While early artificial intelligence (AI) programs were a one-trick pony, typically only able to excel at one task, today it's about becoming a jack of all trades. The goal is to write one program that can solve multi-variant problems without the need to be rewritten when conditions change--write once, run anywhere. Digital heavyweights--notably Amazon, Google, IBM, and Microsoft--are now open sourcing their machine learning (ML) libraries in pursuit of that goal as competitive pressures shift focus from proprietary technologies to proprietary data for differentiation. Machine learning is the study of algorithms that learn from examples and experience, rather than relying on hard-coded rules that do not always adapt well to real-world environments. ABI Research forecasts ML-based IoT analytics revenues will grow from 2 billion in 2016 to more than 19 billion in 2021, with more than 90% of 2021 revenue to be attributed to more advanced analytics phases.

Futurist Gerd Leonhard has argued that we are at a crossroads. We have to decide what form our technology will take in the coming decades. Will we change it, or will it change us? If things continue in their current direction, it is our ever more powerful technology that will shape us – the machines will control their creators. Already we are seeing the psychological and physical effects of constant connection to the internet.

The technology and working of Artificial Intelligence is much appreciated. The world has great plans for this nascent technology and Arnold Schwarzenegger in The Terminator gave us enough reasons to believe in its goodness. Yet, when tech gurus and geniuses like Steve Wozniak, Bill Gates, Elon Musk and Stephen Hawking themselves fear the advancement of artificial technology to a level of electronic apocalypse, we can hardly accept it in the right spirits. However, AI has evolved and will evolve and we are definitely moving towards a more robotic future but artificial intelligence does not have to be necessarily creepy. It is being used in different sectors and will have huge impact on our day to day lives in days to come.

AI is making waves in the tech industry, with everyone from Google to the smallest start-up trying to profit from artificial intelligence. Chatbots, roboadvisors, virtual assistants and smart cars have been brought out of the realms of science fiction and embedded in everyday life, with these technologies seemingly only scratching the surface of what AI can actually do. The promise of AI is a hard one to measure and an even harder one on which to make a bet on. But now, as AI begins to deliver and starts to disrupt, some are making huge bets on what they view as the future of technology. Talk of unicorns and huge IPOs have fuelled the rush to invest in tech – with investors pouring money into innovators of all sizes, from established enterprises to those who start out in the basement or garage.

BEIJING, CHINA - GPU Technology Conference China -- NVIDIA (NASDAQ: NVDA) today unveiled a palm-sized, energy-efficient artificial intelligence (AI) computer that automakers can use to power automated and autonomous vehicles for driving and mapping. The new single-processor configuration of the NVIDIA DRIVE PX 2 AI computing platform for AutoCruise functions -- which include highway automated driving and HD mapping -- consumes just 10 watts of power and enables vehicles to use deep neural networks to process data from multiple cameras and sensors. It will be deployed by China's Baidu as the in-vehicle car computer for its self-driving cloud-to-car system. DRIVE PX 2 enables automakers and their tier 1 suppliers to accelerate production of automated and autonomous vehicles. A car using the small form-factor DRIVE PX 2 for AutoCruise can understand in real time what is happening around it, precisely locate itself on an HD map and plan a safe path forward.

We won't have robot doctors for a long time, but the human doctors we have now are beginning to lean on specialized artificial intelligence to help save time. Google DeepMind just announced a partnership with University College London Hospital which will explore using artificial intelligence to treat patients with head and neck cancers. The goal is to develop tools to automatically identify cancerous cells for radiology machines. Currently, radiologists employ a manual process, called image segmentation, to take CT and MRI scans and use them to create a map of the patient's anatomy with clear guidelines of where to direct the radiation. Avoiding healthy areas of the head and neck requires that map to be extraordinarily detailed; typically it takes four hours to create.

We live in an age of disruption -- when new technology and behavioral shifts that would have seemed unimaginable even a few years ago are transforming the way we live. Artificial intelligence and robotics are fundamentally reinventing the workforce. Drones and driverless cars are transforming supply chains and logistics, even as they challenge policymakers to rethink existing approaches to infrastructure and regulation. Millennials are changing consumption patterns and demand for everything from cars to real estate. Responding to disruption has become one of the biggest strategic imperatives facing today's business leaders and policymakers.

Carol has extensive experience as a developer and architect building complex, mission-critical applications in the Banking, Health Insurance and Telecom industries. As a Java Technology Evangelist at Sun Microsystems, Carol traveled all over the world speaking at Sun Tech Days, JUGs, companies, and conferences. She is a recognized speaker in Java communities.

Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine.

This is a tough task, I was in this precarious situation trying to explain to my younger son. I had a curated list of top 10 frequently used Machine Learning algorithms, but the key was to do a backward mapping of these Machine Learning techniques to solve problems which are of interest and relevance to my son. As a starting point to the conversation I asked him, list down your decision making points, meaning there may be many situations when you had to make decisions but you may not have all the information. I took those and matched it to the machine learning algorithms while explaining the core concept behind the problem solving. A classifier is a machine learning technique that takes a bunch of data and attempts to predict which class the new data belongs to. Sure, remember sometime back you were asking me who you want to invite to your birthday party and whether they will accept your invitation or not! Now, assume that you have got a data set about all the other 29 kids in your class. The information contains their hobby, kind of books they read, do they share their tiffin or not, are they friendly, in your last birthday did they come and did they bring nice gifts, etc. Now, given these information, you want to predict whether your classmates will accept your invitation or not.