As artificial intelligence becomes more sophisticated and its ability to perform human tasks accelerates exponentially, we're finally seeing some attempts to wrestle with what that means, not just for business, but for humanity as a whole. From the first stone ax to the printing press to the latest ERP solution, technology that reduces or even eliminates physical and mental effort is as old as the human race itself. However, that doesn't make each step forward any less uncomfortable for the people whose work is directly affected – and the rise of AI is qualitatively different from past developments. Until now, we developed technology to handle specific routine tasks. A human needed to break down complex processes into their component tasks, determine how to automate each of those tasks, and finally create and refine the automation process.

This powerful quote by William Shakespeare applies well to techniques used in data science & analytics as well. Allow me to prove it using a short story. In May ' 2015, we conducted a Data Hackathon ( a data science competition) in Delhi-NCR, India. We gave participants the challenge to identify Human Activity Recognition Using Smartphones Data Set. The data set had 561 variables for training model used for the identification of Human activity in test data set.

In this post I'll share how I've been studying Deep Learning and using it to solve data science problems. It's an informal post but with interesting content (I hope). I come from physics and computer engineering. I studied both in Venezuela, and then I did a Master in Physics in Mexico. But I consider myself a Data Scientist.

Very good overview of NN, and met all my expectations. What I hoped for was to gain an overall understanding of different NN approaches and understand how to design a NN model architecture. A bonus would be a few exercises on how to use Keras/Tensorflow, as that is what people are mostly expected to use for NN. It shouldn't be so hard to treat comma and space similarly to comma without a space.

Surely it's entered your mind that one day a computer could replace you or a colleague. In this dystopian vision of the future, all of your hard-earned education and on the job training leads to an early severance package (or perhaps some kind of universal basic income). In the distant future, research does indicate that Artificial Intelligence (AI) will be able to perform many jobs. And while it is true that AI will transform our world, McKinsey and Company found that only 5 percent of jobs can be fully performed by artificial intelligence in the near term. What's more likely is that AI will create new human jobs itself as well as empower humans to work more efficiently.

Everywhere you turn, there's another news story emphasizing the impact artificial intelligence will have on the enterprise this year. Along with that are headlines screaming about Silicon Valley giants siphoning off talent at a time when machine learning has become a huge driver in the battle for digital transformation dominance. So if you're looking to make good on the promise of AI, where can you turn for talent? Desperate times, it is said, call for desperate measures. Many organizations are dealing with the AI talent shortage by forming partnerships with universities and by training and building from within. If you think this is all a lot of hype, consider that by 2030, the global GDP could be up to 14% higher, or $15.7 trillion as a result of AI, making it the biggest commercial opportunity in today's economy, according to the recent PwC report "Sizing the Prize."

Takeshi Niinami, chief executive officer of Suntory Holdings Ltd., believes that companies and governments need to offer proper training and education to their employees with a long-term view toward new technologies as people still lack the skills needed for "the fourth industrial revolution." "Technology will be the rule-changer in the future, and we need to maximize the sensitivity of our antenna for technological innovation," Niinami told The Japan Times in an interview during the World Economic Forum's annual meeting in Davos, Switzerland, last month. Niinami pointed out that the fourth industrial revolution, along with the growth of protectionist sentiment and female empowerment, were the topics that dominated discussions among business and political leaders. The fourth industrial revolution refers to the drastic social and industrial changes being caused by the recent emergence of disruptive technologies including the internet of things, robotics, virtual reality and artificial intelligence that will fundamentally alter the way we live and work. Last March, the WEF established the Center for the Fourth Industrial Revolution in San Francisco to bring together business leaders, governments, startups, academics and international organizations to accelerate cross-sector cooperation and to co-design policies for such emerging technologies as artificial intelligence and drones.

That's all for now, I hope this collection of algorithmic and quantitative trading based websites will help you strengthen your skills. Last, and certainly not the least, I would like to mention QuantInsti's Executive Programme in Algorithmic Trading (EPAT) that equips you with the required skill sets via training modules like Statistics & Econometrics, Financial Computing & Technology, and Algorithmic & Quantitative Trading to be a successful trader.

Nothing seemed suspect when Jill Watson, a teaching assistant at the Georgia Institute of Technology (Georgia Tech), emailed students about assignments and answered questions during Professor Ashok Goel's online knowledge-based artificial intelligence course. In fact, it wasn't until the end of the semester that the students realized they hadn't been emailing a human at all -- they'd been corresponding with a chatbot. Goel had built an artificially intelligent teaching assistant that could answer routine questions so that he and the human teaching assistants could focus on responding to more complex issues, Business Insider reports. And he isn't the only person using artificial intelligence to improve education. The teams at companies including Thinkster Math, Brainly, Content Technologies Inc., and Gradescope are creating artificial intelligence tools to aid students and educators.

"Automated machine learning promised data scientists a better, faster way to build models, but the reality never matched the hype," said Dr. Ingo Mierswa, founder and president of RapidMiner. When I looked closely at automated machine learning solutions, I found them to be black boxes. They restricted my ability as a data scientist to understand how the models worked and tune them when necessary. We built Auto Model on top of RapidMiner Studio to improve the productivity of data scientists without hiding the ability to understand how and why a model works. As data scientists need to tune or tweak models, they have the full power of the RapidMiner Studio visual workflow designer at their disposal." RapidMiner Auto Model accelerates the entire data science lifecycle using automated machine learning.