Across the globe, energy systems are changing, creating unprecedented challenges for the organisations tasked with ensuring the lights stay on. In the UK, large fossil fuelled power stations are being replaced by increasing levels of widely distributed wind and solar generation. This renewable power is clean and free at the point of use but it cannot always be relied upon. To date National Grid has managed this intermittency by keeping polluting power stations online to make up the difference but Artificial Intelligence offers an alternative approach. What's needed is a smart grid which can integrate renewable energy efficiently at scale without having to keep polluting power stations online to manage intermittency.

Data is the new natural resource that can empower every citizen in a country like India, Microsoft's India-born CEO Satya Nadella said on Monday during a discussion on the digital world, cloud computing and artificial intelligence. There is need for "recognising that data is the new natural resource instead of looking at it as being unevenly spread" for empowering every citizen of India, Nadella said in a dialogue with Infosys co-founder Nandan Nilekani. In India, several state governments are using data resources and computing power for governance, Nadella said. The government of Andhra Pradesh is using cloud services to track high school dropouts in order to build interventions while ICRISAT is using machine learning to improve the yield of farmers, he said. "I was looking at the big consumers of the public cloud (computing) and one thing that came up was the Election Commission in Tamil Nadu,'' Nadella said with reference to the usage of CCTV cameras in Tamil Nadu at election booths in 2016. Nadella said that the broad technology areas that he is currently excited about is cloud computing, artificial intelligence and augmented reality. Much of the entrepreneurial energy in India is around cloud computing, he said. What has been seen so far in terms of artificial intelligence is statistical machine learning by using the power of data and real AI is still in the future, Nadella said. "In Punjab, I believe we took all the call centre data … We analysed the speech so that the government can get a better handle on what are the issues that can be automated and what needs human services.

The Microsoft chief's comments come in the wake of Bill Gates' call for levying taxes on robots that take away jobs from people, reports Ayan Pramanik Microsoft chief executive Satya Nadella has warned that embracing Artificial Intelligence without reskilling people will have an impact on jobs, even as it has displayed efficiency in delivery of government services. "I think it is an exciting future, but at the same time, you have to be very mindful of the impact of AI on jobs. That's why I want to make sure some of the skilling work we are doing in India is going to help people," said Nadella at an interaction with the architect of Aadhaar, Nandan Nilekani. Nadella's comments come in the wake of Microsoft founder Bill Gates' call for levying taxes on robots that take away jobs from people. Nadella pointed out that Microsoft had been able to use AI to improve efficiency in government services in Punjab and Andhra Pradesh.

Machine learning (ML) has evolved in leaps and bounds. While some still remain skeptical about its progress, most enterprises are excited about the ample opportunities that would open up with the growth of machine learning. Machine learning is a part of the gigantic field of Artificial Intelligence (AI). It has been just a few decades since AI was introduced. With machine learning, like the name implies, the machine learns.

Machine learning has simplified the heavy lifting of collecting and analyzing data. Even with the most advanced techniques, data scientists spend countless hours developing, testing and retooling analytic models one step at a time. Worse yet, most organizations cannot find enough data scientists to complete this labor-intensive work. The impact is that we have not yet fully realized the promise of continuous intelligence; until now. The field of machine learning offers promises to streamline the application of analytics and create a new era of autonomous data. It adds massive efficiencies to the process by automating the construction of these models.

Artificial intelligence (AI) is powering the next wave of customer experience. Virtual buying assistants, chatbots and voice-activated apps all are changing the way customers interact with brands. The future of e-commerce will look entirely different, and brands are taking steps now -- big or small -- to make this vision a reality. This is the sexy side of AI, the stuff of headlines mixed in with sci-fi fanfare. It's exciting for sure, but the love story between customer and brand, quietly driven by AI for years -- and just skimming the surface of what's possible with access to more data today -- is what gets our heart rate up at Boxever.

Oxford Winter Intelligence - Abstract: In this paper we will address an important issue of reward function integrity in artificially intelligent systems. Throughout the paper, we will analyze historical examples of wireheading in man and machine and evaluate a number of approaches proposed for dealing with reward-function corruption. While simplistic optimizers driven to maximize a proxy measure for a particular goal will always be a subject to corruption, sufficiently rational self-improving machines are believed by many to be safe from wireheading problems. Claims are often made that such machines will know that their true goals are different from the proxy measures, utilized to represent the progress towards goal achievement in their fitness functions, and will choose not to modify their reward functions in a way which does not improve chances for the true goal achievement. Likewise, supposedly such advanced machines will choose to avoid corrupting other system components such as input sensors, memory, internal and external communication channels, CPU architecture and software modules.

For those out there who know me, it'll be no surprise to learn that I'm going long on the transformative power of artificial intelligence (AI). Since 2013, I've spent most of my energy studying, researching, investing (e.g. Mapillary, Numerai, Ravelin) and building AI communities (AI Summit 2015and 2016, LondonAI meetup), with a mission to accelerate its real-world applications. I am passionate about seeking out and bringing technology advancements to markets that can enable us to solve the high-value (and often complex) problems we face in business and society. Importantly, this includes ones that were previously intractable from either a technical or commercial standpoint.

Chatbots are appearing on Facebook, Slack, consumer retail sites, you name it and they're getting really easy to build and with some systems, really smart. At the low end where serious smarts aren't as important, chatbots are becoming projects that non-programmers can build with ease and at the top end, while we're not at HAL 9000 level yet, chatbots that use machine learning and understanding techniques are getting more powerful all the time. So, if you're looking for new, shiny ways to interact with your customers or you want to add slick support for your staff on Slack, this collection of chatbot platforms is what you need to get started. We've got 10 chatbots to consider that range from "throw it together in an afternoon" for fast, simple results, to systems that require a CS degree and deliver impressive results. Let the chatbot takeover begin!

In the last few months, I have had several people contact me about their enthusiasm for venturing into the world of data science and using Machine Learning (ML) techniques to probe statistical regularities and build impeccable data-driven products. However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post. Recently, there has been an upsurge in the availability of many easy-to-use machine and deep learning packages such as scikit-learn, Weka, Tensorflow etc. Machine Learning theory is a field that intersects statistical, probabilistic, computer science and algorithmic aspects arising from learning iteratively from data and finding hidden insights which can be used to build intelligent applications. Despite the immense possibilities of Machine and Deep Learning, a thorough mathematical understanding of many of these techniques is necessary for a good grasp of the inner workings of the algorithms and getting good results. There are many reasons why the mathematics of Machine Learning is important and I'll highlight some of them below: The main question when trying to understand an interdisciplinary field such as Machine Learning is the amount of maths necessary and the level of maths needed to understand these techniques.