketing commitments.

Elon Musk's secretive "brain-machine interface" startup, Neuralink, stepped out of the shadows on Tuesday evening, revealing its progress in creating a wireless implantable device that can – theoretically – read your mind. At an event at the California Academy of Sciences in San Francisco, Musk touted the startup's achievements since he founded it in 2017 with the goal of staving off what he considers to be an "existential threat": artificial intelligence (AI) surpassing human intelligence. Two years later, Neuralink claims to have achieved major advances toward Musk's goal of having human and machine intelligence work in "symbiosis". Neurolink says it has designed very small "threads" – smaller than a human hair – that can be injected into the brain to detect the activity of neurons. It also says it has developed a robot to insert those threads in the brain, under the direction of a neurosurgeon.

Summary: Despite our concerns about China taking the lead in AI, our own government efforts mostly through DARPA continue powerful leadership and funding to maintain our lead. Here's their plan to maintain that lead over the next decade. Think all those great ideas that have powered AI/ML for the last 10 years came from Silicon Valley and a few universities? Hard as it may be to admit it's the seed money in the billions that our government has spent that got pretty much all of these breakthroughs to the doorway of commercial acceptability. Dozens of articles bemoan the huge investments that China is making in AI with the threat that they will pull ahead.

According to the new market research report "Artificial Intelligence Market by Offering (Hardware, Software, Services), Technology (Machine Learning, Natural Language Processing, Context-Aware Computing, Computer Vision), End-User Industry, and Geography - Global Forecast to 2025", published by MarketsandMarkets, the Artificial Intelligence Market is expected to be valued at USD 21.5 billion in 2018 and is likely to reach USD 190.6 billion by 2025, at a CAGR of 36.6% during the forecast period. Major drivers for the market are growing big data, the increasing adoption of cloud-based applications and services, and an increase in demand for intelligent virtual assistants. The major restraint for the market is the limited number of AI technology experts. Critical challenges facing the AI market include concerns regarding data privacy and the unreliability of AI algorithms. Underlying opportunities in the artificial intelligence market include improving operational efficiency in the manufacturing industry and the adoption of AI to improve customer service.

#Megvii, a Chinese #AI #startup that supplies facial recognition software for the Chinese government's Wsurveillance program, is expanding its technology beyond humans to recognize different faces of pets.