Microsoft Machine Learning & Data Science Summit is taking place in conjunction with Microsoft Ignite at Georgia World Congress Center. Today, day 1 started with keynote by Dr. Joseph Sirosh who identified three axes of innovation along with various customer case studies. Thought leaders and Microsoft engineers discuss the latest Big Data, Machine Learning, Artificial Intelligence, and Open Source techniques and technologies along with important case studies. There were various great take aways from sessions.
Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.
Bruce Newsome reviews the recently published book: "Strategy, Evolution, and War: From Apes to Artificial Intelligence," authored by Kenneth Payne and published by Georgetown University Press. Artificial intelligence (AI) has been explicit in the practices and policies of defence since at least the 1970s, at least in high-capacity countries, given the exponential growth in the power of electronic computing per unit cost. It was already specified in training and forecasting simulations, decision-making aids, targeting aids, robotics, adaptive navigation systems (as in the Tomahawk Cruise Missile), and ballistic missile defence. Any child with a video game could experience AI. AI raced up Western governmental priorities in the 2000s by application to countering terrorism; in 2009, the US escalated its cyber capabilities and authorities, partly on the promise of AI; in 2014, the Russians seemed to know first what the defenders of Ukraine were doing, in part because of integration of AI; and in 2016, Western governments consensually blamed Russia for unprecedented interference in American and other elections, partly aided by AI.
I have been asked by quite a few people on how to start Machine Learning and Deep Learning. Here, I have curated a list of resources which I used and the path I took when I first learnt Machine Learning. I will keep on updating this article as I find more helpful resources. This will teach you the ropes of Machine Learning and will brush up your Linear Algebra skill a little bit. Make sure you do all the assignments and after you have completed the course, you will get a hold of Machine Learning concepts such as; Linear Regression, Logistics Regression, SVM, Neural Networks and K-means clustering.
In this tutorial, we'll be using a GA to find a solution to the traveling salesman problem (TSP). Let's start with a few definitions, rephrased in the context of the TSP: Now, let's see this in action. While each part of our GA is built from scratch, we'll use a few standard packages to make things easier: We first create a City class that will allow us to create and handle our cities. These are simply our (x, y) coordinates. Within the City class, we add a distance calculation (making use of the Pythagorean theorem) in line 6 and a cleaner way to output the cities as coordinates with __repr__ in line 12.
Stefan Jovanovic is a very experienced Android Developer with a demonstrated history of working in the information technology and services industry. Skilled in Android, Solidity, Linux and Object-Oriented Programming (OOP), Stefan Jovanovic led CryptoAngel to be specialized in AI, blockchain, augmented reality, and IoT. JC: CryptoAngel is a very skill-oriented company, in which you have a team of extraordinary creative people dedicated to their mission to enable people to robust their potentials, talents and knowledge using disruptive modern technologies based on AI and Blockchain. Does that mean CryptoAngel offer a very customized service for your customer? Stefan: Crypto Angel offers a customize service for every each costumer.
TensorFlow, the open source software library developed by the Google Brain team, is a framework for building deep learning neural networks. It is also considered one of the best ways to build deep learning models by machine learning practitioners across the globe. In deep learning models, which rely on a lot of data and computing resources, TensorFlow is used significantly. Given its flexible architecture for easy deployment on various platforms such as CPUs, GPUs and TPUs, TensorFlow remains one of the favourite libraries to get into ML. Its huge popularity also means that tech enthusiasts are on a constant lookout to learn more and work more with this library.
Artificial intelligence (AI) and machine learning (ML) are the most widely chosen domains for reskilling among working tech professionals in India, according to the findings of education technology company Simplilearn. The firm's'Career Impact Survey 2018' which was aimed at analyzing the impact of professional certifications and reskilling among working professionals revealed that AI and ML domains were chosen by 25% of respondents. This was followed by big data and data science domains chosen by 20% of the participants. Other new age categories such as'digital marketing, cloud computing, cybersecurity, DevOps and Agile and Scrum' together saw 55% uptake in reskilling among professionals. The certification courses helped 31% of professionals to enhance their performance, gain manager and peer appreciation, according to the survey.
The research found that while AI could displace roughly seven million jobs in the country, it could also create 7.2 million roles, resulting in a modest net boost of around 200,000 jobs. It has also estimated that about 20 percent of jobs would be automated over the next 20 years and no sector would be unaffected. Technologies such as robotics, drones and driverless vehicles would replace human workers in some areas, but also create many additional jobs as productivity and real incomes rise and new and better products are developed. In the health and social work sector the number of people employed could rise by almost one million, while jobs in manufacturing could fall by roughly 25 percent, a net loss of almost 700,000 roles. "Major new technologies, from steam engines to computers, displace some existing jobs but also generate large productivity gains," PwC's Chief Economist John Hawksworth said in a press release.
The management of digital and online data continues to be on the rise as emerging technologies, like AI and Analytics, makes it more convenient for businesses to gather vital details and insights on their customers. In fact, by 2020, the world will have accumulated 44 zettabytes of information, according to market research firm International Data Corp. The solution, according to Dan Baird, Founder & CEO of Wrench.ai, is to use "private" machine learning approach that allows the technology to only pull insights from data sets, while not having to access peoples' personally identifiable information. He explains how in an interview with Digital Journal. Digital Journal: How important is digital transformation for business?